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: Tuesday, 2026-07-07 </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**: 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) - **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) - **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) - **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) - **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-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) - **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) - **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) - **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-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) - **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) - **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) - **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) - **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>

Output from command in shell b24ae4: 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=okIpbu1tp_A [youtube] okIpbu1tp_A: Downloading webpage [youtube] okIpbu1tp_A: Downloading tv client config [youtube] okIpbu1tp_A: Downloading player f551602d-main [youtube] okIpbu1tp_A: Downloading tv player API JSON [youtube] okIpbu1tp_A: 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] okIpbu1tp_A: 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] okIpbu1tp_A: Downloading subtitles: en [info] okIpbu1tp_A: Downloading 1 format(s): 139-11 [info] Writing video subtitles to: Why PC Gaming Audio is Broken (and how it’s holding you back) [okIpbu1tp_A].en.json3 [download] Destination: Why PC Gaming Audio is Broken (and how it’s holding you back) [okIpbu1tp_A].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 5.04MiB/s (00:00:00) [download] 31.00KiB at 4.10MiB/s (00:00:00) [download] 63.00KiB at 3.49MiB/s (00:00:00) [download] 127.00KiB at 3.98MiB/s (00:00:00) [download] 199.21KiB at 5.20MiB/s (00:00:00) [download] 100% of 199.21KiB in 00:00:00 at 1.46MiB/s Exit code: 0

Tool 'read' validation failed: Failed to read file '/Users/root1/Why PC Gaming Audio is Broken (and how it's holding you back) [okIpbu1tp_A].en.json3': not found: /Users/root1/Why PC Gaming Audio is Broken (and how it's holding you back) [okIpbu1tp_A].en.json3

Output from command in shell c3cdd5: total 2304 drwxr-xr-x 4 root1 staff 128 Jun 5 04:50 __pycache__ drwxr-x---+ 146 root1 staff 4672 Jul 7 01:29 . drwxr-xr-x 5 root admin 160 Jun 5 03:47 .. drwxr-x--x 3 root1 staff 96 Apr 22 2025 .adobe drwxr-xr-x 2 root1 staff 64 Jun 12 16:58 .agent-browser drwxr-xr-x 5 root1 staff 160 Jul 3 02:04 .agents drwxr-xr-x@ 4 root1 staff 128 Feb 8 16:44 .amp drwxr-xr-x 6 root1 staff 192 Apr 23 01:58 .augment drwxr-xr-x@ 7 root1 staff 224 Dec 19 2025 .aws -rw-------@ 1 root1 staff 9479 Jul 1 23:13 .bash_history -rw-r--r-- 1 root1 staff 643 Jul 1 01:37 .bash_profile -rw-r--r-- 1 root1 staff 105 Jul 1 01:37 .bashrc -rw-------@ 1 root1 staff 21486 Jun 10 2025 .boto -rw-r--r--@ 1 root1 staff 10 Sep 12 2025 .brave-urls.state -rw-r--r--@ 1 root1 staff 274 Sep 10 2025 .brave-urls.txt drwxr-xr-x 4 root1 staff 128 Jun 19 00:21 .bun drwxr-xr-x@ 3 root1 staff 96 Jul 29 2025 .bundle drwx------@ 5 root1 staff 160 Oct 25 2025 .BurpSuite drwxr-xr-x 29 root1 staff 928 Jul 7 01:29 .cache drwxr-xr-x@ 2 root1 staff 64 Oct 29 2025 .calcurse drwxr-xr-x@ 12 root1 staff 384 Jun 19 14:10 .cargo drwxr-xr-x@ 5 root1 staff 160 Oct 29 2025 .Chad drwxr-xr-x 14 root1 staff 448 Jun 13 02:06 .claude -rw------- 1 root1 staff 36244 Jun 13 02:06 .claude.json -rw-r--r-- 1 root1 staff 0 May 5 07:56 .claude.json.tmp.13432.1777992984690 -rw------- 1 root1 staff 28113 May 