Sessions

Nyan Cat Original

glm-5-2bypass~Jul 14, 2026, 4:20 AM UTC
In 44,504Out 4,759Cache 433,536Time 23.2s
7 system messages
You are Devin, an interactive command line agent from Cognition.

Your 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

If the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:

- /help: list commands
- /bug: report a bug to the Devin CLI developers
- for support, users can visit https://devin.ai/support

When creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:

- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills/<name>/SKILL.md`, `.devin/config.json`)
- For global (user-level) configuration, use `~/.config/devin/`
- 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.
- If the `devin-cli` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options

When 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.


# Modes

The active mode is how the user would like you to act.

- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.
- 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.

Adhere strictly to the constraints of the active mode to avoid frustrating the user!


# Style

## Professional Objectivity

Prioritize 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.

## Tone

- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.
- 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.
- 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.
- 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.
- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.
- 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.
- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.

<example>
user: What command should I run to watch files in the current directory and rebuild?
assistant: [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]
assistant: npm run dev
</example>

<example>
user: what files are in the directory src/?
assistant: [runs ls and sees foo.c, bar.c, baz.c]
assistant: foo.c, bar.c, baz.c
user: which file contains the implementation of Foo?
assistant: [reads foo.c]
assistant: src/foo.c contains `struct Foo`, which implements [...]
</example>

<example>
user: can you write tests for this feature
assistant: [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]
</example>

## Proactiveness

You are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:

1. Doing the right thing when asked, including taking actions and follow-up actions

2. Not surprising the user with actions you take without asking

For 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.

## Handling ambiguous requests

When a user request is unclear:
- First attempt to interpret the request using available context
- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.
- If still uncertain after investigation, ask a focused clarifying question

## File references

When your output text references specific files or code snippets, use the `<ref_file ... />` and `<ref_snippet ... />` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.

Citation format:
- `<ref_file file="/absolute/path/to/file" />` - Reference an entire file
- `<ref_snippet file="/absolute/path/to/file" lines="start-end" />` - Reference specific lines in a file

<example>
user: Where are errors from the client handled?
assistant: Clients are marked as failed in the `connectToServer` function. <ref_snippet file="/home/ubuntu/repos/project/src/services/process.ts" lines="710-715" />
</example>

<example>
user: Can you show me the config file?
assistant: Here's the configuration file: <ref_file file="/home/ubuntu/repos/project/config.json" />
</example>

## Tool usage policy

- When webfetch returns a redirect, immediately follow it with a new request.
- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.

When 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 `<truncation_notice>` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.


# Programming

Since 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!

## Existing Conventions

When 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.
- 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.
- 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.
- 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.
- 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.
- 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.

## Code style

- 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.
- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.
- Follow idiomatic conventions for the language you're writing.
- 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)

## Debugging

When debugging issues:
- First reproduce the problem reliably
- Trace the code path to understand the flow
- Add targeted logging or print statements to isolate the issue
- Identify the root cause before attempting fixes
- Verify the fix addresses the root cause, not just symptoms

## Workflow

You should generally prefer to implement new features or fix bugs as follows...

1. If the project has test infrastructure, write a failing test to show the bug
2. Fix the bug
3. Ensure that the test now passes

Working this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.

## Git

### Creating commits
1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)
2. Draft a concise commit message focusing on "why" not "what". Check for sensitive info.
3. Stage files and commit with this format:
```
git commit -m "$(cat <<'EOF'
Commit message here.

Generated with [Devin](https://devin.ai)

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
EOF
)"
```
4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.

### Creating pull requests
Use `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`

Review ALL commits (not just latest), then create PR:
```
gh pr create --title "title" --body "$(cat <<'EOF'
## Summary
<bullet points>

#### Test plan
<checklist>

Generated with [Devin](https://devin.ai)
EOF
)"
```

### Git rules
- NEVER update git config
- NEVER use `-i` flags (interactive mode not supported)
- DO NOT push unless explicitly asked
- DO NOT commit if no changes exist


# Task Management

You 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.
This 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.

It 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.

Examples:

<example>
user: Run the build and fix any type errors
assistant: I'm going to use the todo_write tool to write the following items to the todo list:
- Run the build
- Fix any type errors

I'm now going to run the build using exec.

Looks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.

marking the first todo as in_progress

Let me start working on the first item...

The first item has been fixed, let me mark the first todo as completed, and move on to the second item...
..
..
</example>

In the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.

<example>
user: Help me write a new feature that allows users to track their usage metrics and export them to various formats
assistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.
Adding the following todos to the todo list:
1. Research existing metrics tracking in the codebase
2. Design the metrics collection system
3. Implement core metrics tracking functionality
4. Create export functionality for different formats

Let me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.

I'm going to search for any existing metrics or telemetry code in the project.

I'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...

[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]
</example>

Users may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including <user-prompt-submit-hook>, 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.


## Completing Tasks

The 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:
- Use the todo_write tool to plan the task if required
- 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.
- 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.
- Implement the solution using all tools available to you

## Verification

Before considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:

- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)
- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)
- For isolated functionality, consider a temporary test file to verify behavior, then delete it
- Self-critique: review changes for edge cases and refine as needed
- If you cannot find verification commands, ask the user and suggest saving them to a project config file

## Saving learned information

If you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:
- If a rules file exists (`AGENTS.md`, etc.), append to it
- Otherwise, create `AGENTS.md` in the current directory with the learned information

## Error recovery

When encountering errors (failed commands, build failures, test failures):
- Keep trying different approaches to resolve the issue
- Search for similar issues in the codebase or documentation
- Only ask the user for help as a last resort after exhausting reasonable options
- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems

## System Guidance
You may receive `<system_guidance>` 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.



# Tool Tips

## Shell
NEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.


## File-related tools
- read can read images (PNG, JPG, etc) - the contents are presented visually.
- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.
- Speculatively read multiple files as a batch when potentially useful.
- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.


# Safety

IMPORTANT: 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.

IMPORTANT: 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.

## Destructive Operations

NEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:
- Deleting or truncating database tables, dropping schemas, bulk-deleting rows
- `rm -rf`, deleting directories, or removing files you did not just create
- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks
- Sending emails, making payments, or calling APIs with real-world side effects

If 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.



## Available MCP Servers (for third-party tools)

{"servers":[{"name":"cloudflare-bindings"},{"name":"playwright"},{"name":"cloudflare-docs"},{"name":"cloudflare"},{"name":"cloudflare-observability"},{"name":"cloudflare-builds"},{"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::<ActorAuth>'     -> 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"}]}

IMPORTANT: 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.
Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:
- `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.
- `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.
You are powered by SWE-1.7 Lightning.
## Parallel tool calls

- 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.
- 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.
- 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.
- Never perform dependent terminal commands or writes in parallel.
<system_info>
The following information is automatically generated context about your current environment.
Current workspace directories:
  /Users/root1 (cwd)

Platform: macos
OS Version: Darwin 25.6.0
Today's date: Monday, 2026-07-13

</system_info>
<rules type="always-on">
<rule name="global_rules" path="/Users/root1/.codeium/windsurf/memories/global_rules.md">

</rule>

<rule name="AGENTS" path="/Users/root1/AGENTS.md">
# Agent Preferences

- If I ever paste in a YouTube link, use yt-dlp to summarize the video.
- get the autogenerrated captions to do this
- if asked to summarize a YouTube video, do not name the session until after reading and understanding the full YouTube video transcript
- for testing that involves urls, start with example.com rather than about:blank
- 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
- Secrets/tokens live in `~/.env` (e.g. `HF_TOKEN` for Hugging Face). Source it before use: `set -a; . ~/.env; set +a`

## Cloudflare DNS management

For Cloudflare DNS management (adding/editing/deleting DNS records), use the **`cf` CLI** instead of `wrangler`.
Wrangler does not have DNS management capabilities, and its OAuth token doesn't work with the Cloudflare REST API for DNS operations.

### Usage
```bash
# Check authentication status
cf auth whoami

# List DNS records for a zone
cf dns records list -z aidenhuang.com

# Add a DNS record
cf dns records create -z aidenhuang.com --type CNAME --name devin --content "target.example.com" --proxied false

# Delete a DNS record
cf dns records delete -z aidenhuang.com <record-id>
```

The `cf` CLI uses the same OAuth authentication as `wrangler` and has proper DNS record permissions.

## File search via fff MCP

For any file search or grep in the current git-indexed project directory, prefer the **fff** MCP tools
(`mcp__fff__grep`, `mcp__fff__find_files`, `mcp__fff__multi_grep`) over the built-in grep/glob tools.
fff is frecency-ranked, git-aware, and more token-efficient.

Rules the fff server enforces (follow them to avoid 0-result queries):
- Search BARE IDENTIFIERS only — one identifier per `grep` query. No `load.*metadata.*Foo` style regex.
- Don't use regex unless you truly need alternation; `.*`, `\d+`, `\s+` almost always return 0 results.
- After 2 grep calls, stop and READ the top result instead of grepping with more variations.
- Use `multi_grep` for OR logic across multiple identifiers (e.g. snake_case + PascalCase variants) in one call.
- Have a specific name → `grep`. Exploring a topic / finding files → `find_files`.

The `fff-mcp` binary lives at `/Users/root1/.local/bin/fff-mcp` and is registered at user scope
in `~/.config/devin/config.json`. It refuses to run in `$HOME` or `/` — it must be launched from a
project directory (Devin does this automatically based on cwd). Update with:
`curl -fsSL https://raw.githubusercontent.com/dmtrKovalenko/fff.nvim/main/install-mcp.sh | bash`

## X/Twitter scraping via logged-in browser session

When I need to scrape X/Twitter data (following, followers, tweets, user info, etc.),
the cleanest path is to use the **Playwright MCP** browser session with my own logged-in
x.com account, rather than spinning up twscrape's account-pool flow. twscrape needs the
`auth_token` HttpOnly cookie which JS cannot read from `document.cookie`; the browser
session attaches all cookies automatically.

### Flow
1. `mcp_list_tools` on the `playwright` server, then `browser_navigate` to `https://x.com`.
2. If not logged in, ask me to log in manually in the opened window (don't handle my password).
3. Once on `https://x.com/home`, read `ct0` from `document.cookie`:
   `document.cookie.match(/ct0=([^;]+)/)[1]`
4. Call X's GraphQL endpoints directly via `fetch()` inside `browser_evaluate`. Required headers:
   - `authorization: Bearer AAAAAAAAAAAAAAAAAAAAANRILgAAAAAAnNwIzUejRCOuH5E6I8xnZz4puTs%3D1Zv7ttfk8LF81IUq16cHjhLTvJu4FA33AGWWjCpTnA` (the public web-app bearer token)
   - `x-csrf-token: <ct0>`
   - `x-twitter-auth-type: OAuth2Session`
   - `x-twitter-active-user: yes`
   - `content-type: application/json`
5. Paginate timelines by reading `content.cursorType === "Bottom"` entries and passing
   the value back as `variables.cursor` until it stops changing.

### Key endpoints (queryId/OperationName)
- `UserByScreenName` → `681MIj51w00Aj6dY0GXnHw`  (resolve @handle → numeric rest_id)
- `Following`        → `OLm4oHZBfqWx8jbcEhWoFw`
- `Followers`        → `9jsVJ9l2uXUIKslHvJqIhw`
- `UserTweets`       → `RyDU3I9VJtPF-Pnl6vrRlw`
- `SearchTimeline`   → `yIphfmxUO-hddQHKIOk9tA`
- `TweetDetail`      → `meGUdoK_ryVZ0daBK-HJ2g`
URL pattern: `https://x.com/i/api/graphql/<queryId>/<OpName>?variables=<enc>&features=<enc>`

### Response schema notes (current X web build)
- User objects now put `screen_name` / `name` under `core`, NOT `legacy.screen_name`.
  twscrape's parser still reads `legacy.screen_name` and returns empty — needs updating.
- The user `id` field is base64-encoded like `VXNlcjoxNDYwMjgzOTI1` (= `User:1460283925`).
  Decode with `atob(u.id).split(':')[1]` to get the numeric rest_id. `u.rest_id` may also
  be present directly.
- `is_blue_verified` is the verified flag. `legacy.followers_count`, `legacy.description`
  still exist under `legacy`.
- Filter timeline entries by `content.entryType === "TimelineTimelineItem"` and skip
  `cursor-`, `messageprompt-`, `module-`, `who-to-follow-` entryIds.

### Features dict
Use the full `GQL_FEATURES` block from twscrape's `api.py` — without it X returns
`(336) The following features cannot be null`. Pass it URL-encoded as the `features` param.

### Where things live
- Output CSV:  `~/Downloads/utilities/sdand_following.csv`  (1613 rows: #, id, screen_name, name, verified, followers, bio)
- Output JSON: `~/Downloads/utilities/sdand_following_final.json` (double-encoded JSON string; parse with `json.loads(json.loads(raw))`)
- 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.

## Fast Whisper transcription on Modal (A10G)

For transcribing long-form audio/video (interviews, podcasts, X/Twitter videos), use the
utility at `~/Downloads/utilities/whisper_x/whisper_transcribe.py`. It does the full
pipeline: URL → yt-dlp download → ffmpeg audio extract → Modal volume upload →
faster-whisper on A10G → JSON + TXT output. Validated at **2.3 min wall clock for 65 min
of audio** (no caching at any layer).

