Devin Sessions

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":"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"},{"name":"playwright"}]} 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.

## 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: Saturday, 2026-07-04 </system_info>

<rules type="always-on"> <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 - 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` ## 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> <rule name="global_rules" path="/Users/root1/.codeium/windsurf/memories/global_rules.md"> </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-email-service**: Send and receive transactional emails with Cloudflare Email Service (Email Sending + Email Routing). Use when building email sending (Workers binding or REST API), email routing, Agents SDK email handling, or integrating email into any app — Workers, Node.js, Python, Go, etc. Also use for email deliverability, SPF/DKIM/DMARC, wrangler email setup, MCP email tools, or when a coding agent needs to send emails. Even for simple requests like "add email to my Worker" — this skill has critical config details. (source: /Users/root1/.agents/skills/cloudflare-email-service/SKILL.md) - **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) - **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/.agents/skills/workers-best-practices/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) - **sandbox-sdk**: Build sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/sandbox-sdk/SKILL.md) - **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/.agents/skills/web-perf/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/.agents/skills/cloudflare-one/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) - **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) - **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) - **cloudflare**: Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/cloudflare/SKILL.md) - **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) - **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/.agents/skills/cloudflare-one-migrations/SKILL.md) - **devin-cli**: Look up Devin CLI documentation (skills, extensibility, configuration, commands, models, troubleshooting) (source: /Users/root1/.local/share/devin/cli/_versions/3000.1.23/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>

{ "command": "yt-dlp --write-auto-sub --sub-langs en --skip-download --sub-format json3 --print \"Writing captions to: %(subtitles.en%.3[json3]s)\" \"https://www.youtube.com/watch?v=d4vHsbRWKfk\"" }

Output from command in shell f3ff68: Usage: yt-dlp [OPTIONS] URL [URL...] yt-dlp: error: invalid video print template "Writing captions to: %(subtitles.en%.3[json3]s)": incomplete format Exit code: 2

Output from command in shell c609ff: 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=d4vHsbRWKfk [youtube] d4vHsbRWKfk: Downloading webpage [youtube] d4vHsbRWKfk: Downloading tv client config [youtube] d4vHsbRWKfk: Downloading player 4918c89a-main [youtube] d4vHsbRWKfk: Downloading tv player API JSON [youtube] d4vHsbRWKfk: 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] d4vHsbRWKfk: 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] d4vHsbRWKfk: Downloading subtitles: en [info] d4vHsbRWKfk: Downloading 1 format(s): 139-20 [info] Writing video subtitles to: Google Fitbit Air – 30 Days Later： An Honest Review [d4vHsbRWKfk].en.json3 [download] Destination: Google Fitbit Air – 30 Days Later： An Honest Review [d4vHsbRWKfk].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 Unknown B/s (00:00:00) [download] 15.00KiB at 2.94MiB/s (00:00:00) [download] 31.00KiB at 2.01MiB/s (00:00:00) [download] 63.00KiB at 1.77MiB/s (00:00:00) [download] 115.13KiB at 1.76MiB/s (00:00:00) [download] 100% of 115.13KiB in 00:00:00 at 586.96KiB/s Exit code: 0

