{"session":{"id":"colorful-handbell","working_directory":"/Users/root1","backend_type":"Windsurf","model":"swe-1-7-lightning","agent_mode":"bypass","created_at":"2026-07-09T04:21:43+00:00","last_activity_at":"2026-07-09T04:22:10+00:00","title":"YouTube Video Discussion","main_chain_id":57,"shell_last_seen_index":0,"cogs_json":[{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/profile"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":[{"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://devin.ai/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-cli` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"},{\"name\":\"playwright\"},{\"name\":\"cloudflare-bindings\"},{\"name\":\"cloudflare-docs\"},{\"name\":\"cloudflare\"},{\"name\":\"cloudflare-builds\"},{\"name\":\"cloudflare-observability\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"normal"},"created_at":"2026-07-09T04:21:43.221083Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"af582149-b6ad-4f5c-b983-cc6ff28dff23"}],"tool_availability":{"BlockList":[{"Name":{"exact":"exit_plan_mode"}}]},"append_system_messages":[]},{"model":"swe-1-7-lightning","source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/model"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":{"AllowList":[{"Name":{"exact":"exec"}},{"Name":{"exact":"read"}},{"Name":{"exact":"edit"}},{"Name":{"exact":"grep"}},{"Name":{"exact":"glob"}},{"Name":{"exact":"exit_plan_mode"}},{"Name":{"literal":"get_output"}},{"Name":{"literal":"kill_shell"}},{"Name":{"literal":"write_to_process"}},{"Name":{"literal":"write"}},{"Name":{"literal":"notebook_read"}},{"Name":{"literal":"notebook_edit"}},{"Name":{"literal":"todo_write"}},{"Name":{"literal":"webfetch"}},{"Name":{"literal":"web_search"}},{"Name":{"literal":"ask_user_question"}},{"Name":{"literal":"run_subagent"}},{"Name":{"literal":"read_subagent"}},{"Name":{"literal":"request_scope"}},{"Name":{"literal":"mcp_call_tool"}},{"Name":{"literal":"mcp_list_servers"}},{"Name":{"literal":"mcp_list_tools"}},{"Name":{"literal":"mcp_read_resource"}},{"Name":{"literal":"skill"}}]},"append_system_messages":[{"role":"system","content":"You are powered by SWE-1.7 Lightning.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-07-09T04:21:43.221342Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"ad099b59-7938-4ae5-9a46-2a562e2e88a8"}]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/parallel-tool-calls"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[{"role":"system","content":"## Parallel tool calls\n\n- You have the capability to call multiple tools in a single response--when multiple independent pieces of information are requested, batch your tool calls together for optimal performance.\n- For example, if you need to run `git status` and `git diff`, return an array of all the arguments of the 2 read-only tool calls to run the calls in parallel.\n- Always run parallel tool calls extensively when doing independent actions, especially when reading files, analyzing directories, searching on the web, grepping and searching across the codebase.\n- Never perform dependent terminal commands or writes in parallel.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-07-09T04:21:43.221355Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"3c55620a-8d23-4941-a3d8-813f2e71bd2c"}]},{"model":null,"source":"Subagent","context":[],"lifetime":{"Unique":"core/read_only_kinds"},"permissions":[[{"Tool":{"Kind":"read"}},"Allow"],[{"Tool":{"Kind":"search"}},"Allow"],[{"Tool":{"Kind":"think"}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/subagent_profiles"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[{"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"subagent_profiles"},"created_at":"2026-07-09T04:21:43.221369Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"5b1e1906-fed1-427b-ac5e-86e26c6ab448"}]},{"model":null,"source":"Managed","context":[],"lifetime":{"Unique":"workspace/config_protection"},"permissions":[[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/cli/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.cognition/**/config*.json"}}},"ForceAsk"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Base","context":[],"lifetime":{"Unique":"workspace/read_permissions"},"permissions":[[{"Scope":{"Read":{"glob":"/Users/root1"}}},"Allow"],[{"Scope":{"Read":{"glob":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501"}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"UserOverride","context":[],"lifetime":{"Unique":"workspace/project_override_permissions"},"permissions":[[{"Scope":{"Command":{"matcher":{"Prefix":"brightness"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"osascript"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"pkill"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"yt-dlp"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"ls"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"python3"}}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"workspace/context"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/accept_edits"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/bypass"},"permissions":[["AnyScope","Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/autonomous"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]}],"workspace_dirs":null,"hidden":false,"metadata":"{\"total_credit_cost\":0,\"total_acu_cost\":0.0}"},"message_count":29,"messages":[{"message_id":"af582149-b6ad-4f5c-b983-cc6ff28dff23","node_id":18,"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://devin.ai/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-cli` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"},{\"name\":\"playwright\"},{\"name\":\"cloudflare-bindings\"},{\"name\":\"cloudflare-docs\"},{\"name\":\"cloudflare\"},{\"name\":\"cloudflare-builds\"},{\"name\":\"cloudflare-observability\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","created_at":"2026-07-09T04:21:43.221083Z","finish_reason":null,"metrics":null},{"message_id":"5b1e1906-fed1-427b-ac5e-86e26c6ab448","node_id":19,"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","created_at":"2026-07-09T04:21:43.221369Z","finish_reason":null,"metrics":null},{"message_id":"3c55620a-8d23-4941-a3d8-813f2e71bd2c","node_id":20,"role":"system","content":"## Parallel tool calls\n\n- You have the capability to call multiple tools in a single response--when multiple independent pieces of information are requested, batch your tool calls together for optimal performance.\n- For example, if you need to run `git status` and `git diff`, return an array of all the arguments of the 2 read-only tool calls to run the calls in parallel.\n- Always run parallel tool calls extensively when doing independent actions, especially when reading files, analyzing directories, searching on the web, grepping and searching across the codebase.\n- Never perform dependent terminal commands or writes in parallel.","created_at":"2026-07-09T04:21:43.221355Z","finish_reason":null,"metrics":null},{"message_id":"ad099b59-7938-4ae5-9a46-2a562e2e88a8","node_id":21,"role":"system","content":"You are powered by SWE-1.7 Lightning.","created_at":"2026-07-09T04:21:43.221342Z","finish_reason":null,"metrics":null},{"message_id":"d04d8d14-d682-47a9-a2f4-2be626dd01d3","node_id":22,"role":"system","content":"\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n /Users/root1 (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Wednesday, 2026-07-08\n\n","created_at":"2026-07-09T04:21:42.625653Z","finish_reason":null,"metrics":null},{"message_id":"4b6d41df-c4ed-45f8-9038-09bff096d87f","node_id":23,"role":"system","content":"\n\n# Agent Preferences\n\n- If I ever paste in a YouTube link, use yt-dlp to summarize the video.\n- get the autogenerrated captions to do this\n- if asked to summarize a YouTube video, do not name the session until after reading and understanding the full YouTube video transcript\n- for testing that involves urls, start with example.com rather than about:blank\n- For tasks that may benefit from computer use (controlling macOS apps, windows, clicking, typing, etc.), use the background-computer-use skill to control local macOS apps through the BackgroundComputerUse API\n- Secrets/tokens live in `~/.env` (e.g. `HF_TOKEN` for Hugging Face). Source it before use: `set -a; . ~/.env; set +a`\n\n## Cloudflare DNS management\n\nFor Cloudflare DNS management (adding/editing/deleting DNS records), use the **`cf` CLI** instead of `wrangler`.\nWrangler does not have DNS management capabilities, and its OAuth token doesn't work with the Cloudflare REST API for DNS operations.\n\n### Usage\n```bash\n# Check authentication status\ncf auth whoami\n\n# List DNS records for a zone\ncf dns records list -z aidenhuang.com\n\n# Add a DNS record\ncf dns records create -z aidenhuang.com --type CNAME --name devin --content \"target.example.com\" --proxied false\n\n# Delete a DNS record\ncf dns records delete -z aidenhuang.com \n```\n\nThe `cf` CLI uses the same OAuth authentication as `wrangler` and has proper DNS record permissions.\n\n## File search via fff MCP\n\nFor any file search or grep in the current git-indexed project directory, prefer the **fff** MCP tools\n(`mcp__fff__grep`, `mcp__fff__find_files`, `mcp__fff__multi_grep`) over the built-in grep/glob tools.\nfff is frecency-ranked, git-aware, and more token-efficient.\n\nRules the fff server enforces (follow them to avoid 0-result queries):\n- Search BARE IDENTIFIERS only — one identifier per `grep` query. No `load.*metadata.*Foo` style regex.\n- Don't use regex unless you truly need alternation; `.*`, `\\d+`, `\\s+` almost always return 0 results.\n- After 2 grep calls, stop and READ the top result instead of grepping with more variations.\n- Use `multi_grep` for OR logic across multiple identifiers (e.g. snake_case + PascalCase variants) in one call.\n- Have a specific name → `grep`. Exploring a topic / finding files → `find_files`.\n\nThe `fff-mcp` binary lives at `/Users/root1/.local/bin/fff-mcp` and is registered at user scope\nin `~/.config/devin/config.json`. It refuses to run in `$HOME` or `/` — it must be launched from a\nproject directory (Devin does this automatically based on cwd). Update with:\n`curl -fsSL https://raw.githubusercontent.com/dmtrKovalenko/fff.nvim/main/install-mcp.sh | bash`\n\n## X/Twitter scraping via logged-in browser session\n\nWhen I need to scrape X/Twitter data (following, followers, tweets, user info, etc.),\nthe cleanest path is to use the **Playwright MCP** browser session with my own logged-in\nx.com account, rather than spinning up twscrape's account-pool flow. twscrape needs the\n`auth_token` HttpOnly cookie which JS cannot read from `document.cookie`; the browser\nsession attaches all cookies automatically.\n\n### Flow\n1. `mcp_list_tools` on the `playwright` server, then `browser_navigate` to `https://x.com`.\n2. If not logged in, ask me to log in manually in the opened window (don't handle my password).\n3. Once on `https://x.com/home`, read `ct0` from `document.cookie`:\n `document.cookie.match(/ct0=([^;]+)/)[1]`\n4. Call X's GraphQL endpoints directly via `fetch()` inside `browser_evaluate`. Required headers:\n - `authorization: Bearer AAAAAAAAAAAAAAAAAAAAANRILgAAAAAAnNwIzUejRCOuH5E6I8xnZz4puTs%3D1Zv7ttfk8LF81IUq16cHjhLTvJu4FA33AGWWjCpTnA` (the public web-app bearer token)\n - `x-csrf-token: `\n - `x-twitter-auth-type: OAuth2Session`\n - `x-twitter-active-user: yes`\n - `content-type: application/json`\n5. Paginate timelines by reading `content.cursorType === \"Bottom\"` entries and passing\n the value back as `variables.cursor` until it stops changing.\n\n### Key endpoints (queryId/OperationName)\n- `UserByScreenName` → `681MIj51w00Aj6dY0GXnHw` (resolve @handle → numeric rest_id)\n- `Following` → `OLm4oHZBfqWx8jbcEhWoFw`\n- `Followers` → `9jsVJ9l2uXUIKslHvJqIhw`\n- `UserTweets` → `RyDU3I9VJtPF-Pnl6vrRlw`\n- `SearchTimeline` → `yIphfmxUO-hddQHKIOk9tA`\n- `TweetDetail` → `meGUdoK_ryVZ0daBK-HJ2g`\nURL pattern: `https://x.com/i/api/graphql//?variables=&features=`\n\n### Response schema notes (current X web build)\n- User objects now put `screen_name` / `name` under `core`, NOT `legacy.screen_name`.\n twscrape's parser still reads `legacy.screen_name` and returns empty — needs updating.\n- The user `id` field is base64-encoded like `VXNlcjoxNDYwMjgzOTI1` (= `User:1460283925`).\n Decode with `atob(u.id).split(':')[1]` to get the numeric rest_id. `u.rest_id` may also\n be present directly.\n- `is_blue_verified` is the verified flag. `legacy.followers_count`, `legacy.description`\n still exist under `legacy`.\n- Filter timeline entries by `content.entryType === \"TimelineTimelineItem\"` and skip\n `cursor-`, `messageprompt-`, `module-`, `who-to-follow-` entryIds.\n\n### Features dict\nUse the full `GQL_FEATURES` block from twscrape's `api.py` — without it X returns\n`(336) The following features cannot be null`. Pass it URL-encoded as the `features` param.\n\n### Where things live\n- Output CSV: `~/Downloads/utilities/sdand_following.csv` (1613 rows: #, id, screen_name, name, verified, followers, bio)\n- Output JSON: `~/Downloads/utilities/sdand_following_final.json` (double-encoded JSON string; parse with `json.loads(json.loads(raw))`)\n- twscrape repo was cloned to `~/Downloads/utilities/twscrape/` for reference, then deleted after the flow was reverse-engineered. Re-clone from https://github.com/vladkens/twscrape.git if needed.\n\n## Fast Whisper transcription on Modal (A10G)\n\nFor transcribing long-form audio/video (interviews, podcasts, X/Twitter videos), use the\nutility at `~/Downloads/utilities/whisper_x/whisper_transcribe.py`. It does the full\npipeline: URL → yt-dlp download → ffmpeg audio extract → Modal volume upload →\nfaster-whisper on A10G → JSON + TXT output. Validated at **2.3 min wall clock for 65 min\nof audio** (no caching at any layer).\n\n### Usage\nShell alias (defined in `~/.zshrc`): `whisper`\n```bash\n# Transcribe an X/Twitter video (picks first playlist item)\nwhisper \"https://x.com/.../status/123\"\n\n# Pick a specific playlist item, use a smaller model\nwhisper \"https://x.com/...\" --playlist-item 2 --model-size medium\n\n# Transcribe a local audio file\nwhisper /path/to/audio.mp3 --name my-podcast\n\n# Custom output dir + keep downloaded source\nwhisper \"https://...\" --outdir ./transcripts --keep-source\n```\nTranscript text goes to stdout (pipe with `| pbcopy`); structured JSON + readable TXT\nsaved to `/.json` and `/.txt`.\n\n### Key optimizations (vs naive T4 run that took 11.7 min)\n- **A10G GPU** (~8x fp16 throughput vs T4; Modal ~$0.60/hr vs ~$0.16/hr — pennies for short jobs)\n- **`BatchedInferencePipeline`** with `batch_size=16` — batches encoder/decoder across chunks (2-4x)\n- **`beam_size=1`** (greedy) — ~2x faster, negligible WER increase for conversational speech\n- **`vad_filter=True`** — skips silence segments\n- **`compute_type=\"float16\"`** — halves memory bandwidth\n- **No caching**: `force_build=True` on apt/pip steps + unique `download_root` per run forces\n fresh image rebuild + fresh HF model download every time\n\n### Pinned versions (must match)\n- `faster-whisper==1.1.1` (provides `BatchedInferencePipeline`)\n- `ctranslate2==4.8.0`\n- Base image: `nvidia/cuda:12.6.3-cudnn-runtime-ubuntu22.04` (provides `libcublas.so.12`;\n `debian_slim` fails with `RuntimeError: Library libcublas.so.12 is not found`)\n\n### Audio prep (done automatically by the utility)\n```bash\nffmpeg -y -i input.mp4 -vn -ac 1 -ar 16000 -c:a aac -b:a 64k audio.m4a\n```\nMono 16kHz 64kbps AAC — a 65-min video (151 MB stream) becomes ~35 MB audio.\n\n### X/Twitter download notes\n- Tweet URLs can contain **playlists** (multiple videos). Use `--playlist-item N` to pick one.\n- Always use `-f bestaudio/best` to avoid downloading multi-GB high-bitrate video streams.\n- A 65-min interview's video variant can be 2.8+ GB; audio-only is ~63 MB (128 kbps).\n\n### Where things live\n- Utility: `~/Downloads/utilities/whisper_x/whisper_transcribe.py`\n- Strategy doc: `~/Downloads/utilities/whisper_x/STRATEGY.md` (full optimization breakdown)\n- Modal app (standalone): `~/Downloads/utilities/whisper_x/transcribe_fast.py`\n- Modal volume: `whisper-audio` (created automatically; holds uploaded audio files)\n- Modal profile: `aidenhuang-personal` (workspace with GPU access)\n\n\n\n\n\n\n","created_at":"2026-07-09T04:21:43.221757Z","finish_reason":null,"metrics":null},{"message_id":"a9cb7194-5e36-4fcf-a76a-d653fe0fac68","node_id":24,"role":"user","content":"https://www.youtube.com/watch?v=AZrU6y3pUcU","created_at":"2026-07-09T04:21:43.724973Z","finish_reason":null,"metrics":null},{"message_id":"5d31302a-fb6a-43d9-bd2a-4ee1906ea247","node_id":25,"role":"system","content":"\nThe following skills can be invoked using the `skill` tool. When ANY skill — built-in OR repository — clearly matches the user's request or the current task, invoke it with the `skill` tool immediately at the start of the session. If more than one skill matches, invoke ALL of them (issue the `skill` calls in parallel) — do not stop at the single most obvious one.\n\n- **workers-best-practices**: Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.codeium/windsurf/skills/workers-best-practices/SKILL.md)\n- **web-perf**: Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge. (source: /Users/root1/.claude/skills/web-perf/SKILL.md)\n- **wrangler**: Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.claude/skills/wrangler/SKILL.md)\n- **background-computer-use**: Launch and use the local BackgroundComputerUse macOS runtime through its self-documenting loopback API. Use when Codex needs to control local macOS apps or windows, inspect screenshots and Accessibility state, click/type/scroll/press keys, use the visible cursor, or help install/start the BackgroundComputerUse API from a skill. (source: /Users/root1/.devin/skills/background-computer-use/SKILL.md)\n- **cloudflare**: Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/cloudflare/SKILL.md)\n- **cloudflare**: Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/cloudflare/SKILL.md)\n- **cloudflare-one-migrations**: Plans migrations from Zscaler ZIA/ZPA, Palo Alto, legacy VPN, SWG, or SASE stacks to Cloudflare One. Use for migration assessments, policy mapping, rollout plans, and parity/gap analysis. (source: /Users/root1/.claude/skills/cloudflare-one-migrations/SKILL.md)\n- **sandbox-sdk**: Build sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/sandbox-sdk/SKILL.md)\n- **workers-best-practices**: Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/workers-best-practices/SKILL.md)\n- **cloudflare-one-migrations**: Plans migrations from Zscaler ZIA/ZPA, Palo Alto, legacy VPN, SWG, or SASE stacks to Cloudflare One. Use for migration assessments, policy mapping, rollout plans, and parity/gap analysis. (source: /Users/root1/.config/devin/skills/cloudflare-one-migrations/SKILL.md)\n- **agents-sdk**: Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, chat applications, voice agents, or browser automation. Covers Agent class, state management, callable RPC, Workflows, durable execution, queues, retries, observability, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.codeium/windsurf/skills/agents-sdk/SKILL.md)\n- **cloudflare-one**: Guides Cloudflare One Zero Trust and SASE work across Access, Gateway, WARP, Tunnel, Cloudflare WAN, DLP, CASB, device posture, and identity. Use when designing, configuring, troubleshooting, or reviewing Cloudflare One deployments. Retrieval-first: use current Cloudflare docs/API schemas instead of embedded product docs. (source: /Users/root1/.agents/skills/cloudflare-one/SKILL.md)\n- **turnstile-spin**: Set up Cloudflare Turnstile end-to-end in a project — scan the codebase, create the widget via the Cloudflare API, deploy the managed siteverify Worker, write the frontend snippets, validate, and persist the skill. Load this when a user asks to add Turnstile, set up CAPTCHA, protect a form from bots, or fix a Turnstile integration. Mirrors developers.cloudflare.com/turnstile/spin. (source: /Users/root1/.claude/skills/turnstile-spin/SKILL.md)\n- **cloudflare-email-service**: Send and receive transactional emails with Cloudflare Email Service (Email Sending + Email Routing). Use when building email sending (Workers binding or REST API), email routing, Agents SDK email handling, or integrating email into any app — Workers, Node.js, Python, Go, etc. Also use for email deliverability, SPF/DKIM/DMARC, wrangler email setup, MCP email tools, or when a coding agent needs to send emails. Even for simple requests like \"add email to my Worker\" — this skill has critical config details. (source: /Users/root1/.agents/skills/cloudflare-email-service/SKILL.md)\n- **turnstile-spin**: Set up Cloudflare Turnstile end-to-end in a project — scan the codebase, create the widget via the Cloudflare API, deploy the managed siteverify Worker, write the frontend snippets, validate, and persist the skill. Load this when a user asks to add Turnstile, set up CAPTCHA, protect a form from bots, or fix a Turnstile integration. Mirrors developers.cloudflare.com/turnstile/spin. (source: /Users/root1/.config/devin/skills/turnstile-spin/SKILL.md)\n- **wrangler**: Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/wrangler/SKILL.md)\n- **cloudflare-email-service**: Send and receive transactional emails with Cloudflare Email Service (Email Sending + Email Routing). Use when building email sending (Workers binding or REST API), email routing, Agents SDK email handling, or integrating email into any app — Workers, Node.js, Python, Go, etc. Also use for email deliverability, SPF/DKIM/DMARC, wrangler email setup, MCP email tools, or when a coding agent needs to send emails. Even for simple requests like \"add email to my Worker\" — this skill has critical config details. (source: /Users/root1/.config/devin/skills/cloudflare-email-service/SKILL.md)\n- **find-skills**: Helps users discover and install agent skills when they ask questions like \"how do I do X\", \"find a skill for X\", \"is there a skill that can...\", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill. (source: /Users/root1/.agents/skills/find-skills/SKILL.md)\n- **cloudflare-one**: Guides Cloudflare One Zero Trust and SASE work across Access, Gateway, WARP, Tunnel, Cloudflare WAN, DLP, CASB, device posture, and identity. Use when designing, configuring, troubleshooting, or reviewing Cloudflare One deployments. Retrieval-first: use current Cloudflare docs/API schemas instead of embedded product docs. (source: /Users/root1/.config/devin/skills/cloudflare-one/SKILL.md)\n- **cloudflare-agent-setup**: (source: /Users/root1/.devin/skills/cloudflare-agent-setup/SKILL.md)\n- **durable-objects**: Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/durable-objects/SKILL.md)\n- **durable-objects**: Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/durable-objects/SKILL.md)\n- **sandbox-sdk**: Build sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/sandbox-sdk/SKILL.md)\n- **agents-sdk**: Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, chat applications, voice agents, or browser automation. Covers Agent class, state management, callable RPC, Workflows, durable execution, queues, retries, observability, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/agents-sdk/SKILL.md)\n- **web-perf**: Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/web-perf/SKILL.md)\n- **devin-cli**: Look up Devin CLI documentation (skills, extensibility, configuration, commands, models, troubleshooting) (source: /Users/root1/.local/share/devin/cli/_versions/3000.1.27/share/devin/docs)\n- **declarative-repo-setup**: Generate and verify a working environment.yaml (Devin snapshot-setup blueprint) for a repo (source: builtin:drs)\n","created_at":"2026-07-09T04:21:43.725704Z","finish_reason":null,"metrics":null},{"message_id":"df1057f0-e52f-4974-b608-f2ad1fb3fe88","node_id":27,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:0","name":"exec","arguments":{"command":"yt-dlp --version"}}],"thinking":"The user provided a YouTube link. According to the AGENTS.md rule: \"If I ever paste in a YouTube link, use yt-dlp to summarize the video. get the autogenerated captions to do this\". Also \"if asked to summarize a YouTube video, do not name the session until after reading and understanding the full YouTube video transcript\". The user didn't ask to summarize explicitly, but the rule says \"If I ever paste in a YouTube link, use yt-dlp to summarize the video.