8 10:35 .claude.json.tmp.17132.aa1ab09dfd47 -rw-r--r--@ 1 root staff 2294 Jul 11 2025 .clean_node_modules.log drwxr-xr-x@ 3 root1 staff 96 Feb 8 16:40 .codeium drwxr-xr-x 36 root1 staff 1152 Jul 3 18:36 .codex drwxr-xr-x@ 2 root1 staff 64 Jan 29 16:31 .concurrent drwxr-xr-x@ 6 root1 staff 192 Jul 31 2025 .conda drwxr-x--x@ 39 root1 staff 1248 Jun 17 18:44 .config drwx------ 3 root1 staff 96 May 20 2025 .cups drwxr-xr-x 16 root1 staff 512 Jul 3 03:33 .cursor drwxr-xr-x 6 root1 staff 192 Jun 30 02:22 .devin drwx------@ 16 root1 staff 512 Jun 8 17:55 .docker -rw-r--r--@ 1 root1 staff 250 Jul 20 2025 .dragon-ui-config.json -rw-r--r--@ 1 root1 staff 233 Jul 20 2025 .dragon-ui-model-prices.json -rw-r--r--@ 1 root1 staff 68 Jul 20 2025 .dragon-ui-price-update.json -rw-r--r--@ 1 root1 staff 34820 Jul 6 21:08 .DS_Store drwxr-xr-x@ 19 root1 staff 608 Dec 17 2025 .dspy_cache drwxr-xr-x@ 6 root1 staff 192 Nov 8 2025 .electron-gyp drwx------@ 5 root1 staff 160 Oct 25 2025 .emacs.d -rw-r--r--@ 1 root1 staff 263 Jun 28 17:16 .env drwxr-xr-x@ 9 root1 staff 288 Oct 25 2025 .expo drwx------@ 33 root1 staff 1056 Jul 2 01:58 .factory drwxr-xr-x@ 5 root1 staff 160 Oct 25 2025 .gem drwxr-xr-x@ 10 root1 staff 320 Jul 1 01:37 .gemini drwxr-xr-x@ 20 root1 staff 640 Nov 17 2025 .ghostty-windows -rw-r--r--@ 1 root1 staff 370 Aug 8 2025 .gitconfig drwxr-xr-x@ 4 root1 staff 128 Dec 12 2025 .gsutil drwxr-xr-x 3 root1 staff 96 Jun 8 00:43 .harbor drwxr-xr-x 4 root1 staff 128 Jun 17 18:44 .homebrew drwxr-xr-x 3 root1 staff 96 Aug 3 2025 .idlerc drwxr-xr-x@ 3 root1 staff 96 Sep 8 2025 .ipython drwxr-xr-x@ 6 root1 staff 192 Oct 25 2025 .jupyter drwxr-xr-x@ 11 root1 staff 352 Jan 8 13:27 .kimi drwxr-xr-x@ 5 root1 staff 160 Jan 11 20:22 .kube drwxr-xr-x 4 root1 staff 128 Jun 18 20:08 .local drwxr-xr-x@ 3 root1 staff 96 Apr 4 2025 .matplotlib -rw-r--r--@ 1 root1 staff 27 Dec 9 2025 .media_control_state.json drwxr-xr-x@ 3 root1 staff 96 Dec 10 2025 .mgrep -rw-r--r--@ 1 root1 staff 218 Jun 22 22:37 .modal.toml drwxr-xr-x@ 3 root1 staff 96 Mar 11 2025 .mono drwxr-xr-x@ 3 root1 staff 96 Jul 22 2025 .next -rw-------@ 1 root1 staff 0 Jul 14 2025 .node_repl_history drwxr-xr-x 6 root1 staff 192 Jun 18 20:09 .npm drwxr-xr-x@ 29 root1 staff 928 Jan 6 14:14 .nvm drwxr-xr-x 7 root1 staff 224 Apr 30 23:47 .ollama drwxr-xr-x 3 root1 staff 96 Jun 18 20:26 .openclaw drwxr-xr-x@ 8 root1 staff 256 Apr 8 13:03 .opencode drwxr-xr-x 3 root1 staff 96 Jun 16 02:13 .pallium drwxr-xr-x@ 15 root1 staff 480 Oct 25 2025 .pearai drwxr-xr-x 3 root1 staff 96 May 2 09:24 .pi drwxr-xr-x@ 232 root1 staff 7424 Jul 6 21:07 .playwright-mcp -rw-------@ 1 root1 staff 132 Sep 10 2025 .polygon_s3_env drwxr-xr-x@ 5 root1 staff 160 Jan 10 21:53 .prime -rw-r--r-- 1 root1 staff 130 Jul 1 01:37 .profile -rw-------@ 1 root1 staff 13 Jun 23 2025 .psql_history -rw-r--r--@ 1 root1 staff 9 Aug 12 2025 .python_history drwxr-xr-x@ 4 root1 staff 128 Sep 29 2025 .railway drwxr-xr-x@ 3 root1 staff 96 Aug 20 2025 .redhat drwxr-xr-x@ 5 root1 staff 160 Jul 22 2025 .roiai drwxr-xr-x 7 root1 staff 224 Jun 19 00:36 .rustup drwxr-xr-x@ 3 root1 staff 96 Apr 21 2025 .skiko drwxr-xr-x@ 10 root1 staff 320 Apr 8 13:04 .slate drwxr-xr-x@ 3 root1 staff 96 Mar 10 2025 .SoulseekQt drwx------@ 11 root1 staff 352 Jun 18 17:48 .ssh drwxr-xr-x@ 3 root1 staff 96 May 2 2025 .start_app_logs drwxr-xr-x@ 3 root1 staff 96 Jul 22 2025 .storybook drwxr-xr-x@ 3 root1 staff 96 Jul 29 2025 .streamlit drwxr-xr-x@ 5 root1 staff 160 Jun 16 17:13 .swiftpm drwxr-xr-x@ 3 root1 staff 96 Mar 27 2025 .swt -rw-r--r-- 1 root1 staff 31 Jun 19 00:36 .tcshrc drwxr-xr-x@ 4 root1 staff 128 Dec 12 2025 .terraform.d -rw-r--r--@ 1 root1 staff 235 