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495| "tOffsetMs": 400 496| }, { 497| "utf8": " tennis", 498| "tOffsetMs": 480 499| }, { 500| "utf8": " court", 501| "tOffsetMs": 800 502| }, { 503| "utf8": " daily,", 504| "tOffsetMs": 1080 505| }, { 506| "utf8": " in", 507| "tOffsetMs": 1520 508| }, { 509| "utf8": " the", 510| "tOffsetMs": 1680 511| } ] 512| }, { 513| "tStartMs": 41190, 514| "dDurationMs": 2090, 515| "wWinId": 1, 516| "aAppend": 1, 517| "segs": [ { 518| "utf8": "\n" 519| } ] 520| }, { 521| "tStartMs": 41200, 522| "dDurationMs": 4320, 523| "wWinId": 1, 524| "segs": [ { 525| "utf8": "gym," 526| }, { 527| "utf8": " running", 528| "tOffsetMs": 520 529| }, { 530| "utf8": " through", 531| "tOffsetMs": 840 532| }, { 533| "utf8": " airports,", 534| "tOffsetMs": 1120 535| }, { 536| "utf8": " sitting", 537| "tOffsetMs": 1800 538| } ] 539| }, { 540| "tStartMs": 43270, 541| "dDurationMs": 2250, 542| "wWinId": 1, 543| "aAppend": 1, 544| "segs": [ { 545| "utf8": "\n" 546| } ] 547| }, { 548| "tStartMs": 43280, 549| "dDurationMs": 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{ 716| "utf8": " bonus", 717| "tOffsetMs": 520 718| }, { 719| "utf8": " feature,", 720| "tOffsetMs": 1160 721| }, { 722| "utf8": " it", 723| "tOffsetMs": 1720 724| }, { 725| "utf8": " has", 726| "tOffsetMs": 1960 727| }, { 728| "utf8": " to", 729| "tOffsetMs": 2240 730| }, { 731| "utf8": " be", 732| "tOffsetMs": 2360 733| } ] 734| }, { 735| "tStartMs": 57870, 736| "dDurationMs": 2210, 737| "wWinId": 1, 738| "aAppend": 1, 739| "segs": [ { 740| "utf8": "\n" 741| } ] 742| }, { 743| "tStartMs": 57880, 744| "dDurationMs": 5000, 745| "wWinId": 1, 746| "segs": [ { 747| "utf8": "everything." 748| }, { 749| "utf8": " I", 750| "tOffsetMs": 920 751| }, { 752| "utf8": " also", 753| "tOffsetMs": 1000 754| }, { 755| "utf8": " have", 756| "tOffsetMs": 1200 757| }, { 758| "utf8": " to", 759| "tOffsetMs": 1320 760| }, { 761| "utf8": " say", 762| "tOffsetMs": 1400 763| }, { 764| "utf8": " the", 765| "tOffsetMs": 1600 766| }, { 767| "utf8": " strap", 768| "tOffsetMs": 1720 769| } ] 770| }, { 771| 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1120 1094| }, { 1095| "utf8": " can", 1096| "tOffsetMs": 1200 1097| }, { 1098| "utf8": " still", 1099| "tOffsetMs": 1280 1100| } ] 1101| }, { 1102| "tStartMs": 82670, 1103| "dDurationMs": 2210, 1104| "wWinId": 1, 1105| "aAppend": 1, 1106| "segs": [ { 1107| "utf8": "\n" 1108| } ] 1109| }, { 1110| "tStartMs": 82680, 1111| "dDurationMs": 3520, 1112| "wWinId": 1, 1113| "segs": [ { 1114| "utf8": "feel" 1115| }, { 1116| "utf8": " it.", 1117| "tOffsetMs": 280 1118| }, { 1119| "utf8": " It's", 1120| "tOffsetMs": 800 1121| }, { 1122| "utf8": " been", 1123| "tOffsetMs": 960 1124| }, { 1125| "utf8": " soaking", </file-view>

{ "command": "python3 -c \"\nimport json\n\nwith open('Google Fitbit Air – 30 Days Later： An Honest Review [d4vHsbRWKfk].en.json3', 'r') as f:\n data = json.load(f)\n\ntext_segments = []\nfor event in data.get('events', []):\n if 'segs' in event:\n for seg in event['segs']:\n if 'utf8' in seg:\n text = seg['utf8']\n if text and text not in ['\\n', '>> [music]']:\n text_segments.append(text)\n\nfull_text = ' '.join(text_segments)\nprint(full_text[:5000]) # Print first 5000 chars to check\n\"" }