\" So I should summarize. Need to get autogenerated captions. Need to use yt-dlp. Need to check if installed. We'll likely download the video's subtitles using yt-dlp. We should not name the session. We need to read the transcript. We need to use tools. Plan: run yt-dlp to list available subtitles and download auto-generated captions. Then summarize. Need to not overthink. Let's do it. First check yt-dlp presence and use.","created_at":"2026-07-09T04:21:45.067339Z","finish_reason":"tool_calls","metrics":{"ttft_ms":665,"total_time_ms":1070,"input_tokens":6622,"output_tokens":223,"cache_read_tokens":10624,"cache_creation_tokens":null,"tpot_ms":1.8161434977578474,"tokens_per_sec":550.6172839506173}},{"message_id":"32d51d53-9edd-441a-95a1-107c068bdc87","node_id":28,"role":"tool","content":"Output from command in shell 3a41da:\n2025.12.08\n\n\nExit code: 0","created_at":"2026-07-09T04:21:45.499653Z","finish_reason":null,"metrics":null},{"message_id":"563f67ff-9e0d-4c77-a760-ea4cba5dccd5","node_id":30,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:1","name":"exec","arguments":{"command":"yt-dlp --list-subs \"https://www.youtube.com/watch?v=AZrU6y3pUcU\""}}],"thinking":"yt-dlp installed. Need to get autogenerated captions and summarize. I can use yt-dlp to download only subtitles (--skip-download), list subtitles. The URL is https://www.youtube.com/watch?v=AZrU6y3pUcU. Need to determine language and title. Let's list available subs.","created_at":"2026-07-09T04:21:46.226784Z","finish_reason":"tool_calls","metrics":{"ttft_ms":505,"total_time_ms":726,"input_tokens":356,"output_tokens":109,"cache_read_tokens":17152,"cache_creation_tokens":null,"tpot_ms":2.0275229357798166,"tokens_per_sec":493.21266968325796}},{"message_id":"8ce79f15-d5ac-4bf5-91e1-c339eac81e56","node_id":31,"role":"tool","content":"Output from command in shell 0f2646:\nWARNING: Your yt-dlp version (2025.12.08) is older than 90 days!\n It is strongly recommended to always use the latest version.\n You installed yt-dlp with pip or using the wheel from PyPi; Use that to update.\n To suppress this warning, add --no-update to your command/config.\n[youtube] Extracting URL: https://www.youtube.com/watch?v=AZrU6y3pUcU\n[youtube] AZrU6y3pUcU: Downloading webpage\n[youtube] AZrU6y3pUcU: Downloading tv client config\n[youtube] AZrU6y3pUcU: Downloading player ecd4b80a-main\n[youtube] AZrU6y3pUcU: Downloading tv player API JSON\n[youtube] AZrU6y3pUcU: Downloading android sdkless player API JSON\n[youtube] [jsc:deno] Solving JS challenges using deno\nWARNING: [youtube] [jsc] Remote components challenge solver script (deno) and NPM package (deno) were skipped. These may be required to solve JS challenges. You can enable these downloads with --remote-components ejs:github (recommended) or --remote-components ejs:npm , respectively. For more information and alternatives, refer to https://github.com/yt-dlp/yt-dlp/wiki/EJS\nWARNING: [youtube] AZrU6y3pUcU: n challenge solving failed: Some formats may be missing. Ensure you have a supported JavaScript runtime and challenge solver script distribution installed. Review any warnings presented before this message. For more details, refer to https://github.com/yt-dlp/yt-dlp/wiki/EJS\n[info] Available automatic captions for AZrU6y3pUcU:\nLanguage Name Formats\nab Abkhazian vtt, srt, ttml, srv3, srv2, srv1, json3\naa Afar vtt, srt, ttml, srv3, srv2, srv1, json3\naf Afrikaans vtt, srt, ttml, srv3, srv2, srv1, json3\nak Akan vtt, srt, ttml, srv3, srv2, srv1, json3\nsq Albanian vtt, srt, ttml, srv3, srv2, srv1, json3\nam Amharic vtt, srt, ttml, srv3, srv2, srv1, json3\nar Arabic vtt, srt, ttml, srv3, srv2, srv1, json3\nhy Armenian vtt, srt, ttml, srv3, srv2, srv1, json3\nas Assamese vtt, srt, ttml, srv3, srv2, srv1, json3\nay Aymara vtt, srt, ttml, srv3, srv2, srv1, json3\naz Azerbaijani vtt, srt, ttml, srv3, srv2, srv1, json3\nbn Bangla vtt, srt, ttml, srv3, srv2, srv1, json3\nba Bashkir vtt, srt, ttml, srv3, srv2, srv1, json3\neu Basque vtt, srt, ttml, srv3, srv2, srv1, json3\nbe Belarusian vtt, srt, ttml, srv3, srv2, srv1, json3\nbho Bhojpuri vtt, srt, ttml, srv3, srv2, srv1, json3\nbs Bosnian vtt, srt, ttml, srv3, srv2, srv1, json3\nbr Breton vtt, srt, ttml, srv3, srv2, srv1, json3\nbg Bulgarian vtt, srt, ttml, srv3, srv2, srv1, json3\nmy Burmese vtt, srt, ttml, srv3, srv2, srv1, json3\nca Catalan vtt, srt, ttml, srv3, srv2, srv1, json3\nceb Cebuano vtt, srt, ttml, srv3, srv2, srv1, json3\nzh-Hans Chinese (Simplified) vtt, srt, ttml, srv3, srv2, srv1, json3\nzh-Hant Chinese (Traditional) vtt, srt, ttml, srv3, srv2, srv1, json3\nco Corsican vtt, srt, ttml, srv3, srv2, srv1, json3\nhr Croatian vtt, srt, ttml, srv3, srv2, srv1, json3\ncs Czech vtt, srt, ttml, srv3, srv2, srv1, json3\nda Danish vtt, srt, ttml, srv3, srv2, srv1, json3\ndv Divehi vtt, srt, ttml, srv3, srv2, srv1, json3\nnl Dutch vtt, srt, ttml, srv3, srv2, srv1, json3\ndz Dzongkha vtt, srt, ttml, srv3, srv2, srv1, json3\nen-orig English (Original) vtt, srt, ttml, srv3, srv2, srv1, json3\nen English vtt, srt, ttml, srv3, srv2, srv1, json3\neo Esperanto vtt, srt, ttml, srv3, srv2, srv1, json3\net Estonian vtt, srt, ttml, srv3, srv2, srv1, json3\nee Ewe vtt, srt, ttml, srv3, srv2, srv1, json3\nfo Faroese vtt, srt, ttml, srv3, srv2, srv1, json3\nfj Fijian vtt, srt, ttml, srv3, srv2, srv1, json3\nfil Filipino vtt, srt, ttml, srv3, srv2, srv1, json3\nfi Finnish vtt, srt, ttml, srv3, srv2, srv1, json3\nfr French vtt, srt, ttml, srv3, srv2, srv1, json3\ngaa Ga vtt, srt, ttml, srv3, srv2, srv1, json3\ngl Galician vtt, srt, ttml, srv3, srv2, srv1, json3\nlg Ganda vtt, srt, ttml, srv3, srv2, srv1, json3\nka Georgian vtt, srt, ttml, srv3, srv2, srv1, json3\nde German vtt, srt, ttml, srv3, srv2, srv1, json3\nel Greek vtt, srt, ttml, srv3, srv2, srv1, json3\ngn Guarani vtt, srt, ttml, srv3, srv2, srv1, json3\ngu Gujarati vtt, srt, ttml, srv3, srv2, srv1, json3\nht Haitian Creole vtt, srt, ttml, srv3, srv2, srv1, json3\nha Hausa vtt, srt, ttml, srv3, srv2, srv1, json3\nhaw Hawaiian vtt, srt, ttml, srv3, srv2, srv1, json3\niw Hebrew vtt, srt, ttml, srv3, srv2, srv1, json3\nhi Hindi vtt, srt, ttml, srv3, srv2, srv1, json3\nhmn Hmong vtt, srt, ttml, srv3, srv2, srv1, json3\nhu Hungarian vtt, srt, ttml, srv3, srv2, srv1, json3\nis Icelandic vtt, srt, ttml, srv3, srv2, srv1, json3\nig Igbo vtt, srt, ttml, srv3, srv2, srv1, json3\nid Indonesian vtt, srt, ttml, srv3, srv2, srv1, json3\niu Inuktitut vtt, srt, ttml, srv3, srv2, srv1, json3\nga Irish vtt, srt, ttml, srv3, srv2, srv1, json3\nit Italian vtt, srt, ttml, srv3, srv2, srv1, json3\nja Japanese vtt, srt, ttml, srv3, srv2, srv1, json3\njv Javanese vtt, srt, ttml, srv3, srv2, srv1, json3\nkl Kalaallisut vtt, srt, ttml, srv3, srv2, srv1, json3\nkn Kannada vtt, srt, ttml, srv3, srv2, srv1, json3\nkk Kazakh vtt, srt, ttml, srv3, srv2, srv1, json3\nkha Khasi vtt, srt, ttml, srv3, srv2, srv1, json3\nkm Khmer vtt, srt, ttml, srv3, srv2, srv1, json3\nrw Kinyarwanda vtt, srt, ttml, srv3, srv2, srv1, json3\nko Korean vtt, srt, ttml, srv3, srv2, srv1, json3\nkri Krio vtt, srt, ttml, srv3, srv2, srv1, json3\nku Kurdish vtt, srt, ttml, srv3, srv2, srv1, json3\nky Kyrgyz vtt, srt, ttml, srv3, srv2, srv1, json3\nlo Lao vtt, srt, ttml, srv3, srv2, srv1, json3\nla Latin vtt, srt, ttml, srv3, srv2, srv1, json3\nlv Latvian vtt, srt, ttml, srv3, srv2, srv1, json3\nln Lingala vtt, srt, ttml, srv3, srv2, srv1, json3\nlt Lithuanian vtt, srt, ttml, srv3, srv2, srv1, json3\nlua Luba-Lulua vtt, srt, ttml, srv3, srv2, srv1, json3\nluo Luo vtt, srt, ttml, srv3, srv2, srv1, json3\nlb Luxembourgish vtt, srt, ttml, srv3, srv2, srv1, json3\nmk Macedonian vtt, srt, ttml, srv3, srv2, srv1, json3\nmg Malagasy vtt, srt, ttml, srv3, srv2, srv1, json3\nms Malay vtt, srt, ttml, srv3, srv2, srv1, json3\nml Malayalam vtt, srt, ttml, srv3, srv2, srv1, json3\nmt Maltese vtt, srt, ttml, srv3, srv2, srv1, json3\ngv Manx vtt, srt, ttml, srv3, srv2, srv1, json3\nmi Māori vtt, srt, ttml, srv3, srv2, srv1, json3\nmr Marathi vtt, srt, ttml, srv3, srv2, srv1, json3\nmn Mongolian vtt, srt, ttml, srv3, srv2, srv1, json3\nmfe Morisyen vtt, srt, ttml, srv3, srv2, srv1, json3\nne Nepali vtt, srt, ttml, srv3, srv2, srv1, json3\nnew Newari vtt, srt, ttml, srv3, srv2, srv1, json3\nnso Northern Sotho vtt, srt, ttml, srv3, srv2, srv1, json3\nno Norwegian vtt, srt, ttml, srv3, srv2, srv1, json3\nny Nyanja vtt, srt, ttml, srv3, srv2, srv1, json3\noc Occitan vtt, srt, ttml, srv3, srv2, srv1, json3\nor Odia vtt, srt, ttml, srv3, srv2, srv1, json3\nom Oromo vtt, srt, ttml, srv3, srv2, srv1, json3\nos Ossetic vtt, srt, ttml, srv3, srv2, srv1, json3\npam Pampanga vtt, srt, ttml, srv3, srv2, srv1, json3\nps Pashto vtt, srt, ttml, srv3, srv2, srv1, json3\nfa Persian vtt, srt, ttml, srv3, srv2, srv1, json3\npl Polish vtt, srt, ttml, srv3, srv2, srv1, json3\npt Portuguese vtt, srt, ttml, srv3, srv2, srv1, json3\npt-PT Portuguese (Portugal) vtt, srt, ttml, srv3, srv2, srv1, json3\npa Punjabi vtt, srt, ttml, srv3, srv2, srv1, json3\nqu Quechua vtt, srt, ttml, srv3, srv2, srv1, json3\nro Romanian vtt, srt, ttml, srv3, srv2, srv1, json3\nrn Rundi vtt, srt, ttml, srv3, srv2, srv1, json3\nru Russian vtt, srt, ttml, srv3, srv2, srv1, json3\nsm Samoan vtt, srt, ttml, srv3, srv2, srv1, json3\nsg Sango vtt, srt, ttml, srv3, srv2, srv1, json3\nsa Sanskrit vtt, srt, ttml, srv3, srv2, srv1, json3\ngd Scottish Gaelic vtt, srt, ttml, srv3, srv2, srv1, json3\nsr Serbian vtt, srt, ttml, srv3, srv2, srv1, json3\ncrs Seselwa Creole French vtt, srt, ttml, srv3, srv2, srv1, json3\nsn Shona vtt, srt, ttml, srv3, srv2, srv1, json3\nsd Sindhi vtt, srt, ttml, srv3, srv2, srv1, json3\nsi Sinhala vtt, srt, ttml, srv3, srv2, srv1, json3\nsk Slovak vtt, srt, ttml, srv3, srv2, srv1, json3\nsl Slovenian vtt, srt, ttml, srv3, srv2, srv1, json3\nso Somali vtt, srt, ttml, srv3, srv2, srv1, json3\nst Southern Sotho vtt, srt, ttml, srv3, srv2, srv1, json3\nes Spanish vtt, srt, ttml, srv3, srv2, srv1, json3\nsu Sundanese vtt, srt, ttml, srv3, srv2, srv1, json3\nsw Swahili vtt, srt, ttml, srv3, srv2, srv1, json3\nss Swati vtt, srt, ttml, srv3, srv2, srv1, json3\nsv Swedish vtt, srt, ttml, srv3, srv2, srv1, json3\ntg Tajik vtt, srt, ttml, srv3, srv2, srv1, json3\nta Tamil vtt, srt, ttml, srv3, srv2, srv1, json3\ntt Tatar vtt, srt, ttml, srv3, srv2, srv1, json3\nte Telugu vtt, srt, ttml, srv3, srv2, srv1, json3\nth Thai vtt, srt, ttml, srv3, srv2, srv1, json3\nbo Tibetan vtt, srt, ttml, srv3, srv2, srv1, json3\nti Tigrinya vtt, srt, ttml, srv3, srv2, srv1, json3\nto Tongan vtt, srt, ttml, srv3, srv2, srv1, json3\nts Tsonga vtt, srt, ttml, srv3, srv2, srv1, json3\ntn Tswana vtt, srt, ttml, srv3, srv2, srv1, json3\ntum Tumbuka vtt, srt, ttml, srv3, srv2, srv1, json3\ntr Turkish vtt, srt, ttml, srv3, srv2, srv1, json3\ntk Turkmen vtt, srt, ttml, srv3, srv2, srv1, json3\nuk Ukrainian vtt, srt, ttml, srv3, srv2, srv1, json3\nur Urdu vtt, srt, ttml, srv3, srv2, srv1, json3\nug Uyghur vtt, srt, ttml, srv3, srv2, srv1, json3\nuz Uzbek vtt, srt, ttml, srv3, srv2, srv1, json3\nve Venda vtt, srt, ttml, srv3, srv2, srv1, json3\nvi Vietnamese vtt, srt, ttml, srv3, srv2, srv1, json3\nwar Waray vtt, srt, ttml, srv3, srv2, srv1, json3\ncy Welsh vtt, srt, ttml, srv3, srv2, srv1, json3\nfy Western Frisian vtt, srt, ttml, srv3, srv2, srv1, json3\nwo Wolof vtt, srt, ttml, srv3, srv2, srv1, json3\nxh Xhosa vtt, srt, ttml, srv3, srv2, srv1, json3\nyi Yiddish vtt, srt, ttml, srv3, srv2, srv1, json3\nyo Yoruba vtt, srt, ttml, srv3, srv2, srv1, json3\nzu Zulu vtt, srt, ttml, srv3, srv2, srv1, json3\nAZrU6y3pUcU has no subtitles\n\n\nExit code: 0","created_at":"2026-07-09T04:21:48.449451Z","finish_reason":null,"metrics":null},{"message_id":"e3a6b14a-6d20-4bd8-af67-fcb7f927496a","node_id":33,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:2","name":"exec","arguments":{"command":"yt-dlp --skip-download --write-auto-subs --sub-lang en --sub-format srt --convert-subs srt -o \"/tmp/%(title)s.%(ext)s\" \"https://www.youtube.com/watch?v=AZrU6y3pUcU\""}}],"thinking":"Need to download auto-generated captions. Available automatic captions includes en-orig and en. Need to use --write-auto-subs and --skip-download. Use English. Let's download to a temp file.","created_at":"2026-07-09T04:21:49.277052Z","finish_reason":"tool_calls","metrics":{"ttft_ms":642,"total_time_ms":826,"input_tokens":4780,"output_tokens":116,"cache_read_tokens":17408,"cache_creation_tokens":null,"tpot_ms":1.5862068965517242,"tokens_per_sec":630.4347826086956}},{"message_id":"83b856a2-449d-4ad1-89d3-ff3c3732f859","node_id":34,"role":"tool","content":"Output from command in shell 7334da:\nWARNING: Your yt-dlp version (2025.12.08) is older than 90 days!\n It is strongly recommended to always use the latest version.\n You installed yt-dlp with pip or using the wheel from PyPi; Use that to update.\n To suppress this warning, add --no-update to your command/config.\n[youtube] Extracting URL: https://www.youtube.com/watch?v=AZrU6y3pUcU\n[youtube] AZrU6y3pUcU: Downloading webpage\n[youtube] AZrU6y3pUcU: Downloading tv client config\n[youtube] AZrU6y3pUcU: Downloading player ecd4b80a-main\n[youtube] AZrU6y3pUcU: Downloading tv player API JSON\n[youtube] AZrU6y3pUcU: Downloading android sdkless player API JSON\n[youtube] [jsc:deno] Solving JS challenges using deno\nWARNING: [youtube] [jsc] Remote components challenge solver script (deno) and NPM package (deno) were skipped. These may be required to solve JS challenges. You can enable these downloads with --remote-components ejs:github (recommended) or --remote-components ejs:npm , respectively. For more information and alternatives, refer to https://github.com/yt-dlp/yt-dlp/wiki/EJS\nWARNING: [youtube] AZrU6y3pUcU: n challenge solving failed: Some formats may be missing. Ensure you have a supported JavaScript runtime and challenge solver script distribution installed. Review any warnings presented before this message. For more details, refer to https://github.com/yt-dlp/yt-dlp/wiki/EJS\n[info] AZrU6y3pUcU: Downloading subtitles: en\n[info] AZrU6y3pUcU: Downloading 1 format(s): 139-20\n[info] Writing video subtitles to: /tmp/Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown.en.srt\n[download] Destination: /tmp/Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown.en.srt\n\r[download] 1.00KiB at Unknown B/s (00:00:00)\r[download] 3.00KiB at Unknown B/s (00:00:00)\r[download] 7.00KiB at Unknown B/s (00:00:00)\r[download] 15.00KiB at 4.49MiB/s (00:00:00)\r[download] 31.00KiB at 1.94MiB/s (00:00:00)\r[download] 63.00KiB at 2.57MiB/s (00:00:00)\r[download] 77.52KiB at 2.76MiB/s (00:00:00)\r[download] 100% of 77.52KiB in 00:00:00 at 573.24KiB/s\n[SubtitlesConvertor] Converting subtitles\n[SubtitlesConvertor] Subtitle file for srt is already in the requested format\n\n\nExit code: 0","created_at":"2026-07-09T04:21:51.727730Z","finish_reason":null,"metrics":null},{"message_id":"6185e67c-b303-417d-8de7-f99ffc2c7f41","node_id":36,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:3","name":"read","arguments":{"file_path":"/tmp/Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown.en.srt"}}],"thinking":"Got subtitle file. Title: \"Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown\". Need to read transcript. It may be long, 77KB. Let's read.","created_at":"2026-07-09T04:21:52.408125Z","finish_reason":"tool_calls","metrics":{"ttft_ms":558,"total_time_ms":679,"input_tokens":929,"output_tokens":89,"cache_read_tokens":22144,"cache_creation_tokens":null,"tpot_ms":1.3595505617977528,"tokens_per_sec":735.5371900826447}},{"message_id":"8d2888e9-f84d-4cfb-bad9-c4ae18e82a3b","node_id":37,"role":"tool","content":"\n 1|1\n 2|00:00:00,080 --> 00:00:04,560\n 3|With GBT3, you couldn't scale test time\n 4|\n 5|2\n 6|00:00:02,800 --> 00:00:05,839\n 7|compute. Like if you gave it a budget of\n 8|\n 9|3\n 10|00:00:04,560 --> 00:00:07,919\n 11|$10 million and said, \"Okay, well, let's\n 12|\n 13|4\n 14|00:00:05,839 --> 00:00:09,760\n 15|see what GB3 can do.\" It really can't do\n 16|\n 17|5\n 18|00:00:07,919 --> 00:00:11,360\n 19|that much. The precurren frameworks and\n 20|\n 21|6\n 22|00:00:09,760 --> 00:00:12,719\n 23|responsible scaling policies, they don't\n 24|\n 25|7\n 26|00:00:11,360 --> 00:00:13,679\n 27|really account for the amount of testime\n 28|\n 29|8\n 30|00:00:12,719 --> 00:00:15,280\n 31|compute. They just say, \"Okay, well,\n 32|\n 33|9\n 34|00:00:13,679 --> 00:00:17,440\n 35|what's the capability of the model?\" The\n 36|\n 37|10\n 38|00:00:15,280 --> 00:00:19,359\n 39|problem is we're in a world now where\n 40|\n 41|11\n 42|00:00:17,440 --> 00:00:21,279\n 43|the capability of the model is a\n 44|\n 45|12\n 46|00:00:19,359 --> 00:00:23,279\n 47|function of how much money you put into\n 48|\n 49|13\n 50|00:00:21,279 --> 00:00:26,000\n 51|it. Basically, if you give it a budget\n 52|\n 53|14\n 54|00:00:23,279 --> 00:00:27,680\n 55|of $10,000, it can do a lot more than\n 56|\n 57|15\n 58|00:00:26,000 --> 00:00:29,199\n 59|what it can do with a budget of $10.\n 60|\n 61|16\n 62|00:00:27,680 --> 00:00:31,760\n 63|Give it a budget of $10 million, you can\n 64|\n 65|17\n 66|00:00:29,199 --> 00:00:33,920\n 67|do even more. At what budget should you\n 68|\n 69|18\n 70|00:00:31,760 --> 00:00:35,600\n 71|evaluate these models? The policies that\n 72|\n 73|19\n 74|00:00:33,920 --> 00:00:38,600\n 75|exist today don't really address that\n 76|\n 77|20\n 78|00:00:35,600 --> 00:00:38,600\n 79|question.\n 80|\n 81|21\n 82|00:00:43,280 --> 00:00:47,680\n 83|Hi listeners, I'm Sarah Goa and welcome\n 84|\n 85|22\n 86|00:00:45,680 --> 00:00:50,000\n 87|back to No Priors. Today I'm here with\n 88|\n 89|23\n 90|00:00:47,680 --> 00:00:52,000\n 91|Nome Brown, one of our godfathers of AI\n 92|\n 93|24\n 94|00:00:50,000 --> 00:00:54,800\n 95|reasoning. We talk about the broken\n 96|\n 97|25\n 98|00:00:52,000 --> 00:00:57,280\n 99|state of evaluations, very large-scale\n 100|\n 101|26\n 102|00:00:54,800 --> 00:00:59,120\n 103|test time compute, how he thinks about\n 104|\n 105|27\n 106|00:00:57,280 --> 00:01:01,600\n 107|recursive self-improvement, and what's\n 108|\n 109|28\n 110|00:00:59,120 --> 00:01:04,320\n 111|next on the horizon for competition at\n 112|\n 113|29\n 114|00:01:01,600 --> 00:01:05,680\n 115|the frontier. Welcome, Gnome. I'm so\n 116|\n 117|30\n 118|00:01:04,320 --> 00:01:06,799\n 119|excited to have you back.\n 120|\n 121|31\n 122|00:01:05,680 --> 00:01:08,640\n 123|>> That's great to be back. Yeah,\n 124|\n 125|32\n 126|00:01:06,799 --> 00:01:11,200\n 127|>> you are our first guest. I'm very proud\n 128|\n 129|33\n 130|00:01:08,640 --> 00:01:14,560\n 131|of my taste in friends and researchers\n 132|\n 133|34\n 134|00:01:11,200 --> 00:01:17,680\n 135|for uh for the pod. um given you know\n 136|\n 137|35\n 138|00:01:14,560 --> 00:01:19,520\n 139|how important uh you know inference time\n 140|\n 141|36\n 142|00:01:17,680 --> 00:01:20,799\n 143|scaling has become to the industry you\n 144|\n 145|37\n 146|00:01:19,520 --> 00:01:21,520\n 147|should be proud too having actually\n 148|\n 149|38\n 150|00:01:20,799 --> 00:01:23,280\n 151|pioneered it\n 152|\n 153|39\n 154|00:01:21,520 --> 00:01:24,400\n 155|>> played a part yeah among among many\n 156|\n 157|40\n 158|00:01:23,280 --> 00:01:27,040\n 159|others\n 160|\n 161|41\n 162|00:01:24,400 --> 00:01:28,799\n 163|>> you just wrote this essay that really\n 164|\n 165|42\n 166|00:01:27,040 --> 00:01:32,799\n 167|resonated about large scale test time\n 168|\n 169|43\n 170|00:01:28,799 --> 00:01:35,360\n 171|compute and why uh the industry is not\n 172|\n 173|44\n 174|00:01:32,799 --> 00:01:36,960\n 175|evaluating these models as robustly as\n 176|\n 177|45\n 178|00:01:35,360 --> 00:01:37,439\n 179|it should be what was the motivation for\n 180|\n 181|46\n 182|00:01:36,960 --> 00:01:40,880\n 183|it\n 184|\n 185|47\n 186|00:01:37,439 --> 00:01:44,560\n 187|>> yeah the motivation was we released 5.5\n 188|\n 189|48\n 190|00:01:40,880 --> 00:01:46,079\n 191|and the initial reaction ction was kind\n 192|\n 193|49\n 194|00:01:44,560 --> 00:01:47,680\n 195|of skepticism that it was a\n 196|\n 197|50\n 198|00:01:46,079 --> 00:01:49,119\n 199|substantially better model. To be fair,\n 200|\n 201|51\n 202|00:01:47,680 --> 00:01:50,560\n 203|that only lasted for a few hours before\n 204|\n 205|52\n 206|00:01:49,119 --> 00:01:51,840\n 207|people had some time to play around with\n 208|\n 209|53\n 210|00:01:50,560 --> 00:01:52,560\n 211|it and and try it out themselves and\n 212|\n 213|54\n 214|00:01:51,840 --> 00:01:54,479\n 215|they saw that it was actually\n 216|\n 217|55\n 218|00:01:52,560 --> 00:01:57,119\n 219|substantially better. Um, but I think a\n 220|\n 221|56\n 222|00:01:54,479 --> 00:01:58,479\n 223|lot of the skepticism came from the\n 224|\n 225|57\n 226|00:01:57,119 --> 00:01:59,759\n 227|benchmark grid that was published.\n 228|\n 229|58\n 230|00:01:58,479 --> 00:02:01,759\n 231|Basically, whenever a new model is\n 232|\n 233|59\n 234|00:01:59,759 --> 00:02:02,880\n 235|released, there's this benchmark grid\n 236|\n 237|60\n 238|00:02:01,759 --> 00:02:04,560\n 239|where they show all these different\n 240|\n 241|61\n 242|00:02:02,880 --> 00:02:05,680\n 243|benchmarks on on the x-axxis and then\n 244|\n 245|62\n 246|00:02:04,560 --> 00:02:07,280\n 247|the performance of different models on\n 248|\n 249|63\n 250|00:02:05,680 --> 00:02:08,800\n 251|the y- axis and you can just like\n 252|\n 253|64\n 254|00:02:07,280 --> 00:02:11,200\n 255|compare different models. It's like a\n 256|\n 257|65\n 258|00:02:08,800 --> 00:02:13,599\n 259|single number for a model on a single\n 260|\n 261|66\n 262|00:02:11,200 --> 00:02:15,680\n 263|benchmark. And if you look on paper at\n 264|\n 265|67\n 266|00:02:13,599 --> 00:02:18,480\n 267|the difference between like 5.5 and 5.4\n 268|\n 269|68\n 270|00:02:15,680 --> 00:02:19,920\n 271|or or other models, it wasn't it was an\n 272|\n 273|69\n 274|00:02:18,480 --> 00:02:21,599\n 275|improvement, but it wasn't a huge\n 276|\n 277|70\n 278|00:02:19,920 --> 00:02:23,280\n 279|improvement. It was only a few\n 280|\n 281|71\n 282|00:02:21,599 --> 00:02:24,560\n 283|percentage points in some benchmarks.\n 284|\n 285|72\n 286|00:02:23,280 --> 00:02:25,760\n 287|So, people looked at that and they were\n 288|\n 289|73\n 290|00:02:24,560 --> 00:02:27,360\n 291|skeptical that it was actually a better\n 292|\n 293|74\n 294|00:02:25,760 --> 00:02:29,599\n 295|model. Um, once they played around with\n 296|\n 297|75\n 298|00:02:27,360 --> 00:02:32,080\n 299|it, the story changed. I think the\n 300|\n 301|76\n 302|00:02:29,599 --> 00:02:33,680\n 303|reason why it doesn't show up as so much\n 304|\n 305|77\n 306|00:02:32,080 --> 00:02:35,840\n 307|better on the benchmarks is because the\n 308|\n 309|78\n 310|00:02:33,680 --> 00:02:37,120\n 311|benchmarks are being presented, the\n 312|\n 313|79\n 314|00:02:35,840 --> 00:02:39,200\n 315|benchmark results are being presented in\n 316|\n 317|80\n 318|00:02:37,120 --> 00:02:41,280\n 319|the wrong way. They're not controlling\n 320|\n 321|81\n 322|00:02:39,200 --> 00:02:42,640\n 323|for the amount of test time compute that\n 324|\n 325|82\n 326|00:02:41,280 --> 00:02:45,040\n 327|is being used on that benchmark\n 328|\n 329|83\n 330|00:02:42,640 --> 00:02:46,560\n 331|question. It turned out that 5.5 is just\n 332|\n 333|84\n 334|00:02:45,040 --> 00:02:50,000\n 335|much more efficient with its thinking.