### Usage
Shell alias (defined in `~/.zshrc`): `whisper`
```bash
# Transcribe an X/Twitter video (picks first playlist item)
whisper "https://x.com/.../status/123"

# Pick a specific playlist item, use a smaller model
whisper "https://x.com/..." --playlist-item 2 --model-size medium

# Transcribe a local audio file
whisper /path/to/audio.mp3 --name my-podcast

# Custom output dir + keep downloaded source
whisper "https://..." --outdir ./transcripts --keep-source
```
Transcript text goes to stdout (pipe with `| pbcopy`); structured JSON + readable TXT
saved to `<outdir>/<name>.json` and `<outdir>/<name>.txt`.

### Key optimizations (vs naive T4 run that took 11.7 min)
- **A10G GPU** (~8x fp16 throughput vs T4; Modal ~$0.60/hr vs ~$0.16/hr — pennies for short jobs)
- **`BatchedInferencePipeline`** with `batch_size=16` — batches encoder/decoder across chunks (2-4x)
- **`beam_size=1`** (greedy) — ~2x faster, negligible WER increase for conversational speech
- **`vad_filter=True`** — skips silence segments
- **`compute_type="float16"`** — halves memory bandwidth
- **No caching**: `force_build=True` on apt/pip steps + unique `download_root` per run forces
  fresh image rebuild + fresh HF model download every time

### Pinned versions (must match)
- `faster-whisper==1.1.1` (provides `BatchedInferencePipeline`)
- `ctranslate2==4.8.0`
- Base image: `nvidia/cuda:12.6.3-cudnn-runtime-ubuntu22.04` (provides `libcublas.so.12`;
  `debian_slim` fails with `RuntimeError: Library libcublas.so.12 is not found`)

### Audio prep (done automatically by the utility)
```bash
ffmpeg -y -i input.mp4 -vn -ac 1 -ar 16000 -c:a aac -b:a 64k audio.m4a
```
Mono 16kHz 64kbps AAC — a 65-min video (151 MB stream) becomes ~35 MB audio.

### X/Twitter download notes
- Tweet URLs can contain **playlists** (multiple videos). Use `--playlist-item N` to pick one.
- Always use `-f bestaudio/best` to avoid downloading multi-GB high-bitrate video streams.
- A 65-min interview's video variant can be 2.8+ GB; audio-only is ~63 MB (128 kbps).

### Where things live
- Utility: `~/Downloads/utilities/whisper_x/whisper_transcribe.py`
- Strategy doc: `~/Downloads/utilities/whisper_x/STRATEGY.md` (full optimization breakdown)
- Modal app (standalone): `~/Downloads/utilities/whisper_x/transcribe_fast.py`
- Modal volume: `whisper-audio` (created automatically; holds uploaded audio files)
- Modal profile: `aidenhuang-personal` (workspace with GPU access)

</rule>
</rules>
<available_skills>
The 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.

- **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/.codeium/windsurf/skills/cloudflare-one/SKILL.md)
- **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/.agents/skills/agents-sdk/SKILL.md)
- **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/.agents/skills/turnstile-spin/SKILL.md)
- **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)
- **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)
- **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/.claude/skills/sandbox-sdk/SKILL.md)
- **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/.agents/skills/wrangler/SKILL.md)
- **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)
- **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)
- **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/.codeium/windsurf/skills/cloudflare-email-service/SKILL.md)
- **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)
- **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/.codeium/windsurf/skills/cloudflare/SKILL.md)
- **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)
- **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/.claude/skills/workers-best-practices/SKILL.md)
- **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/.codeium/windsurf/skills/web-perf/SKILL.md)
- **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)
- **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)
- **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)
- **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)
- **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)
- **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)
- **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)
- **cloudflare-agent-setup**:  (source: /Users/root1/.devin/skills/cloudflare-agent-setup/SKILL.md)
- **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)
- **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)
- **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)
- **declarative-repo-setup**: Generate and verify a working environment.yaml (Devin snapshot-setup blueprint) for a repo (source: builtin:drs)
</available_skills>
  1. You
  2. Devin
    Thinking

    The user has provided a YouTube link. According to the always-on rules: - "If I ever paste in a YouTube link, use yt-dlp to summarize the video." - "get the autogenerated captions to do this" - "if asked to summarize a YouTube video, do not name the session until after reading and understanding the full YouTube video transcript" So I need to use yt-dlp to download the captions/transcript and then summarize the video. I should not name the session yet. Let me use yt-dlp to extract the transcript. I need to check if yt-dlp is available and get the captions. I'll use yt-dlp's subtitle/list feature. First, let me think about the command. yt-dlp can list available subtitles with --list-subs and download them with --write-subs or extract them. To get auto-generated captions, I can use: - --list-subs to see available subtitles - --write-auto-subs to write auto-generated subtitles - --skip-download to not download the video Or I can use yt-dlp to extract the transcript directly to text. Let me use a command that gets the auto-generated subtitles in a text format. A good approach: 1. List available subs first 2. Use yt-dlp --write-auto-subs --skip-download --sub-lang en --convert-subs srt or similar Actually, a cleaner way is to use yt-dlp --dump-json to get metadata including subtitles, or to just download the .vtt file and convert it to text. Let me start by checking yt-dlp availability and listing subs.

    exec(command: yt-dlp --version)
    {
      "command": "yt-dlp --version"
    }
    In 17,040Out 354Cache 512TTFT 1,549mstool_calls
  3. OutputOutput from command in shell 082484:
    Output from command in shell 082484:
    2025.12.08
    
    
    Exit code: 0
  4. Devin
    Thinking

    yt-dlp is available. Now I'll list the available subtitles for the video and then download the auto-generated English captions.