Jul 15 2025 .ticker.yaml drwx------+ 6 root1 staff 192 Jul 3 01:13 .Trash drwxr-xr-x 8 root1 staff 256 Apr 14 23:36 .venv drwxr-xr-x@ 10 root1 staff 320 Dec 17 2025 .venv_uv -rw-------@ 1 root1 staff 18769 Jan 20 13:59 .viminfo drwxr-xr-x@ 3 root1 staff 96 Jan 9 01:37 .vmux drwxr-xr-x 3 root1 staff 96 Jul 3 00:56 .warp drwxr-xr-x@ 4 root1 staff 128 Nov 2 2025 .wdm drwxr-xr-x@ 4 root1 staff 128 Feb 8 16:39 .windsurf -rwxr-xr-x@ 1 root1 staff 288 Nov 28 2025 .yabairc drwxr-xr-x@ 3 root1 staff 96 Oct 15 2025 .yarn -rw-r--r--@ 1 root1 staff 116 Apr 14 15:47 .yarnrc -rw-r--r--@ 1 root1 staff 49215 Jan 14 01:53 .zcompdump -rw-r--r-- 1 root1 staff 83 Jul 1 01:37 .zprofile -rw-r--r--@ 1 root1 staff 85 Jan 6 14:13 .zprofile.backup -rw------- 1 root1 staff 13902 Jul 6 22:07 .zsh_history drwx------ 5 root1 staff 160 Apr 22 20:18 .zsh_sessions -rw-r--r--@ 1 root1 staff 83 Jun 11 2025 .zshenv -rw-r--r-- 1 root1 staff 771 Jul 1 01:37 .zshrc -rw-r--r--@ 1 root1 staff 10862 Jan 6 14:12 .zshrc.backup -rw-r--r--@ 1 root1 staff 10042 Dec 10 2025 .zshrc.bak-2025-12-10 -rw-r--r-- 1 root1 staff 140562 Jul 5 22:55 2 Months With Logitech's New Aimbot Mouse (It's Gone) [-GvDP1sOyS4].en.json3 -rw-r--r-- 1 root1 staff 7858 Jun 28 18:18 agents.md drwx------@ 14 root1 staff 448 Jul 6 20:50 Applications drwxr-xr-x@ 4 root1 staff 128 May 17 21:40 Applications (Parallels) drwxr-xr-x 14 root1 staff 448 Jun 16 17:13 background-computer-use drwxr-xr-x 2 root1 staff 64 Jun 21 18:06 bin drwxr-xr-x 2 root1 staff 64 Jun 22 23:37 CascadeProjects -rw-r--r-- 1 root1 staff 221634 Jul 6 18:17 Cheap vs Expensive OLED Monitors： The Brutal Truth. [hBCypm3pojk].en.json3 drwx------@ 553 root1 staff 17696 Jul 6 07:57 Desktop drwx------@ 51 root1 staff 1632 Jul 7 00:05 Documents drwx------@ 23 root1 staff 736 Jul 6 21:07 Downloads drwxr-xr-x 4 root1 staff 128 Jun 16 02:12 go -rw-r--r-- 1 root1 staff 5157 Jul 4 04:32 index_articles.py drwxr-xr-x@ 2 root1 staff 64 Jun 18 20:03 installers drwx------@ 116 root1 staff 3712 May 17 20:55 Library drwxr--r-- 6 root1 staff 192 Jun 20 18:02 logs drwxr-xr-x 15 root1 staff 480 Jun 17 18:43 MicGuard drwxr-xr-x@ 112 root1 staff 3584 Jul 2 04:31 misc drwx------@ 10 root1 staff 320 Apr 14 14:35 Movies drwx------@ 6 root1 staff 192 Apr 14 14:14 Music -rw-r--r-- 1 root1 staff 18070 Jul 3 01:39 Naomi Klein： AI is a fascist idea [iEf-MNsyUiE].en.srt -rw-r--r-- 1 root1 staff 1126 Jul 4 14:19 parse_vtt.py drwx------@ 7 root1 staff 224 Apr 14 14:14 Pictures drwxr-xr-x+ 4 root1 staff 128 Mar 10 2025 Public -rw-r--r-- 1 root1 staff 1446 Jul 4 04:32 search_articles.py drwxr-xr-x 4 root1 staff 128 May 19 00:19 Soulseek Downloads -rw-r--r-- 1 root1 staff 10589 Jul 4 03:55 techfundingnews_scraper.py -rw-r--r-- 1 root1 staff 193178 Jul 4 14:19 The 1000 FPS Gaming PC [VEaI8OUcMvM].en.vtt drwxr-xr-x 11 root1 staff 352 Jul 6 20:31 WebcamStreamer -rw-r--r-- 1 root1 staff 203995 Jul 7 01:29 Why PC Gaming Audio is Broken (and how it’s holding you back) [okIpbu1tp_A].en.json3 Exit code: 0

/Users/root1/2 Months With Logitech's New Aimbot Mouse (It's Gone) [-GvDP1sOyS4].en.json3 /Users/root1/Cheap vs Expensive OLED Monitors： The Brutal Truth. [hBCypm3pojk].en.json3 /Users/root1/Downloads/old-files/youtube_transcripts/8vqboaa2U1Y/8vqboaa2U1Y.en-orig.json3 /Users/root1/Library/Mobile Documents/com~apple~CloudDocs/downloads archive/Downloads_Archive_20260618/The Mode SixtyFive — I Still Love This Thing. [b2eXp4G5XP8].en.json3 /Users/root1/Library/Mobile Documents/com~apple~CloudDocs/downloads archive/Downloads_Archive_20260618/yt-summary-7_T1L2c5C6k.en-orig.json3 /Users/root1/Why PC Gaming Audio is Broken (and how it’s holding you back) [okIpbu1tp_A].en.json3 /Users/root1/misc/$1000 Gaming PC Build Guide (for 2026) [-CaETwdxZ_k].en.json3