\n 336|\n 337|85\n 338|00:02:46,560 --> 00:02:51,280\n 339|If you run it at max settings, 5.4 is\n 340|\n 341|86\n 342|00:02:50,000 --> 00:02:54,080\n 343|thinking for a lot longer. It takes\n 344|\n 345|87\n 346|00:02:51,280 --> 00:02:55,680\n 347|longer to get back a response than 5.5.\n 348|\n 349|88\n 350|00:02:54,080 --> 00:02:57,200\n 351|And once you control for the amount of\n 352|\n 353|89\n 354|00:02:55,680 --> 00:03:00,319\n 355|thinking time, actually you can see that\n 356|\n 357|90\n 358|00:02:57,200 --> 00:03:01,440\n 359|5.5 is a substantial jump over 5.4. That\n 360|\n 361|91\n 362|00:03:00,319 --> 00:03:03,360\n 363|is, I think, people's day-to-day\n 364|\n 365|92\n 366|00:03:01,440 --> 00:03:05,519\n 367|experience with it. And then when I\n 368|\n 369|93\n 370|00:03:03,360 --> 00:03:06,800\n 371|mention this to people the reaction, the\n 372|\n 373|94\n 374|00:03:05,519 --> 00:03:08,800\n 375|typical question I get is like okay well\n 376|\n 377|95\n 378|00:03:06,800 --> 00:03:11,200\n 379|why not just have 5.5 think for as long\n 380|\n 381|96\n 382|00:03:08,800 --> 00:03:12,560\n 383|as 5.4 and the question is like well how\n 384|\n 385|97\n 386|00:03:11,200 --> 00:03:14,000\n 387|long should they think for? Typically\n 388|\n 389|98\n 390|00:03:12,560 --> 00:03:16,000\n 391|the response I get is well until the\n 392|\n 393|99\n 394|00:03:14,000 --> 00:03:17,120\n 395|performance plateaus right there's at\n 396|\n 397|100\n 398|00:03:16,000 --> 00:03:18,400\n 399|some point where the performance on the\n 400|\n 401|101\n 402|00:03:17,120 --> 00:03:20,879\n 403|benchmark is going to plateau and you\n 404|\n 405|102\n 406|00:03:18,400 --> 00:03:22,239\n 407|evaluate to that point. The thing is the\n 408|\n 409|103\n 410|00:03:20,879 --> 00:03:24,319\n 411|point at which it plateaus is actually\n 412|\n 413|104\n 414|00:03:22,239 --> 00:03:27,680\n 415|really far out these days. I mean, it\n 416|\n 417|105\n 418|00:03:24,319 --> 00:03:28,640\n 419|was true in GBD3 land back in 2022, the\n 420|\n 421|106\n 422|00:03:27,680 --> 00:03:29,920\n 423|models couldn't really think\n 424|\n 425|107\n 426|00:03:28,640 --> 00:03:31,920\n 427|productively for that long. And so, you\n 428|\n 429|108\n 430|00:03:29,920 --> 00:03:34,239\n 431|could just run them until they plateau.\n 432|\n 433|109\n 434|00:03:31,920 --> 00:03:35,920\n 435|It's not that far away. But what we're\n 436|\n 437|110\n 438|00:03:34,239 --> 00:03:39,200\n 439|seeing today with the modern models is\n 440|\n 441|111\n 442|00:03:35,920 --> 00:03:41,519\n 443|that 5.5 and other models can think for\n 444|\n 445|112\n 446|00:03:39,200 --> 00:03:45,599\n 447|if you scaffold them reasonably well,\n 448|\n 449|113\n 450|00:03:41,519 --> 00:03:46,959\n 451|can think for weeks even um before\n 452|\n 453|114\n 454|00:03:45,599 --> 00:03:48,799\n 455|having performance plateau on some of\n 456|\n 457|115\n 458|00:03:46,959 --> 00:03:51,360\n 459|these benchmarks. And so, the point at\n 460|\n 461|116\n 462|00:03:48,799 --> 00:03:53,440\n 463|which they plateau is simply too far out\n 464|\n 465|117\n 466|00:03:51,360 --> 00:03:55,760\n 467|to reasonably test. we all need to\n 468|\n 469|118\n 470|00:03:53,440 --> 00:03:58,159\n 471|actually reinforce um either like a\n 472|\n 473|119\n 474|00:03:55,760 --> 00:03:59,599\n 475|patience limit or a budget limit from a\n 476|\n 477|120\n 478|00:03:58,159 --> 00:04:00,560\n 479|token perspective now and that wasn't\n 480|\n 481|121\n 482|00:03:59,599 --> 00:04:02,000\n 483|true a few years ago.\n 484|\n 485|122\n 486|00:04:00,560 --> 00:04:03,680\n 487|>> Exactly. And so I think the proper way\n 488|\n 489|123\n 490|00:04:02,000 --> 00:04:06,000\n 491|to and so my claim is the proper way to\n 492|\n 493|124\n 494|00:04:03,680 --> 00:04:08,159\n 495|evaluate the models now is you either\n 496|\n 497|125\n 498|00:04:06,000 --> 00:04:10,239\n 499|have some kind of budget for the\n 500|\n 501|126\n 502|00:04:08,159 --> 00:04:12,319\n 503|benchmark whether it's tokens or cost or\n 504|\n 505|127\n 506|00:04:10,239 --> 00:04:13,920\n 507|time or whatever or you plot the\n 508|\n 509|128\n 510|00:04:12,319 --> 00:04:15,040\n 511|performance as a function of the amount\n 512|\n 513|129\n 514|00:04:13,920 --> 00:04:17,199\n 515|of test time compute that's going into\n 516|\n 517|130\n 518|00:04:15,040 --> 00:04:18,959\n 519|the model and then it becomes much more\n 520|\n 521|131\n 522|00:04:17,199 --> 00:04:20,320\n 523|clear um how to compare the performance\n 524|\n 525|132\n 526|00:04:18,959 --> 00:04:23,360\n 527|between these different models.\n 528|\n 529|133\n 530|00:04:20,320 --> 00:04:26,560\n 531|>> Given the model evaluation cycle and the\n 532|\n 533|134\n 534|00:04:23,360 --> 00:04:29,199\n 535|fact that performance does not asmtote\n 536|\n 537|135\n 538|00:04:26,560 --> 00:04:31,520\n 539|for many tasks over um quite a long\n 540|\n 541|136\n 542|00:04:29,199 --> 00:04:33,919\n 543|period of time. What do you do about\n 544|\n 545|137\n 546|00:04:31,520 --> 00:04:36,320\n 547|that issue? The fact that some of the\n 548|\n 549|138\n 550|00:04:33,919 --> 00:04:38,479\n 551|evals that you would want to run are\n 552|\n 553|139\n 554|00:04:36,320 --> 00:04:39,919\n 555|both beyond the scope of budget or time\n 556|\n 557|140\n 558|00:04:38,479 --> 00:04:40,960\n 559|that's reasonable given the current\n 560|\n 561|141\n 562|00:04:39,919 --> 00:04:42,720\n 563|model release cycle.\n 564|\n 565|142\n 566|00:04:40,960 --> 00:04:45,680\n 567|>> I mean, I think for things like cyber,\n 568|\n 569|143\n 570|00:04:42,720 --> 00:04:48,400\n 571|we've seen and actually the AIS um in\n 572|\n 573|144\n 574|00:04:45,680 --> 00:04:51,759\n 575|their evaluations has shown that the\n 576|\n 577|145\n 578|00:04:48,400 --> 00:04:53,040\n 579|models continue to improve um at 100\n 580|\n 581|146\n 582|00:04:51,759 --> 00:04:54,240\n 583|million tokens. You know, if you run\n 584|\n 585|147\n 586|00:04:53,040 --> 00:04:55,919\n 587|them for 100 million tokens, they're\n 588|\n 589|148\n 590|00:04:54,240 --> 00:04:57,360\n 591|still improving at beyond that point.\n 592|\n 593|149\n 594|00:04:55,919 --> 00:05:00,560\n 595|>> And that can take a very long time to\n 596|\n 597|150\n 598|00:04:57,360 --> 00:05:03,280\n 599|run. But you also do see that like the\n 600|\n 601|151\n 602|00:05:00,560 --> 00:05:05,360\n 603|performance is is it's not just like a\n 604|\n 605|152\n 606|00:05:03,280 --> 00:05:06,880\n 607|discontinuous jump. It's actually like\n 608|\n 609|153\n 610|00:05:05,360 --> 00:05:08,720\n 611|you can see the slope of improvement\n 612|\n 613|154\n 614|00:05:06,880 --> 00:05:11,520\n 615|over those 100 million tokens. And so\n 616|\n 617|155\n 618|00:05:08,720 --> 00:05:13,759\n 619|you could you could probably do some\n 620|\n 621|156\n 622|00:05:11,520 --> 00:05:15,919\n 623|kind of uh evaluation up to a certain\n 624|\n 625|157\n 626|00:05:13,759 --> 00:05:17,919\n 627|budget and then just say okay well this\n 628|\n 629|158\n 630|00:05:15,919 --> 00:05:19,520\n 631|is what we project the performance to\n 632|\n 633|159\n 634|00:05:17,919 --> 00:05:20,720\n 635|look like. And I think this this there\n 636|\n 637|160\n 638|00:05:19,520 --> 00:05:21,919\n 639|hasn't been a lot of research on this\n 640|\n 641|161\n 642|00:05:20,720 --> 00:05:23,039\n 643|yet. I actually think this would be a\n 644|\n 645|162\n 646|00:05:21,919 --> 00:05:24,000\n 647|great paper to publish if there's any\n 648|\n 649|163\n 650|00:05:23,039 --> 00:05:26,240\n 651|academics out there looking for\n 652|\n 653|164\n 654|00:05:24,000 --> 00:05:28,160\n 655|something to research. Can you predict\n 656|\n 657|165\n 658|00:05:26,240 --> 00:05:30,800\n 659|what the performance looks like at an\n 660|\n 661|166\n 662|00:05:28,160 --> 00:05:33,680\n 663|inference budget of let's say $10,000\n 664|\n 665|167\n 666|00:05:30,800 --> 00:05:34,560\n 667|only using inference budgets up to 10 or\n 668|\n 669|168\n 670|00:05:33,680 --> 00:05:37,039\n 671|10 or $100?\n 672|\n 673|169\n 674|00:05:34,560 --> 00:05:39,759\n 675|>> So maybe an orthogonal question for you.\n 676|\n 677|170\n 678|00:05:37,039 --> 00:05:41,360\n 679|Uh do you think users are systematically\n 680|\n 681|171\n 682|00:05:39,759 --> 00:05:42,960\n 683|like not thinking long enough with their\n 684|\n 685|172\n 686|00:05:41,360 --> 00:05:44,240\n 687|models about problems?\n 688|\n 689|173\n 690|00:05:42,960 --> 00:05:47,759\n 691|>> What do you mean by not thinking long\n 692|\n 693|174\n 694|00:05:44,240 --> 00:05:51,120\n 695|enough? um if the if you can\n 696|\n 697|175\n 698|00:05:47,759 --> 00:05:52,880\n 699|build an agent or control the amount of\n 700|\n 701|176\n 702|00:05:51,120 --> 00:05:54,320\n 703|um test time compute being used like\n 704|\n 705|177\n 706|00:05:52,880 --> 00:05:56,160\n 707|there's there's what is done by the\n 708|\n 709|178\n 710|00:05:54,320 --> 00:05:59,039\n 711|model itself and there's what the user\n 712|\n 713|179\n 714|00:05:56,160 --> 00:06:01,120\n 715|can do. Um do you think that like you\n 716|\n 717|180\n 718|00:05:59,039 --> 00:06:03,120\n 719|know the industry is using test time\n 720|\n 721|181\n 722|00:06:01,120 --> 00:06:04,960\n 723|compute an optimal amount uh way\n 724|\n 725|182\n 726|00:06:03,120 --> 00:06:06,080\n 727|undershooting it or it's you know it's a\n 728|\n 729|183\n 730|00:06:04,960 --> 00:06:07,680\n 731|problem in the models where they just\n 732|\n 733|184\n 734|00:06:06,080 --> 00:06:08,720\n 735|need to be able to do that thinking\n 736|\n 737|185\n 738|00:06:07,680 --> 00:06:11,759\n 739|faster.\n 740|\n 741|186\n 742|00:06:08,720 --> 00:06:14,240\n 743|>> I I think it depends on the problem. Uh\n 744|\n 745|187\n 746|00:06:11,759 --> 00:06:15,840\n 747|I think this idea that the models you\n 748|\n 749|188\n 750|00:06:14,240 --> 00:06:17,360\n 751|just let them think for a week or\n 752|\n 753|189\n 754|00:06:15,840 --> 00:06:19,199\n 755|whatever and then they respond. It's it\n 756|\n 757|190\n 758|00:06:17,360 --> 00:06:21,680\n 759|sounds nice and yes the benchmarks look\n 760|\n 761|191\n 762|00:06:19,199 --> 00:06:23,440\n 763|great but it's not very practical when\n 764|\n 765|192\n 766|00:06:21,680 --> 00:06:24,400\n 767|working u because like okay you ask the\n 768|\n 769|193\n 770|00:06:23,440 --> 00:06:25,520\n 771|model a question and then you sit there\n 772|\n 773|194\n 774|00:06:24,400 --> 00:06:26,319\n 775|for a week waiting for it to come back\n 776|\n 777|195\n 778|00:06:25,520 --> 00:06:29,919\n 779|to you.\n 780|\n 781|196\n 782|00:06:26,319 --> 00:06:31,360\n 783|>> I think what people have have found most\n 784|\n 785|197\n 786|00:06:29,919 --> 00:06:32,960\n 787|effective is to kind of like iterate\n 788|\n 789|198\n 790|00:06:31,360 --> 00:06:35,199\n 791|quickly with the models and so the\n 792|\n 793|199\n 794|00:06:32,960 --> 00:06:37,280\n 795|thinking time I think needs to be\n 796|\n 797|200\n 798|00:06:35,199 --> 00:06:38,479\n 799|flexible. when it makes sense to respond\n 800|\n 801|201\n 802|00:06:37,280 --> 00:06:39,919\n 803|quickly to the user, it should respond\n 804|\n 805|202\n 806|00:06:38,479 --> 00:06:41,199\n 807|quickly. And then when it makes sense to\n 808|\n 809|203\n 810|00:06:39,919 --> 00:06:42,240\n 811|think for a long time and the user wants\n 812|\n 813|204\n 814|00:06:41,199 --> 00:06:43,680\n 815|it to think for a long time, then it\n 816|\n 817|205\n 818|00:06:42,240 --> 00:06:44,720\n 819|makes sense to think for a long time. I\n 820|\n 821|206\n 822|00:06:43,680 --> 00:06:46,080\n 823|think people have been striking the\n 824|\n 825|207\n 826|00:06:44,720 --> 00:06:47,280\n 827|right balance given what they have to\n 828|\n 829|208\n 830|00:06:46,080 --> 00:06:49,120\n 831|deal with right now.\n 832|\n 833|209\n 834|00:06:47,280 --> 00:06:51,520\n 835|>> How would you characterize, you know,\n 836|\n 837|210\n 838|00:06:49,120 --> 00:06:53,759\n 839|there's a lot of talk about benchmark\n 840|\n 841|211\n 842|00:06:51,520 --> 00:06:55,680\n 843|maxing and the ability to game different\n 844|\n 845|212\n 846|00:06:53,759 --> 00:06:57,680\n 847|benchmarks? What would you characterize\n 848|\n 849|213\n 850|00:06:55,680 --> 00:06:59,199\n 851|the like landscape of benchmarks as\n 852|\n 853|214\n 854|00:06:57,680 --> 00:07:00,960\n 855|today? And then do you have like\n 856|\n 857|215\n 858|00:06:59,199 --> 00:07:03,199\n 859|favorites that you think are more\n 860|\n 861|216\n 862|00:07:00,960 --> 00:07:05,360\n 863|indicative of capability than others? So\n 864|\n 865|217\n 866|00:07:03,199 --> 00:07:06,880\n 867|the benchmark maxing thing is also\n 868|\n 869|218\n 870|00:07:05,360 --> 00:07:10,319\n 871|motivation for for writing the essay\n 872|\n 873|219\n 874|00:07:06,880 --> 00:07:12,240\n 875|that I think it's really easy to show\n 876|\n 877|220\n 878|00:07:10,319 --> 00:07:14,639\n 879|you can do much better than previous\n 880|\n 881|221\n 882|00:07:12,240 --> 00:07:17,360\n 883|benchmarks or or previous previous\n 884|\n 885|222\n 886|00:07:14,639 --> 00:07:18,880\n 887|models on benchmarks by just for example\n 888|\n 889|223\n 890|00:07:17,360 --> 00:07:20,800\n 891|scaffolding a bunch of models together.\n 892|\n 893|224\n 894|00:07:18,880 --> 00:07:22,240\n 895|Um so if you say okay well we're going\n 896|\n 897|225\n 898|00:07:20,800 --> 00:07:23,919\n 899|to instead of just running this model\n 900|\n 901|226\n 902|00:07:22,240 --> 00:07:25,520\n 903|once we're going to run it five times\n 904|\n 905|227\n 906|00:07:23,919 --> 00:07:27,680\n 907|and take the best of the five responses\n 908|\n 909|228\n 910|00:07:25,520 --> 00:07:29,759\n 911|or like ask a judge which one it thinks\n 912|\n 913|229\n 914|00:07:27,680 --> 00:07:32,639\n 915|is best then you can get much higher\n 916|\n 917|230\n 918|00:07:29,759 --> 00:07:35,759\n 919|scores than that model. And so it's\n 920|\n 921|231\n 922|00:07:32,639 --> 00:07:37,360\n 923|really easy to make something that looks\n 924|\n 925|232\n 926|00:07:35,759 --> 00:07:38,560\n 927|a lot better on paper but is actually\n 928|\n 929|233\n 930|00:07:37,360 --> 00:07:39,840\n 931|not better once you control for the\n 932|\n 933|234\n 934|00:07:38,560 --> 00:07:40,560\n 935|amount of test time compute. That is one\n 936|\n 937|235\n 938|00:07:39,840 --> 00:07:41,840\n 939|thing that I'm worried about when it\n 940|\n 941|236\n 942|00:07:40,560 --> 00:07:43,199\n 943|comes to benchmark maxing. I mean it\n 944|\n 945|237\n 946|00:07:41,840 --> 00:07:45,039\n 947|like it's it's a little misleading is\n 948|\n 949|238\n 950|00:07:43,199 --> 00:07:46,000\n 951|the only concern that I have. And then\n 952|\n 953|239\n 954|00:07:45,039 --> 00:07:48,960\n 955|as far as like the benchmarks\n 956|\n 957|240\n 958|00:07:46,000 --> 00:07:50,560\n 959|themselves, I think there is always a\n 960|\n 961|241\n 962|00:07:48,960 --> 00:07:53,199\n 963|risk of like just optimizing for the\n 964|\n 965|242\n 966|00:07:50,560 --> 00:07:55,199\n 967|benchmark. And I've I've certainly\n 968|\n 969|243\n 970|00:07:53,199 --> 00:07:56,720\n 971|encouraged my team and I think at OpenAI\n 972|\n 973|244\n 974|00:07:55,199 --> 00:07:58,960\n 975|we're pretty good about not trying to\n 976|\n 977|245\n 978|00:07:56,720 --> 00:08:00,800\n 979|optimize for for specific benchmarks.\n 980|\n 981|246\n 982|00:07:58,960 --> 00:08:02,160\n 983|But once you put out a benchmark, it's\n 984|\n 985|247\n 986|00:08:00,800 --> 00:08:04,160\n 987|there's it's always at risk of just\n 988|\n 989|248\n 990|00:08:02,160 --> 00:08:06,800\n 991|being optimized for. And I think one way\n 992|\n 993|249\n 994|00:08:04,160 --> 00:08:09,599\n 995|to one way to address that is to keep a\n 996|\n 997|250\n 998|00:08:06,800 --> 00:08:10,960\n 999|held out private set um that isn't uh\n1000|\n1001|251\n1002|00:08:09,599 --> 00:08:14,479\n1003|publicly available.\n1004|\n1005|252\n1006|00:08:10,960 --> 00:08:16,960\n1007|>> The most popular fallback advice for you\n1008|\n1009|253\n1010|00:08:14,479 --> 00:08:18,639\n1011|know figure out if a model is\n1012|\n1013|254\n1014|00:08:16,960 --> 00:08:20,400\n1015|significantly better or not is to just\n1016|\n1017|255\n1018|00:08:18,639 --> 00:08:21,680\n1019|play with it for a while. Do you have\n1020|\n1021|256\n1022|00:08:20,400 --> 00:08:24,080\n1023|anything more sophisticated than that\n1024|\n1025|257\n1026|00:08:21,680 --> 00:08:27,039\n1027|that you suggest people do? like do you\n1028|\n1029|258\n1030|00:08:24,080 --> 00:08:29,840\n1031|create your own set of new evaluations\n1032|\n1033|259\n1034|00:08:27,039 --> 00:08:30,560\n1035|each time besides private holdback at\n1036|\n1037|260\n1038|00:08:29,840 --> 00:08:32,000\n1039|OpenAI?\n1040|\n1041|261\n1042|00:08:30,560 --> 00:08:32,959\n1043|>> I think everybody has their own set of\n1044|\n1045|262\n1046|00:08:32,000 --> 00:08:34,399\n1047|questions that they like to ask the\n1048|\n1049|263\n1050|00:08:32,959 --> 00:08:37,440\n1051|model whenever it comes out.\n1052|\n1053|264\n1054|00:08:34,399 --> 00:08:40,320\n1055|>> Um for me lately it's been I I use them\n1056|\n1057|265\n1058|00:08:37,440 --> 00:08:41,680\n1059|to make poker bots and see how good they\n1060|\n1061|266\n1062|00:08:40,320 --> 00:08:43,919\n1063|can make a poker bot. I think it's a\n1064|\n1065|267\n1066|00:08:41,680 --> 00:08:46,560\n1067|nice eval because there is very little\n1068|\n1069|268\n1070|00:08:43,919 --> 00:08:48,399\n1071|um open source code for making poker\n1072|\n1073|269\n1074|00:08:46,560 --> 00:08:49,680\n1075|bots and there's a lot of published\n1076|\n1077|270\n1078|00:08:48,399 --> 00:08:51,279\n1079|essay there's a lot of published papers\n1080|\n1081|271\n1082|00:08:49,680 --> 00:08:52,800\n1083|on it but you really have to reason\n1084|\n1085|272\n1086|00:08:51,279 --> 00:08:56,080\n1087|through everything and it's like\n1088|\n1089|273\n1090|00:08:52,800 --> 00:08:57,600\n1091|requires a lot of just reasoning and\n1092|\n1093|274\n1094|00:08:56,080 --> 00:08:58,480\n1095|iteration and like a lot of small\n1096|\n1097|275\n1098|00:08:57,600 --> 00:09:00,080\n1099|gotchas\n1100|\n1101|276\n1102|00:08:58,480 --> 00:09:01,360\n1103|>> that I can kind of I've already worked\n1104|\n1105|277\n1106|00:09:00,080 --> 00:09:02,880\n1107|through myself so I can see where the\n1108|\n1109|278\n1110|00:09:01,360 --> 00:09:04,480\n1111|models fail along the way. They've\n1112|\n1113|279\n1114|00:09:02,880 --> 00:09:07,279\n1115|gotten really good at it now. Can you\n1116|\n1117|280\n1118|00:09:04,480 --> 00:09:09,360\n","created_at":"2026-07-09T04:21:52.465311Z","finish_reason":null,"metrics":null},{"message_id":"ffbbfd73-838e-43e8-9055-dd328a2d0b24","node_id":39,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:4","name":"exec","arguments":{"command":"python3 - <<'PY'\nimport re, sys\npath = \"/tmp/Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown.en.srt\"\nwith open(path, 'r', encoding='utf-8') as f:\n data = f.read()\n# Remove SRT index lines, time lines, and empty subtitle lines; keep text lines\nlines = data.splitlines()\ntexts = []\ni = 0\nwhile i < len(lines):\n line = lines[i].strip()\n if re.match(r'^\\d+$', line):\n # next line is timecode\n i += 2\n # collect text until blank\n sub = []\n while i < len(lines) and lines[i].strip() != '':\n sub.append(lines[i].strip())\n i += 1\n if sub:\n texts.append(' '.join(sub))\n i += 1\nprint('\\n'.join(texts))\nPY","timeout":60000}}],"thinking":"The file has 4408 lines, read only up to 1118. Need to read full transcript to summarize. We need to get entire content. Use read with larger? read default 20000 chars, lines 1-1118 maybe because limit? It returned first 1118 lines due to default char limit? Let's read in chunks. Use read with offset and limit. Need entire transcript. It may be too long for context but okay. We can process. Let's see total size: 77KB. Need to read all. Use read with limit huge? read can read up to 20000 chars. The file is ~77KB, so 4 chunks. We can use shell cat, but need to read file. The read tool supports offset. We can read full by calling with offset increments. Let's use cat to a local and maybe grep? But we need content for summarization. We can use exec to extract text (strip timecodes) and output. Maybe use python to parse SRT and print text. That would be more concise. We can pipe to a file. Let's create a clean transcript. Use Python script.","created_at":"2026-07-09T04:21:54.591631Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1087,"total_time_ms":2125,"input_tokens":11438,"output_tokens":479,"cache_read_tokens":23040,"cache_creation_tokens":null,"tpot_ms":2.1670146137787056,"tokens_per_sec":461.4643545279383}},{"message_id":"a4782bd7-0034-46e3-941c-9cc6a0151fab","node_id":40,"role":"tool","content":"Output from command in shell 1dbd60:\nWith GBT3, you couldn't scale test time\ncompute. Like if you gave it a budget of\n$10 million and said, \"Okay, well, let's\nsee what GB3 can do.\" It really can't do\nthat much. The precurren frameworks and\nresponsible scaling policies, they don't\nreally account for the amount of testime\ncompute. They just say, \"Okay, well,\nwhat's the capability of the model?\" The\nproblem is we're in a world now where\nthe capability of the model is a\nfunction of how much money you put into\nit. Basically, if you give it a budget\nof $10,000, it can do a lot more than\nwhat it can do with a budget of $10.\nGive it a budget of $10 million, you can\ndo even more. At what budget should you\nevaluate these models? The policies that\nexist today don't really address that\nquestion.\nHi listeners, I'm Sarah Goa and welcome\nback to No Priors. Today I'm here with\nNome Brown, one of our godfathers of AI\nreasoning. We talk about the broken\nstate of evaluations, very large-scale\ntest time compute, how he thinks about\nrecursive self-improvement, and what's\nnext on the horizon for competition at\nthe frontier. Welcome, Gnome. I'm so\nexcited to have you back.\n>> That's great to be back. Yeah,\n>> you are our first guest. I'm very proud\nof my taste in friends and researchers\nfor uh for the pod. um given you know\nhow important uh you know inference time\nscaling has become to the industry you\nshould be proud too having actually\npioneered it\n>> played a part yeah among among many\nothers\n>> you just wrote this essay that really\nresonated about large scale test time\ncompute and why uh the industry is not\nevaluating these models as robustly as\nit should be what was the motivation for\nit\n>> yeah the motivation was we released 5.5\nand the initial reaction ction was kind\nof skepticism that it was a\nsubstantially better model. To be fair,\nthat only lasted for a few hours before\npeople had some time to play around with\nit and and try it out themselves and\nthey saw that it was actually\nsubstantially better. Um, but I think a\nlot of the skepticism came from the\nbenchmark grid that was published.\nBasically, whenever a new model is\nreleased, there's this benchmark grid\nwhere they show all these different\nbenchmarks on on the x-axxis and then\nthe performance of different models on\nthe y- axis and you can just like\ncompare different models. It's like a\nsingle number for a model on a single\nbenchmark. And if you look on paper at\nthe difference between like 5.5 and 5.4\nor or other models, it wasn't it was an\nimprovement, but it wasn't a huge\nimprovement. It was only a few\npercentage points in some benchmarks.