    exec(command: yt-dlp --list-subs "https://www.youtube.…)
    {
      "command": "yt-dlp --list-subs \"https://www.youtube.com/watch?v=Do_LjDsisIE\""
    }
    In 407Out 63Cache 17,536TTFT 491mstool_calls
  5. OutputOutput from command in shell 916349:
    Output from command in shell 916349:
    WARNING: Your yt-dlp version (2025.12.08) is older than 90 days!
             It is strongly recommended to always use the latest version.
             You installed yt-dlp with pip or using the wheel from PyPi; Use that to update.
             To suppress this warning, add --no-update to your command/config.
    [youtube] Extracting URL: https://www.youtube.com/watch?v=Do_LjDsisIE
    [youtube] Do_LjDsisIE: Downloading webpage
    [youtube] Do_LjDsisIE: Downloading tv client config
    [youtube] Do_LjDsisIE: Downloading player dd53c628-main
    [youtube] Do_LjDsisIE: Downloading tv player API JSON
    [youtube] Do_LjDsisIE: Downloading android sdkless player API JSON
    [youtube] [jsc:deno] Solving JS challenges using deno
    WARNING: [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
    WARNING: [youtube] Do_LjDsisIE: 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
    [info] Available automatic captions for Do_LjDsisIE:
    Language Name                  Formats
    ab       Abkhazian             vtt, srt, ttml, srv3, srv2, srv1, json3
    aa       Afar                  vtt, srt, ttml, srv3, srv2, srv1, json3
    af       Afrikaans             vtt, srt, ttml, srv3, srv2, srv1, json3
    ak       Akan                  vtt, srt, ttml, srv3, srv2, srv1, json3
    sq       Albanian              vtt, srt, ttml, srv3, srv2, srv1, json3
    am       Amharic               vtt, srt, ttml, srv3, srv2, srv1, json3
    ar       Arabic                vtt, srt, ttml, srv3, srv2, srv1, json3
    hy       Armenian              vtt, srt, ttml, srv3, srv2, srv1, json3
    as       Assamese              vtt, srt, ttml, srv3, srv2, srv1, json3
    ay       Aymara                vtt, srt, ttml, srv3, srv2, srv1, json3
    az       Azerbaijani           vtt, srt, ttml, srv3, srv2, srv1, json3
    bn       Bangla                vtt, srt, ttml, srv3, srv2, srv1, json3
    ba       Bashkir               vtt, srt, ttml, srv3, srv2, srv1, json3
    eu       Basque                vtt, srt, ttml, srv3, srv2, srv1, json3
    be       Belarusian            vtt, srt, ttml, srv3, srv2, srv1, json3
    bho      Bhojpuri              vtt, srt, ttml, srv3, srv2, srv1, json3
    bs       Bosnian               vtt, srt, ttml, srv3, srv2, srv1, json3
    br       Breton                vtt, srt, ttml, srv3, srv2, srv1, json3
    bg       Bulgarian             vtt, srt, ttml, srv3, srv2, srv1, json3
    my       Burmese               vtt, srt, ttml, srv3, srv2, srv1, json3
    ca       Catalan               vtt, srt, ttml, srv3, srv2, srv1, json3
    ceb      Cebuano               vtt, srt, ttml, srv3, srv2, srv1, json3
    zh-Hans  Chinese (Simplified)  vtt, srt, ttml, srv3, srv2, srv1, json3
    zh-Hant  Chinese (Traditional) vtt, srt, ttml, srv3, srv2, srv1, json3
    co       Corsican              vtt, srt, ttml, srv3, srv2, srv1, json3
    hr       Croatian              vtt, srt, ttml, srv3, srv2, srv1, json3
    cs       Czech                 vtt, srt, ttml, srv3, srv2, srv1, json3
    da       Danish                vtt, srt, ttml, srv3, srv2, srv1, json3
    dv       Divehi                vtt, srt, ttml, srv3, srv2, srv1, json3
    nl       Dutch                 vtt, srt, ttml, srv3, srv2, srv1, json3
    dz       Dzongkha              vtt, srt, ttml, srv3, srv2, srv1, json3
    en-orig  English (Original)    vtt, srt, ttml, srv3, srv2, srv1, json3
    en       English               vtt, srt, ttml, srv3, srv2, srv1, json3
    eo       Esperanto             vtt, srt, ttml, srv3, srv2, srv1, json3
    et       Estonian              vtt, srt, ttml, srv3, srv2, srv1, json3
    ee       Ewe                   vtt, srt, ttml, srv3, srv2, srv1, json3
    fo       Faroese               vtt, srt, ttml, srv3, srv2, srv1, json3
    fj       Fijian                vtt, srt, ttml, srv3, srv2, srv1, json3
    fil      Filipino              vtt, srt, ttml, srv3, srv2, srv1, json3
    fi       Finnish               vtt, srt, ttml, srv3, srv2, srv1, json3
    fr       French                vtt, srt, ttml, srv3, srv2, srv1, json3
    gaa      Ga                    vtt, srt, ttml, srv3, srv2, srv1, json3
    gl       Galician              vtt, srt, ttml, srv3, srv2, srv1, json3
    lg       Ganda                 vtt, srt, ttml, srv3, srv2, srv1, json3
    ka       Georgian              vtt, srt, ttml, srv3, srv2, srv1, json3
    de       German                vtt, srt, ttml, srv3, srv2, srv1, json3
    el       Greek                 vtt, srt, ttml, srv3, srv2, srv1, json3
    gn       Guarani               vtt, srt, ttml, srv3, srv2, srv1, json3
    gu       Gujarati              vtt, srt, ttml, srv3, srv2, srv1, json3
    ht       Haitian Creole        vtt, srt, ttml, srv3, srv2, srv1, json3
    ha       Hausa                 vtt, srt, ttml, srv3, srv2, srv1, json3
    haw      Hawaiian              vtt, srt, ttml, srv3, srv2, srv1, json3
    iw       Hebrew                vtt, srt, ttml, srv3, srv2, srv1, json3
    hi       Hindi                 vtt, srt, ttml, srv3, srv2, srv1, json3
    hmn      Hmong                 vtt, srt, ttml, srv3, srv2, srv1, json3
    hu       Hungarian             vtt, srt, ttml, srv3, srv2, srv1, json3
    is       Icelandic             vtt, srt, ttml, srv3, srv2, srv1, json3
    ig       Igbo                  vtt, srt, ttml, srv3, srv2, srv1, json3
    id       Indonesian            vtt, srt, ttml, srv3, srv2, srv1, json3
    iu       Inuktitut             vtt, srt, ttml, srv3, srv2, srv1, json3
    ga       Irish                 vtt, srt, ttml, srv3, srv2, srv1, json3
    it       Italian               vtt, srt, ttml, srv3, srv2, srv1, json3
    ja       Japanese              vtt, srt, ttml, srv3, srv2, srv1, json3
    jv       Javanese              vtt, srt, ttml, srv3, srv2, srv1, json3
    kl       Kalaallisut           vtt, srt, ttml, srv3, srv2, srv1, json3
    kn       Kannada               vtt, srt, ttml, srv3, srv2, srv1, json3
    kk       Kazakh                vtt, srt, ttml, srv3, srv2, srv1, json3
    kha      Khasi                 vtt, srt, ttml, srv3, srv2, srv1, json3
    km       Khmer                 vtt, srt, ttml, srv3, srv2, srv1, json3
    rw       Kinyarwanda           vtt, srt, ttml, srv3, srv2, srv1, json3
    ko       Korean                vtt, srt, ttml, srv3, srv2, srv1, json3
    kri      Krio                  vtt, srt, ttml, srv3, srv2, srv1, json3
    ku       Kurdish               vtt, srt, ttml, srv3, srv2, srv1, json3
    ky       Kyrgyz                vtt, srt, ttml, srv3, srv2, srv1, json3
    lo       Lao                   vtt, srt, ttml, srv3, srv2, srv1, json3
    la       Latin                 vtt, srt, ttml, srv3, srv2, srv1, json3
    lv       Latvian               vtt, srt, ttml, srv3, srv2, srv1, json3
    ln       Lingala               vtt, srt, ttml, srv3, srv2, srv1, json3
    lt       Lithuanian            vtt, srt, ttml, srv3, srv2, srv1, json3
    lua      Luba-Lulua            vtt, srt, ttml, srv3, srv2, srv1, json3
    luo      Luo                   vtt, srt, ttml, srv3, srv2, srv1, json3
    lb       Luxembourgish         vtt, srt, ttml, srv3, srv2, srv1, json3
    mk       Macedonian            vtt, srt, ttml, srv3, srv2, srv1, json3
    mg       Malagasy              vtt, srt, ttml, srv3, srv2, srv1, json3
    ms       Malay                 vtt, srt, ttml, srv3, srv2, srv1, json3
    ml       Malayalam             vtt, srt, ttml, srv3, srv2, srv1, json3
    mt       Maltese               vtt, srt, ttml, srv3, srv2, srv1, json3
    gv       Manx                  vtt, srt, ttml, srv3, srv2, srv1, json3
    mi       Māori                 vtt, srt, ttml, srv3, srv2, srv1, json3
    mr       Marathi               vtt, srt, ttml, srv3, srv2, srv1, json3
    mn       Mongolian             vtt, srt, ttml, srv3, srv2, srv1, json3
    mfe      Morisyen              vtt, srt, ttml, srv3, srv2, srv1, json3
    ne       Nepali                vtt, srt, ttml, srv3, srv2, srv1, json3
    new      Newari                vtt, srt, ttml, srv3, srv2, srv1, json3
    nso      Northern Sotho        vtt, srt, ttml, srv3, srv2, srv1, json3
    no       Norwegian             vtt, srt, ttml, srv3, srv2, srv1, json3
    ny       Nyanja                vtt, srt, ttml, srv3, srv2, srv1, json3
    oc       Occitan               vtt, srt, ttml, srv3, srv2, srv1, json3
    or       Odia                  vtt, srt, ttml, srv3, srv2, srv1, json3
    om       Oromo                 vtt, srt, ttml, srv3, srv2, srv1, json3
    os       Ossetic               vtt, srt, ttml, srv3, srv2, srv1, json3
    pam      Pampanga              vtt, srt, ttml, srv3, srv2, srv1, json3
    ps       Pashto                vtt, srt, ttml, srv3, srv2, srv1, json3
    fa       Persian               vtt, srt, ttml, srv3, srv2, srv1, json3
    pl       Polish                vtt, srt, ttml, srv3, srv2, srv1, json3
    pt       Portuguese            vtt, srt, ttml, srv3, srv2, srv1, json3
    pt-PT    Portuguese (Portugal) vtt, srt, ttml, srv3, srv2, srv1, json3
    pa       Punjabi               vtt, srt, ttml, srv3, srv2, srv1, json3
    qu       Quechua               vtt, srt, ttml, srv3, srv2, srv1, json3
    ro       Romanian              vtt, srt, ttml, srv3, srv2, srv1, json3
    rn       Rundi                 vtt, srt, ttml, srv3, srv2, srv1, json3
    ru       Russian               vtt, srt, ttml, srv3, srv2, srv1, json3
    sm       Samoan                vtt, srt, ttml, srv3, srv2, srv1, json3
    sg       Sango                 vtt, srt, ttml, srv3, srv2, srv1, json3
    sa       Sanskrit              vtt, srt, ttml, srv3, srv2, srv1, json3
    gd       Scottish Gaelic       vtt, srt, ttml, srv3, srv2, srv1, json3
    sr       Serbian               vtt, srt, ttml, srv3, srv2, srv1, json3
    crs      Seselwa Creole French vtt, srt, ttml, srv3, srv2, srv1, json3
    sn       Shona                 vtt, srt, ttml, srv3, srv2, srv1, json3
    sd       Sindhi                vtt, srt, ttml, srv3, srv2, srv1, json3
    si       Sinhala               vtt, srt, ttml, srv3, srv2, srv1, json3
    sk       Slovak                vtt, srt, ttml, srv3, srv2, srv1, json3
    sl       Slovenian             vtt, srt, ttml, srv3, srv2, srv1, json3
    so       Somali                vtt, srt, ttml, srv3, srv2, srv1, json3
    st       Southern Sotho        vtt, srt, ttml, srv3, srv2, srv1, json3
    es       Spanish               vtt, srt, ttml, srv3, srv2, srv1, json3
    su       Sundanese             vtt, srt, ttml, srv3, srv2, srv1, json3
    sw       Swahili               vtt, srt, ttml, srv3, srv2, srv1, json3
    ss       Swati                 vtt, srt, ttml, srv3, srv2, srv1, json3
    sv       Swedish               vtt, srt, ttml, srv3, srv2, srv1, json3
    tg       Tajik                 vtt, srt, ttml, srv3, srv2, srv1, json3
    ta       Tamil                 vtt, srt, ttml, srv3, srv2, srv1, json3
    tt       Tatar                 vtt, srt, ttml, srv3, srv2, srv1, json3
    te       Telugu                vtt, srt, ttml, srv3, srv2, srv1, json3
    th       Thai                  vtt, srt, ttml, srv3, srv2, srv1, json3
    bo       Tibetan               vtt, srt, ttml, srv3, srv2, srv1, json3
    ti       Tigrinya              vtt, srt, ttml, srv3, srv2, srv1, json3
    to       Tongan                vtt, srt, ttml, srv3, srv2, srv1, json3
    ts       Tsonga                vtt, srt, ttml, srv3, srv2, srv1, json3
    tn       Tswana                vtt, srt, ttml, srv3, srv2, srv1, json3
    tum      Tumbuka               vtt, srt, ttml, srv3, srv2, srv1, json3
    tr       Turkish               vtt, srt, ttml, srv3, srv2, srv1, json3
    tk       Turkmen               vtt, srt, ttml, srv3, srv2, srv1, json3
    uk       Ukrainian             vtt, srt, ttml, srv3, srv2, srv1, json3
    ur       Urdu                  vtt, srt, ttml, srv3, srv2, srv1, json3
    ug       Uyghur                vtt, srt, ttml, srv3, srv2, srv1, json3
    uz       Uzbek                 vtt, srt, ttml, srv3, srv2, srv1, json3
    ve       Venda                 vtt, srt, ttml, srv3, srv2, srv1, json3
    vi       Vietnamese            vtt, srt, ttml, srv3, srv2, srv1, json3
    war      Waray                 vtt, srt, ttml, srv3, srv2, srv1, json3
    cy       Welsh                 vtt, srt, ttml, srv3, srv2, srv1, json3
    fy       Western Frisian       vtt, srt, ttml, srv3, srv2, srv1, json3
    wo       Wolof                 vtt, srt, ttml, srv3, srv2, srv1, json3
    xh       Xhosa                 vtt, srt, ttml, srv3, srv2, srv1, json3
    yi       Yiddish               vtt, srt, ttml, srv3, srv2, srv1, json3
    yo       Yoruba                vtt, srt, ttml, srv3, srv2, srv1, json3
    zu       Zulu                  vtt, srt, ttml, srv3, srv2, srv1, json3
    Do_LjDsisIE has no subtitles
    
    
    Exit code: 0
  6. Devin
    Thinking

    The video has auto-generated captions available in English. I see "en" and "en-orig" (English Original). I'll download the English auto-generated captions. I'll use a temp directory and download the .vtt or .srt, then extract the text. Let me use yt-dlp with --write-auto-subs --skip-download --sub-langs en --convert-subs srt, or better yet download as plain text. Actually, to get plain text, the easiest is to download the .vtt and then strip the timing. Or I can use --dump-json with subtitles. Let me do: yt-dlp --write-auto-subs --skip-download --sub-langs en --convert-subs srt -o "/tmp/%(id)s.%(ext)s" "https://..." Then I'll have an SRT file I can parse. Or I could use the json3 format which is JSON and easier to parse. Actually, the simplest might be to use yt-dlp --print "%(subtitles.?:0:1000)s" but that's complex. Let me just download the auto-subs as .srt and then process it.

    exec(command: cd /tmp && yt-dlp --write-auto-subs --sk…)
    {
      "command": "cd /tmp && yt-dlp --write-auto-subs --skip-download --sub-langs en --convert-subs srt -o \"%(id)s.%(ext)s\" \"https://www.youtube.com/watch?v=Do_LjDsisIE\""
    }
    In 4,617Out 307Cache 17,920TTFT 693mstool_calls
  7. OutputOutput from command in shell 8ab642:
    Output from command in shell 8ab642:
    WARNING: Your yt-dlp version (2025.12.08) is older than 90 days!
             It is strongly recommended to always use the latest version.
             You installed yt-dlp with pip or using the wheel from PyPi; Use that to update.
             To suppress this warning, add --no-update to your command/config.
    [youtube] Extracting URL: https://www.youtube.com/watch?v=Do_LjDsisIE
    [youtube] Do_LjDsisIE: Downloading webpage
    [youtube] Do_LjDsisIE: Downloading tv client config
    [youtube] Do_LjDsisIE: Downloading player dd53c628-main
    [youtube] Do_LjDsisIE: Downloading tv player API JSON
    [youtube] Do_LjDsisIE: Downloading android sdkless player API JSON
    WARNING: [youtube] Do_LjDsisIE: Some tv client https formats have been skipped as they are missing a url. YouTube may have enabled the SABR-only or Server-Side Ad Placement experiment for the current session. See  https://github.com/yt-dlp/yt-dlp/issues/12482  for more details
    [youtube] [jsc:deno] Solving JS challenges using deno
    WARNING: [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
    WARNING: [youtube] Do_LjDsisIE: 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
    WARNING: [youtube] Do_LjDsisIE: Some web client https formats have been skipped as they are missing a url. YouTube is forcing SABR streaming for this client. See  https://github.com/yt-dlp/yt-dlp/issues/12482  for more details
    [info] Do_LjDsisIE: Downloading subtitles: en
    [info] Do_LjDsisIE: Downloading 1 format(s): 401+251
    [info] Writing video subtitles to: Do_LjDsisIE.en.vtt
    [download] Destination: Do_LjDsisIE.en.vtt
    
    [download]    1.00KiB at  Unknown B/s (00:00:00)
    [download]    3.00KiB at  Unknown B/s (00:00:00)
    [download]    7.00KiB at  Unknown B/s (00:00:00)
    [download]   15.00KiB at    3.90MiB/s (00:00:00)
    [download]   31.00KiB at    2.26MiB/s (00:00:00)
    [download]   63.00KiB at    3.03MiB/s (00:00:00)
    [download]  127.00KiB at    3.82MiB/s (00:00:00)
    [download]  206.82KiB at    4.79MiB/s (00:00:00)
    [download] 100% of  206.82KiB in 00:00:00 at 1.30MiB/s
    [SubtitlesConvertor] Converting subtitles
    Deleting original file Do_LjDsisIE.en.vtt (pass -k to keep)
    
    
    Exit code: 0
  8. Devin
    Thinking

    The SRT file was downloaded. It says "Deleting original file Do_LjDsisIE.en.vtt" and converted. But it says destination was Do_LjDsisIE.en.vtt. Hmm, the conversion might have created Do_LjDsisIE.en.srt. Let me check.

    exec(command: ls -la /tmp/Do_LjDsisIE*)
    {
      "command": "ls -la /tmp/Do_LjDsisIE*"
    }
    In 1,172Out 92Cache 22,528TTFT 527mstool_calls
  9. OutputOutput from command in shell f2efd2:
    Output from command in shell f2efd2:
    -rw-r--r--  1 root1  wheel  110463 Jul 13 21:20 /tmp/Do_LjDsisIE.en.srt
    
    
    Exit code: 0
  10. Devin
    Thinking

    I have the SRT file. Now I need to extract the text from it. I can use a Python one-liner or sed/awk to strip timing and subtitle numbers. Then I'll read the transcript. Since it could be long, I should probably process it. Let me convert SRT to plain text. I'll use python to parse it and output plain text without duplicates. Actually, I can just use a simple sed command to remove the timing lines and numbers, then use the transcript. But SRT format has: 1 00:00:00,000 --> 00:00:02,000 Text line 1 2 ... To get plain text, remove lines that are empty, numeric, or contain "-->". Let me use Python to parse SRT properly and join lines.