Output from command in shell 30c00e: So, I've worn the new Fitbit Air every day for a month through international airports, a week-long tennis camp in Bali, and intensive training sessions in tropical heat. So, after a month, here's my honest review. Is this a $99 novelty or actually an incredible fitness tracker without a subscription fee? >> Let's start with what I think is the Fitbit Air's biggest strength, the design. The thing completely disappears on your wrist more so than my Whoop, Apple Watch, and Oura Ring. During the month, I went everywhere with it, 5-6 hours on the tennis court daily, in the gym, running through airports, sitting in a sauna, and I genuinely forgot that I was wearing it. And for a screenless tracker, that's arguably the most important feature because if you're supposed to wear something 24/7, comfort isn't just a bonus feature, it has to be everything. I also have to say the strap mechanism is also significantly better than Whoop's. I've complained about Whoop's clasp for years because it constantly pops open when you're trying to put it on, and it's surprisingly frustrating for something you interact with every day. The Fitbit Air feels much cleaner and simpler by comparison, as you can see here. >> I'd say this has disappeared on my wrist, whereas the Whoop, you can still feel it. It's been soaking up the sweat, and you can really feel that sweatiness, whereas this, it just feels like it's not even there. Uh the Fitbit Air really does live up to its air name. >> I will say though, real quick, that the blue color here, because I've sweat so much in it, there is a bit of discoloration on the wristband. It looks kind of brownish. It's kind of nasty, actually, on the underside, but that's kind of to be expected, especially when you're, I guess, sweating for hours and hours, but it doesn't look too great. I would suggest getting a different color than the blue one here. The battery life is also pretty solid. I traveled for 10 days and only needed to charge it once at the end of the trip. Whoop, however, still wins here because I comfortably went the entire trip without even packing a charger and it still had more juice to give, but Fitbit's fast charging helps make up for it. Around 5 minutes on a charger gets you roughly a full day of use while a full charge takes only around 90 minutes. Now, how about the accuracy of its tracking and data? I really put this through its paces, you know, on court 5 to 6 hours a day for over a week and I have to say it's good, but it's not great. In real time, the heart rate tracking is neck and neck with the Whoop, but its processing of data is noticeably slower. As you can see in the screenshot, the Whoop was faster by 10 to 20 minutes in auto detecting the activity and processing it. There were also a few days at the training camp where I knew I was absolutely cooked. We'd been on court for a long time in the Bali heat and I felt completely exhausted. The Whoop reflected that a bit better than the Fitbit, I'd say. And there was one of those moments where you could feel the difference between a wellness tracker and performance focused tracker. The reason isn't actually Google's software, it's the hardware. Fitbit has made this tracker, you know, incredibly comfortable and thin, but there's a trade-off. The thinner you make a wearable, the harder it becomes to capture perfectly accurate heart rate during uh intense exercise. And that's especially noticeable in a sport like tennis. You're constantly sprinting, stopping, recovering, then exploding into the next point and your heart rate is moving all over the place. Those rapid changes are difficult for any wrist-based tracker to follow accurately and Whoop just simply did a better job keeping up. Now, if you're doing longer runs, though, cycling sessions or any activity where your heart rate stays relatively stable, the Fitbit Air performs much better. Step counting was also surprisingly accurate in my testing and the cardio load feature gives you a useful snapshot of whether you're building fitness or recovering throughout the week. It's not as sophisticated as Whoop's strain score, but hey, it's also free. And for most people, it's probably enough. Where this thing really impressed me over the month is sleep tracking. And I noticed that over the month it knows the difference between lying in bed an Exit code: 0