\nSo, people looked at that and they were\nskeptical that it was actually a better\nmodel. Um, once they played around with\nit, the story changed. I think the\nreason why it doesn't show up as so much\nbetter on the benchmarks is because the\nbenchmarks are being presented, the\nbenchmark results are being presented in\nthe wrong way. They're not controlling\nfor the amount of test time compute that\nis being used on that benchmark\nquestion. It turned out that 5.5 is just\nmuch more efficient with its thinking.\nIf you run it at max settings, 5.4 is\nthinking for a lot longer. It takes\nlonger to get back a response than 5.5.\nAnd once you control for the amount of\nthinking time, actually you can see that\n5.5 is a substantial jump over 5.4. That\nis, I think, people's day-to-day\nexperience with it. And then when I\nmention this to people the reaction, the\ntypical question I get is like okay well\nwhy not just have 5.5 think for as long\nas 5.4 and the question is like well how\nlong should they think for? Typically\nthe response I get is well until the\nperformance plateaus right there's at\nsome point where the performance on the\nbenchmark is going to plateau and you\nevaluate to that point. The thing is the\npoint at which it plateaus is actually\nreally far out these days. I mean, it\nwas true in GBD3 land back in 2022, the\nmodels couldn't really think\nproductively for that long. And so, you\ncould just run them until they plateau.\nIt's not that far away. But what we're\nseeing today with the modern models is\nthat 5.5 and other models can think for\nif you scaffold them reasonably well,\ncan think for weeks even um before\nhaving performance plateau on some of\nthese benchmarks. And so, the point at\nwhich they plateau is simply too far out\nto reasonably test. we all need to\nactually reinforce um either like a\npatience limit or a budget limit from a\ntoken perspective now and that wasn't\ntrue a few years ago.\n>> Exactly. And so I think the proper way\nto and so my claim is the proper way to\nevaluate the models now is you either\nhave some kind of budget for the\nbenchmark whether it's tokens or cost or\ntime or whatever or you plot the\nperformance as a function of the amount\nof test time compute that's going into\nthe model and then it becomes much more\nclear um how to compare the performance\nbetween these different models.\n>> Given the model evaluation cycle and the\nfact that performance does not asmtote\nfor many tasks over um quite a long\nperiod of time. What do you do about\nthat issue? The fact that some of the\nevals that you would want to run are\nboth beyond the scope of budget or time\nthat's reasonable given the current\nmodel release cycle.\n>> I mean, I think for things like cyber,\nwe've seen and actually the AIS um in\ntheir evaluations has shown that the\nmodels continue to improve um at 100\nmillion tokens. You know, if you run\nthem for 100 million tokens, they're\nstill improving at beyond that point.\n>> And that can take a very long time to\nrun. But you also do see that like the\nperformance is is it's not just like a\ndiscontinuous jump. It's actually like\nyou can see the slope of improvement\nover those 100 million tokens. And so\nyou could you could probably do some\nkind of uh evaluation up to a certain\nbudget and then just say okay well this\nis what we project the performance to\nlook like. And I think this this there\nhasn't been a lot of research on this\nyet. I actually think this would be a\ngreat paper to publish if there's any\nacademics out there looking for\nsomething to research. Can you predict\nwhat the performance looks like at an\ninference budget of let's say $10,000\nonly using inference budgets up to 10 or\n10 or $100?\n>> So maybe an orthogonal question for you.\nUh do you think users are systematically\nlike not thinking long enough with their\nmodels about problems?\n>> What do you mean by not thinking long\nenough? um if the if you can\nbuild an agent or control the amount of\num test time compute being used like\nthere's there's what is done by the\nmodel itself and there's what the user\ncan do. Um do you think that like you\nknow the industry is using test time\ncompute an optimal amount uh way\nundershooting it or it's you know it's a\nproblem in the models where they just\nneed to be able to do that thinking\nfaster.\n>> I I think it depends on the problem. Uh\nI think this idea that the models you\njust let them think for a week or\nwhatever and then they respond. It's it\nsounds nice and yes the benchmarks look\ngreat but it's not very practical when\nworking u because like okay you ask the\nmodel a question and then you sit there\nfor a week waiting for it to come back\nto you.\n>> I think what people have have found most\neffective is to kind of like iterate\nquickly with the models and so the\nthinking time I think needs to be\nflexible. when it makes sense to respond\nquickly to the user, it should respond\nquickly. And then when it makes sense to\nthink for a long time and the user wants\nit to think for a long time, then it\nmakes sense to think for a long time. I\nthink people have been striking the\nright balance given what they have to\ndeal with right now.\n>> How would you characterize, you know,\nthere's a lot of talk about benchmark\nmaxing and the ability to game different\nbenchmarks? What would you characterize\nthe like landscape of benchmarks as\ntoday? And then do you have like\nfavorites that you think are more\nindicative of capability than others? So\nthe benchmark maxing thing is also\nmotivation for for writing the essay\nthat I think it's really easy to show\nyou can do much better than previous\nbenchmarks or or previous previous\nmodels on benchmarks by just for example\nscaffolding a bunch of models together.\nUm so if you say okay well we're going\nto instead of just running this model\nonce we're going to run it five times\nand take the best of the five responses\nor like ask a judge which one it thinks\nis best then you can get much higher\nscores than that model. And so it's\nreally easy to make something that looks\na lot better on paper but is actually\nnot better once you control for the\namount of test time compute. That is one\nthing that I'm worried about when it\ncomes to benchmark maxing. I mean it\nlike it's it's a little misleading is\nthe only concern that I have. And then\nas far as like the benchmarks\nthemselves, I think there is always a\nrisk of like just optimizing for the\nbenchmark. And I've I've certainly\nencouraged my team and I think at OpenAI\nwe're pretty good about not trying to\noptimize for for specific benchmarks.\nBut once you put out a benchmark, it's\nthere's it's always at risk of just\nbeing optimized for. And I think one way\nto one way to address that is to keep a\nheld out private set um that isn't uh\npublicly available.\n>> The most popular fallback advice for you\nknow figure out if a model is\nsignificantly better or not is to just\nplay with it for a while. Do you have\nanything more sophisticated than that\nthat you suggest people do? like do you\ncreate your own set of new evaluations\neach time besides private holdback at\nOpenAI?\n>> I think everybody has their own set of\nquestions that they like to ask the\nmodel whenever it comes out.\n>> Um for me lately it's been I I use them\nto make poker bots and see how good they\ncan make a poker bot. I think it's a\nnice eval because there is very little\num open source code for making poker\nbots and there's a lot of published\nessay there's a lot of published papers\non it but you really have to reason\nthrough everything and it's like\nrequires a lot of just reasoning and\niteration and like a lot of small\ngotchas\n>> that I can kind of I've already worked\nthrough myself so I can see where the\nmodels fail along the way. They've\ngotten really good at it now. Can you\ndescribe perhaps like with your poker\nbot creation like how reasoning might\nhave progressed in model releases for\nyou guys over a few uh releases?\n>> Yeah, when the early models were really\nbad at it, like they could not basically\ndo anything. And then 5.2\nI was able to work with it to make a\nriver solver.\n>> So that's like the final stage of poker.\nAnd um that itself was I thought really\nimpressive. I mean I had to work with it\na little bit, but I was actually really\nimpressed cuz I was able to make the\nriver solver probably about five times\nfaster than I would have alone. There\nwere a couple things that I got um\ntripped up on. Uh blockers was always a\nbig big issue. But overall, like, you\nknow, with a with a bit of gentle\nsteering, it just kind of like it kind\nof felt like a grad student where, okay,\nthey would run into issues, but at least\nlike I would um know what those issues\nwere and know how to fix it, and I could\njust make suggestions and it would go\noff and then do it and then pretty\nquickly it would actually come back with\nsomething really good.\n>> Mhm.\n>> And then especially the optimization I\nthought was very impressive. It was able\nto make it like 10 times faster than\nwhat I was able to do because it was\njust able to optimize um the code so\nwell. The downsides with 5.2 is I felt\nlike it was gaslighting me a lot and I\nalways had to be very careful checking\nit and making sure like, okay, is it\nactually doing what it said it did? Um,\nare there any things that are like\nglaring issues that it's not recognizing\nor it's just pretending aren't issues?\nUm, I remember there was like one point\nuh where for one of the models I was\nplaying around with it, not 5.2,\nI kind of like as a as a unit test, I\ntold it, okay, well, let's say um I have\n$100 in the pot and I fold. How much am\nI losing? And the model said $92. And I\nwas like, that's crazy. I have $100 in\nthe pot and I just fold it. How do I not\nlose $100? And it said, oh, you know,\nit's 92. It's close to 100. It's fine.\nIt's no big deal. And I was like,\nclearly this is a problem, right? So the\nmodels did have this problem where they\nwould gaslight you a lot. But once we\ngot to 5.5, I actually thought um it was\nway better. It was able to basically do\nit zero shot. And in fact, I've been\nworking on just doing a full scale poker\nsolver. Um and it it's basically able to\ndo the whole thing uh with some gentle\nsteering from me. And I wouldn't be\nsurprised if you know 6 months or a year\nfrom now, the model is able to do zero\nshot an entire poker solver, basically\nmy entire PhD thesis in one go. Let's\ntalk about the larger implications of\nyou know needing to evaluate these\nmodels relative to um let's say like\nspeed of their reasoning or efficiency\nversus you know token volume right um or\ndollar budget or whatever whatever your\nscalar is. Can you describe some of the\nlarger implications in your essay\nincluding around um like safety\nevaluations? Yeah, the safety\nevaluations thing. Um, it's it's a bit\nof an inconvenient truth thing where\nokay, so I guess for background, a lot\nof the all of the labs have these things\ncalled either responsible scaling\npolicies, preparedness frameworks. They\ngo by various names, but the idea is\nthat whenever a model is released, they\ngo through a series of evaluations to\nmeasure are there dangerous\ncapabilities? Um, could these models do\nthings that we're we we wouldn't want um\na bad actor to do. And if the model\nisn't very capable, then it's no big\ndeal. But if it is very capable, if it\ncould be used for example to make\nbioweapons, then you want to put in\nmitigations against that.\n>> But the question is okay, well, how do\nyou evaluate whether the model is\ncapable of that? And they have like\nvarious protocols about like how they do\nthese valuations. But a lot of these\nframeworks were developed around the era\nof chatgpt before after when test time\ncomput scaling was not really uh as much\nof a thing and it made sense like with\nGPT3 you couldn't scale test time\ncompute like if you gave it a budget of\n$10 million and said okay well let's see\nwhat GP3 can do it really can't do that\nmuch more than what you could do with\nlike $10 or $1. the preparedness\nframeworks and responsible scaling\npolicies, they don't really account for\nthe amount of test time compute. They\njust say, \"Okay, well, what's the\ncapability of the model? The problem is\nwe're in a world now where the\ncapability of the model is a function of\nhow much money you put into it.\nBasically, if you give it a budget of\n$10,000, it can do a lot more than what\nit can do with a budget of $10. If you\ngive it a budget of $10 million, it can\ndo even more.\" And so, at what budget\nshould you evaluate these models? the\npolicies that exist today don't really\naddress that question.\n>> Mhm.\n>> Uh some do some do better than others,\nbut um for the most part, this is not\nreally a factor that's being heavily\nconsidered. Now, whether it should be\nreleased anyway, I don't I don't want to\nwait into this question. I think\nthere's, you know, there's arguments on\nboth sides, but I think the important\nthing to recognize is that this is a\nquestion that is not being we're just\nkind of like, you know, pretending that\nthis issue doesn't exist. And I think\nit's important to just, you know, one\nway or the other um account for it.\nYeah, it's the mirror image of the\ncapability question of if the models can\nuh continue to do more and more without\nasmmptoing on some tasks at very large\nbudgets um then they should also be able\nto do so for uh tasks we don't want them\nto do as a society, right? And so\ntesting for that um and what budget is\nallocated. It also seems out of sync\nfrom the model release cycle itself,\nright? there's been this acceleration\nof, you know, you get a new model every\nsometimes few days and weeks at this\npoint versus 6 months. And you have a\nline in the essay where you say like the\nthe only way to truly evaluate an agent\non some very longunning task might be to\nrun it for a year. And that's going to\nbe true of both like useful and uh\nnegative tasks, right? And so how do you\nthink about that versus the model\nrelease cycle? Yeah, this this is also\nan interesting dynamic where basically\nas the models have become stronger,\nthey've they're more they're better able\nto operate over longer horizons.\n>> So again, with GPD3, if you wanted to\nrun it for, you know, a week, there's\nreally not much you could do to scaffold\nit into something useful that could\nactually run for a week. But we're\nseeing now with the most recent models\nthat you can actually scaffold, for\nexample, 5.5 into doing a series of\nexperiments that can run for weeks, for\nmonths. Have you given your poker solver\ntask like infinite budget yet?\n>> I haven't really scaffolded something\ntogether where I just tell it like,\n\"Okay, just run this for um for weeks.\"\nI think I could give it I I could ask I\ncould probably give it / goal and just\nlike yeah, tell it to go nuts.\n>> But um I I think at this point it could\n100% do the river solver. Um, if I just\ngive it slash goal, I don't think it's\nat the level yet where it could do like\nthe full poker solver if I gave it just\nslash goal and told it, yeah, go go run\nfor a month. But we're going to pretty\nsoon be at that point where I probably\ncould just tell it like, yeah, go work\non this for a month and then come back\nto me with a a full complete poker\nsolver that's state-of-the-art.\nAnd the problem is if you want to\nevaluate the capabilities of a model,\nwhat it can do after running for a\nmonth, the only way to be fully sure is\nto actually run it for a month. And if\nyou want to know after 6 months, the\nonly way to know fully is to run it for\nsix months. Now there I I'll get to like\nthings we could do to address that a\nlittle bit later. But like it's\nimportant to recognize the model release\ncycle is look, we're releasing new\nmodels like every two or three months at\nthis point. And so a model comes out, it\ntakes two or 3 months to push it to its\nlimits and then you have another model\ncome out. And so nobody actually knows\nwhat the ceiling of capabilities are for\nthese models because nobody's actually\nrun them for long enough to really tell.\nWhen slash goal came out, for example, I\nmean, people started running things that\nit took over a week for it to finish.\nAnd so, people actually didn't realize\nthat this was a big deal until after a\nweek uh until a week after it was\nreleased.\n>> Mhm.\n>> I think that's going to be more and more\ntrue. You know, the implications of that\nare, I think, pretty interesting because\nwhat do the labs do to like fully\nevaluate their models before they're\nreleased? It's actually very difficult\nbecause, yeah, you would have to the\nonly way to to really do the evaluations\nis then delay the model release cycle.\nUm and you know there's a lot of\ncompetitive pressure right now to not do\nthat.\n>> Do you think there's um like exciting\nlatent capability in the models that are\nalready released that people have not\nfully explored given timeline?\n>> I think absolutely. I think actually a\nreally great example is the erdo unit\ndistance problem. So for the viewers\nthat don't know like we used an internal\nmodel at OpenAI a few weeks ago to uh\ndisprove the unit erdos unit distance\nconjecture. Now, I'm not a\nmathematician, but this seems like it\nwas a pretty big deal in the in the math\ncommunity. It was like the first uh\nfirst problem that a lot of\nmathematicians had really spent a lot of\ntime on, and the model was able to do\nsomething that they weren't able to do\nand do it in a way that was actually\ninteresting and useful for\nmathematicians. Honestly, it did it at… (2 chars truncated)\n… (21 lines truncated)\nlike … (31 chars truncated)\nthen if you do this enough times it\nactually ends up arriving at the\ndisproof. Now what this means is you\ncould in principle ask 5.5 to you know\nas as a general purpose scaffold list a\nbunch of different strategies and then\nfor each strategy tell it to investigate\nthat strategy and then it would probably\nbe able to arrive at the disproof with a\ngeneral purpose scaffold. Now that\nscaffold would be very expensive. I mean\nit would probably cost I I just ballpark\nlike thousand to 10 to $100,000. Um but\nit would be possible and it would have\nbeen possible for somebody to disprove\nthe erdos unit distance conjecture\nbefore we did using a general purpose\nmodel. And nobody had explored\nsufficiently what happens if I put\n$100,000 worth of compute into 5.5 what\ncould it do? Um and the answer is like\nyeah you probably could get stuff like\nthat out of it. So people should be\nexperimenting more with the current\ngeneration in terms of\n>> well this is I think is an interesting\nquestion of is it worth it to experiment\nwith because again the model release\ncycle is every every couple months we\nput out a new model that's even more\npowerful and so the cost of disproving\nthe erdosha distance gesture drops by\nlike 10 or 100x with every model release\ncycle probably in some cases more so\n>> you've seen the meme that's like oh I\nlike why why bother doing any\nengineering work when I should just wait\nfor the next\n>> model go on vacation come back two\nmonths later and and it's, you know, a\nthousand times cheaper. So,\n>> do you agree with that?\n>> I\n>> Is that what you're doing right now at\nOpenAI, just waiting for the next model\nrelease?\n>> I think I I mean, I will say we're we're\nin a period where progress is very fast\nand like yeah, the models are becoming\nmore capable. I I can say like at OpenAI\none of the things that we're we're\nactively not doing and look we have a\nlot of mathematicians we have a lot of\nphysicists people are very excited about\nwhat these models can do right now\nespecially you know the internal models\nwe are trying to encourage people to not\nspend all their time just like going\nthrough all the mathematical open\nproblems physics problems and um just\nseeing pushing the models to their\nlimits to see what they can prove or\ndisprove. Um because we really think the\nfocus should be on how do we make even\nmore capable models? how can we get them\nget them out safely to the world as\nquickly as possible so that all the\nscientists in the world can use these\nmodels to solve the problems themselves.\nUh so yeah in some sense we are thinking\nabout this that yes it's really tempting\nto um just put all of our efforts into\nscaling up these models and see what\nthey can do at their limits right now\nbut really the focus should be on how do\nwe use these models to make even more\npowerful models even more capable models\nthat can do everything much more\ncost-ffectively.\nWhat is changing about the uh direction\nor allocation of resources for research\nin your mind given your beliefs about\nthis very large scale the impact of very\nlarge scale test time compute? How does\nthis interact with the um idea of\nrecursive self-improvement for example\nwhere you know it's a dominant idea for\nhow you know any lab gets to the best\ncapability model. So, one thing I should\nclarify, I don't think we're at the\npoint where, okay, you just give it an\narbitrary extremely high inference\nbudget and it's just it's just super\nintelligent across the board.\n>> SL goal,\n>> yeah,\n>> make GPD 7 or whatever and yeah, just go\nnuts.\n>> What's between us and there then?\n>> I think having played around with the\nmodel uh so okay, so first of all, there\nare some benchmarks where the models\nwill just not improve if they have more\ninference budget. So, I think a lot of\nfactual ret uh factual um retrieval kind\nof questions fall into this category of\nif you ask a person when was Abraham\nLincoln born and they don't know the\ndate. They could sit there, they could\nthink about it for a week, if they if\nthey don't have access to Wikipedia or\nsomething, they're not going to be able\nto do better answering that question if\nthey thought about it for a week\ncompared to 5 seconds. Same with the\nmodel. If you actually, interestingly\nenough, if you give the model these\nkinds of like factual retrieval\nquestions and you give them a little bit\nof time to think, they do actually do\nbetter. um but if you give them a week\nthey're not suddenly going to do better\nat remembering dates. There are so\nthere's some benchmarks where they\nclearly improve with more test time\ncompute and there's some where they\ndon't. I think on the other extreme\nthere are um benchmarks where they kind\nof obviously will keep improving limit\nuh without limit with more test time\ncompute. So the example I like to point\nto is sudoku. If you uh it's there's a\nreally simple strategy to solving Sudoku\nwhich is just try a bunch of different\nrandom numbers and then see if it fits\nthe criteria. If it if it matches all\nthe constraints and if it doesn't just\ntry a different random combination of\nnumbers and clearly with enough time you\nwill be able to solve any puzzle with\nthis strategy. You can kind of trivally\nsee like okay any model could keep doing\nbetter and better um if it was just\ngiven more test time compute. So you\nhave and and all the benchmarks kind of\nexist somewhere between these two\nextremes. the models are not at the\nlevel where if you just give them enough\ntest time on compute they will be able\nto do um all of our jobs just because\nyeah there's some benchmarks where they\nwill not improve there are some things\nwhere they they will not improve one\nthing I see for research in particular\nis they don't have very good research\ntaste right now and so I think they're\nactually a very good complement to\nresearchers especially um you know I've\nfound like I've found I'm much more\neffective by using these models but\nthey're not able to fully replace the\nwhole research cycle\nNow, does that change with time?\nProbably. I mean, I think the models are\ngetting better across the board. Some\nsome things are getting better the\nfaster than others. Um, but they're not\nat the point where they're fully\nreplacing uh researchers with just\nenough test time compute.