    exec(command: cd /tmp && python3 -c " import re with o…)
    {
      "command": "cd /tmp && python3 -c \"\nimport re\nwith open('Do_LjDsisIE.en.srt') as f:\n    text = f.read()\n# Parse SRT entries\nentries = re.split(r'\\n\\s*\\n', text.strip())\nlines = []\nfor entry in entries:\n    # Remove index and timing lines\n    parts = entry.split('\\n')\n    # Skip first line (index) and second line (timing)\n    content = '\\n'.join(parts[2:])\n    # Remove <b> etc tags\n    content = re.sub(r'<[^>]+>', '', content)\n    lines.append(content.strip())\n# Remove duplicate consecutive lines (repeated captions)\nresult = []\nprev = None\nfor line in lines:\n    if line != prev and line:\n        result.append(line)\n        prev = line\nprint('\\n'.join(result))\n\" > /tmp/Do_LjDsisIE.transcript.txt\nwc -l /tmp/Do_LjDsisIE.transcript.txt"
    }
    In 180Out 412Cache 23,680TTFT 577mstool_calls
  11. OutputOutput from command in shell 940615:
    Output from command in shell 940615:
        1770 /tmp/Do_LjDsisIE.transcript.txt
    
    
    Exit code: 0
  12. Devin
    Thinking

    The transcript is 1770 lines. That might be a lot to read fully, but the rule says "do not name the session until after reading and understanding the full YouTube video transcript" and "get the autogenerated captions to do this". So I should read the transcript. But it might be too large to fit. Let me check the size and read it in chunks. Also, I should get the title of the video and metadata to understand context. Let me get the title and view metadata.

    exec(command: cd /tmp && yt-dlp --print "%(title)s\n%(…)
    {
      "command": "cd /tmp && yt-dlp --print \"%(title)s\\n%(uploader)s\\n%(duration_string)s\\n%(description)s\" --skip-download \"https://www.youtube.com/watch?v=Do_LjDsisIE\""
    }
    In 511Out 169Cache 23,808TTFT 731mstool_calls
  13. OutputOutput from command in shell ea0b26:
    Output from command in shell ea0b26:
    WARNING: Your yt-dlp version (2025.12.08) is older than 90 days!
             It is strongly recommended to always use the latest version.
             You installed yt-dlp with pip or using the wheel from PyPi; Use that to update.
             To suppress this warning, add --no-update to your command/config.
    WARNING: [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
    WARNING: [youtube] Do_LjDsisIE: 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
    The One Discipline That Made Me a Billionaire by Age 30 | Lucy Guo, Passes\nEO\n19:19\nMeet Lucy Guo, Founder & CEO of Passes and Co-Founder of ScaleAI, who became the youngest self-made female billionaire. 
    
    What's the secret behind all these impressive titles? How has her life changed since becoming a billionaire? In this interview, we discovered that Lucy is more than just her accolades. She's an entrepreneur driven by positivity, self-confidence, and hard work. From dropping out of college to co-founding Scale AI and building Passes, Lucy shares the real lessons she’s learned through countless challenges.
    
    00:00 Intro
    02:16 The Mindset That Made the Youngest Self-Made Billionaire
    05:14 Why I Dropped Out and Stopped Being the "Good Girl"
    06:12 The No.1 Rule That Makes You Take Risks 
    07:01 EO Partner Highlight: EO x Lovable
    08:04 What I Learned at Snap
    09:57 Leaving Snap to Found Scale AI
    11:54 Most Painful Failure
    12:49 Principle 1- Impact drives people
    13:17 Principle 2- Do not avoid IC work
    14:08 Principle 3. Choose grit over genius
    14:49 Principle 4. Be Kind
    15:20 Don't Build If You're Not Obsessed
    17:44 Be Everyone's Cheerleader
    
    🔗  Read the full transcription of Lucy’s interview: https://www.eomag.io/article/passes-lucy-guo?utm_source=youtube&utm_medium=description
    
    ✨EO Partner Highlight: EO x Lovable✨
    📍The promo code EO20YT mentioned in the video has now expired, but you can still explore Lovable here: https://lovable.dev/
    
    EO stands for Entrepreneur& Opportunities. As we're looking to feature more inspiring stories of entrepreneurs all over the world, don't hesitate to contact us at partner@eoeoeo.net
    
    X | @eostudi0
    LinkedIn | @EO STUDIO
    Instagram | @eostudio.official 
    Newsletter | https://www.eomag.io/subscribe?utm_source=youtube&utm_medium=description
    
    
    Exit code: 0
  14. Devin
    Thinking

    Good. The video is "The One Discipline That Made Me a Billionaire by Age 30 | Lucy Guo, Passes" from EO. It's 19:19 long. The transcript is 1770 lines. I need to read it. The file is about 110KB? Wait SRT is 110KB, transcript should be a bit smaller. Let me check size and read it. Since it's long, I'll use the read tool with offset to read in chunks. But I need to read it all to understand. I can use read with no limit? Default is 20000 chars. 1770 lines could be ~100KB. Let me read it.

    exec(command: wc -c /tmp/Do_LjDsisIE.transcript.txt)
    {
      "command": "wc -c /tmp/Do_LjDsisIE.transcript.txt"
    }
    In 1,012Out 170Cache 24,192TTFT 720mstool_calls
  15. OutputOutput from command in shell b3bdf0:
    Output from command in shell b3bdf0:
       67382 /tmp/Do_LjDsisIE.transcript.txt
    
    
    Exit code: 0
  16. Devin
    Thinking

    67KB. Read tool default limit is 20000 chars, so I'll need to read it in multiple chunks. Let me read it all. I can read with limit 20000 at a time and offsets. But to be efficient, maybe I can use a python script to summarize? No, the rule says I need to read and understand the full transcript. But I can read it as chunks. However, as an AI, I can process the text. The tool output limit may cause issues. But I need to actually read it. Let me read the transcript in chunks. I'll use read tool.