Output from command in shell 9d08de: So, I've worn the new Fitbit Air every day for a month through international airports, a week-long tennis camp in Bali, and intensive training sessions in tropical heat. So, after a month, here's my honest review. Is this a $99 novelty or actually an incredible fitness tracker without a subscription fee? >> Let's start with what I think is the Fitbit Air's biggest strength, the design. The thing completely disappears on your wrist more so than my Whoop, Apple Watch, and Oura Ring. During the month, I went everywhere with it, 5-6 hours on the tennis court daily, in the gym, running through airports, sitting in a sauna, and I genuinely forgot that I was wearing it. And for a screenless tracker, that's arguably the most important feature because if you're supposed to wear something 24/7, comfort isn't just a bonus feature, it has to be everything. I also have to say the strap mechanism is also significantly better than Whoop's. I've complained about Whoop's clasp for years because it constantly pops open when you're trying to put it on, and it's surprisingly frustrating for something you interact with every day. The Fitbit Air feels much cleaner and simpler by comparison, as you can see here. >> I'd say this has disappeared on my wrist, whereas the Whoop, you can still feel it. It's been soaking up the sweat, and you can really feel that sweatiness, whereas this, it just feels like it's not even there. Uh the Fitbit Air really does live up to its air name. >> I will say though, real quick, that the blue color here, because I've sweat so much in it, there is a bit of discoloration on the wristband. It looks kind of brownish. It's kind of nasty, actually, on the underside, but that's kind of to be expected, especially when you're, I guess, sweating for hours and hours, but it doesn't look too great. I would suggest getting a different color than the blue one here. The battery life is also pretty solid. I traveled for 10 days and only needed to charge it once at the end of the trip. Whoop, however, still wins here because I comfortably went the entire trip without even packing a charger and it still had more juice to give, but Fitbit's fast charging helps make up for it. Around 5 minutes on a charger gets you roughly a full day of use while a full charge takes only around 90 minutes. Now, how about the accuracy of its tracking and data? I really put this through its paces, you know, on court 5 to 6 hours a day for over a week and I have to say it's good, but it's not great. In real time, the heart rate tracking is neck and neck with the Whoop, but its processing of data is noticeably slower. As you can see in the screenshot, the Whoop was faster by 10 to 20 minutes in auto detecting the activity and processing it. There were also a few days at the training camp where I knew I was absolutely cooked. We'd been on court for a long time in the Bali heat and I felt completely exhausted. The Whoop reflected that a bit better than the Fitbit, I'd say. And there was one of those moments where you could feel the difference between a wellness tracker and performance focused tracker. The reason isn't actually Google's software, it's the hardware. Fitbit has made this tracker, you know, incredibly comfortable and thin, but there's a trade-off. The thinner you make a wearable, the harder it becomes to capture perfectly accurate heart rate during uh intense exercise. And that's especially noticeable in a sport like tennis. You're constantly sprinting, stopping, recovering, then exploding into the next point and your heart rate is moving all over the place. Those rapid changes are difficult for any wrist-based tracker to follow accurately and Whoop just simply did a better job keeping up. Now, if you're doing longer runs, though, cycling sessions or any activity where your heart rate stays relatively stable, the Fitbit Air performs much better. Step counting was also surprisingly accurate in my testing and the cardio load feature gives you a useful snapshot of whether you're building fitness or recovering throughout the week. It's not as sophisticated as Whoop's strain score, but hey, it's also free. And for most people, it's probably enough. Where this thing really impressed me over the month is sleep tracking. And I noticed that over the month it knows the difference between lying in bed and scrolling my phone and actually falling asleep. It catches naps accurately and it catches overnight wake-ups pretty well, too. On the first night of the camp, actually, I woke up at 3:00 a.m. sweating and feeling absolutely terrible. And the next morning, I was trying to convince myself I could still get on court. And looking back through the Fitbit data later, you could actually see the readiness score and recovering metrics dropping before I fully acknowledge how sick I was feeling. And all of this without a subscription fee is just kind of OP in this day and age. For context, Whoop charges, as you probably already know, $199 a year minimum and its entry-level tier doesn't even include skin temperature data. You have to upgrade to the peak or life tier for those. The Fitbit Air gives you everything you need out of the box for $99. And that value proposition is just hard to argue with. However, of course, there is always a trade-off and it's a big one in my opinion. The Google Health app right now, it's kind of rough. Core metrics are buried under layers of user interface. Workouts take up to 30 minutes to show up after you finish. And the AI health coach, powered by Gemini, fills your screen with paragraph-long essays you didn't really ask for. Nobody needs an AI pep talk for a 5-minute walk, for example. But Google clearly knows this and within days of launch, they dropped a public road map with over 39 fixes coming through the summer. They've already shipped a first major update fixing a bunch of things. Meanwhile, other apps like Whoop, Oura, 8 Sleep, they may have a fee attached to them, but the user experience is genuinely more enjoyable to use right now. I've been using Whoop for years now, and Whoop's three-pillar layout, the recovery, strain, sleep, is one of the cleanest wellness apps I've used yet. And the Google Health app will get there, I think, but it isn't there today. So, the app isn't great, but it is moving fast. So, after a month wearing this in all sorts of situations, this is how I think I'd frame it. The Fitbit Air is a $99 sleep and wellness tracker that happens to do some fitness tracking, not the other way around. If you want training-grade data, you still need a Whoop or Garmin. But, if you want 80% of the value at 20% of the cost, even less, with no subscription, this is it, because the thing that I appreciated most after a month wasn't actually the hardware, it was the absence of another subscription cost. I didn't feel like I was renting access to my own health data. So, by not paying a subscription, you do lose some things, and the sensor hardware is older, which means it simply can't match Whoop or the latest Garmin watches for accuracy, but what you get is a genuinely solid health and sleep tracker. It's beautifully designed, comfortable enough that you can actually wear it 24/7, and you buy it once for $99, and you don't worry about it again. So, over 5 years, that's a $1,000 plus saving over Whoop. And for most people who aren't training at an elite level, that's the smarter call, I think. So, yeah, overall, it's not the most advanced tracker I've tested, it's not the most accurate, and it definitely doesn't have the best app, but after a month, I never once wanted to take it off, and for a wearable, that's probably the highest compliment I can give it. Exit code: 0