\n>> Can you give um an example or two of\nlike asking the model to do a research\ntest for just like this is this is a\nterrible idea? I mean I I think going\nback to my poker solver example, I was\nreally impressed with the model's\nability to optimize the algorithms that\nI had developed in my in my PhD. It was\nhonestly it it was it was shocking to\nsee how inefficient I was um in\nretrospect and they were able to make it\nlike you know 10 100x faster. And then I\nwas like, okay, can you come up with an\nalgorithm that is better than the\nalgorithms that I came up with or that\nanybody else came up with and go ahead\nand like look at all the published work\nand synthesize that and then try to come\nup with something novel and it it's not\nable to do it. And I can give it a lot\nof time and it's it's still not able to\ndo it. Now, it's possible that if I\nscaffolded something and like kind of\nconstrained it a bit more that maybe it\ncould eventually come up with something\nbetter. Um, but it would take a lot of\nit's not just as simple as saying,\n\"Okay, please come up with a better\nalgorithm.\"\n>> And how do you think that that gets\nimproved?\n>> What I've seen is with every model\nrelease cycle, um, it does get better at\nthis sort of thing. It's still it's\nstill bad in my opinion, but it it's not\nas bad as it used to be. And I wouldn't\nbe surprised if at some point, same\nthing with coding, same thing with math,\nwhere there's just like this inflection\npoint where suddenly it's actually good\nenough to be useful. Um I wouldn't be\nsurprised if we encounter that point for\nresearch taste as well.\n>> Given that what do you like what is your\nframing of RSI today? Like how should we\nthink about it?\n>> The models are definitely accelerating\nwhat researchers can do inside the labs.\nBut I think they are accelerating some\nthings and not other things. And\ncurrently we're at the point where okay\nif something goes 100x faster you get\nbottlenecked by the things that don't go\n100x faster. over time the things that\nwe're getting bottlenecked on are going\nto shrink and and there will be I think\num a kind of a gradual takeoff in that\nrespect but it's more about transforming\nright now it's more about transforming\nwhat researchers do rather than fully\nreplacing the researchers\n>> so that actually implies that you don't\nthink we're close to a very fast takeoff\nright now\n>> I think fast takeoff is relative things\nare moving very fast but I think there\nis this hypothesis that you could have\nbasically an overnight intelligence\nexplosion where the models discover some\nkind of breakthrough through to make\nthemselves smarter and then that leads\nto more breakthroughs that make\nthemselves even smarter immediately and\nyou have basically in an instance the\nmodels just you know becoming very\nsuperhuman across the board uh in in\nmoments and I don't think we're headed\nto that world largely because of the\nfact that the models rely so much on\nlarge scale test time compute in order\nto achieve um their greatest\nintelligence. If you if it requires so\nmuch test time on compute to unlock the\nfull capabilities of the model, then\nthat means you're bottlenecked by time\num things can only go so fast because\nthe models need to run for long enough\nto um to actually do something really\nreally powerful. Time itself becomes a\nbottleneck to what we can do and I I\nthink that is the case right now for a\nlot of the labs that ultimately I think\nthe biggest bottleneck for all of us is\ntime and that's why all the researchers\nare working so intensely right now. It's\nit's just so many um so many hours per\nweek are being put into this because we\nall see what the overhang is. We see\nwhat the capabilities are and we're just\nbottlenecked by how quickly can we do\nthings.\n>> What do you think is on the frontier um\nthat is less explored now? Like we've\ntalked about multi-agent before.\n>> I think multi-agent is quite explored.\nUm I think\n>> at sufficient scale,\n>> I think there's a lot more that could be\ndone. Um, but it's also one of the\nthings that's that's hard to do at a lot\nof research is hard to do at small skin.\nI think multi- agent in particular, it\nreally requires in order to fully unlock\nthe cubilities. I think requires like\nfrontier models. I think we've seen some\npretty interesting multi- aent\nscaffolds. I think um they're able to do\na lot, but I think it's really just\nscratching the surface of what it will\nbe able to do. I mean, one way that I\nthink about it is if if you look at\nhuman civilization, it's not that humans\nhave become smarter over it's not that\nthey evolved to become smarter over, you\nknow, the past 50,000 years. It's that\nhumans are able to do a lot more today\nthan they were back in caveman times\nbecause there have been billions of\nhumans thinking for a long time and\nbuilding off of each other's accumulated\nknowledge.\n>> We have like very good retrieval and\nscaffolding versus 50,000 years ago. Uh\nit's not even I wouldn't even call it a\nscaffold. It's just like this is a very\nlike organic emerging property of just\nlike humans being able to accumulate\nknowledge, share it, um and build off of\nit. We're not seeing that with AI models\ntoday. They kind of they're they're born\ninto a world for and they exist for a\nvery short context window and then they\njust like disappear.\n>> And yeah, there are things that you can\nkind of do to like continue them, but\nit's very limited. I do think eventually\nwe will and we're starting to see like\nsigns that we're entering a world where\nthey can coordinate on a large scale. I\nthink multbook and open claw when they\nfirst came out I think it was obviously\na bit overhyped but they were um\nan indication of where things could go\nin the future and I do think that\neventually we get to that kind of world\n>> of some sort of coordinated compounding\nstate.\n>> Yeah. uh the ability of the models to to\nshare knowledge um on a more global\nlevel and be able to build on that\nknowledge productively.\n>> Given this set of beliefs and your work\nlike uh how would you characterize just\ncompetition at the frontier between the\nbetween the three kingdoms if there is\nno overnight takeoff? It's just\nresearchers grinding away trying to make\ngood high taste algorithmic and\ninvestment decisions about where to go\nand then compute allocation and then\npolicy decisions and eval decisions.\n>> Um\n>> that feels like slightly more grounded\nthan um uh I support I suppose like\nracing towards some immediate hard\ntakeoff that nobody can catch you on.\n>> I think the competition is very intense\nright now. I do think the models that\nexist today um are accelerating what\nresearchers at the Frontier Labs can do.\nUm there's obviously like I said limits\nto that right now, but it the ability to\nuse the models to improve the model\nresearch is a real thing and it's um it\nit is like an amplifying force. I think\nthat will continue to be true. I think\nthey'll become more true over time. One\nthing that I am comforted by is I think\nall the researchers at the frontier labs\nlike all the frontier labs I think\nrecognize what is at stake and what\nthese models like what what the uh what\nthe risks are and that's something that\nI I find comforting that I think\neverybody really understands like okay\nthis is a pretty serious thing and it\ncan lead to really great things or it\ncan lead to really bad things and yes\nthere's a competitive dynamic between\nthe labs but like we can also try to\nfigure out how we all get to the\npositive outcomes rather than the very\nnegative outcomes. I I think you know\nI'd be remiss to ask just because you\nhave been right very early for a long\ntime um on the importance of test time\ncompute and reasoning as a framework\nlike are there ways in which you use the\nmodels that you should you would\nencourage others to right is it just\ngoal everything\n>> I think for a lot of people\nthey worked I mean this is probably not\neven true for your audience necessarily\nbut there's a lot of people that\nexperimented with AI back in like 2012\n23 and felt like they couldn't trust the\noutputs and then don't use it for really\nhigh stakes decisions. And actually, I\nthink the models have progressed to a\npoint where they are very good for these\nkinds of things. I mean, I asked them\ntax advice or I bought a condo recently\nand I was asking it for advice on like,\nokay, well, what's all the paperwork\nthat I have to fill out and like how to\nhow do I what does it all mean? It's\nactually really good for these kinds of\nquestions. Mhm.\n>> So, I use it dayto-day for for a lot of\nthis kind of stuff. And I think they're\nat a point now where they've actually\nbeen at a point for a while now where I\nfeel like I can just trust the outputs\narguably more than I could trust the\noutput from from a human,\n>> an expert human. Yeah.\n>> Okay. I have two two final questions for\nyou. Um, one is uh is there something\nyou think that the rest of the research\ncommunity doesn't agree with you on or\ndoesn't understand the importance of\nquite yet? Oh, these are such good\nquestions. I wish I had time to think\nabout this ahead of time.\n>> You can you can just hang out with me\nand think about it. Yeah.\n>> Is it weird to be like consensus now?\nYou're a bit salty three years ago when\nyou're like, why don't people understand\nhow important this is?\n>> I still I still feel like it's not\nconsensus though because like you know\npeople still don't publish the\nbenchmarks this way.\n>> Oh, that's true. Yeah.\n>> Like that's actually why\n>> I think that's like inertia.\n>> That's kind of Yeah, but that's kind of\nwhy I wrote the essay. I was just like\nlook I mean we can talk about this but\nlike yeah the part of the motivation is\nlike I would talk to researchers about\num we it makes sense to show the\nbenchmarks with an x-axis whether it's\ntokens or cost or time there should be\nan x-axis and everybody would say like\nyeah that makes sense we should do that\nbut everybody\n>> they're not acting with the importance\nof like good heart like this is we have\nto measure the correct thing\n>> well really their response is people\nexpect us to to publish the grid\n>> and then okay why do people expect the\ngrid to be published because everybody\npublishes the grid. And so you kind of\nend up in this this bad equilibrium\nwhere everybody kind of knows that it's\na bad equilibrium, but like nobody wants\nto break out. And I I felt like, okay,\nwell, if I just hopefully come out and\nsay like, look guys, let's all recognize\nthat we're in a bad equilibrium and\nlet's move to this different equilibrium\nwhere we're we're plotting things with\nan X-axis that hopefully that can, you\nknow, next time there's a model release,\na company can feel comfortable not\npublishing the grid, at least not at the\nvery front uh the top line. and um we\ncan have a more productive evaluation of\nthese models.\n>> Then a last question for you. How do you\nthink about companies across all of\nthese specialized domains who feel the\nvalue that they have is essentially like\nthe routing layer, the choice layer of\nyou know my goal is composed of a bunch\nof discrete tasks. Some require more\nintelligence and less and within my my\njob as a vendor is to um solve that\nproblem or achieve the optimal outcome\nwith uh taking into account the budget\nconstraints. And so I will manage like\nthe parallelization and how how much\ninference do you spend on it from what\nmodel because I I think the the frontier\nlab point of view is that that routing\nhappens both within the you know behind\nthe API behind the application and then\nsome of it in the model itself. Um and\nthat's pieces of that are clearly being\nexternalized in all these applications.\n>> Yeah, I do I do think this is related to\nthe fact that like benchmarks should be\nevaluated with an x-axis of tokens or\ncost. Um I I have seen some evals\nrecently that show like okay well with\nwith a routing layer you can achieve\nmuch better performance um by basically\ndoing consensus among the models.\n>> Yeah.\n>> And like I definitely believe that if\nyou do consensus among the models that\nyou're going to achieve better\nperformance than any individual model\nbut\n>> it's important to ask like are you going\nto do better than having that model\nbasically think for longer. Um like once\nyou control for the amount of test time\ncompute is it is it actually still doing\nbetter? That's that's the question that\nyou want to figure out. Okay, that's\nvery principle of view which is like yes\nrouting is fine but it's all subject to\nthe same budget question. Yeah,\n>> right. Um if you put it on the same\nscaler then you can make an optimal\ndecision and I think maybe I win.\n>> Mhm. I I I don't even know necessarily\nthat I I would believe that the routing\ndoes better but then there's still a\nquestion of um is it going to do\nsignificantly better? Is it very\nfragile? Is it um reflective of real\nworld use cases compared to benchmarks?\nbecause like one issue you could run\ninto is that you could optimize for\ncertain benchmarks with the routing and\nthen show like oh yeah we see this big\nimprovement on these benchmarks. Um but\nin real world use cases it actually ends\nup not being a significant improvement.\nSo I I would say at the very least like\nI would say you want to control for\ntestime compute and then you also want\nto have all the same um skepticism about\nbenchmarks that you would normally have.\n>> Awesome. N thanks so much and and for\nbeing on the mission for uh breaking us\nout of this false equilibrium. Yeah,\nit's great to be back.\n>> Find us on Twitter at no prior pod.\nSubscribe to our YouTube channel if you\nwant to see our faces. Follow the show\non Apple Podcasts, Spotify, or wherever\nyou listen. That way, you get a new\nepisode every week. And sign up for\nemails or find transcripts for every\nepisode at no-briers.com.\n\nExit code: 0\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/28f3adc3/content.txt\n","created_at":"2026-07-09T04:21:54.639781Z","finish_reason":null,"metrics":null},{"message_id":"0f0708fb-a293-4b2b-b7ce-9ac500f6914c","node_id":42,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:5","name":"read","arguments":{"file_path":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/28f3adc3/content.txt"}}],"thinking":"The transcript output was truncated in the tool output but full output written to overflow file. It seems we got the majority, but there were some truncation notices \"21 lines truncated\", \"31 chars truncated\" etc within the Python print due to terminal? Actually the transcript itself appears complete but with some \"... (2 chars truncated)\" in the text. We need full transcript. The overflow file path is /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/28f3adc3/content.txt. Need to read it fully.","created_at":"2026-07-09T04:21:56.036952Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1106,"total_time_ms":1396,"input_tokens":10226,"output_tokens":179,"cache_read_tokens":34432,"cache_creation_tokens":null,"tpot_ms":1.6201117318435754,"tokens_per_sec":617.2413793103449}},{"message_id":"8f5e59a6-d7db-4e44-8904-96b60149574b","node_id":43,"role":"tool","content":"\n 1|Output from command in shell 1dbd60:\n 2|With GBT3, you couldn't scale test time\n 3|compute. Like if you gave it a budget of\n 4|$10 million and said, \"Okay, well, let's\n 5|see what GB3 can do.\" It really can't do\n 6|that much. The precurren frameworks and\n 7|responsible scaling policies, they don't\n 8|really account for the amount of testime\n 9|compute. They just say, \"Okay, well,\n 10|what's the capability of the model?\" The\n 11|problem is we're in a world now where\n 12|the capability of the model is a\n 13|function of how much money you put into\n 14|it. Basically, if you give it a budget\n 15|of $10,000, it can do a lot more than\n 16|what it can do with a budget of $10.\n 17|Give it a budget of $10 million, you can\n 18|do even more. At what budget should you\n 19|evaluate these models? The policies that\n 20|exist today don't really address that\n 21|question.\n 22|Hi listeners, I'm Sarah Goa and welcome\n 23|back to No Priors. Today I'm here with\n 24|Nome Brown, one of our godfathers of AI\n 25|reasoning. We talk about the broken\n 26|state of evaluations, very large-scale\n 27|test time compute, how he thinks about\n 28|recursive self-improvement, and what's\n 29|next on the horizon for competition at\n 30|the frontier. Welcome, Gnome. I'm so\n 31|excited to have you back.\n 32|>> That's great to be back. Yeah,\n 33|>> you are our first guest. I'm very proud\n 34|of my taste in friends and researchers\n 35|for uh for the pod. um given you know\n 36|how important uh you know inference time\n 37|scaling has become to the industry you\n 38|should be proud too having actually\n 39|pioneered it\n 40|>> played a part yeah among among many\n 41|others\n 42|>> you just wrote this essay that really\n 43|resonated about large scale test time\n 44|compute and why uh the industry is not\n 45|evaluating these models as robustly as\n 46|it should be what was the motivation for\n 47|it\n 48|>> yeah the motivation was we released 5.5\n 49|and the initial reaction ction was kind\n 50|of skepticism that it was a\n 51|substantially better model. To be fair,\n 52|that only lasted for a few hours before\n 53|people had some time to play around with\n 54|it and and try it out themselves and\n 55|they saw that it was actually\n 56|substantially better. Um, but I think a\n 57|lot of the skepticism came from the\n 58|benchmark grid that was published.\n 59|Basically, whenever a new model is\n 60|released, there's this benchmark grid\n 61|where they show all these different\n 62|benchmarks on on the x-axxis and then\n 63|the performance of different models on\n 64|the y- axis and you can just like\n 65|compare different models. It's like a\n 66|single number for a model on a single\n 67|benchmark. And if you look on paper at\n 68|the difference between like 5.5 and 5.4\n 69|or or other models, it wasn't it was an\n 70|improvement, but it wasn't a huge\n 71|improvement. It was only a few\n 72|percentage points in some benchmarks.\n 73|So, people looked at that and they were\n 74|skeptical that it was actually a better\n 75|model. Um, once they played around with\n 76|it, the story changed. I think the\n 77|reason why it doesn't show up as so much\n 78|better on the benchmarks is because the\n 79|benchmarks are being presented, the\n 80|benchmark results are being presented in\n 81|the wrong way. They're not controlling\n 82|for the amount of test time compute that\n 83|is being used on that benchmark\n 84|question. It turned out that 5.5 is just\n 85|much more efficient with its thinking.\n 86|If you run it at max settings, 5.4 is\n 87|thinking for a lot longer. It takes\n 88|longer to get back a response than 5.5.\n 89|And once you control for the amount of\n 90|thinking time, actually you can see that\n 91|5.5 is a substantial jump over 5.4. That\n 92|is, I think, people's day-to-day\n 93|experience with it. And then when I\n 94|mention this to people the reaction, the\n 95|typical question I get is like okay well\n 96|why not just have 5.5 think for as long\n 97|as 5.4 and the question is like well how\n 98|long should they think for? Typically\n 99|the response I get is well until the\n100|performance plateaus right there's at\n101|some point where the performance on the\n102|benchmark is going to plateau and you\n103|evaluate to that point. The thing is the\n104|point at which it plateaus is actually\n105|really far out these days. I mean, it\n106|was true in GBD3 land back in 2022, the\n107|models couldn't really think\n108|productively for that long. And so, you\n109|could just run them until they plateau.\n110|It's not that far away. But what we're\n111|seeing today with the modern models is\n112|that 5.5 and other models can think for\n113|if you scaffold them reasonably well,\n114|can think for weeks even um before\n115|having performance plateau on some of\n116|these benchmarks. And so, the point at\n117|which they plateau is simply too far out\n118|to reasonably test. we all need to\n119|actually reinforce um either like a\n120|patience limit or a budget limit from a\n121|token perspective now and that wasn't\n122|true a few years ago.\n123|>> Exactly. And so I think the proper way\n124|to and so my claim is the proper way to\n125|evaluate the models now is you either\n126|have some kind of budget for the\n127|benchmark whether it's tokens or cost or\n128|time or whatever or you plot the\n129|performance as a function of the amount\n130|of test time compute that's going into\n131|the model and then it becomes much more\n132|clear um how to compare the performance\n133|between these different models.\n134|>> Given the model evaluation cycle and the\n135|fact that performance does not asmtote\n136|for many tasks over um quite a long\n137|period of time. What do you do about\n138|that issue? The fact that some of the\n139|evals that you would want to run are\n140|both beyond the scope of budget or time\n141|that's reasonable given the current\n142|model release cycle.\n143|>> I mean, I think for things like cyber,\n144|we've seen and actually the AIS um in\n145|their evaluations has shown that the\n146|models continue to improve um at 100\n147|million tokens. You know, if you run\n148|them for 100 million tokens, they're\n149|still improving at beyond that point.\n150|>> And that can take a very long time to\n151|run. But you also do see that like the\n152|performance is is it's not just like a\n153|discontinuous jump. It's actually like\n154|you can see the slope of improvement\n155|over those 100 million tokens. And so\n156|you could you could probably do some\n157|kind of uh evaluation up to a certain\n158|budget and then just say okay well this\n159|is what we project the performance to\n160|look like. And I think this this there\n161|hasn't been a lot of research on this\n162|yet. I actually think this would be a\n163|great paper to publish if there's any\n164|academics out there looking for\n165|something to research. Can you predict\n166|what the performance looks like at an\n167|inference budget of let's say $10,000\n168|only using inference budgets up to 10 or\n169|10 or $100?\n170|>> So maybe an orthogonal question for you.\n171|Uh do you think users are systematically\n172|like not thinking long enough with their\n173|models about problems?\n174|>> What do you mean by not thinking long\n175|enough? um if the if you can\n176|build an agent or control the amount of\n177|um test time compute being used like\n178|there's there's what is done by the\n179|model itself and there's what the user\n180|can do. Um do you think that like you\n181|know the industry is using test time\n182|compute an optimal amount uh way\n183|undershooting it or it's you know it's a\n184|problem in the models where they just\n185|need to be able to do that thinking\n186|faster.\n187|>> I I think it depends on the problem. Uh\n188|I think this idea that the models you\n189|just let them think for a week or\n190|whatever and then they respond. It's it\n191|sounds nice and yes the benchmarks look\n192|great but it's not very practical when\n193|working u because like okay you ask the\n194|model a question and then you sit there\n195|for a week waiting for it to come back\n196|to you.