Output from command in shell cbaebc: { "wireMagic": "pb3", "pens": [ { } ], "wsWinStyles": [ { }, { "mhModeHint": 2, "juJustifCode": 0, "sdScrollDir": 3 } ], "wpWinPositions": [ { }, { "apPoint": 6, "ahHorPos": 20, "avVerPos": 100, "rcRows": 2, "ccCols": 40 } ], "events": [ { "tStartMs": 0, "dDurationMs": 681680, "id": 1, "wpWinPosId": 1, "wsWinStyleId": 1 }, { "tStartMs": 80, "dDurationMs": 4800, "wWinId": 1, "segs": [ { "utf8": "This", "acAsrConf": 0 }, { "utf8": " must", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " be", "tOffsetMs": 400, "acAsrConf": 0 }, { "utf8": " one", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " of", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 800, "acAsrConf": 0 }, { "utf8": " most", "tOffsetMs": 1040, "acAsrConf": 0 }, { "utf8": " legendary", "tOffsetMs": 1440, "acAsrConf": 0 } ] }, { "tStartMs": 2230, "dDurationMs": 2650, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 2240, "dDurationMs": 5200, "wWinId": 1, "segs": [ { "utf8": "clips", "acAsrConf": 0 }, { "utf8": " in", "tOffsetMs": 399, "acAsrConf": 0 }, { "utf8": " esports", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " history.", "tOffsetMs": 1280, "acAsrConf": 0 } ] }, { "tStartMs": 4870, "dDurationMs": 2570, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 4880, "dDurationMs": 4320, "wWinId": 1, "segs": [ { "utf8": "\u003e\u003e But", "acAsrConf": 0, "isSpeakerChange": 1 }, { "utf8": " it", "tOffsetMs": 160, "acAsrConf": 0 }, { "utf8": " is", "tOffsetMs": 399, "acAsrConf": 0 }, { "utf8": " too", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " late.", "tOffsetMs": 800, "acAsrConf": 0 }, { "utf8": " Garite", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " is", "tOffsetMs": 2240, "acAsrConf": 0 }, { "utf8": " going", "tOffsetMs": 2399, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 2479, "acAsrConf": 0 } ] }, { "tStartMs": 7430, "dDurationMs": 1770, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 7440, "dDurationMs": 2400, "wWinId": 1, "segs": [ { "utf8": "be", "acAsrConf": 0 }, { "utf8": " waiting.", "tOffsetMs": 160, "acAsrConf": 0 }, { "utf8": " Heo", "tOffsetMs": 479, "acAsrConf": 0 }, { "utf8": " charges", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " in.", "tOffsetMs": 1199, "acAsrConf": 0 }, { "utf8": " Doesn't", "tOffsetMs": 1520, "acAsrConf": 0 } ] }, { "tStartMs": 9190, "dDurationMs": 650, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 9200, "dDurationMs": 1600, "wWinId": 1, "segs": [ { "utf8": "check.", "acAsrConf": 0 } ] }, { "tStartMs": 9830, "dDurationMs": 970, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 9840, "dDurationMs": 5040, "wWinId": 1, "segs": [ { "utf8": "\u003e\u003e What?", "acAsrConf": 0, "isSpeakerChange": 1 } ] }, { "tStartMs": 10790, "dDurationMs": 4090, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 10800, "dDurationMs": 6399, "wWinId": 1, "segs": [ { "utf8": "\u003e\u003e Oh", "acAsrConf": 0, "isSpeakerChange": 1 }, { "utf8": " my", "tOffsetMs": 480, "acAsrConf": 0 }, { "utf8": " god.", "tOffsetMs": 1040, "acAsrConf": 0 }, { "utf8": " Inhuman", "tOffsetMs": 1759, "acAsrConf": 0 }, { "utf8": " reactions.", "tOffsetMs": 2640, "acAsrConf": 0 } ] }, { "tStartMs": 14870, "dDurationMs": 2329, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 14880, "dDurationMs": 4159, "wWinId": 1, "segs": [ { "utf8": "\u003e\u003e Inhuman", "acAsrConf": 0, "isSpeakerChange": 1 }, { "utf8": " reactions.", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " It", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " looks", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " like", "tOffsetMs": 