Video Summary: Google Fitbit Air - 30 Days Later: An Honest Review

Overview: The reviewer tested the Fitbit Air for 30 days in various conditions including international travel, a week-long tennis camp in Bali, and intensive training sessions in tropical heat. The core question: Is this a $99 novelty or an incredible fitness tracker without a subscription fee?

Key Findings:

Strengths:

Design & Comfort: The biggest strength - it "completely disappears" on the wrist, more so than Whoop, Apple Watch, and Oura Ring. The reviewer often forgot they were wearing it during 5-6 hour daily tennis sessions, gym workouts, airport runs, and sauna sessions
Strap Mechanism: Significantly better than Whoop's problematic clasp that constantly pops open
Battery Life: Solid - lasted 10 days of travel with one charge. Fast charging helps (5 minutes ≈ 1 day of use, full charge ≈ 90 minutes)
Sleep Tracking: Impressive accuracy - distinguishes between lying in bed scrolling phone vs. actual sleep, catches naps and overnight wake-ups well
Value Proposition: $99 one-time purchase vs. Whoop's $199/year minimum subscription. Over 5 years, that's $1,000+ savings

Weaknesses:

Heart Rate Accuracy: Good but not great. Real-time tracking is neck-and-neck with Whoop, but data processing is noticeably slower (10-20 minutes behind in auto-detecting activities)
Intense Exercise Performance: During tennis with rapid heart rate changes (sprinting, stopping, recovering), Whoop performed better due to hardware limitations - the thinner design makes accurate heart rate capture harder during intense exercise
Google Health App: Currently "rough" - core metrics buried under UI layers, workouts take 30 minutes to appear, AI health coach gives unwanted paragraph-long essays
Discoloration: Blue wristband showed brownish discoloration from heavy sweating

Performance Context:

Better for steady-state activities (long runs, cycling) vs. stop-and-go sports like tennis
Cardio load feature useful but not as sophisticated as Whoop's strain score
Step counting surprisingly accurate
Detected illness/readiness dropping before reviewer fully acknowledged feeling sick

The Trade-off: The Fitbit Air is positioned as a "$99 sleep and wellness tracker that happens to do some fitness tracking" - not the other way around. For training-grade data, you still need Whoop or Garmin. But for 80% of the value at 20% of the cost with no subscription, this is it.

Final Verdict: Not the most advanced or accurate tracker, and definitely doesn't have the best app. However, after a month the reviewer "never once wanted to take it off" - which for a wearable is probably the highest compliment. For most people not training at an elite level, this is the smarter call.