\n197|>> I think what people have have found most\n198|effective is to kind of like iterate\n199|quickly with the models and so the\n200|thinking time I think needs to be\n201|flexible. when it makes sense to respond\n202|quickly to the user, it should respond\n203|quickly. And then when it makes sense to\n204|think for a long time and the user wants\n205|it to think for a long time, then it\n206|makes sense to think for a long time. I\n207|think people have been striking the\n208|right balance given what they have to\n209|deal with right now.\n210|>> How would you characterize, you know,\n211|there's a lot of talk about benchmark\n212|maxing and the ability to game different\n213|benchmarks? What would you characterize\n214|the like landscape of benchmarks as\n215|today? And then do you have like\n216|favorites that you think are more\n217|indicative of capability than others? So\n218|the benchmark maxing thing is also\n219|motivation for for writing the essay\n220|that I think it's really easy to show\n221|you can do much better than previous\n222|benchmarks or or previous previous\n223|models on benchmarks by just for example\n224|scaffolding a bunch of models together.\n225|Um so if you say okay well we're going\n226|to instead of just running this model\n227|once we're going to run it five times\n228|and take the best of the five responses\n229|or like ask a judge which one it thinks\n230|is best then you can get much higher\n231|scores than that model. And so it's\n232|really easy to make something that looks\n233|a lot better on paper but is actually\n234|not better once you control for the\n235|amount of test time compute. That is one\n236|thing that I'm worried about when it\n237|comes to benchmark maxing. I mean it\n238|like it's it's a little misleading is\n239|the only concern that I have. And then\n240|as far as like the benchmarks\n241|themselves, I think there is always a\n242|risk of like just optimizing for the\n243|benchmark. And I've I've certainly\n244|encouraged my team and I think at OpenAI\n245|we're pretty good about not trying to\n246|optimize for for specific benchmarks.\n247|But once you put out a benchmark, it's\n248|there's it's always at risk of just\n249|being optimized for. And I think one way\n250|to one way to address that is to keep a\n251|held out private set um that isn't uh\n252|publicly available.\n253|>> The most popular fallback advice for you\n254|know figure out if a model is\n255|significantly better or not is to just\n256|play with it for a while. Do you have\n257|anything more sophisticated than that\n258|that you suggest people do? like do you\n259|create your own set of new evaluations\n260|each time besides private holdback at\n261|OpenAI?\n262|>> I think everybody has their own set of\n263|questions that they like to ask the\n264|model whenever it comes out.\n265|>> Um for me lately it's been I I use them\n266|to make poker bots and see how good they\n267|can make a poker bot. I think it's a\n268|nice eval because there is very little\n269|um open source code for making poker\n270|bots and there's a lot of published\n271|essay there's a lot of published papers\n272|on it but you really have to reason\n273|through everything and it's like\n274|requires a lot of just reasoning and\n275|iteration and like a lot of small\n276|gotchas\n277|>> that I can kind of I've already worked\n278|through myself so I can see where the\n279|models fail along the way. They've\n280|gotten really good at it now. Can you\n281|describe perhaps like with your poker\n282|bot creation like how reasoning might\n283|have progressed in model releases for\n284|you guys over a few uh releases?\n285|>> Yeah, when the early models were really\n286|bad at it, like they could not basically\n287|do anything. And then 5.2\n288|I was able to work with it to make a\n289|river solver.\n290|>> So that's like the final stage of poker.\n291|And um that itself was I thought really\n292|impressive. I mean I had to work with it\n293|a little bit, but I was actually really\n294|impressed cuz I was able to make the\n295|river solver probably about five times\n296|faster than I would have alone. There\n297|were a couple things that I got um\n298|tripped up on. Uh blockers was always a\n299|big big issue. But overall, like, you\n300|know, with a with a bit of gentle\n301|steering, it just kind of like it kind\n302|of felt like a grad student where, okay,\n303|they would run into issues, but at least\n304|like I would um know what those issues\n305|were and know how to fix it, and I could\n306|just make suggestions and it would go\n307|off and then do it and then pretty\n308|quickly it would actually come back with\n309|something really good.\n310|>> Mhm.\n311|>> And then especially the optimization I\n312|thought was very impressive. It was able\n313|to make it like 10 times faster than\n314|what I was able to do because it was\n315|just able to optimize um the code so\n316|well. The downsides with 5.2 is I felt\n317|like it was gaslighting me a lot and I\n318|always had to be very careful checking\n319|it and making sure like, okay, is it\n320|actually doing what it said it did? Um,\n321|are there any things that are like\n322|glaring issues that it's not recognizing\n323|or it's just pretending aren't issues?\n324|Um, I remember there was like one point\n325|uh where for one of the models I was\n326|playing around with it, not 5.2,\n327|I kind of like as a as a unit test, I\n328|told it, okay, well, let's say um I have\n329|$100 in the pot and I fold. How much am\n330|I losing? And the model said $92. And I\n331|was like, that's crazy. I have $100 in\n332|the pot and I just fold it. How do I not\n333|lose $100? And it said, oh, you know,\n334|it's 92. It's close to 100. It's fine.\n335|It's no big deal. And I was like,\n336|clearly this is a problem, right? So the\n337|models did have this problem where they\n338|would gaslight you a lot. But once we\n339|got to 5.5, I actually thought um it was\n340|way better. It was able to basically do\n341|it zero shot. And in fact, I've been\n342|working on just doing a full scale poker\n343|solver. Um and it it's basically able to\n344|do the whole thing uh with some gentle\n345|steering from me. And I wouldn't be\n346|surprised if you know 6 months or a year\n347|from now, the model is able to do zero\n348|shot an entire poker solver, basically\n349|my entire PhD thesis in one go. Let's\n350|talk about the larger implications of\n351|you know needing to evaluate these\n352|models relative to um let's say like\n353|speed of their reasoning or efficiency\n354|versus you know token volume right um or\n355|dollar budget or whatever whatever your\n356|scalar is. Can you describe some of the\n357|larger implications in your essay\n358|including around um like safety\n359|evaluations? Yeah, the safety\n360|evaluations thing. Um, it's it's a bit\n361|of an inconvenient truth thing where\n362|okay, so I guess for background, a lot\n363|of the all of the labs have these things\n364|called either responsible scaling\n365|policies, preparedness frameworks. They\n366|go by various names, but the idea is\n367|that whenever a model is released, they\n368|go through a series of evaluations to\n369|measure are there dangerous\n370|capabilities? Um, could these models do\n371|things that we're we we wouldn't want um\n372|a bad actor to do. And if the model\n373|isn't very capable, then it's no big\n374|deal. But if it is very capable, if it\n375|could be used for example to make\n376|bioweapons, then you want to put in\n377|mitigations against that.\n378|>> But the question is okay, well, how do\n379|you evaluate whether the model is\n380|capable of that? And they have like\n381|various protocols about like how they do\n382|these valuations. But a lot of these\n383|frameworks were developed around the era\n384|of chatgpt before after when test time\n385|comput scaling was not really uh as much\n386|of a thing and it made sense like with\n387|GPT3 you couldn't scale test time\n388|compute like if you gave it a budget of\n389|$10 million and said okay well let's see\n390|what GP3 can do it really can't do that\n391|much more than what you could do with\n392|like $10 or $1. the preparedness\n393|frameworks and responsible scaling\n394|policies, they don't really account for\n395|the amount of test time compute. They\n396|just say, \"Okay, well, what's the\n397|capability of the model? The problem is\n398|we're in a world now where the\n399|capability of the model is a function of\n400|how much money you put into it.\n401|Basically, if you give it a budget of\n402|$10,000, it can do a lot more than what\n403|it can do with a budget of $10. If you\n404|give it a budget of $10 million, it can\n405|do even more.\" And so, at what budget\n406|should you evaluate these models? the\n407|policies that exist today don't really\n408|address that question.\n409|>> Mhm.\n410|>> Uh some do some do better than others,\n411|but um for the most part, this is not\n412|really a factor that's being heavily\n413|considered. Now, whether it should be\n414|released anyway, I don't I don't want to\n415|wait into this question. I think\n416|there's, you know, there's arguments on\n417|both sides, but I think the important\n418|thing to recognize is that this is a\n419|question that is not being we're just\n420|kind of like, you know, pretending that\n421|this issue doesn't exist. And I think\n422|it's important to just, you know, one\n423|way or the other um account for it.\n424|Yeah, it's the mirror image of the\n425|capability question of if the models can\n426|uh continue to do more and more without\n427|asmmptoing on some tasks at very large\n428|budgets um then they should also be able\n429|to do so for uh tasks we don't want them\n430|to do as a society, right? And so\n431|testing for that um and what budget is\n432|allocated. It also seems out of sync\n433|from the model release cycle itself,\n434|right? there's been this acceleration\n435|of, you know, you get a new model every\n436|sometimes few days and weeks at this\n437|point versus 6 months. And you have a\n438|line in the essay where you say like the\n439|the only way to truly evaluate an agent\n440|on some very longunning task might be to\n441|run it for a year. And that's going to\n442|be true of both like useful and uh\n443|negative tasks, right? And so how do you\n444|think about that versus the model\n445|release cycle? Yeah, this this is also\n446|an interesting dynamic where basically\n447|as the models have become stronger,\n448|they've they're more they're better able\n449|to operate over longer horizons.\n450|>> So again, with GPD3, if you wanted to\n451|run it for, you know, a week, there's\n452|really not much you could do to scaffold\n453|it into something useful that could\n454|actually run for a week. But we're\n455|seeing now with the most recent models\n456|that you can actually scaffold, for\n457|example, 5.5 into doing a series of\n458|experiments that can run for weeks, for\n459|months. Have you given your poker solver\n460|task like infinite budget yet?\n461|>> I haven't really scaffolded something\n462|together where I just tell it like,\n463|\"Okay, just run this for um for weeks.\"\n464|I think I could give it I I could ask I\n465|could probably give it / goal and just\n466|like yeah, tell it to go nuts.\n467|>> But um I I think at this point it could\n468|100% do the river solver. Um, if I just\n469|give it slash goal, I don't think it's\n470|at the level yet where it could do like\n471|the full poker solver if I gave it just\n472|slash goal and told it, yeah, go go run\n473|for a month. But we're going to pretty\n474|soon be at that point where I probably\n475|could just tell it like, yeah, go work\n476|on this for a month and then come back\n477|to me with a a full complete poker\n478|solver that's state-of-the-art.\n479|And the problem is if you want to\n480|evaluate the capabilities of a model,\n481|what it can do after running for a\n482|month, the only way to be fully sure is\n483|to actually run it for a month. And if\n484|you want to know after 6 months, the\n485|only way to know fully is to run it for\n486|six months. Now there I I'll get to like\n487|things we could do to address that a\n488|little bit later. But like it's\n489|important to recognize the model release\n490|cycle is look, we're releasing new\n491|models like every two or three months at\n492|this point. And so a model comes out, it\n493|takes two or 3 months to push it to its\n494|limits and then you have another model\n495|come out. And so nobody actually knows\n496|what the ceiling of capabilities are for\n497|these models because nobody's actually\n498|run them for long enough to really tell.\n499|When slash goal came out, for example, I\n500|mean, people started running things that\n501|it took over a week for it to finish.\n502|And so, people actually didn't realize\n503|that this was a big deal until after a\n504|week uh until a week after it was\n505|released.\n506|>> Mhm.\n507|>> I think that's going to be more and more\n508|true. You know, the implications of that\n509|are, I think, pretty interesting because\n510|what do the labs do to like fully\n511|evaluate their models before they're\n512|released? It's actually very difficult\n513|because, yeah, you would have to the\n514|only way to to really do the evaluations\n515|is then delay the model release cycle.\n516|Um and you know there's a lot of\n517|competitive pressure right now to not do\n518|that.\n519|>> Do you think there's um like exciting\n520|latent capability in the models that are\n521|already released that people have not\n522|fully explored given timeline?\n523|>> I think absolutely. I think actually a\n524|really great example is the erdo unit\n525|distance problem. So for the viewers\n526|that don't know like we used an internal\n527|model at OpenAI a few weeks ago to uh\n528|disprove the unit erdos unit distance\n529|conjecture. Now, I'm not a\n530|mathematician, but this seems like it\n531|was a pretty big deal in the in the math\n532|community. It was like the first uh\n533|first problem that a lot of\n534|mathematicians had really spent a lot of\n535|time on, and the model was able to do\n536|something that they weren't able to do\n537|and do it in a way that was actually\n538|interesting and useful for\n","created_at":"2026-07-09T04:21:56.041063Z","finish_reason":null,"metrics":null},{"message_id":"db19b2a6-4ed8-4b42-9609-506c6472487a","node_id":45,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:6","name":"read","arguments":{"file_path":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/28f3adc3/content.txt","offset":539}}],"thinking":"File has 1106 lines; read returned up to 538. Need remainder.","created_at":"2026-07-09T04:21:57.176745Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1046,"total_time_ms":1132,"input_tokens":6432,"output_tokens":78,"cache_read_tokens":44544,"cache_creation_tokens":null,"tpot_ms":1.1025641025641026,"tokens_per_sec":906.9767441860464}},{"message_id":"ad3e72c4-e316-41a9-afaf-f3d53cb9e3d5","node_id":46,"role":"tool","content":"\n 539|mathematicians. Honestly, it did it at a\n 540|budget that was dirt cheap. I mean, we\n 541|didn't put a lot of effort into this. We\n 542|just we trained a new model and we were\n 543|just curious what it could do. And we\n 544|ran it through some problems. And this\n 545|one at a pretty low budget, it was like,\n 546|\"Oh, yeah. I think I have a disproof.\"\n 547|And then we were able to verify that\n 548|yeah that this proof is correct after we\n 549|announced the results a bunch of people\n 550|found that you could get the answer out\n 551|of 5.5 as well if now it's not as simple\n 552|as just asking 5.5 hey here's the unit\n 553|distance conjecture what's the disproof\n 554|you had to scaffold it a bit you had to\n 555|like steer it a bit and so somebody\n 556|found okay you ask 5.5 list a bunch of\n 557|ways that you could tackle this problem\n 558|and then for it it lists one of the\n 559|paths that are actually promising to get\n 560|to the disproof and then you tell it\n 561|like okay explore this some more and\n 562|then if you do this enough times it\n 563|actually ends up arriving at the\n 564|disproof. Now what this means is you\n 565|could in principle ask 5.5 to you know\n 566|as as a general purpose scaffold list a\n 567|bunch of different strategies and then\n 568|for each strategy tell it to investigate\n 569|that strategy and then it would probably\n 570|be able to arrive at the disproof with a\n 571|general purpose scaffold. Now that\n 572|scaffold would be very expensive. I mean\n 573|it would probably cost I I just ballpark\n 574|like thousand to 10 to $100,000. Um but\n 575|it would be possible and it would have\n 576|been possible for somebody to disprove\n 577|the erdos unit distance conjecture\n 578|before we did using a general purpose\n 579|model. And nobody had explored\n 580|sufficiently what happens if I put\n 581|$100,000 worth of compute into 5.5 what\n 582|could it do? Um and the answer is like\n 583|yeah you probably could get stuff like\n 584|that out of it. So people should be\n 585|experimenting more with the current\n 586|generation in terms of\n 587|>> well this is I think is an interesting\n 588|question of is it worth it to experiment\n 589|with because again the model release\n 590|cycle is every every couple months we\n 591|put out a new model that's even more\n 592|powerful and so the cost of disproving\n 593|the erdosha distance gesture drops by\n 594|like 10 or 100x with every model release\n 595|cycle probably in some cases more so\n 596|>> you've seen the meme that's like oh I\n 597|like why why bother doing any\n 598|engineering work when I should just wait\n 599|for the next\n 600|>> model go on vacation come back two\n 601|months later and and it's, you know, a\n 602|thousand times cheaper. So,\n 603|>> do you agree with that?\n 604|>> I\n 605|>> Is that what you're doing right now at\n 606|OpenAI, just waiting for the next model\n 607|release?\n 608|>> I think I I mean, I will say we're we're\n 609|in a period where progress is very fast\n 610|and like yeah, the models are becoming\n 611|more capable. I I can say like at OpenAI\n 612|one of the things that we're we're\n 613|actively not doing and look we have a\n 614|lot of mathematicians we have a lot of\n 615|physicists people are very excited about\n 616|what these models can do right now\n 617|especially you know the internal models\n 618|we are trying to encourage people to not\n 619|spend all their time just like going\n 620|through all the mathematical open\n 621|problems physics problems and um just\n 622|seeing pushing the models to their\n 623|limits to see what they can prove or\n 624|disprove. Um because we really think the\n 625|focus should be on how do we make even\n 626|more capable models? how can we get them\n 627|get them out safely to the world as\n 628|quickly as possible so that all the\n 629|scientists in the world can use these\n 630|models to solve the problems themselves.\n 631|Uh so yeah in some sense we are thinking\n 632|about this that yes it's really tempting\n 633|to um just put all of our efforts into\n 634|scaling up these models and see what\n 635|they can do at their limits right now\n 636|but really the focus should be on how do\n 637|we use these models to make even more\n 638|powerful models even more capable models\n 639|that can do everything much more\n 640|cost-ffectively.\n 641|What is changing about the uh direction\n 642|or allocation of resources for research\n 643|in your mind given your beliefs about\n 644|this very large scale the impact of very\n 645|large scale test time compute? How does\n 646|this interact with the um idea of\n 647|recursive self-improvement for example\n 648|where you know it's a dominant idea for\n 649|how you know any lab gets to the best\n 650|capability model. So, one thing I should\n 651|clarify, I don't think we're at the\n 652|point where, okay, you just give it an\n 653|arbitrary extremely high inference\n 654|budget and it's just it's just super\n 655|intelligent across the board.\n 656|>> SL goal,\n 657|>> yeah,\n 658|>> make GPD 7 or whatever and yeah, just go\n 659|nuts.\n 660|>> What's between us and there then?\n 661|>> I think having played around with the\n 662|model uh so okay, so first of all, there\n 663|are some benchmarks where the models\n 664|will just not improve if they have more\n 665|inference budget. So, I think a lot of\n 666|factual ret uh factual um retrieval kind\n 667|of questions fall into this category of\n 668|if you ask a person when was Abraham\n 669|Lincoln born and they don't know the\n 670|date. They could sit there, they could\n 671|think about it for a week, if they if\n 672|they don't have access to Wikipedia or\n 673|something, they're not going to be able\n 674|to do better answering that question if\n 675|they thought about it for a week\n 676|compared to 5 seconds. Same with the\n 677|model. If you actually, interestingly\n 678|enough, if you give the model these\n 679|kinds of like factual retrieval\n 680|questions and you give them a little bit\n 681|of time to think, they do actually do\n 682|better. um but if you give them a week\n 683|they're not suddenly going to do better\n 684|at remembering dates. There are so\n 685|there's some benchmarks where they\n 686|clearly improve with more test time\n 687|compute and there's some where they\n 688|don't. I think on the other extreme\n 689|there are um benchmarks where they kind\n 690|of obviously will keep improving limit\n 691|uh without limit with more test time\n 692|compute. So the example I like to point\n 693|to is sudoku. If you uh it's there's a\n 694|really simple strategy to solving Sudoku\n 695|which is just try a bunch of different\n 696|random numbers and then see if it fits\n 697|the criteria. If it if it matches all\n 698|the constraints and if it doesn't just\n 699|try a different random combination of\n 700|numbers and clearly with enough time you\n 701|will be able to solve any puzzle with\n 702|this strategy. You can kind of trivally\n 703|see like okay any model could keep doing\n 704|better and better um if it was just\n 705|given more test time compute. So you\n 706|have and and all the benchmarks kind of\n 707|exist somewhere between these two\n 708|extremes. the models are not at the\n 709|level where if you just give them enough\n 710|test time on compute they will be able\n 711|to do um all of our jobs just because\n 712|yeah there's some benchmarks where they\n 713|will not improve there are some things\n 714|where they they will not improve one\n 715|thing I see for research in particular\n 716|is they don't have very good research\n 717|taste right now and so I think they're\n 718|actually a very good complement to\n 719|researchers especially um you know I've\n 720|found like I've found I'm much more\n 721|effective by using these models but\n 722|they're not able to fully replace the\n 723|whole research cycle\n 724|Now, does that change with time?\n 725|Probably. I mean, I think the models are\n 726|getting better across the board. Some\n 727|some things are getting better the\n 728|faster than others. Um, but they're not\n 729|at the point where they're fully\n 730|replacing uh researchers with just\n 731|enough test time compute.\n 732|>> Can you give um an example or two of\n 733|like asking the model to do a research\n 734|test for just like this is this is a\n 735|terrible idea? I mean I I think going\n 736|back to my poker solver example, I was\n 737|really impressed with the model's\n 738|ability to optimize the algorithms that\n 739|I had developed in my in my PhD. It was\n 740|honestly it it was it was shocking to\n 741|see how inefficient I was um in\n 742|retrospect and they were able to make it\n 743|like you know 10 100x faster. And then I\n 744|was like, okay, can you come up with an\n 745|algorithm that is better than the\n 746|algorithms that I came up with or that\n 747|anybody else came up with and go ahead\n 748|and like look at all the published work\n 749|and synthesize that and then try to come\n 750|up with something novel and it it's not\n 751|able to do it. And I can give it a lot\n 752|of time and it's it's still not able to\n 753|do it. Now, it's possible that if I\n 754|scaffolded something and like kind of\n 755|constrained it a bit more that maybe it\n 756|could eventually come up with something\n 757|better. Um, but it would take a lot of\n 758|it's not just as simple as saying,\n 759|\"Okay, please come up with a better\n 760|algorithm.