1920, "acAsrConf": 0 }, { "utf8": " it", "tOffsetMs": 2080, "acAsrConf": 0 } ] }, { "tStartMs": 17189, "dDurationMs": 1850, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 17199, "dDurationMs": 3761, "wWinId": 1, "segs": [ { "utf8": "shouldn't", "acAsrConf": 0 }, { "utf8": " be", "tOffsetMs": 241, "acAsrConf": 0 }, { "utf8": " possible", "tOffsetMs": 481, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 881, "acAsrConf": 0 }, { "utf8": " react", "tOffsetMs": 1121, "acAsrConf": 0 }, { "utf8": " this", "tOffsetMs": 1521, "acAsrConf": 0 } ] }, { "tStartMs": 19029, "dDurationMs": 1931, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 19039, "dDurationMs": 4480, "wWinId": 1, "segs": [ { "utf8": "fast,", "acAsrConf": 0 }, { "utf8": " but", "tOffsetMs": 721, "acAsrConf": 0 }, { "utf8": " that's", "tOffsetMs": 961, "acAsrConf": 0 }, { "utf8": " what", "tOffsetMs": 1121, "acAsrConf": 0 }, { "utf8": " you", "tOffsetMs": 1281, "acAsrConf": 0 }, { "utf8": " get", "tOffsetMs": 1441, "acAsrConf": 0 }, { "utf8": " when", "tOffsetMs": 1601, "acAsrConf": 0 }, { "utf8": " you", "tOffsetMs": 1761, "acAsrConf": 0 } ] }, { "tStartMs": 20950, "dDurationMs": 2569, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 20960, "dDurationMs": 4399, "wWinId": 1, "segs": [ { "utf8": "react", "acAsrConf": 0 }, { "utf8": " based", "tOffsetMs": 319, "acAsrConf": 0 }, { "utf8": " on", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " sound", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " instead", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " of", "tOffsetMs": 1840, "acAsrConf": 0 }, { "utf8": " visuals.", "tOffsetMs": 2000, "acAsrConf": 0 } ] }, { "tStartMs": 23509, "dDurationMs": 1850, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 23519, "dDurationMs": 3920, "wWinId": 1, "segs": [ { "utf8": "I", "acAsrConf": 0 }, { "utf8": " don't", "tOffsetMs": 241, "acAsrConf": 0 }, { "utf8": " want", "tOffsetMs": 321, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 481, "acAsrConf": 0 }, { "utf8": " downplay", "tOffsetMs": 561, "acAsrConf": 0 }, { "utf8": " how", "tOffsetMs": 1041, "acAsrConf": 0 }, { "utf8": " insane", "tOffsetMs": 1361, "acAsrConf": 0 } ] }, { "tStartMs": 25349, "dDurationMs": 2090, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 25359, "dDurationMs": 4881, "wWinId": 1, "segs": [ { "utf8": "Kiko's", "acAsrConf": 0 }, { "utf8": " reaction", "tOffsetMs": 561, "acAsrConf": 0 }, { "utf8": " and", "tOffsetMs": 961, "acAsrConf": 0 }, { "utf8": " aim", "tOffsetMs": 1201, "acAsrConf": 0 }, { "utf8": " were", "tOffsetMs": 1521, "acAsrConf": 0 }, { "utf8": " in", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " this", "tOffsetMs": 1920, "acAsrConf": 0 } ] }, { "tStartMs": 27429, "dDurationMs": 2811, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 27439, "dDurationMs": 5280, "wWinId": 1, "segs": [ { "utf8": "clip,", "acAsrConf": 0 }, { "utf8": " but", "tOffsetMs": 561, "acAsrConf": 0 }, { "utf8": " it's", "tOffsetMs": 721, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 961, "acAsrConf": 0 }, { "utf8": " simple", "tOffsetMs": 1121, "acAsrConf": 0 }, { "utf8": " biological", "tOffsetMs": 1361, "acAsrConf": 0 }, { "utf8": " fact.", "tOffsetMs": 2080, "acAsrConf": 0 } ] }, { "tStartMs": 30230, "dDurationMs": 2489, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 30240, "dDurationMs": 4800, "wWinId": 1, "segs": [ { "utf8": "Human", "acAsrConf": 0 }, { "utf8": " beings", "tOffsetMs": 320, "acAsrConf": 0 }, { "utf8": " react", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " faster", "tOffsetMs": 