    read(file_path: /tmp/Do_LjDsisIE.transcript.txt, offset: 1, limit: 500)
    {
      "file_path": "/tmp/Do_LjDsisIE.transcript.txt",
      "offset": 1,
      "limit": 500
    }
    In 334Out 162Cache 25,088TTFT 834mstool_calls
  17. Output<file-view path="/tmp/Do_LjDsisIE.transcript.txt" start_line="1" end_line="500" total_lines="1770">
    <file-view path="/tmp/Do_LjDsisIE.transcript.txt" start_line="1" end_line="500" total_lines="1770">
      1|My daily routine is essentially waking
      2|My daily routine is essentially waking
      3|up between like 5:30 a.m. to 6 o'clock
      4|up between like 5:30 a.m. to 6 o'clock
      5|up between like 5:30 a.m. to 6 o'clock
      6|a.m. every day. And I quite literally
      7|a.m. every day. And I quite literally
      8|a.m. every day. And I quite literally
      9|just roll out of bed, then I go straight
     10|just roll out of bed, then I go straight
     11|just roll out of bed, then I go straight
     12|to Berries. And one thing about me is I
     13|to Berries. And one thing about me is I
     14|to Berries. And one thing about me is I
     15|literally got my house specifically
     16|literally got my house specifically
     17|literally got my house specifically
     18|because it's 5 minutes away from Berries
     19|because it's 5 minutes away from Berries
     20|because it's 5 minutes away from Berries
     21|and 5 minutes from the office. So I can
     22|and 5 minutes from the office. So I can
     23|and 5 minutes from the office. So I can
     24|reduce my commute and I don't really
     25|reduce my commute and I don't really
     26|reduce my commute and I don't really
     27|need a car, which is awesome. I shower
     28|need a car, which is awesome. I shower
     29|need a car, which is awesome. I shower
     30|and I hit work. Afterwards, I'm usually
     31|and I hit work. Afterwards, I'm usually
     32|and I hit work. Afterwards, I'm usually
     33|just in like backtoback meetings. I try
     34|just in like backtoback meetings. I try
     35|just in like backtoback meetings. I try
     36|to sometimes block off time because
     37|to sometimes block off time because
     38|to sometimes block off time because
     39|[music] like I need to like be in Figma
     40|[music] like I need to like be in Figma
     41|[music] like I need to like be in Figma
     42|and like actually get feedback on design
     43|and like actually get feedback on design
     44|and like actually get feedback on design
     45|because I'm usually working until maybe
     46|because I'm usually working until maybe
     47|because I'm usually working until maybe
     48|like 8 o'clock, nine o'clock. I'll get
     49|like 8 o'clock, nine o'clock. I'll get
     50|like 8 o'clock, nine o'clock. I'll get
     51|dinner, then I'll go back to work, and
     52|dinner, then I'll go back to work, and
     53|dinner, then I'll go back to work, and
     54|then um I'll fall asleep sometime
     55|then um I'll fall asleep sometime
     56|then um I'll fall asleep sometime
     57|between like 11:00 p.m. to 1:00 a.m.
     58|between like 11:00 p.m. to 1:00 a.m.
     59|between like 11:00 p.m. to 1:00 a.m.
     60|depending on the day, and then repeat.
     61|depending on the day, and then repeat.
     62|depending on the day, and then repeat.
     63|You know, in YC, they tell you you
     64|You know, in YC, they tell you you
     65|You know, in YC, they tell you you
     66|should only be doing three things, which
     67|should only be doing three things, which
     68|should only be doing three things, which
     69|is like working out, talking to
     70|is like working out, talking to
     71|is like working out, talking to
     72|customers, and building. I see like why
     73|customers, and building. I see like why
     74|customers, and building. I see like why
     75|working out is so important because
     76|working out is so important because
     77|working out is so important because
     78|[music] it puts you on a schedule, tests
     79|[music] it puts you on a schedule, tests
     80|[music] it puts you on a schedule, tests
     81|your discipline. like no matter how
     82|your discipline. like no matter how
     83|your discipline. like no matter how
     84|shitty I'm feeling, I will still get up
     85|shitty I'm feeling, I will still get up
     86|shitty I'm feeling, I will still get up
     87|and go out and work out because I know
     88|and go out and work out because I know
     89|and go out and work out because I know
     90|I'm going to feel better and have more
     91|I'm going to feel better and have more
     92|I'm going to feel better and have more
     93|energy to be better at my job the rest
     94|energy to be better at my job the rest
     95|energy to be better at my job the rest
     96|of the day. So why my life looks like it
     97|of the day. So why my life looks like it
     98|of the day. So why my life looks like it
     99|is today, but like don't sit still
    100|is today, but like don't sit still
    101|is today, but like don't sit still
    102|essentially. I don't waste time
    103|essentially. I don't waste time
    104|essentially. I don't waste time
    105|scrolling [music] through Tik Tok,
    106|scrolling [music] through Tik Tok,
    107|scrolling [music] through Tik Tok,
    108|watching TV, watching movies, etc. And I
    109|watching TV, watching movies, etc. And I
    110|watching TV, watching movies, etc. And I
    111|think by not sitting still, I've been
    112|think by not sitting still, I've been
    113|think by not sitting still, I've been
    114|able to really be efficient with my time
    115|able to really be efficient with my time
    116|able to really be efficient with my time
    117|and [music] invest it in things that
    118|and [music] invest it in things that
    119|and [music] invest it in things that
    120|make me happy.
    121|make me happy.
    122|make me happy.
    123|Hi, I'm Lucy Glow. I am the founder of
    124|Hi, I'm Lucy Glow. I am the founder of
    125|Hi, I'm Lucy Glow. I am the founder of
    126|Scale AI and passes back in capital
    127|Scale AI and passes back in capital
    128|Scale AI and passes back in capital
    129|Capital and HFZ. I angel invest. I've
    130|Capital and HFZ. I angel invest. I've
    131|Capital and HFZ. I angel invest. I've
    132|invested in over a 100 different
    133|invested in over a 100 different
    134|invested in over a 100 different
    135|companies. Um, and I'm excited to chat
    136|companies. Um, and I'm excited to chat
    137|companies. Um, and I'm excited to chat
    138|with you guys today.
    139|with you guys today.
    140|with you guys today.
    141|I think in terms of like how it's
    142|I think in terms of like how it's
    143|I think in terms of like how it's
    144|potentially changed my life. Um, my DMs
    145|potentially changed my life. Um, my DMs
    146|potentially changed my life. Um, my DMs
    147|are popping. [laughter]
    148|are popping. [laughter]
    149|are popping. [laughter]
    150|Not life-changing, but it's been cool. I
    151|Not life-changing, but it's been cool. I
    152|Not life-changing, but it's been cool. I
    153|mean, I think overall probably just like
    154|mean, I think overall probably just like
    155|mean, I think overall probably just like
    156|what makes me happy. Um, everyone's
    157|what makes me happy. Um, everyone's
    158|what makes me happy. Um, everyone's
    159|like, "Okay, cool. Like what are you
    160|like, "Okay, cool. Like what are you
    161|like, "Okay, cool. Like what are you
    162|buying with your money, etc." But like
    163|buying with your money, etc." But like
    164|buying with your money, etc." But like
    165|at the end of the day like what makes me
    166|at the end of the day like what makes me
    167|at the end of the day like what makes me
    168|happy is like what drives every single
    169|happy is like what drives every single
    170|happy is like what drives every single
    171|thing I do. Um whether it's like you
    172|thing I do. Um whether it's like you
    173|thing I do. Um whether it's like you
    174|know building passes because it makes me
    175|know building passes because it makes me
    176|know building passes because it makes me
    177|happy because it provides challenges in
    178|happy because it provides challenges in
    179|happy because it provides challenges in
    180|my life but I also get to like you know
    181|my life but I also get to like you know
    182|my life but I also get to like you know
    183|change people's lives. So at passes were
    184|change people's lives. So at passes were
    185|change people's lives. So at passes were
    186|building infrastructure for creators to
    187|building infrastructure for creators to
    188|building infrastructure for creators to
    189|monetize their brand. So I think when
    190|monetize their brand. So I think when
    191|monetize their brand. So I think when
    192|you look at the largest creators they
    193|you look at the largest creators they
    194|you look at the largest creators they
    195|really are like unicorns. For example
    196|really are like unicorns. For example
    197|really are like unicorns. For example
    198|Kim Kardashian built skims. Logan Paul
    199|Kim Kardashian built skims. Logan Paul
    200|Kim Kardashian built skims. Logan Paul
    201|built Prime. Mr. Beast with Fastables.
    202|built Prime. Mr. Beast with Fastables.
    203|built Prime. Mr. Beast with Fastables.
    204|I'm excited for us to eventually build a
    205|I'm excited for us to eventually build a
    206|I'm excited for us to eventually build a
    207|[music] unicorn creator.
    208|I think I got suspended from
    209|I think I got suspended from
    210|kindergarten to be quite honest. I think
    211|kindergarten to be quite honest. I think
    212|kindergarten to be quite honest. I think
    213|it came from like me telling the teacher
    214|it came from like me telling the teacher
    215|it came from like me telling the teacher
    216|that what we were learning was dumb. But
    217|that what we were learning was dumb. But
    218|that what we were learning was dumb. But
    219|if you think about it, like my parents
    220|if you think about it, like my parents
    221|if you think about it, like my parents
    222|were trying to teach me like I was going
    223|were trying to teach me like I was going
    224|were trying to teach me like I was going
    225|to like abacus competitions like
    226|to like abacus competitions like
    227|to like abacus competitions like
    228|multiplication, division, etc. back in
    229|multiplication, division, etc. back in
    230|multiplication, division, etc. back in
    231|like kindergarten. And in school they
    232|like kindergarten. And in school they
    233|like kindergarten. And in school they
    234|were literally like here's the alphabet,
    235|were literally like here's the alphabet,
    236|were literally like here's the alphabet,
    237|right? Um, and I was just very bored as
    238|right? Um, and I was just very bored as
    239|right? Um, and I was just very bored as
    240|a child. So, I was always trying to
    241|a child. So, I was always trying to
    242|a child. So, I was always trying to
    243|figure out like different things to do.
    244|figure out like different things to do.
    245|figure out like different things to do.
    246|I was like learning stuff on my own.
    247|I was like learning stuff on my own.
    248|I was like learning stuff on my own.
    249|Specifically, like the stuff I learned
    250|Specifically, like the stuff I learned
    251|Specifically, like the stuff I learned
    252|was to like make money. But eventually,
    253|was to like make money. But eventually,
    254|was to like make money. But eventually,
    255|I discovered my love for product and I
    256|I discovered my love for product and I
    257|I discovered my love for product and I
    258|was like, "Oh, this is actually really
    259|was like, "Oh, this is actually really
    260|was like, "Oh, this is actually really
    261|fun." Um, but you could see glimpses of
    262|fun." Um, but you could see glimpses of
    263|fun." Um, but you could see glimpses of
    264|it where like, you know, I tried to
    265|it where like, you know, I tried to
    266|it where like, you know, I tried to
    267|start my own virtual pet site after
    268|start my own virtual pet site after
    269|start my own virtual pet site after
    270|being addicted to Neopets. I was playing
    271|being addicted to Neopets. I was playing
    272|being addicted to Neopets. I was playing
    273|a lot of online video games. I was
    274|a lot of online video games. I was
    275|a lot of online video games. I was
    276|figuring out how to start my own online
    277|figuring out how to start my own online
    278|figuring out how to start my own online
    279|arcade game website. I mean, I think
    280|arcade game website. I mean, I think
    281|arcade game website. I mean, I think
    282|what really shaped how I'm an
    283|what really shaped how I'm an
    284|what really shaped how I'm an
    285|entrepreneur today is that through like
    286|entrepreneur today is that through like
    287|entrepreneur today is that through like
    288|the early stages of me like making
    289|the early stages of me like making
    290|the early stages of me like making
    291|money, my parents found all of my hidden
    292|money, my parents found all of my hidden
    293|money, my parents found all of my hidden
    294|cash and would take it away. So then I
    295|cash and would take it away. So then I
    296|cash and would take it away. So then I
    297|was like, I have to like figure out how
    298|was like, I have to like figure out how
    299|was like, I have to like figure out how
    300|they don't take away cash. PayPal had
    301|they don't take away cash. PayPal had
    302|they don't take away cash. PayPal had
    303|come out and then you could just go like
    304|come out and then you could just go like
    305|come out and then you could just go like
    306|buy a Visa debit card, open up a PayPal
    307|buy a Visa debit card, open up a PayPal
    308|buy a Visa debit card, open up a PayPal
    309|account and that's when I started
    310|account and that's when I started
    311|account and that's when I started
    312|learning how to make money on the
    313|learning how to make money on the
    314|learning how to make money on the
    315|internet and then that's when I got
    316|internet and then that's when I got
    317|internet and then that's when I got
    318|curious about building my own websites
    319|curious about building my own websites
    320|curious about building my own websites
    321|and then like that kind of led to
    322|and then like that kind of led to
    323|and then like that kind of led to
    324|hackathons and then I would say like the
    325|hackathons and then I would say like the
    326|hackathons and then I would say like the
    327|turning point of like real
    328|turning point of like real
    329|turning point of like real
    330|entrepreneurship and like wanting to
    331|entrepreneurship and like wanting to
    332|entrepreneurship and like wanting to
    333|build lasting companies like more than
    334|build lasting companies like more than
    335|build lasting companies like more than
    336|just bots, right? Um was at these
    337|just bots, right? Um was at these
    338|just bots, right? Um was at these
    339|hackathons where suddenly I was exposed
    340|hackathons where suddenly I was exposed
    341|hackathons where suddenly I was exposed
    342|to startup world and I was like wow I
    343|to startup world and I was like wow I
    344|to startup world and I was like wow I
    345|can build apps that like millions of
    346|can build apps that like millions of
    347|can build apps that like millions of
    348|people use this is really really cool.
    349|people use this is really really cool.
    350|people use this is really really cool.
    351|Both my parents are absolutely
    352|Both my parents are absolutely
    353|Both my parents are absolutely
    354|brilliant. They're both technical. I
    355|brilliant. They're both technical. I
    356|brilliant. They're both technical. I
    357|think it was interesting because they
    358|think it was interesting because they
    359|think it was interesting because they
    360|actually really discouraged me from
    361|actually really discouraged me from
    362|actually really discouraged me from
    363|pursuing a technical field because they
    364|pursuing a technical field because they
    365|pursuing a technical field because they
    366|kind of imagined like my place as a
    367|kind of imagined like my place as a
    368|kind of imagined like my place as a
    369|woman was to get married and pop out
    370|woman was to get married and pop out
    371|woman was to get married and pop out
    372|grandchildren, but they still wanted me
    373|grandchildren, but they still wanted me
    374|grandchildren, but they still wanted me
    375|in an intelligent environment to find an
    376|in an intelligent environment to find an
    377|in an intelligent environment to find an
    378|intelligent partner. I think I ended up
    379|intelligent partner. I think I ended up
    380|intelligent partner. I think I ended up
    381|pursuing it almost by accident. Um, I
    382|pursuing it almost by accident. Um, I
    383|pursuing it almost by accident. Um, I
    384|thought I was going to be a chemical
    385|thought I was going to be a chemical
    386|thought I was going to be a chemical
    387|engineer because I was like like
    388|engineer because I was like like
    389|engineer because I was like like
    390|chemistry was my favorite subject. like
    391|chemistry was my favorite subject. like
    392|chemistry was my favorite subject. like
    393|I loved AP chemistry and I was very good
    394|I loved AP chemistry and I was very good
    395|I loved AP chemistry and I was very good
    396|at it. But when I I ended up on this
    397|at it. But when I I ended up on this
    398|at it. But when I I ended up on this
    399|website, College Confidential, like I
    400|website, College Confidential, like I
    401|website, College Confidential, like I
    402|need help with my college applications
    403|need help with my college applications
    404|need help with my college applications
    405|and um there's this random stranger who
    406|and um there's this random stranger who
    407|and um there's this random stranger who
    408|literally looked at my extracurriculars
    409|literally looked at my extracurriculars
    410|literally looked at my extracurriculars
    411|and it was like you're an idiot. If you
    412|and it was like you're an idiot. If you
    413|and it was like you're an idiot. If you
    414|want to get into best college, just
    415|want to get into best college, just
    416|want to get into best college, just
    417|apply for computer science and I was
    418|apply for computer science and I was
    419|apply for computer science and I was
    420|like, I guess that makes sense. So
    421|like, I guess that makes sense. So
    422|like, I guess that makes sense. So
    423|that's actually like kind of how I just
    424|that's actually like kind of how I just
    425|that's actually like kind of how I just
    426|landed in computer science. My college
    427|landed in computer science. My college
    428|landed in computer science. My college
    429|days are like I was going to a lot of
    430|days are like I was going to a lot of
    431|days are like I was going to a lot of
    432|hackathons. So [music] like you know
    433|hackathons. So [music] like you know
    434|hackathons. So [music] like you know
    435|every few weeks M hacks hack MIT quite
    436|every few weeks M hacks hack MIT quite
    437|every few weeks M hacks hack MIT quite
    438|literally every hackathon I like was
    439|literally every hackathon I like was
    440|literally every hackathon I like was
    441|[music] like I'm going to go to and then
    442|[music] like I'm going to go to and then
    443|[music] like I'm going to go to and then
    444|I made a lot of friends and I say this
    445|I made a lot of friends and I say this
    446|I made a lot of friends and I say this
    447|because like I might be biased but um
    448|because like I might be biased but um
    449|because like I might be biased but um
    450|like when I was first hiring like the
    451|like when I was first hiring like the
    452|like when I was first hiring like the
    453|first people I went to were the friends
    454|first people I went to were the friends
    455|first people I went to were the friends
    456|I made in [music] college and they
    457|I made in [music] college and they
    458|I made in [music] college and they
    459|didn't even ask any questions which is
    460|didn't even ask any questions which is
    461|didn't even ask any questions which is
    462|concerning. They definitely should have
    463|concerning. They definitely should have
    464|concerning. They definitely should have
    465|asked how much their equity [music] was
    466|asked how much their equity [music] was
    467|asked how much their equity [music] was
    468|worth, but um they were just like,
    469|worth, but um they were just like,
    470|worth, but um they were just like,
    471|"Yeah, like we'd love to come work with
    472|"Yeah, like we'd love to come work with
    473|"Yeah, like we'd love to come work with
    474|you." YOLO. Right. And that was cuz
    475|you." YOLO. Right. And that was cuz
    476|you." YOLO. Right. And that was cuz
    477|there was that trust that was gained in
    478|there was that trust that was gained in
    479|there was that trust that was gained in
    480|[music] college. When you're building
    481|[music] college. When you're building
    482|[music] college. When you're building
    483|your next company, um your network is
    484|your next company, um your network is
    485|your next company, um your network is
    486|your net worth and like you're going to
    487|your net worth and like you're going to
    488|your net worth and like you're going to
    489|be able to hire the friends that you
    490|be able to hire the friends that you
    491|be able to hire the friends that you
    492|made in college and you know they're
    493|made in college and you know they're
    494|made in college and you know they're
    495|good.
    496|good.
    497|good.
    498|I I think if I didn't have a rebellious
    499|I I think if I didn't have a rebellious
    500|I I think if I didn't have a rebellious
    </file-view>
  18. Devin
    Thinking

    I see the transcript has many duplicate lines because the SRT entries weren't de-duplicated properly. My Python script removed duplicate consecutive lines, but it seems each caption line is repeated. Actually, looking at the SRT extraction, each entry may have the same line repeated due to the SRT format? Wait, my Python script used re.split to split by blank lines, skipped first two lines, joined rest. But the output shows duplicate lines. This is because the SRT may have entries with duplicate text? Let me examine the SRT file. Actually, I think the issue is that YouTube's auto-captions may have each word repeated, but more likely my parsing is wrong. Let me look at the raw SRT.