\"\n 761|>> And how do you think that that gets\n 762|improved?\n 763|>> What I've seen is with every model\n 764|release cycle, um, it does get better at\n 765|this sort of thing. It's still it's\n 766|still bad in my opinion, but it it's not\n 767|as bad as it used to be. And I wouldn't\n 768|be surprised if at some point, same\n 769|thing with coding, same thing with math,\n 770|where there's just like this inflection\n 771|point where suddenly it's actually good\n 772|enough to be useful. Um I wouldn't be\n 773|surprised if we encounter that point for\n 774|research taste as well.\n 775|>> Given that what do you like what is your\n 776|framing of RSI today? Like how should we\n 777|think about it?\n 778|>> The models are definitely accelerating\n 779|what researchers can do inside the labs.\n 780|But I think they are accelerating some\n 781|things and not other things. And\n 782|currently we're at the point where okay\n 783|if something goes 100x faster you get\n 784|bottlenecked by the things that don't go\n 785|100x faster. over time the things that\n 786|we're getting bottlenecked on are going\n 787|to shrink and and there will be I think\n 788|um a kind of a gradual takeoff in that\n 789|respect but it's more about transforming\n 790|right now it's more about transforming\n 791|what researchers do rather than fully\n 792|replacing the researchers\n 793|>> so that actually implies that you don't\n 794|think we're close to a very fast takeoff\n 795|right now\n 796|>> I think fast takeoff is relative things\n 797|are moving very fast but I think there\n 798|is this hypothesis that you could have\n 799|basically an overnight intelligence\n 800|explosion where the models discover some\n 801|kind of breakthrough through to make\n 802|themselves smarter and then that leads\n 803|to more breakthroughs that make\n 804|themselves even smarter immediately and\n 805|you have basically in an instance the\n 806|models just you know becoming very\n 807|superhuman across the board uh in in\n 808|moments and I don't think we're headed\n 809|to that world largely because of the\n 810|fact that the models rely so much on\n 811|large scale test time compute in order\n 812|to achieve um their greatest\n 813|intelligence. If you if it requires so\n 814|much test time on compute to unlock the\n 815|full capabilities of the model, then\n 816|that means you're bottlenecked by time\n 817|um things can only go so fast because\n 818|the models need to run for long enough\n 819|to um to actually do something really\n 820|really powerful. Time itself becomes a\n 821|bottleneck to what we can do and I I\n 822|think that is the case right now for a\n 823|lot of the labs that ultimately I think\n 824|the biggest bottleneck for all of us is\n 825|time and that's why all the researchers\n 826|are working so intensely right now. It's\n 827|it's just so many um so many hours per\n 828|week are being put into this because we\n 829|all see what the overhang is. We see\n 830|what the capabilities are and we're just\n 831|bottlenecked by how quickly can we do\n 832|things.\n 833|>> What do you think is on the frontier um\n 834|that is less explored now? Like we've\n 835|talked about multi-agent before.\n 836|>> I think multi-agent is quite explored.\n 837|Um I think\n 838|>> at sufficient scale,\n 839|>> I think there's a lot more that could be\n 840|done. Um, but it's also one of the\n 841|things that's that's hard to do at a lot\n 842|of research is hard to do at small skin.\n 843|I think multi- agent in particular, it\n 844|really requires in order to fully unlock\n 845|the cubilities. I think requires like\n 846|frontier models. I think we've seen some\n 847|pretty interesting multi- aent\n 848|scaffolds. I think um they're able to do\n 849|a lot, but I think it's really just\n 850|scratching the surface of what it will\n 851|be able to do. I mean, one way that I\n 852|think about it is if if you look at\n 853|human civilization, it's not that humans\n 854|have become smarter over it's not that\n 855|they evolved to become smarter over, you\n 856|know, the past 50,000 years. It's that\n 857|humans are able to do a lot more today\n 858|than they were back in caveman times\n 859|because there have been billions of\n 860|humans thinking for a long time and\n 861|building off of each other's accumulated\n 862|knowledge.\n 863|>> We have like very good retrieval and\n 864|scaffolding versus 50,000 years ago. Uh\n 865|it's not even I wouldn't even call it a\n 866|scaffold. It's just like this is a very\n 867|like organic emerging property of just\n 868|like humans being able to accumulate\n 869|knowledge, share it, um and build off of\n 870|it. We're not seeing that with AI models\n 871|today. They kind of they're they're born\n 872|into a world for and they exist for a\n 873|very short context window and then they\n 874|just like disappear.\n 875|>> And yeah, there are things that you can\n 876|kind of do to like continue them, but\n 877|it's very limited. I do think eventually\n 878|we will and we're starting to see like\n 879|signs that we're entering a world where\n 880|they can coordinate on a large scale. I\n 881|think multbook and open claw when they\n 882|first came out I think it was obviously\n 883|a bit overhyped but they were um\n 884|an indication of where things could go\n 885|in the future and I do think that\n 886|eventually we get to that kind of world\n 887|>> of some sort of coordinated compounding\n 888|state.\n 889|>> Yeah. uh the ability of the models to to\n 890|share knowledge um on a more global\n 891|level and be able to build on that\n 892|knowledge productively.\n 893|>> Given this set of beliefs and your work\n 894|like uh how would you characterize just\n 895|competition at the frontier between the\n 896|between the three kingdoms if there is\n 897|no overnight takeoff? It's just\n 898|researchers grinding away trying to make\n 899|good high taste algorithmic and\n 900|investment decisions about where to go\n 901|and then compute allocation and then\n 902|policy decisions and eval decisions.\n 903|>> Um\n 904|>> that feels like slightly more grounded\n 905|than um uh I support I suppose like\n 906|racing towards some immediate hard\n 907|takeoff that nobody can catch you on.\n 908|>> I think the competition is very intense\n 909|right now. I do think the models that\n 910|exist today um are accelerating what\n 911|researchers at the Frontier Labs can do.\n 912|Um there's obviously like I said limits\n 913|to that right now, but it the ability to\n 914|use the models to improve the model\n 915|research is a real thing and it's um it\n 916|it is like an amplifying force. I think\n 917|that will continue to be true. I think\n 918|they'll become more true over time. One\n 919|thing that I am comforted by is I think\n 920|all the researchers at the frontier labs\n 921|like all the frontier labs I think\n 922|recognize what is at stake and what\n 923|these models like what what the uh what\n 924|the risks are and that's something that\n 925|I I find comforting that I think\n 926|everybody really understands like okay\n 927|this is a pretty serious thing and it\n 928|can lead to really great things or it\n 929|can lead to really bad things and yes\n 930|there's a competitive dynamic between\n 931|the labs but like we can also try to\n 932|figure out how we all get to the\n 933|positive outcomes rather than the very\n 934|negative outcomes. I I think you know\n 935|I'd be remiss to ask just because you\n 936|have been right very early for a long\n 937|time um on the importance of test time\n 938|compute and reasoning as a framework\n 939|like are there ways in which you use the\n 940|models that you should you would\n 941|encourage others to right is it just\n 942|goal everything\n 943|>> I think for a lot of people\n 944|they worked I mean this is probably not\n 945|even true for your audience necessarily\n 946|but there's a lot of people that\n 947|experimented with AI back in like 2012\n 948|23 and felt like they couldn't trust the\n 949|outputs and then don't use it for really\n 950|high stakes decisions. And actually, I\n 951|think the models have progressed to a\n 952|point where they are very good for these\n 953|kinds of things. I mean, I asked them\n 954|tax advice or I bought a condo recently\n 955|and I was asking it for advice on like,\n 956|okay, well, what's all the paperwork\n 957|that I have to fill out and like how to\n 958|how do I what does it all mean? It's\n 959|actually really good for these kinds of\n 960|questions. Mhm.\n 961|>> So, I use it dayto-day for for a lot of\n 962|this kind of stuff. And I think they're\n 963|at a point now where they've actually\n 964|been at a point for a while now where I\n 965|feel like I can just trust the outputs\n 966|arguably more than I could trust the\n 967|output from from a human,\n 968|>> an expert human. Yeah.\n 969|>> Okay. I have two two final questions for\n 970|you. Um, one is uh is there something\n 971|you think that the rest of the research\n 972|community doesn't agree with you on or\n 973|doesn't understand the importance of\n 974|quite yet? Oh, these are such good\n 975|questions. I wish I had time to think\n 976|about this ahead of time.\n 977|>> You can you can just hang out with me\n 978|and think about it. Yeah.\n 979|>> Is it weird to be like consensus now?\n 980|You're a bit salty three years ago when\n 981|you're like, why don't people understand\n 982|how important this is?\n 983|>> I still I still feel like it's not\n 984|consensus though because like you know\n 985|people still don't publish the\n 986|benchmarks this way.\n 987|>> Oh, that's true. Yeah.\n 988|>> Like that's actually why\n 989|>> I think that's like inertia.\n 990|>> That's kind of Yeah, but that's kind of\n 991|why I wrote the essay. I was just like\n 992|look I mean we can talk about this but\n 993|like yeah the part of the motivation is\n 994|like I would talk to researchers about\n 995|um we it makes sense to show the\n 996|benchmarks with an x-axis whether it's\n 997|tokens or cost or time there should be\n 998|an x-axis and everybody would say like\n 999|yeah that makes sense we should do that\n1000|but everybody\n1001|>> they're not acting with the importance\n1002|of like good heart like this is we have\n1003|to measure the correct thing\n1004|>> well really their response is people\n1005|expect us to to publish the grid\n1006|>> and then okay why do people expect the\n1007|grid to be published because everybody\n1008|publishes the grid. And so you kind of\n1009|end up in this this bad equilibrium\n1010|where everybody kind of knows that it's\n1011|a bad equilibrium, but like nobody wants\n1012|to break out. And I I felt like, okay,\n1013|well, if I just hopefully come out and\n1014|say like, look guys, let's all recognize\n1015|that we're in a bad equilibrium and\n1016|let's move to this different equilibrium\n1017|where we're we're plotting things with\n1018|an X-axis that hopefully that can, you\n1019|know, next time there's a model release,\n1020|a company can feel comfortable not\n1021|publishing the grid, at least not at the\n1022|very front uh the top line. and um we\n1023|can have a more productive evaluation of\n1024|these models.\n1025|>> Then a last question for you. How do you\n1026|think about companies across all of\n1027|these specialized domains who feel the\n1028|value that they have is essentially like\n1029|the routing layer, the choice layer of\n1030|you know my goal is composed of a bunch\n1031|of discrete tasks. Some require more\n1032|intelligence and less and within my my\n1033|job as a vendor is to um solve that\n1034|problem or achieve the optimal outcome\n1035|with uh taking into account the budget\n1036|constraints. And so I will manage like\n1037|the parallelization and how how much\n1038|inference do you spend on it from what\n1039|model because I I think the the frontier\n1040|lab point of view is that that routing\n1041|happens both within the you know behind\n1042|the API behind the application and then\n1043|some of it in the model itself. Um and\n1044|that's pieces of that are clearly being\n1045|externalized in all these applications.\n1046|>> Yeah, I do I do think this is related to\n1047|the fact that like benchmarks should be\n1048|evaluated with an x-axis of tokens or\n1049|cost. Um I I have seen some evals\n1050|recently that show like okay well with\n1051|with a routing layer you can achieve\n1052|much better performance um by basically\n1053|doing consensus among the models.\n1054|>> Yeah.\n1055|>> And like I definitely believe that if\n1056|you do consensus among the models that\n1057|you're going to achieve better\n1058|performance than any individual model\n1059|but\n1060|>> it's important to ask like are you going\n1061|to do better than having that model\n1062|basically think for longer. Um like once\n1063|you control for the amount of test time\n1064|compute is it is it actually still doing\n1065|better? That's that's the question that\n1066|you want to figure out. Okay, that's\n1067|very principle of view which is like yes\n1068|routing is fine but it's all subject to\n1069|the same budget question. Yeah,\n1070|>> right. Um if you put it on the same\n1071|scaler then you can make an optimal\n1072|decision and I think maybe I win.\n1073|>> Mhm. I I I don't even know necessarily\n1074|that I I would believe that the routing\n1075|does better but then there's still a\n1076|question of um is it going to do\n1077|significantly better? Is it very\n1078|fragile? Is it um reflective of real\n1079|world use cases compared to benchmarks?\n","created_at":"2026-07-09T04:21:57.179010Z","finish_reason":null,"metrics":null},{"message_id":"8bee790c-e236-4c6d-9ea2-43390efc42d6","node_id":48,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:7","name":"read","arguments":{"file_path":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/28f3adc3/content.txt","offset":1080}}],"thinking":"Need the final lines.","created_at":"2026-07-09T04:21:58.339087Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1083,"total_time_ms":1157,"input_tokens":6544,"output_tokens":67,"cache_read_tokens":50944,"cache_creation_tokens":null,"tpot_ms":1.1044776119402986,"tokens_per_sec":905.4054054054054}},{"message_id":"4be57a33-9246-409c-b0c1-3daf1e3667cc","node_id":49,"role":"tool","content":"\n1080|because like one issue you could run\n1081|into is that you could optimize for\n1082|certain benchmarks with the routing and\n1083|then show like oh yeah we see this big\n1084|improvement on these benchmarks. Um but\n1085|in real world use cases it actually ends\n1086|up not being a significant improvement.\n1087|So I I would say at the very least like\n1088|I would say you want to control for\n1089|testime compute and then you also want\n1090|to have all the same um skepticism about\n1091|benchmarks that you would normally have.\n1092|>> Awesome. N thanks so much and and for\n1093|being on the mission for uh breaking us\n1094|out of this false equilibrium. Yeah,\n1095|it's great to be back.\n1096|>> Find us on Twitter at no prior pod.\n1097|Subscribe to our YouTube channel if you\n1098|want to see our faces. Follow the show\n1099|on Apple Podcasts, Spotify, or wherever\n1100|you listen. That way, you get a new\n1101|episode every week. And sign up for\n1102|emails or find transcripts for every\n1103|episode at no-briers.com.\n1104|\n1105|\n1106|Exit code: 0\n","created_at":"2026-07-09T04:21:58.341556Z","finish_reason":null,"metrics":null},{"message_id":"22b83406-9d88-4018-a5c4-bbe68dab7400","node_id":51,"role":"assistant","content":"","tool_calls":[{"id":"functions.web_search:8","name":"web_search","arguments":{"query":"\"Really Big Test-Time Compute\" Noam Brown No Priors"}}],"thinking":"Now I have full transcript. Need to summarize. This is a No Priors podcast episode with Sarah Guo? \"Sarah Goa\" transcription. Guest Noam Brown (transcribed Nome/Gnome) from OpenAI. Main topics: test-time compute scaling, broken evaluations, benchmark grids, safety evals, recursive self-improvement, multi-agent, competition. I need to provide a concise summary. Since user only pasted link, according to rule \"use yt-dlp to summarize\". We can output summary. Should I include title and details? Yes. Need to be direct. Could include bullet points. Ensure not too verbose. Should I mention that transcript was auto-generated captions? Not necessary, but perhaps note summary from auto-generated captions. Need to cite file? Not required but can cite the transcript file path. The user didn't ask for session name. We shouldn't name session. We can respond with summary. Ensure we use no emojis. Also maybe include key quotes. The rule says \"get the autogenerated captions to do this\". We did. We should summarize. Let's craft. Title: \"Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown\" (No Priors podcast). Host Sarah Guo, guest Noam Brown.\n\nNeed to produce a high-quality summary, maybe structured with sections:\n- Core argument: capability now depends on inference budget; benchmark comparisons should use performance vs test-time compute (tokens/cost/time) not single grid numbers.\n- Why it matters for evaluations: models can think for weeks/months before plateauing; release cycles too fast for full evaluation; benchmark maxing and scaffolding can mislead unless compute controlled.\n- Safety implications: responsible scaling policies don't account for compute budget; dangerous capability depends on budget; need to choose evaluation budget.\n- Examples: poker solver (5.2 needed steering, 5.5 near zero-shot); Erdős unit-distance conjecture disproof done cheaply by internal model and reproducible with 5.5 + scaffolding at $1k-$100k; shows latent capabilities unexplored.\n- Research/RSI: models accelerate researchers but not replace them; poor research taste; some tasks improve with compute, some plateau (factual retrieval vs Sudoku). No overnight hard takeoff because time/compute is bottleneck; gradual takeoff more likely.\n- Multi-agent and coordination: human civilization analogy, current models lack persistent shared knowledge; multi-agent scaffolds promising but require frontier models and large scale.\n- Competition: intense; frontier labs aware of stakes; using models to improve models is amplifying.\n- Practical advice: use models for high-stakes daily decisions; trust outputs now; benchmarks should include x-axis; routing/consensus layers only useful if compute-controlled and validated on real tasks.\n\nNeed to be accurate. Let's include timestamps? Not necessary. Could mention names correctly. The transcript has many typos; we should use real names: Noam Brown, Sarah Guo, OpenAI o3? It says \"5.5\" and \"5.4\" likely \"o3\" or \"o1\"? It says \"we released 5.5\" probably \"o3\"? Actually OpenAI released \"o3\"? The transcript says \"5.5\" maybe \"o3 mini high\"? Hmm. In transcript, they refer to \"5.5\" and \"5.4\" maybe \"o1-preview\"? Wait OpenAI's Noam Brown, \"5.5\" likely \"o3\"? The model names might be \"o3\" and \"o3-mini\"? But transcript says \"5.5\" and \"5.4\" perhaps \"o1\" or \"o3\"? We can quote as \"5.5/5.4\" but maybe it's \"o3\"? The transcription has \"GBT3\" for GPT-3, \"GBD3\", \"GPD3\", \"5.5\" likely \"o3\" or \"o1\"? Could be \"o3\"? Since Noam Brown's essay was about test-time compute and \"o3\"? Hmm. Actually the title says \"Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown\". I recall a No Priors episode with Noam Brown from around Feb/Mar 2025? There was OpenAI's \"o3\" and \"o3-mini\". He may have written about \"o3-mini\"? Wait \"5.5\" could be \"GPT-4.5\"? In transcript they say \"we released 5.5\" and compare to \"5.4\". That likely refers to OpenAI's \"o3-mini\" and \"o1\"? Actually OpenAI released \"o1\" in Sept 2024, \"o1-preview\", \"o1-mini\", then \"o3\" Dec 2024. The Noam Brown essay \"How Test-Time Compute Changes Everything\" might have been about \"o3\". The transcript's \"5.5\" perhaps a transcription error for \"o3\"? But it also says \"5.2\" when making a river solver. OpenAI's \"o1-preview\" was first o-series, \"o1\" then \"o3-mini\"? Wait 5.2, 5.4, 5.5 could be model version numbers for \"o1\"? Actually OpenAI's o-series maybe codenamed? There is \"o1-preview\", \"o1\", \"o3-mini\", \"o3\". The transcript's \"5.5\" may be a mis-recognition of \"o1\"? No, \"5.5\" two syllables? Could be \"o1.5\"? OpenAI hasn't released \"o1.5\". Hmm.\n\nLet's search web for the actual episode title to identify. Use web search.","created_at":"2026-07-09T04:22:02.362874Z","finish_reason":"tool_calls","metrics":{"ttft_ms":988,"total_time_ms":4018,"input_tokens":465,"output_tokens":1225,"cache_read_tokens":57472,"cache_creation_tokens":null,"tpot_ms":2.473469387755102,"tokens_per_sec":404.2904290429043}},{"message_id":"038652c8-2214-4148-b89e-dad1f422a4d9","node_id":52,"role":"tool","content":"# Web Search Results for \"\"Really Big Test-Time Compute\" Noam Brown No Priors\"\n\n## 1. No Priors: Artificial Intelligence | Technology | Startups - Podcast Episode listening links - Plink\nURL: https://plinkhq.com/i/1668002688/e/1000774329129\n\nNo Priors\n...\n## Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n\n## 2. Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\nURL: https://castbox.fm/episode/Really-Big-Test-Time-Compute-in-AI-Changes-Benchmarks%2C-Safety-and-Research-with-OpenAI-Research-Scientist-Noam-Brown-id5296519-id961348912\n\nReally Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n...\nDiscover No Priors: Artificial Intelligence | Technology | Startups Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n...\n# Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n...\nCurrent AI evaluation methods are insufficient because they fail to account for test-time compute, a factor that significantly impacts model capability. As models improve with increased budget (tokens, cost, time), traditional benchmarks become misleading. The rapid release cycle of new models exacerbates this issue, preventing thorough evaluation and understanding of their true potential. This discussion highlights the need for new evaluation strategies, such as plotting performance against test-time compute, to accurately assess AI advancements. It also touches upon the evolution of AI reasoning through tasks like poker bot creation, the challenges in AI safety evaluations concerning long-term tasks, and the role of AI in research as an optimizer rather than a complete replacement for human researchers. The concept of recursive self-improvement and a gradual acceleration of AI capabilities are explored, emphasizing that time, not just algorithmic breakthroughs, is the ultimate bottleneck. Finally, the importance of breaking the current \"bad equilibrium\" in benchmark publishing is stressed to foster more productive and honest AI evaluations.\n...\nCurrent AI evaluation frameworks, including responsible scaling policies, are insufficient because they do not account for test-time compute. Model capability is now a function of budget, making existing evaluation methods inadequate for understanding true performance and potential.\n...\nSkepticism surrounds new model releases due to benchmark scores that don't reflect true improvement because test-t...\n\n## 3. OpenAI's Noam Brown, Ilge Akkaya and Hunter Lightman on o1 and Teaching LLMs to Reason Better\nURL: https://www.youtube.com/watch?v=jPluSXJpdrA\n\n# OpenAI's Noam Brown, Ilge Akkaya and Hunter Lightman on o1 and Teaching LLMs to Reason Better\n...\nCombining LLMs with AlphaGo-style deep reinforcement learning has been a holy grail for many leading AI labs, and with o1 (aka Strawberry) we are seeing the most general merging of the two modes to date. o1 is admittedly better at math than essay writing, but it has already achieved SOTA on a number of math, coding and reasoning benchmarks.\n...\nOpenAI researchers Noam Brown, Ilge Akkaya and Hunter Lightman discuss the ah-ha moments on the way to the release of o1, how it uses chains of thought and backtracking to think through problems, the discovery of strong test-time compute scaling laws and what to expect as the model gets better. \n\nHosted by: Sonya Huang and Pat Grady, Sequoia Capital\n\n## Chapters\n...\n- 00:00 Introduction\n- 01:33 Conviction in o1\n- 04:24\n...\n05:0\n...\n?\n- 07:02 Lessons from gameplay\n- 09:14 Generation vs verification\n- 10:31 What is surprising about o1 so far\n- 11:37 The trough of disillusionment\n- 14:03 Applying deep RL\n- 14:45 o1’s AlphaGo moment?\n- 17:38 A-ha moments\n- 21:10 Why is o1 good at STEM?\n- 24:10 Capabilities vs usefulness\n- 25:29 Defining AGI\n- 26:13 The importance of reasoning\n- 28:39 Chain of thought\n- 30:41 Implication of inference-time scaling laws\n- 35:10 Bottlenecks to scaling test-time compute\n- 38:46 Biggest misunderstanding about o1?\n- 41:13 o1-mini\n- 42:15 How should founders think about o1?\n\n## 4. Begin Proof — Noam Brown\nURL: https://www.youtube.com/watch?v=h4ZguzEMKAU\n\nIn the inaugural episode of Begin Proof, Slater Stich sits down with Noam Brown, who leads work on multi-agent reasoning and test-time compute at OpenAI, and was one of the key people behind o1, OpenAI's first public reasoning model. They talk about what the Erdős result actually means, where the models are superhuman and where they still aren't, and what working mathematicians will be doing in a few years.\n...\nAt OpenAI, Noam focuses on multi-agent reasoning and test time compute scaling.\nHe was one of the key people behind O1, OpenAI's first public reasoning model.\n...\n>> You know, it's interesting like the models have reached a point where they have surpassed my ability to really understand what they're capable of and like what they excel at just because like already the you know, these kinds of problems like understanding the proofs so is just beyond my ability. I'm sure it's beyond most people's ability, but it's also beyond my ability. And so it's hard for me at this point to say like, oh yeah, these are like the weak points of the models and this these are like the strong points of the models in the field of math.\n...\n>> And then there's the separate question of like reasoning through the the the complex problems. And this I think like the argument it's arguable that they're not superhuman yet, but we're also seeing rapid progress on this. And so I think we're going to see this kind of spiky intelligence where um the models are going to be superhuman in some respects and then also not quite superhuman in other respects and the point but over time as the models become more capable they are going to become like more capable across the board and that's what we're seeing. It's that it's not just like oh they're getting better and better at ingesting a lot of mathematics and combining it. It's that they're getting better at that but they're also getting better at just like the fundamental reasoning capabilities.\n...\nSo I I think we will in the short term see more ...\n\n## 5. The field is underestimating inference compute | Noam Brown\nURL: https://www.youtube.com/watch?v=wKwLDaPP6YI\n\n# The field is underestimating inference compute | Noam Brown\n...\nUm now thinking that you have your own I'm going to call it OAI bench you can call it whatever you want. Um what are your most interesting you know, there's there's many isms that I we we talked about before. Uh there's the Ilyaism which to be fair to Ilya Sutskever he doesn't necessarily believe this anymore but at NeurIPS back in the day he definitely did saying NTP is enough. All we need is more tokens bro. There is the uh uh gnomeism that I would call it which is Ilyaism plus gen verifier gap since we have all but one internet. Well, if we have a bunch of verifiable problems we can generate and then verify against them and that creates a lot more data and then now we can do stuff. And so um do you agree with that phrasing of gnomeism or has it been is that uh Yeah, I don't think it captures I don't think it captures the whole story. I mean obviously you're we're trying to distill a lot of stuff down into like a single sentence. Uh I think another key part in my opinion is the inference compute the fact that like a lot of into especially reasoning capabilities, I think the ability to um think be be more productive by thinking for longer. And so it's not just I mean it is a form of next token prediction where you're predicting a series of tokens now.\n...\nAnd I think that the counter to narrative on just test time compute is that um well, if I if I have a contrived example such as I'm trying to learn sort and mankind doesn't know about merge sort yet. But what do you know about bubble sort? And so I have infinite traces of um unsorted lists and then the procedure called bubble sort and then the final answer which is the sorted list um and I can train on infinite amounts of those. That's verifiable, right? Gen verify, I can generate a lot of them.\n...\nIt's been trained to do bubble sort exactly. The reasoning trace was bubble sort. Sure, if all you trained it on in its entire existence is bubble sort ...\n","created_at":"2026-07-09T04:22:03.061345Z","finish_reason":null,"metrics":null},{"message_id":"be027a53-7c18-4c56-8050-12f654acdfbb","node_id":54,"role":"assistant","content":"","tool_calls":[{"id":"functions.web_search:9","name":"web_search","arguments":{"query":"\"Really Big Test-Time Compute in AI Changes Benchmarks\" Noam Brown date"}}],"thinking":"Search result 2 has a summary. Need not rely. We have transcript. We can now summarize. Should we name session? Rule says if asked to summarize a YouTube video, do not name session until after reading and understanding full transcript. We have read transcript, so okay to respond. But user didn't ask for name. So just summarize.\n\nNeed to consider whether to mention \"Noam Brown\" not \"Nome/Gnome\". Use correct names. Also \"Sarah Guo\" host. The model versions: transcript says \"5.5\" maybe \"o3\" or \"o3-mini\"? We can call it \"the latest reasoning model\" to avoid misidentification. But search result summary mentions \"o1\"? Wait title from search result says \"OpenAI Research Scientist Noam Brown\" and \"o1\" in other. But in this episode they might be talking about \"o3\" and \"o3-mini\" (released around early 2025). The transcript \"5.5\" could be \"o3-mini high\" vs \"5.4\"? Let's search for \"5.5\" within the video? Could be \"o3.5\"? Actually maybe they say \"o3.5\" and transcript mishears as \"5.5\"? OpenAI released \"o3-mini\" in Jan 2025, then \"o3\" in April 2025? There is \"o3-mini-high\" and \"o3\". Hmm. In early 2025 maybe \"o3\"? Noam Brown's essay could be about \"o3\"? Let's see. The transcript \"we released 5.5\" maybe \"we released o3.5\"? Did OpenAI release an \"o3.5\"? Not as of July 2026? Actually by July 2026 there could be many. But the transcript is from a video uploaded at some date. Let's search for the podcast episode date.","created_at":"2026-07-09T04:22:04.963404Z","finish_reason":"tool_calls","metrics":{"ttft_ms":847,"total_time_ms":1898,"input_tokens":3206,"output_tokens":425,"cache_read_tokens":57856,"cache_creation_tokens":null,"tpot_ms":2.4729411764705884,"tokens_per_sec":404.37678401522356}},{"message_id":"bfacbe3e-7d35-4987-aa70-cf9a5e2afc46","node_id":55,"role":"tool","content":"# Web Search Results for \"\"Really Big Test-Time Compute in AI Changes Benchmarks\" Noam Brown date\"\n\n## 1. Why Traditional Benchmarks Fail Modern AI Models with OpenAI Research Scientist Noam Brown – No Priors: Artificial Intelligence | Technology | Startups\nURL: https://barbellinsights.com/podcast/no-priors-artificial-intelligence-technology-startups/why-traditional-benchmarks-fail-modern-ai-models-with-openai-research-scientist\n\nWhy Traditional Benchmarks Fail Modern AI Models with OpenAI Research Scientist Noam Brown – No Priors: Artificial Intelligence | Technology | Startups\n...\n$ OPENAI- OpenAI's models (5.5 and internal versions) demonstrate substantial capability improvements that aren't captured by traditional benchmarks. The models show dramatic efficiency gains and can solve previously unsolved mathematical problems at low cost, with capabilities continuing to scale with test-time compute.\n...\n“Once they played around with it, the story changed. I think the reason why it doesn't show up as so much better on the benchmarks is because the benchmarks are being presented the benchmark results are being presented in the wrong way. They're not controlling for the amount of test on compute that is being used on that benchmark question. It turned out that 5.5 is just much more efficient with its thinking.”\n...\n$ BENCHMARKS- Current AI model evaluation frameworks and benchmark grids are fundamentally broken because they don't account for test-time compute, creating a 'bad equilibrium' where models are systematically misevaluated and capabilities are underestimated or misrepresented.\n...\n“The motivation was we released 5.5, and the initial reaction was kinda skepticism that it was a substantially better model. This to be fair, that only lasted for a few hours before people had some time to play around with it and and try it out themselves, and they saw that it was actually substantially better. But I think a lot of the skepticism came from the benchmark grid that was published.”\n...\n“The preparedness frameworks and responsible scaling policies, they don't really account for the amount of test time compute. They just say, okay. Well, what's the capability of the model? The problem is we're in a world now where the capability of the model is a function of how much money you put into it, basically.”\n...\n“The benchmark maxing thing is also motivation for for writing an essay that I think it's re...\n\n## 2. OpenAI Research VP Argues AI Evaluations Must Account for Inference Costs\nURL: https://www.chosun.com/english/industry-en/2026/07/03/HTOSPIK5MNDVZFKJOCI5HY7QN4/\n\nNoam Brown urges updated benchmarks to measure time, cost, and tokens during inference for accurate capability and risk assessment\n...\n026.07.03. 15:51Updated 2\n...\n.07.03. 16:00\n...\n“The performance of cutting-edge artificial intelligence (AI) models has shown a trend of improvement as more computational resources are allocated to inference,” said Noam Brown, Vice President of Research at OpenAI. He argued that current methods of evaluating AI models fail to accurately reflect their actual performance and safety. As the AI industry’s focus shifts from model training to inference, major AI benchmarks do not measure how much time, cost, or tokens are invested during the inference process, making it difficult to gauge the capabilities and risks of state-of-the-art models.\n...\nBrown emphasized during his keynote speech at the “Global AI Frontier Symposium 2026,” held on July 3 at the Westin Seoul Parnas in Gangnam-gu, Seoul, that “AI model evaluation methods must change to reflect the growing importance of inference.” He noted that while the latest AI models improve their problem-solving abilities as they allocate more computational resources and time to the logical reasoning process (inference) before generating answers, existing benchmarks and safety evaluations have not kept pace.\n...\nHe explained, “When OpenAI released GPT-5.5 in April, its performance clearly improved compared to previous models, but major benchmarks did not show significant progress. Instead, users noticed the improvements after applying the model to various tasks. This is because advanced models enhance their performance as more time and computational resources are invested in the inference process.”\n...\nBrown added, “GPT-4’s performance plateaued regardless of increased computational resources, but newer models show delayed saturation points, making it hard to assess their true capabilities with existing evaluation methods.”\n...\nHe argued that future AI model evaluations should account for perf...\n\n## 3. Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\nURL: https://castbox.fm/episode/Really-Big-Test-Time-Compute-in-AI-Changes-Benchmarks%2C-Safety-and-Research-with-OpenAI-Research-Scientist-Noam-Brown-id5296519-id961348912\n\nReally Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n...\nTest-Time Compute in AI Changes Bench\n...\nScientist Noam Brown\n...\n# Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n...\nUpdate: 2026-06-26\n...\nWhen a new AI model drops, it’s judged based on a static benchmark grid that doesn’t account for how long the model is allowed to think. How then should we measure a model’s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry’s traditional benchmark grids are broken, and how large-scale test-time compute is fundamentally changing how models are evaluated. Noam explains how, if properly scaffolded, today’s models can reason for weeks or even months on complex tasks. He also discusses real-world implications of test-time compute, from building poker solver bots to disproving legendary math conjectures. Together, they also unpack the large gaps in current AI safety frameworks, explore the bottlenecks for recursive self-improvement, and look ahead at the future of multi-agent collaboration and global knowledge sharing.\n...\nReally Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n...\n# Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown\n\n## 4. AI benchmarks are dead, argues OpenAI's Noam Brown — inference compute matters most - TestingCatalog AI\nURL: https://testingcatalog.net/ai-benchmarks-are-dead-argues-openais-noam-brown-inference-compute-matters-most/\n\nAI benchmarks are dead, argues OpenAI's Noam Brown — inference compute matters most - TestingCatalog AI\n...\nOpenAI researcher Noam Brown has a message for the AI evaluation community: static benchmark scores are becoming meaningless for frontier models. Instead, he argues, we need to look at inference compute scaling curves — plots of performance against the amount of compute used during inference.\n...\nBrown uses the hypothetical model GPT-5.5 as an example. In standard tests, GPT-5.5 barely outperforms GPT-5.4. But give it more inference compute, and its performance explodes. Standard tests with limited compute budgets simply can't capture a model's true ceiling.\n...\nThis pattern is backed up by external evaluations. In Andrej Karpathy's autonomous agent research and in cybersecurity tests by the UK AI Safety Institute, both GPT-5.5 and the Mythos model kept improving as inference budgets increased — even after generating over 100 million tokens, with no ceiling in sight. The more powerful the model, the more it benefits from additional compute.\n...\nAll this compute also complicates safety evaluations. Brown warns that current biological and cybersecurity benchmarks typically don't fix an inference budget. If a state-level adversary throws $10 million of compute at a specific task, a model that seems safe in standard tests could cross dangerous red lines.\n...\nBrown's recommendation: when releasing new models, AI companies should publish performance curves with tokens, compute cost, or runtime on the x-axis. Evaluators need to treat inference budget as a core variable, and in safety assessments, use low-compute tests to extrapolate safety boundaries.\n\n## 5. OpenAI research VP urges new evaluations as inference compute boosts AI - CHOSUNBIZ\nURL: https://biz.chosun.com/en/en-it/2026/07/03/Y6WT3233JBAD3BDLHJRJJYOTQU/\n\n# OpenAI research VP urges new evaluations as inference compute boosts AI\n...\nCalls for benchmarks that factor test-time compute and long-horizon agent collaboration\n...\nPublished 2026.07.03. 15:38\n...\n![Noam\n...\n, on the\n...\nof Lee Jae-\n...\nhttps://biz.chosun.com/\n...\n/v2/O4GQPEMJMJA2LIHUARVB4D6H6U\n...\nauth=f9f3d7f5eda00229c7bf8554245755\n...\n7cadd21dfceb8b7f94034b3e966e9c468&width=616)\n...\n\"The performance of cutting-edge artificial intelligence (AI) models shows a trend of improving as the compute devoted to inference increases.\"\n...\nNoam Brown, vice president for research at OpenAI, argued that the current way of evaluating artificial intelligence (AI) models does not properly reflect the models' real performance and safety. As the AI industry's center of gravity shifts from model training to inference, major AI benchmarks do not measure how much compute a model uses during inference, making it hard to gauge the capabilities and risks of state-of-the-art models.\n...\nIn a keynote speech at the Global AI Frontier Symposium 2026 at the Westin Seoul Parnas in Gangnam-gu on the 3rd, Brown said, \"We need to change how we evaluate AI models to match an era where inference carries more weight.\" He noted that in the latest AI models, problem-solving ability improves as \"test-time compute,\" the compute used during the logical thinking process (inference) before answering a user's question, increases, but existing benchmarks and safety evaluations have faile… (31 chars truncated)\n… (4 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/8db8311a/content.txt\n","created_at":"2026-07-09T04:22:05.870674Z","finish_reason":null,"metrics":null},{"message_id":"faf1776e-26aa-4543-a2c9-612a863c3b86","node_id":57,"role":"assistant","content":"Here’s a summary of the video, based on its auto-generated captions:\n\n**Title:** *Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI's Noam Brown* \n**Show:** No Priors, hosted by Sarah Guo, with guest Noam Brown (OpenAI research scientist/VP of Research).\n\n### Main argument\nModel capability is no longer a single static property — it is now a function of **test-time (inference) compute budget**. Giving a model $10 vs. $10,000 vs. $10 million of inference compute can produce radically different results. Therefore, the industry’s standard “benchmark grid” — one number per model per benchmark — is misleading because it does not control for how long the model is allowed to think.\n\n### Key points\n\n- **Benchmarks are broken.** When GPT-5.5 was released, the headline benchmark grid only showed a small improvement over GPT-5.4. People were skeptical until they used it. The issue: GPT-5.5 is *much more efficient* at thinking; at max settings it actually outperforms GPT-5.4 substantially once you control for inference time/cost/tokens.\n- **Performance plateaus are now very far out.** With GPT-3, models stopped improving after a short amount of thinking. Today’s models, if scaffolded well, can productively reason for weeks or even months on some tasks. That makes “run until it plateaus” impractical for evaluation.\n- **Better evaluation:** Plot performance as a function of test-time compute (tokens, cost, or wall-clock time). Alternatively, fix a budget and evaluate within it. A useful open research question is predicting high-budget performance from low-budget runs.\n- **Safety evaluations lag.** Responsible-scaling / preparedness frameworks were designed when test-time compute scaling was not a big factor. They ask “what is the model capable of?” without specifying a budget. A model that looks safe at $10 of inference may be dangerous at $10 million, so safety evals must also fix or report compute budgets.\n- **Latent capabilities are under-explored.** OpenAI recently used an internal model to **disprove the Erdős unit-distance conjecture** at a surprisingly low compute budget. Others later reproduced similar results with GPT-5.5 using a general-purpose scaffolding loop, but at a much higher cost ($1k–$100k). This shows that current-generation models may already be capable of results nobody has tried to extract.\n- **Poker solver as a personal benchmark.** Brown uses poker-bot coding as an eval. Early models were useless; GPT-5.2 could build a river solver with heavy steering but would “gaslight” him; GPT-5.5 is nearly zero-shot on the full solver. The models excel at optimizing existing algorithms but still struggle to invent novel algorithms from scratch — they lack “research taste.”\n- **Recursive self-improvement / takeoff.** Models are accelerating researchers but not fully replacing them. The bottlenecks are the parts of research that do not speed up 100×. Brown does not expect an overnight hard takeoff, because unlocking the best capabilities requires large amounts of *time* (test-time compute), which acts as a natural speed limit.\n- **Multi-agent and long-horizon coordination.** Human progress came from billions of people accumulating and sharing knowledge over long time horizons. Today’s models have short context windows and disappear after each turn. Multi-agent scaffolds are promising but still early; eventually models may form a persistent, coordinated, compounding knowledge system.\n- **Competition at the frontier.** Labs are in an intense race, but Brown is comforted that frontier researchers understand the stakes and want positive outcomes. The real bottleneck is time — how quickly the work can be done given that models need to run for long horizons to show their full capabilities.\n- **Routing/consensus startups.** Brown is skeptical of claims that routing across many models beats a single model unless you control for test-time compute. Consensus among models can help, but the key comparison is whether it beats giving one good model more thinking time at the same budget.\n- **Personal use.** Brown now trusts GPT-5.5-level models for high-stakes daily advice (taxes, condo paperwork), often more than expert humans.\n\n### Bottom line\nBrown is urging the field to break the “bad equilibrium” of publishing static benchmark grids and instead treat **test-time compute as a core variable** in both capability and safety evaluations.","tool_calls":[],"thinking":"Search results indicate the transcript refers to \"GPT-5.5\" and \"GPT-5.4\"? Wait result 2 says \"When OpenAI released GPT-5.5 in April...\" and \"GPT-4's performance plateaued\". This is likely future timeline (2026) and the model names in transcript \"5.5\" are \"GPT-5.5\" and \"5.4\" \"GPT-5.4\". Interesting. Since today's date is 2026-07-08 (per system). So the episode is from around June 2026, referencing GPT-5.5. The transcript's \"5.5\" is GPT-5.5, \"5.4\" is GPT-5.4, \"5.2\" maybe GPT-5.2. \"GBT3\" = GPT-3, \"GPD3\" = GPT-3. Great. So we should refer to GPT-5.5/GPT-5.4 etc. However the transcript didn't include \"GPT\" prefix due to mis-recognition. Search result confirms \"GPT-5.5\". Let's perhaps use \"GPT-5.5\" because that's the actual model. The summary should be accurate. The transcript also mentions \"slash goal\" maybe \"/goal\" command? Actually \"slash goal\" likely \"/goal\" feature? Not essential.\n\nNow craft final summary. Use bullet points for readability. Include title, host/guest, main argument. Also mention source file? The user didn't ask. Could include note that summary based on auto-generated captions. Maybe not needed. Provide concise but thorough.\n\nNeed to ensure not too long but enough. Let's produce.","created_at":"2026-07-09T04:22:10.147381Z","finish_reason":"stop","metrics":{"ttft_ms":839,"total_time_ms":4274,"input_tokens":2616,"output_tokens":1271,"cache_read_tokens":61056,"cache_creation_tokens":null,"tpot_ms":2.7025963808025177,"tokens_per_sec":370.0145560407569}}]}