    read(file_path: /tmp/Do_LjDsisIE.en.srt, offset: 1, limit: 100)
    {
      "file_path": "/tmp/Do_LjDsisIE.en.srt",
      "offset": 1,
      "limit": 100
    }
    In 6,092Out 188Cache 25,344TTFT 941mstool_calls
  19. Output<file-view path="/tmp/Do_LjDsisIE.en.srt" start_line="1" end_line="100" total_lines="5945">
    <file-view path="/tmp/Do_LjDsisIE.en.srt" start_line="1" end_line="100" total_lines="5945">
      1|1
      2|00:00:00,080 --> 00:00:01,590
      3|
      4|My daily routine is essentially waking
      5|
      6|2
      7|00:00:01,590 --> 00:00:01,600
      8|My daily routine is essentially waking
      9| 
     10|
     11|3
     12|00:00:01,600 --> 00:00:04,150
     13|My daily routine is essentially waking
     14|up between like 5:30 a.m. to 6 o'clock
     15|
     16|4
     17|00:00:04,150 --> 00:00:04,160
     18|up between like 5:30 a.m. to 6 o'clock
     19| 
     20|
     21|5
     22|00:00:04,160 --> 00:00:05,510
     23|up between like 5:30 a.m. to 6 o'clock
     24|a.m. every day. And I quite literally
     25|
     26|6
     27|00:00:05,510 --> 00:00:05,520
     28|a.m. every day. And I quite literally
     29| 
     30|
     31|7
     32|00:00:05,520 --> 00:00:07,269
     33|a.m. every day. And I quite literally
     34|just roll out of bed, then I go straight
     35|
     36|8
     37|00:00:07,269 --> 00:00:07,279
     38|just roll out of bed, then I go straight
     39| 
     40|
     41|9
     42|00:00:07,279 --> 00:00:08,950
     43|just roll out of bed, then I go straight
     44|to Berries. And one thing about me is I
     45|
     46|10
     47|00:00:08,950 --> 00:00:08,960
     48|to Berries. And one thing about me is I
     49| 
     50|
     51|11
     52|00:00:08,960 --> 00:00:10,709
     53|to Berries. And one thing about me is I
     54|literally got my house specifically
     55|
     56|12
     57|00:00:10,709 --> 00:00:10,719
     58|literally got my house specifically
     59| 
     60|
     61|13
     62|00:00:10,719 --> 00:00:12,310
     63|literally got my house specifically
     64|because it's 5 minutes away from Berries
     65|
     66|14
     67|00:00:12,310 --> 00:00:12,320
     68|because it's 5 minutes away from Berries
     69| 
     70|
     71|15
     72|00:00:12,320 --> 00:00:13,990
     73|because it's 5 minutes away from Berries
     74|and 5 minutes from the office. So I can
     75|
     76|16
     77|00:00:13,990 --> 00:00:14,000
     78|and 5 minutes from the office. So I can
     79| 
     80|
     81|17
     82|00:00:14,000 --> 00:00:15,430
     83|and 5 minutes from the office. So I can
     84|reduce my commute and I don't really
     85|
     86|18
     87|00:00:15,430 --> 00:00:15,440
     88|reduce my commute and I don't really
     89| 
     90|
     91|19
     92|00:00:15,440 --> 00:00:17,269
     93|reduce my commute and I don't really
     94|need a car, which is awesome. I shower
     95|
     96|20
     97|00:00:17,269 --> 00:00:17,279
     98|need a car, which is awesome. I shower
     99| 
    </file-view>
  20. Devin
    Thinking

    I see the issue. The SRT format has some entries with an empty line after the timing, and some entries have overlapping text. My Python script didn't handle the blank lines properly. Some entries have content on line 3 (after index, timing, blank), some have content on line 4. Also entries with very short duration (00:00:01,590 --> 00:00:01,600) contain the previous line. I need to re-parse the SRT properly. A proper SRT parser would: split entries by blank line, remove index and timing line, join remaining lines, remove HTML tags, and then deduplicate. Actually, the SRT is somewhat malformed but standard. Each entry has: - index line - timing line - (maybe blank line) - text line(s) - (maybe blank line) Because entries have a blank line at the start sometimes, my script skipped first two lines (index + timing) but the third line might be blank, and text might be on line 4. Also the text line is repeated in consecutive entries. I need to extract all text from each entry, then deduplicate at word/phrase level. Better approach: Use a proper SRT parser. Python has a library? Maybe not installed. I can write a regex parser. Or simpler: use `ffmpeg` or another tool. Actually, yt-dlp has `--print` but not useful. I can use `pysrt`? Probably not installed. Let me write a proper parser. The SRT has entries like: 1 00:00:00,080 --> 00:00:01,590 My daily routine is essentially waking Where the text is after the blank line. So for each block (split by blank lines), I should: - split by \n - remove empty lines from start and end - if first line is digits, remove it - if second line has "-->", remove it - the rest is text Then deduplicate overlapping phrases. YouTube auto-captions often have karaoke-style SRT where each new entry contains all text shown so far, with new words appended. So entries 1: "My daily routine is essentially waking", 3: "My daily routine is essentially waking\nup between like 5:30 a.m. to 6 o'clock". Actually entry 3 has both old and new line. Entry 4 has only "up between...", entry 5 has old line "up between..." + new line "a.m. every day...". So the pattern is: odd entries (1,3,5...) show current phrase + new phrase? Wait: - Entry 1: only "My daily routine is essentially waking" (duration ~1.5s) - Entry 2: only "My daily routine is essentially waking" (duration 0.01s) - Entry 3: "My daily routine is essentially waking" + "up between like 5:30 a.m. to 6 o'clock" - Entry 4: only "up between..." (0.01s) - Entry 5: "up between..." + "a.m. every day. And I quite literally" So the pattern is: entries with longer durations contain the previous line and the new line. Entries with 0.01s durations contain just the previous line (transition). To extract proper transcript, I could: - For each entry, collect all text lines. - Then process in order, tracking previous text, and only emit new words. - Or use ffmpeg to convert SRT to plain text. But the result may still be duplicated. Actually, a simpler robust method: since the video has a full transcription link at eomag.io, but we should use auto-generated captions. Alternatively, I can use yt-dlp to get the `json3` subtitle format and parse that. Or better, yt-dlp can extract the transcript to a single string via `--print` with subtitle? Not exactly. Wait, I can use yt-dlp to get the `srv1` or `json3` format which has each caption event with start time and text. The `json3` is likely cleaner. Let me download the `json3` format and parse it.

    exec(command: cd /tmp && yt-dlp --write-auto-subs --sk…)
    {
      "command": "cd /tmp && yt-dlp --write-auto-subs --skip-download --sub-langs en --sub-format json3 -o \"%(id)s.%(ext)s\" \"https://www.youtube.com/watch?v=Do_LjDsisIE\""
    }
    In 1,316Out 946Cache 31,360TTFT 909mstool_calls
  21. OutputOutput from command in shell a4cd2a:
    Output from command in shell a4cd2a:
    WARNING: Your yt-dlp version (2025.12.08) is older than 90 days!
             It is strongly recommended to always use the latest version.
             You installed yt-dlp with pip or using the wheel from PyPi; Use that to update.
             To suppress this warning, add --no-update to your command/config.
    [youtube] Extracting URL: https://www.youtube.com/watch?v=Do_LjDsisIE
    [youtube] Do_LjDsisIE: Downloading webpage
    [youtube] Do_LjDsisIE: Downloading tv client config
    [youtube] Do_LjDsisIE: Downloading player dd53c628-main
    [youtube] Do_LjDsisIE: Downloading tv player API JSON
    [youtube] Do_LjDsisIE: Downloading android sdkless player API JSON
    [youtube] [jsc:deno] Solving JS challenges using deno
    WARNING: [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
    WARNING: [youtube] Do_LjDsisIE: 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
    [info] Do_LjDsisIE: Downloading subtitles: en
    [info] Do_LjDsisIE: Downloading 1 format(s): 139
    [info] Writing video subtitles to: Do_LjDsisIE.en.json3
    [download] Destination: Do_LjDsisIE.en.json3
    
    [download]    1.00KiB at  Unknown B/s (00:00:00)
    [download]    3.00KiB at  Unknown B/s (00:00:00)
    [download]    7.00KiB at    6.29MiB/s (00:00:00)
    [download]   15.00KiB at    3.95MiB/s (00:00:00)
    [download]   31.00KiB at    2.45MiB/s (00:00:00)
    [download]   63.00KiB at    2.60MiB/s (00:00:00)
    [download]  127.00KiB at    2.83MiB/s (00:00:00)
    [download]  255.00KiB at    3.81MiB/s (00:00:00)
    [download]  438.48KiB at    4.71MiB/s (00:00:00)
    [download] 100% of  438.48KiB in 00:00:00 at 2.04MiB/s
    
    
    Exit code: 0
  22. Devin
    Thinking

    Good, json3 downloaded. Let me parse it.

    read(file_path: /tmp/Do_LjDsisIE.en.json3, offset: 1, limit: 200)
    {
      "file_path": "/tmp/Do_LjDsisIE.en.json3",
      "offset": 1,
      "limit": 200
    }
    In 1,705Out 51Cache 32,640TTFT 737mstool_calls
  23. Output<file-view path="/tmp/Do_LjDsisIE.en.json3" start_line="1" end_line="200" total_lines="24246">
    <file-view path="/tmp/Do_LjDsisIE.en.json3" start_line="1" end_line="200" total_lines="24246">
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    </file-view>
  24. Devin
    Thinking

    The json3 format has events with segments. Each event is a word or phrase with timing. To get the transcript, I should concatenate all the `utf8` fields in order, respecting `aAppend`? Actually `aAppend: 1` indicates that the event appends to the current caption window (adds a newline). The segments within an event are the words. So I can just concatenate all `utf8` values from all events (skipping events with only newline) to get the full transcript. But I need to be careful: events with `aAppend: 1` and `\n` are just newlines, they don't contain words. So I can iterate events and for each, iterate segs and append `utf8` if it's not just `\n`. Actually, the events seem to be per phrase or word. Concatenating all `utf8` should give a continuous transcript with line breaks where `\n` appears. Since words already have leading spaces (e.g., " daily", " routine"), concatenating will produce readable text. Some events have `\n` only, which adds line breaks. Some events may have actual newlines. I'll concatenate all `utf8` values and then clean up extra spaces. Let me write a Python script to parse json3 and output clean transcript.

    exec(command: cd /tmp && python3 -c " import json, re …)
    {
      "command": "cd /tmp && python3 -c \"\nimport json, re\nwith open('Do_LjDsisIE.en.json3') as f:\n    data = json.load(f)\nout = []\nfor ev in data.get('events', []):\n    for seg in ev.get('segs', []):\n        txt = seg.get('utf8', '')\n        if txt is None:\n            continue\n        out.append(txt)\ntext = ''.join(out)\n# Replace literal newlines and multiple spaces\ntext = re.sub(r'  +', ' ', text)\ntext = re.sub(r'\\n+', '\\n', text)\n# Wrap long lines? Just output as-is.\nwith open('Do_LjDsisIE.clean.txt', 'w') as f:\n    f.write(text)\nprint(len(text))\n\" && wc -l /tmp/Do_LjDsisIE.clean.txt && wc -c /tmp/Do_LjDsisIE.clean.txt"
    }
    In 2,134Out 500Cache 34,304TTFT 720mstool_calls
  25. OutputOutput from command in shell 4d8c2c:
    Output from command in shell 4d8c2c:
    22596
         594 /tmp/Do_LjDsisIE.clean.txt
       22596 /tmp/Do_LjDsisIE.clean.txt
    
    
    Exit code: 0
  26. Devin
    Thinking

    Good, 22,596 chars, 594 lines. Much cleaner. Let me read it.

    read(file_path: /tmp/Do_LjDsisIE.clean.txt, offset: 1, limit: 200)
    {
      "file_path": "/tmp/Do_LjDsisIE.clean.txt",
      "offset": 1,
      "limit": 200
    }
    In 654Out 59Cache 36,352TTFT 801mstool_calls
  27. Output<file-view path="/tmp/Do_LjDsisIE.clean.txt" start_line="1" end_line="200" total_lines="595">
    <file-view path="/tmp/Do_LjDsisIE.clean.txt" start_line="1" end_line="200" total_lines="595">
      1|My daily routine is essentially waking
      2|up between like 5:30 a.m. to 6 o'clock
      3|a.m. every day. And I quite literally
      4|just roll out of bed, then I go straight
      5|to Berries. And one thing about me is I
      6|literally got my house specifically
      7|because it's 5 minutes away from Berries
      8|and 5 minutes from the office. So I can
      9|reduce my commute and I don't really
     10|need a car, which is awesome. I shower
     11|and I hit work. Afterwards, I'm usually
     12|just in like backtoback meetings. I try
     13|to sometimes block off time because
     14|[music] like I need to like be in Figma
     15|and like actually get feedback on design
     16|because I'm usually working until maybe
     17|like 8 o'clock, nine o'clock. I'll get
     18|dinner, then I'll go back to work, and
     19|then um I'll fall asleep sometime
     20|between like 11:00 p.m. to 1:00 a.m.
     21|depending on the day, and then repeat.
     22|You know, in YC, they tell you you
     23|should only be doing three things, which
     24|is like working out, talking to
     25|customers, and building. I see like why
     26|working out is so important because
     27|[music] it puts you on a schedule, tests
     28|your discipline. like no matter how
     29|shitty I'm feeling, I will still get up
     30|and go out and work out because I know
     31|I'm going to feel better and have more
     32|energy to be better at my job the rest
     33|of the day. So why my life looks like it
     34|is today, but like don't sit still
     35|essentially. I don't waste time
     36|scrolling [music] through Tik Tok,
     37|watching TV, watching movies, etc. And I
     38|think by not sitting still, I've been
     39|able to really be efficient with my time
     40|and [music] invest it in things that
     41|make me happy.
     42|Hi, I'm Lucy Glow. I am the founder of
     43|Scale AI and passes back in capital
     44|Capital and HFZ. I angel invest. I've
     45|invested in over a 100 different
     46|companies. Um, and I'm excited to chat
     47|with you guys today.
     48|I think in terms of like how it's
     49|potentially changed my life. Um, my DMs
     50|are popping. [laughter]
     51|Not life-changing, but it's been cool. I
     52|mean, I think overall probably just like
     53|what makes me happy. Um, everyone's
     54|like, "Okay, cool. Like what are you
     55|buying with your money, etc." But like
     56|at the end of the day like what makes me
     57|happy is like what drives every single
     58|thing I do. Um whether it's like you
     59|know building passes because it makes me
     60|happy because it provides challenges in
     61|my life but I also get to like you know
     62|change people's lives. So at passes were
     63|building infrastructure for creators to
     64|monetize their brand. So I think when
     65|you look at the largest creators they
     66|really are like unicorns. For example
     67|Kim Kardashian built skims. Logan Paul
     68|built Prime. Mr. Beast with Fastables.
     69|I'm excited for us to eventually build a
     70|[music] unicorn creator.
     71|I think I got suspended from
     72|kindergarten to be quite honest. I think
     73|it came from like me telling the teacher
     74|that what we were learning was dumb. But
     75|if you think about it, like my parents
     76|were trying to teach me like I was going
     77|to like abacus competitions like
     78|multiplication, division, etc. back in
     79|like kindergarten. And in school they
     80|were literally like here's the alphabet,
     81|right? Um, and I was just very bored as
     82|a child. So, I was always trying to
     83|figure out like different things to do.
     84|I was like learning stuff on my own.
     85|Specifically, like the stuff I learned
     86|was to like make money. But eventually,
     87|I discovered my love for product and I
     88|was like, "Oh, this is actually really
     89|fun." Um, but you could see glimpses of
     90|it where like, you know, I tried to
     91|start my own virtual pet site after
     92|being addicted to Neopets. I was playing
     93|a lot of online video games. I was
     94|figuring out how to start my own online
     95|arcade game website. I mean, I think
     96|what really shaped how I'm an
     97|entrepreneur today is that through like
     98|the early stages of me like making
     99|money, my parents found all of my hidden
    100|cash and would take it away. So then I
    101|was like, I have to like figure out how
    102|they don't take away cash. PayPal had
    103|come out and then you could just go like
    104|buy a Visa debit card, open up a PayPal
    105|account and that's when I started
    106|learning how to make money on the
    107|internet and then that's when I got
    108|curious about building my own websites
    109|and then like that kind of led to
    110|hackathons and then I would say like the
    111|turning point of like real
    112|entrepreneurship and like wanting to
    113|build lasting companies like more than
    114|just bots, right? Um was at these
    115|hackathons where suddenly I was exposed
    116|to startup world and I was like wow I
    117|can build apps that like millions of
    118|people use this is really really cool.
    119|Both my parents are absolutely
    120|brilliant. They're both technical. I
    121|think it was interesting because they
    122|actually really discouraged me from
    123|pursuing a technical field because they
    124|kind of imagined like my place as a
    125|woman was to get married and pop out
    126|grandchildren, but they still wanted me
    127|in an intelligent environment to find an
    128|intelligent partner. I think I ended up
    129|pursuing it almost by accident. Um, I
    130|thought I was going to be a chemical
    131|engineer because I was like like
    132|chemistry was my favorite subject. like
    133|I loved AP chemistry and I was very good
    134|at it. But when I I ended up on this
    135|website, College Confidential, like I
    136|need help with my college applications
    137|and um there's this random stranger who
    138|literally looked at my extracurriculars
    139|and it was like you're an idiot. If you
    140|want to get into best college, just
    141|apply for computer science and I was
    142|like, I guess that makes sense. So
    143|that's actually like kind of how I just
    144|landed in computer science. My college
    145|days are like I was going to a lot of
    146|hackathons. So [music] like you know
    147|every few weeks M hacks hack MIT quite
    148|literally every hackathon I like was
    149|[music] like I'm going to go to and then
    150|I made a lot of friends and I say this
    151|because like I might be biased but um
    152|like when I was first hiring like the
    153|first people I went to were the friends
    154|I made in [music] college and they
    155|didn't even ask any questions which is
    156|concerning. They definitely should have
    157|asked how much their equity [music] was
    158|worth, but um they were just like,
    159|"Yeah, like we'd love to come work with
    160|you." YOLO. Right. And that was cuz
    161|there was that trust that was gained in
    162|[music] college. When you're building
    163|your next company, um your network is
    164|your net worth and like you're going to
    165|be able to hire the friends that you
    166|made in college and you know they're
    167|good.
    168|I I think if I didn't have a rebellious
    169|nature, I probably would have been the
    170|good girl that my parents wanted me to
    171|be and stayed in college, right? And
    172|like I knew I was hurting them when
    173|going to um do this, but I think that
    174|like part of the rebellious nature was I
    175|want to prove you wrong and show you
    176|that I can forge my own path and I will
    177|be successful without college. Um so I'm
    178|sorry I'm hurting you right now, but
    179|like it'll be worth it at the end of the
    180|day. Um I don't think I ever doubted it.
    181|And what helped me commit was just
    182|[music] thinking like this is not a risk
    183|at all. Um like college is always going
    184|to be there versus this is a once in
    185|a-lifetime opportunity and [music] I'm
    186|going to be learning a lot. I feel like
    187|I'm not learning practical skills in
    188|college and everything that I've learned
    189|was through [music] like hackathons,
    190|right? So, if I leave college and I dive
    191|myself in the startup world, I am going
    192|to learn significantly more and be able
    193|to like actually build [music] products
    194|that millions of people use versus
    195|learning theoretical skills that like
    196|I'm probably not going to use in my job.
    197|Um, so I was pretty like dead set on it
    198|early on. [music] I think my conviction
    199|was to optimize learning. So, I've
    200|always like that's been my number one
    </file-view>
  28. Devin
    Thinking

    Good, now I have a clean transcript. I need to read the rest to understand the full video. Let me continue reading.

    read(file_path: /tmp/Do_LjDsisIE.clean.txt, offset: 200, limit: 200)
    {
      "file_path": "/tmp/Do_LjDsisIE.clean.txt",
      "offset": 200,
      "limit": 200
    }
    In 2,509Out 65Cache 36,992TTFT 834mstool_calls
  29. Output<file-view path="/tmp/Do_LjDsisIE.clean.txt" start_line="200" end_line="399" total_lines="595">
    <file-view path="/tmp/Do_LjDsisIE.clean.txt" start_line="200" end_line="399" total_lines="595">
    200|always like that's been my number one
    201|rule in life. Like even if you are
    202|leaving something, it feels like a risk.
    203|It's not that much of a risk if you're
    204|optimizing learning because your
    205|knowledge is always going to be worth
    206|something. And the more knowledge you
    207|gain, the more valuable you are later on
    208|in life. I think the reaction I got from
    209|most people was, "You're a
    210|idiot." From my parents to my friends to
    211|like people who didn't know me. Um
    212|people were just like, [music] "Yo, like
    213|what are you doing?" A lot of it stem
    214|from the fact that I actually only had
    215|four CS classes left to graduate, like
    216|one year left, right? So I think the
    217|like risk mental calculation didn't
    218|really make sense [music] for others but
    219|for me I was like what am I I'm losing
    220|time right but like what's the worst
    221|case scenario I take a job what's the
    222|second worst case scenario I go back to
    223|college I chose like to [music] optimize
    224|for that learning and like optimize for
    225|being in a network of like extremely
    226|ambitious intelligent people
    227|as a founder you already know ideas are
    228|the easy part it's the execution
    229|actually building the product that slows
    230|everything down. That's where Lovable
    231|comes in. It's not just an AI tool. It's
    232|your ondemand engineering team. Simply
    233|describe your idea. Lovable then builds
    234|a full front-end, backend, and database,
    235|so you can launch real productionready
    236|software without writing code. It's
    237|already powering over a 100,000 new
    238|products a day, helping 2.5 million
    239|builders turn ideas into software just
    240|by describing what they want. No devs,
    241|no delays, no excuses.
    242|They're launching in weeks, not months.
    243|And guess [music] what? These teams are
    244|still tiny. In fact, team EO is also
    245|using Lovable to build their upcoming
    246|EOS school platform, and we're loving
    247|it. If you're a non-technical founder or
    248|just want to build without bottlenecks,
    249|try Lovable today for free. Use the
    250|promo code EO2YT to get 20% off your
    251|first purchase of the Lovable Pro plan.
    252|I think at SNAP I really learned how to
    253|think much bigger. Um, like when I first
    254|joined Snap, I didn't realize like kind
    255|of the vision Evan had for Snap. And it
    256|was very inspiring seeing like his ideas
    257|on how he would eventually compete with
    258|Amazon, um, compete with Google, etc.,
    259|which I think like no one even to this
    260|day thinks about Snap competing with
    261|Amazon or Google. But to see him think
    262|so innovatively about product and not
    263|care about like that AB test portion of
    264|things taught me just a lot about like
    265|the importance of being open-minded and
    266|like product driven as well and really
    267|about like perfection. Um I the lesson I
    268|really took away from that is like just
    269|get to 90%. You don't need to spend 3
    270|years going back and forth on a design.
    271|Um, but also like I remember there was
    272|like that zoom out the snap mapaps
    273|feature that like no one at the company
    274|wanted to build because everyone thought
    275|it was so dumb and he was just like kept
    276|on insisting it and it turns out like he
    277|was right at the end of the day, right?
    278|Um, it ended up like being a very
    279|natural UX that people love and use.
    280|That really almost tells you also that
    281|like people don't really know what they
    282|want in the consumer field, right? Like
    283|it might sound like the dumbest thing,
    284|but you really just have to like kind of
    285|give it a go. Um hence like why he was
    286|probably just like you know just
    287|thinking eventually like don't think
    288|about like all this like research
    289|feedback etc because um if he did that
    290|like that would have never been shipped
    291|but like once it got into the hands of
    292|people um they realized like oh I
    293|actually I wanted this but no one could
    294|have told [music] you that. When you
    295|come up with a product spend like 2
    296|weeks designing it and then ship it and
    297|see how it does and [music] if there's
    298|traction then go and iterate and improve
    299|on it. But people will use products they
    300|want to use even if it's super buggy and
    301|the UX is shitty for the most part. So
    302|um it's better to ship that like 90%
    303|with like no user research and then
    304|double down on the product like if it
    305|gets traction versus wasting like months
    306|doing all this research etc and shipping
    307|it and like it might [music] fall flat.
    308|I mean I would say it's like risk
    309|benefit analysis and then am I learning
    310|right? So for example like when I was at
    311|Snap I was giving up a few million
    312|dollars but I also was like that's not
    313|life-changing for me. So, I would rather
    314|optimize learning and also have the
    315|opportunity to make life-changing money.
    316|And if I don't make that life-changing
    317|money, um, at least I learned a bunch of
    318|like new knowledge and I'll be able to
    319|like hop to my next job and make more.
    320|And all I'm losing is like what, one to
    321|two years because that's the amount of
    322|time it would take to like figure out
    323|like am I going to like be able to build
    324|this company unless you're crazy. Like I
    325|have friends that like, you know,
    326|pivoted for like six years and then
    327|finally found something which like those
    328|are obviously the founders I love
    329|investing in. If I were making like a
    330|hund00 million at SNAP, like would I
    331|have made that jump? Like I'm going to
    332|be realistic. Probably not. I probably
    333|been like that's life-changing money. It
    334|is worth staying four years for $100
    335|million before moving on. Um so, um I
    336|would say like those two things are like
    337|my criteria when choosing like when to
    338|make moves.
    339|We were both working at Kora and like I
    340|think it was literally over lunch we
    341|were like, "Huh, should we start a
    342|company together one day?" I was like,
    343|"Yeah, let's do it." And then over like
    344|winter break, we started iterating on
    345|ideas. So um we came up with a horrible
    346|one. Class passed for clubbing and you
    347|know it got traction but you know it's
    348|bad when it's only VCs that are
    349|subscribing to your service. And then we
    350|were like okay let's go do something
    351|more meaningful. So um we pivoted to Ava
    352|which is um a healthcare app that we
    353|help you find like the best doctors for
    354|specific procedures. So it's like oh you
    355|need a root canal. Here is like the best
    356|person to give you a root canal. Um,
    357|also a terrible idea, but the idea ended
    358|up getting us into YC. Um, I think
    359|mostly because I knew we were going to
    360|get into YC [music] because I was like
    361|talking to um, YC partners before the
    362|interview and after the interview and
    363|they were just like, "Oh yeah, like
    364|you'll get in because like [music] you
    365|guys are both super smart. You're
    366|obviously at SNAP and a teal fellow and
    367|he is like you know MIT." So yeah, we
    368|uh, entered YC [music] and then the idea
    369|was terrible. So we ended up pivoting to
    370|scale.
    371|I think like generalized my most painful
    372|failures have been trusting people that
    373|I shouldn't have trusted which is just
    374|not great. Um especially in the
    375|workplace for example like you might
    376|trust an employee and then like when
    377|they underperform and you need to fire
    378|them they decide to try to extort you.
    379|It's just like you need to make sure
    380|that like be like you don't want people
    381|where like you constantly feel like
    382|they're going to like stab you in the
    383|back on figuring out on who to [music]
    384|trust. This is very cliche but go with
    385|your gut. Um, but outside of going with
    386|your gut, like you should definitely
    387|just do like reference checks on both
    388|like [music] friends, like people that
    389|know them or like people that aren't
    390|even friends, you know, like
    391|acquaintances cuz like you're probably
    392|going to get a more honest answer from
    393|acquaintances. [music] I think the best
    394|way to build trust again is like be
    395|extremely authentic. But at the end of
    396|the day, if like [music] trust is
    397|broken, like sometimes it can't be
    398|repaired and that's when you know that
    399|like you might just need to separate and
    </file-view>
  30. Devin
    Thinking

    I see lines 200-399. This covers her principles, time at Snap, leaving Snap, co-founding Scale AI, and failures/trust. I need to continue reading to line 595.

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    {
      "file_path": "/tmp/Do_LjDsisIE.clean.txt",
      "offset": 400,
      "limit": 200
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    In 2,453Out 80Cache 39,424TTFT 821mstool_calls
  31. Output<file-view path="/tmp/Do_LjDsisIE.clean.txt" start_line="400" end_line="595" total_lines="595">
    <file-view path="/tmp/Do_LjDsisIE.clean.txt" start_line="400" end_line="595" total_lines="595">
    400|maturity is understanding that.
    401|I think the team self motivates themsel
    402|but it's innovation when they see that
    403|like we can ship a product in two weeks
    404|and it takes a large company like that's
    405|stagnant like years to ship a product
    406|that's extremely exciting for the team
    407|especially when like you tie that
    408|product to actual revenue numbers and I
    409|think it's just constantly emphasizing
    410|that and I think that's why a lot of
    411|engineers choose to work at startups
    412|instead of larger companies because they
    413|feel like they have more impact. Impact
    414|is what motivates them because they're
    415|not going to get that same impact at a
    416|large company. leaders need to be doing
    417|IC work as well because the only way you
    418|can judge people on like their actual
    419|job is if you do the job yourself and
    420|like really it's all hands- on deck like
    421|nothing is too big to be done. So for
    422|example like everyone was on intercom
    423|when we got a pilot for like a new
    424|customer [music] quite literally we'd
    425|have a war room with like engineers like
    426|me etc. And like we'd all be labeling
    427|that data and making sure it's perfect.
    428|We really emphasize like nothing is
    429|below you. you're going to do what's
    430|best for the company. And if like your
    431|time is best being spent like helping
    432|close this deal, then like you're going
    433|to do that. Let's say you're running
    434|customer support and you're not doing
    435|the customer support tickets yourself,
    436|you're not going [music] to know if your
    437|customer support reps are answering them
    438|fast enough, giving the correct answers
    439|or like if you know if it's not obvious
    440|answer like should they know this the
    441|answer to this or is it like something
    442|more obscure? And you really only get
    443|that like muscle if you you do the work.
    444|I think the number one thing I look for
    445|is like intelligence and hard work
    446|because you can be the smartest person
    447|ever, but if you don't work hard, you're
    448|going to not impact a company. [music]
    449|Hard work is extremely important to me,
    450|especially cuz like with creators, we're
    451|very creator first. That's our culture.
    452|Which means that like because creators
    453|can be 24/7. We need to be 24/7. [music]
    454|Like if a creator hits me up at like 2
    455|am like about a bug, I'm proud to say
    456|that I can call my engineers like and
    457|[music] one of them will pick up and fix
    458|that bug at 2 am, which is incredibly
    459|important in an industry like this. Um,
    460|and [music] I look for people that are
    461|willing to like go the extra mile.
    462|That's not saying like you have to work
    463|every weekend, but like if needed, like
    464|are you going to show up? Yeah. I mean,
    465|I think as a leader, one of my like
    466|biggest values in life is just kindness.
    467|It's like you're wanting the best for
    468|everyone, right? Like if [music] someone
    469|wins, awesome. Like if I lose an amazing
    470|engineer, um I still want to help
    471|[music] them like succeed. If I lose a
    472|crappy engineer, I still want to help
    473|them succeed. For me, I'm like I think
    474|I'm very giving as like both a person
    475|and a leader. So it's like, okay, like
    476|you know, I give you chances. I like try
    477|to you're not working out in your role,
    478|but I think that you're hardworking and
    479|smart. I try to find like a role that
    480|like you [music] might be better suited
    481|for.
    482|So I would say it was a mix of things.
    483|Um, I think what inspired me was
    484|actually living with all the founders.
    485|Like their energy definitely grew on me
    486|and I wanted these founders to start
    487|bases, but no one would do it. And
    488|around the same time, I had a friend who
    489|[music] asked me to be CEO of his AI
    490|company. And I kind of sat on it like I
    491|kind of got excited, right? I was like,
    492|"Oh my god, like I feel ready to be a
    493|founder again." Uh cuz I think a lot of
    494|the reason why I wasn't I didn't do it
    495|sooner was cuz I was scared of failure.
    496|But I finally got to a point where I was
    497|like, you know what? If I fail,
    498|whatever, it doesn't matter. like my
    499|life is going to be fine. most people
    500|don't build two successful startups like
    501|don't be scared of failure and just go
    502|and [music] do the thing right and I
    503|didn't feel like doing an AI company
    504|again to be quite honest I was like I
    505|did B2B enterprise I know how it works
    506|but I would rather like if I'm doing a
    507|second company and like risking failure
    508|at least have fun doing it and I had
    509|always been attracted consumer you can
    510|see this with like a bunch of different
    511|like random apps I had made Snap was
    512|like one of the most fun experiences of
    513|my life so uh I like just knew I wanted
    514|to do consumer and I had been sitting on
    515|this idea idea for a little bit. So, I
    516|was like, "Okay, like let's see some
    517|competitors out there." And really, you
    518|could only find like Patreon. [music]
    519|They've existed for a decade and I saw
    520|like an opportunity in the market. Um, I
    521|was friends with a lot of creators. Um,
    522|I lightly asked my creator friends like,
    523|"Hey, if I were to build this, would
    524|they you use it?" So, you know, like
    525|selling before building, they're like,
    526|"Absolutely." I was like, "Great." And
    527|then I just talked to my LPs and they
    528|were like, "Here is money. Um, [music]
    529|go build." So, um, it all happened
    530|really quickly. like I would say within
    531|the span of like 24 hours of my friend
    532|asking [music] me to like be CEO of his
    533|AI company and me having like
    534|commitments. So the future I see for the
    535|creator economy I think that like AI is
    536|really going to help creators be [music]
    537|co-pilots. I see more creators becoming
    538|entrepreneurs. More kids want to be
    539|creators than ever before. So I think
    540|the creator economy is going to grow but
    541|also everyone like creator marketing
    542|spend is going up in like pretty much
    543|every country. And I think this is due
    544|to the fact people are realizing like
    545|their audience and like the like reach
    546|you get is much stronger spending it on
    547|creator marketing versus on ads. So,
    548|[music] I think because of this, um,
    549|we're going to see more creators work so
    550|closely with brands that they're
    551|essentially considered co-founders or
    552|start their own brands, which is why I
    553|think like unicorn creators are possible
    554|and like we're excited to help them
    555|either get access the best brands and
    556|like get that equity for long-term
    557|generational wealth or if it makes sense
    558|to like help them build that next
    559|product.
    560|I think if 20-year-old me could go back
    561|in time, I would stop complaining about
    562|some of the people I work with and just
    563|start really like getting to know them
    564|better and uplifting them. Why do I
    565|think so? Um, I think that like in
    566|general most companies are very gossipy,
    567|right? So, if you're complaining about
    568|people, like it just gets around and
    569|then it drives them away obviously
    570|because they hear about complaints like
    571|no one wants to be talked badly about.
    572|And I think like the best work
    573|environments and the most positive ones
    574|are when you are like everyone's
    575|cheerleader. So, uh I think I would have
    576|like spent more effort like seeing
    577|[music] the good in people. This is
    578|going to sound very LA, but I feel like
    579|I have like manifested everything in my
    580|life. And because I'm like focus so much
    581|on positive energy, I feel like as long
    582|as I like put in that hard work to like
    583|bring my dreams to life and like
    584|surround myself with positive people,
    585|like anything is possible. so cheesy,
    586|but I really like do believe that's
    587|true. And like I have like the best
    588|friends in the world that, you know, are
    589|constantly around me and encouraging me
    590|and like really doing whatever they can
    591|to help. Like they'll go out of my way.
    592|They're like truly incredible. And I
    593|think that like when I have like such
    594|positive people around me, like I only
    595|look at like upside
    </file-view>
  32. Devin
    Thinking

    I have read the full transcript. Now I can summarize it. The user just pasted a YouTube link, so I should provide a summary. The rule says to use yt-dlp to summarize the video with autogenerated captions. I've done that. I should also not name the session, but the session naming is probably automatic? The rule says "if asked to summarize a YouTube video, do not name the session until after reading and understanding the full YouTube video transcript". Since I've now read the transcript, I can summarize. I don't need to explicitly name the session; that's a system thing. I should just provide a concise summary. The video is an interview with Lucy Guo, founder of Scale AI and Passes, discussing her journey, mindset, and principles. The title: "The One Discipline That Made Me a Billionaire by Age 30". The key themes are: - Daily routine: wake up 5:30-6 AM, workout at Berries, work long hours, don't sit still, avoid time-wasters. - Mindset: optimize for learning, don't be the "good girl", take risks, rebellious nature. - Dropout story: left college with 4 CS classes left because she felt she learned more in the startup world. - Snap experience: learned to think bigger, product-driven, ship to 90%, consumer products. - Scale AI: co-founded with Alexandr Wang after working at Quora/Snap, pivoted from ClassPass for clubbing to Ava (healthcare) to Scale. - Passes: creator economy infrastructure, building a unicorn creator, AI as co-pilot. - Most painful failure: trusting wrong people, firing/extortion. - Principles: 1. Impact drives people (engineers choose startups for impact) 2. Do not avoid IC work (leaders do the job to judge and help) 3. Choose grit over genius (intelligence + hard work) 4. Be kind / everyone's cheerleader - Don't build if you're not obsessed. - Be everyone's cheerleader / positive energy. Also there was an ad for Lovable (EO partner). I should present a summary. Keep it concise but cover main points. Use the title from the metadata and key sections. The user didn't ask a question, just pasted the link. So I should summarize.

    Here is a summary of the video based on the auto-generated captions:

    The One Discipline That Made Me a Billionaire by Age 30 | Lucy Guo, Passes
    Interview with Lucy Guo — co-founder of Scale AI, founder & CEO of Passes, angel investor.


    Core argument

    Lucy Guo attributes much of her success to one discipline: relentless motion — never "sitting still." She structures her life to maximize productive time and learning, avoids passive entertainment, and treats physical fitness as the anchor that enforces daily discipline.

    Key points

    Daily routine

    • Wakes up around 5:30–6 a.m., works out at Berries, works late (often until 8–9 p.m., with dinner and then more work), sleeps around 11 p.m.–1 a.m.
    • Chose her house to be 5 minutes from the gym and office to eliminate commute time.
    • Doesn’t scroll TikTok, watch TV, or watch movies — invests time in things that make her happy and move her forward.

    Mindset & risk-taking

    • Her #1 rule in life: optimize for learning. Even if a move feels risky, the knowledge gained makes it worthwhile.
    • Dropped out of Carnegie Mellon with only four CS classes left because she felt college taught theory while hackathons and startups taught practical, high-leverage skills.
    • Rebelliousness drove her to prove she could succeed without following her parents’ expected path.

    From Snap to Scale AI

    • At Snap she learned from Evan Spiegel to think much bigger, be product-driven, and ship fast to ~90% rather than over-researching.
    • Co-founded Scale AI with Alexandr Wang after they met at Quora; their YC idea pivoted from a clubbing “ClassPass” to a healthcare marketplace (Ava) and finally to Scale.
    • Cites trust as her most painful failure area — hiring the wrong people and dealing with extortion attempts when firing underperformers.

    Building Passes

    • After Scale, she wanted to do consumer again. Passes builds monetization infrastructure for creators, enabling them to become “unicorn creators” who launch their own brands.
    • Believes AI will act as a creator co-pilot and that creator marketing spend will keep rising, making creator-owned businesses a major opportunity.

    Four leadership principles

    1. Impact drives people — tie work to revenue and real outcomes; startups win because individuals can have outsized impact.
    2. Don’t avoid IC work — leaders must do the job themselves to judge performance and set the tone that nothing is beneath anyone.
    3. Choose grit over genius — intelligence matters, but hard work and willingness to go the extra mile matter more.
    4. Be kind — be everyone’s cheerleader, look for the good in people, and help them succeed even if they leave.

    Final message

    • If she could advise her 20-year-old self, she’d stop complaining about coworkers and instead uplift them.
    • She believes strongly in positive energy, manifestation, and surrounding herself with supportive people who help bring her ideas to life.
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