1039, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 1439, "acAsrConf": 0 }, { "utf8": " sound", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " than", "tOffsetMs": 2240, "acAsrConf": 0 } ] }, { "tStartMs": 32709, "dDurationMs": 2331, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 32719, "dDurationMs": 4161, "wWinId": 1, "segs": [ { "utf8": "they", "acAsrConf": 0 }, { "utf8": " do", "tOffsetMs": 161, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 321, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 481, "acAsrConf": 0 }, { "utf8": " visual", "tOffsetMs": 721, "acAsrConf": 0 }, { "utf8": " stimulus.", "tOffsetMs": 1121, "acAsrConf": 0 }, { "utf8": " And", "tOffsetMs": 1921, "acAsrConf": 0 }, { "utf8": " this", "tOffsetMs": 2081, "acAsrConf": 0 } ] }, { "tStartMs": 35030, "dDurationMs": 1850, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 35040, "dDurationMs": 4880, "wWinId": 1, "segs": [ { "utf8": "can", "acAsrConf": 0 }, { "utf8": " make", "tOffsetMs": 160, "acAsrConf": 0 }, { "utf8": " clips", "tOffsetMs": 480, "acAsrConf": 0 }, { "utf8": " like", "tOffsetMs": 800, "acAsrConf": 0 }, { "utf8": " this", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " look", "tOffsetMs": 1359, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 1600, "acAsrConf": 0 }, { "utf8": " bit", "tOffsetMs": 1679, "acAsrConf": 0 } ] }, { "tStartMs": 36870, "dDurationMs": 3050, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 36880, "dDurationMs": 4800, "wWinId": 1, "segs": [ { "utf8": "uncanny", "acAsrConf": 0 }, { "utf8": " or", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " inhuman", "tOffsetMs": 1440, "acAsrConf": 0 }, { "utf8": " even.", "tOffsetMs": 1999, "acAsrConf": 0 }, { "utf8": " Most", "tOffsetMs": 2480, "acAsrConf": 0 }, { "utf8": " of", "tOffsetMs": 2720, "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 2800, "acAsrConf": 0 } ] }, { "tStartMs": 39910, "dDurationMs": 1770, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 39920, "dDurationMs": 4319, "wWinId": 1, "segs": [ { "utf8": "studies", "acAsrConf": 0 }, { "utf8": " conclude", "tOffsetMs": 400, "acAsrConf": 0 }, { "utf8": " that", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " humans", "tOffsetMs": 1120, "acAsrConf": 0 }, { "utf8": " react", "tOffsetMs": 1440, "acAsrConf": 0 } ] }, { "tStartMs": 41670, "dDurationMs": 2569, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 41680, "dDurationMs": 4640, "wWinId": 1, "segs": [ { "utf8": "roughly", "acAsrConf": 0 }, { "utf8": " 50", "tOffsetMs": 640, "acAsrConf": 0 }, { "utf8": " milliseconds", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " faster", "tOffsetMs": 1600, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 2000, "acAsrConf": 0 }, { "utf8": " sound", "tOffsetMs": 2240, "acAsrConf": 0 } ] }, { "tStartMs": 44229, "dDurationMs": 2091, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 44239, "dDurationMs": 4480, "wWinId": 1, "segs": [ { "utf8": "than", "acAsrConf": 0 }, { "utf8": " they", "tOffsetMs": 241, "acAsrConf": 0 }, { "utf8": " do", "tOffsetMs": 401, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 561, "acAsrConf": 0 }, { "utf8": " light.", "tOffsetMs": 721, "acAsrConf": 0 }, { "utf8": " So,", "tOffsetMs": 1201, "acAsrConf": 0 }, { "utf8": " you", "tOffsetMs": 1441, "acAsrConf": 0 }, { "utf8": " can", "tOffsetMs": 1601, "acAsrConf": 0 } ] }, { "tStartMs": 46310, "dDurationMs": 2409, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 46320, "dDurationMs": 4800, "wWinId": 1, "segs": [ { "utf8": "literally", "acAsrConf": 0 }, { "utf8": " react", "tOffsetMs": 719, "acAsrConf": 0 }, { "utf8": " faster", "tOffsetMs": 1200, "acAsrConf": 0 }, { "utf8": " than", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " your", "tOffsetMs": 1840, "acAsrConf": 0 }, { "utf8": " eyes", "tOffsetMs": 2079, "acAsrConf": 0 } ] }, { "tStartMs": 48709, "dDurationMs": 2411, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 48719, "dDurationMs": 4961, "wWinId": 1, "segs": [ { "utf8": "can", "acAsrConf": 0 }, { "utf8": " see.", "tOffsetMs": 241, "acAsrConf": 0 }, { "utf8": " And", "tOffsetMs": 961, "acAsrConf": 0 }, { "utf8": " that", "tOffsetMs": 1121, "acAsrConf": 0 }, { "utf8": " 50", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " millisecond", "tOffsetMs": 1921, "acAsrConf": 0 } ] }, { "tStartMs": 51110, "dDurationMs": 2570, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 51120, "dDurationMs": 4640, "wWinId": 1, "segs": [ { "utf8": "advantage", "acAsrConf": 0 }, { "utf8": " is", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 1040, "acAsrConf": 0 }, { "utf8": " lot", "tOffsetMs": 1279, "acAsrConf": 0 }, { "utf8": " when", "tOffsetMs": 1840, "acAsrConf": 0 }, { "utf8": " you", "tOffsetMs": 2079, "acAsrConf": 0 }, { "utf8": " consider", "tOffsetMs": 2240, "acAsrConf": 0 } ] }, { "tStartMs": 53670, "dDurationMs": 2090, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 53680, "dDurationMs": 4640, "wWinId": 1, "segs": [ { "utf8": "that", "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " visual", "tOffsetMs": 480, "acAsrConf": 0 }, { "utf8": " reaction", "tOffsetMs": 800, "acAsrConf": 0 }, { "utf8": " time", "tOffsetMs": 1280, "acAsrConf": 0 }, { "utf8": " of", "tOffsetMs": 1519, "acAsrConf": 0 }, { "utf8": " most", "tOffsetMs": 1680, "acAsrConf": 0 } ] }, { "tStartMs": 55750, "dDurationMs": 2570, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 55760, "dDurationMs": 4799, "wWinId": 1, "segs": [ { "utf8": "pro", "acAsrConf": 0 }, { "utf8": " players", "tOffsetMs": 319, "acAsrConf": 0 }, { "utf8": " is", "tOffsetMs": 799, "acAsrConf": 0 }, { "utf8": " around", "tOffsetMs": 1040, "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 1279, "acAsrConf": 0 }, { "utf8": " 150", "tOffsetMs": 2160, "acAsrConf": 0 } ] }, { "tStartMs": 58310, "dDurationMs": 2249, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 58320, "dDurationMs": 6080, "wWinId": 1, "segs": [ { "utf8": "millisecond", "acAsrConf": 0 }, { "utf8": " mark.", "tOffsetMs": 640, "acAsrConf": 0 }, { "utf8": " Being", "tOffsetMs": 1280, "acAsrConf": 0 }, { "utf8": " able", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " react", "tOffsetMs": 1840, "acAsrConf": 0 } ] }, { "tStartMs": 60549, "dDurationMs": 3851, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 60559, "dDurationMs": 5920, "wWinId": 1, "segs": [ { "utf8": "about", "acAsrConf": 0 }, { "utf8": " 30%", "tOffsetMs": 561, "acAsrConf": 0 }, { "utf8": " faster", "tOffsetMs": 1361, "acAsrConf": 0 }, { "utf8": " is", "tOffsetMs": 1921, "acAsrConf": 0 }, { "utf8": " not", "tOffsetMs": 2401, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 2561, "acAsrConf": 0 }, { "utf8": " small", "tOffsetMs": 2800, "acAsrConf": 0 }, { "utf8": " feat.", "tOffsetMs": 3041, "acAsrConf": 0 } ] }, { "tStartMs": 64390, "dDurationMs": 2089, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 64400, "dDurationMs": 4240, "wWinId": 1, "segs": [ { "utf8": "And", "acAsrConf": 0 }, { "utf8": " pl… (7 chars truncated) … (8829 lines truncated) "tStar… (13 chars truncated) "dDurationMs": 5759, "wWinId": 1, "segs": [ { "utf8": "was", "acAsrConf": 0 }, { "utf8": " using", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " your", "tOffsetMs": 480, "acAsrConf": 0 }, { "utf8": " audio", "tOffsetMs": 639, "acAsrConf": 0 }, { "utf8": " device", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " via", "tOffsetMs": 1360, "acAsrConf": 0 }, { "utf8": " Azio,", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " no", "tOffsetMs": 2639, "acAsrConf": 0 } ] }, { "tStartMs": 615509, "dDurationMs": 2890, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 615519, "dDurationMs": 5201, "wWinId": 1, "segs": [ { "utf8": "other", "acAsrConf": 0 }, { "utf8": " application", "tOffsetMs": 320, "acAsrConf": 0 }, { "utf8": " could.", "tOffsetMs": 961, "acAsrConf": 0 }, { "utf8": " No", "tOffsetMs": 1521, "acAsrConf": 0 }, { "utf8": " Discord,", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " no", "tOffsetMs": 2560, "acAsrConf": 0 } ] }, { "tStartMs": 618389, "dDurationMs": 2331, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 618399, "dDurationMs": 4801, "wWinId": 1, "segs": [ { "utf8": "Spotify.", "acAsrConf": 0 }, { "utf8": " You're", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " just", "tOffsetMs": 1120, "acAsrConf": 0 }, { "utf8": " going", "tOffsetMs": 1281, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 1440, "acAsrConf": 0 }, { "utf8": " get", "tOffsetMs": 1601, "acAsrConf": 0 }, { "utf8": " game", "tOffsetMs": 2000, "acAsrConf": 0 } ] }, { "tStartMs": 620710, "dDurationMs": 2490, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 620720, "dDurationMs": 5280, "wWinId": 1, "segs": [ { "utf8": "audio.", "acAsrConf": 0 }, { "utf8": " However,", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " multiclient", "tOffsetMs": 1280, "acAsrConf": 0 }, { "utf8": " Azure", "tOffsetMs": 2000, "acAsrConf": 0 } ] }, { "tStartMs": 623190, "dDurationMs": 2810, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 623200, "dDurationMs": 5360, "wWinId": 1, "segs": [ { "utf8": "drivers", "acAsrConf": 0 }, { "utf8": " already", "tOffsetMs": 480, "acAsrConf": 0 }, { "utf8": " exist.", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " My", "tOffsetMs": 1600, "acAsrConf": 0 }, { "utf8": " RME", "tOffsetMs": 1840, "acAsrConf": 0 }, { "utf8": " interface", "tOffsetMs": 2400, "acAsrConf": 0 } ] }, { "tStartMs": 625990, "dDurationMs": 2570, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 626000, "dDurationMs": 5600, "wWinId": 1, "segs": [ { "utf8": "has", "acAsrConf": 0 }, { "utf8": " those", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " and", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " happily", "tOffsetMs": 1120, "acAsrConf": 0 }, { "utf8": " accepts", "tOffsetMs": 1519, "acAsrConf": 0 }, { "utf8": " playback", "tOffsetMs": 2079, "acAsrConf": 0 } ] }, { "tStartMs": 628550, "dDurationMs": 3050, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 628560, "dDurationMs": 5360, "wWinId": 1, "segs": [ { "utf8": "from", "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 399, "acAsrConf": 0 }, { "utf8": " door", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " via", "tOffsetMs": 959, "acAsrConf": 0 }, { "utf8": " Azio", "tOffsetMs": 1279, "acAsrConf": 0 }, { "utf8": " where", "tOffsetMs": 2160, "acAsrConf": 0 }, { "utf8": " I", "tOffsetMs": 2399, "acAsrConf": 0 }, { "utf8": " can", "tOffsetMs": 2560, "acAsrConf": 0 }, { "utf8": " still", "tOffsetMs": 2719, "acAsrConf": 0 } ] }, { "tStartMs": 631590, "dDurationMs": 2330, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 631600, "dDurationMs": 4960, "wWinId": 1, "segs": [ { "utf8": "play", "acAsrConf": 0 }, { "utf8": " something", "tOffsetMs": 320, "acAsrConf": 0 }, { "utf8": " else", "tOffsetMs": 640, "acAsrConf": 0 }, { "utf8": " through", "tOffsetMs": 1440, "acAsrConf": 0 }, { "utf8": " Chrome,", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " for", "tOffsetMs": 2160, "acAsrConf": 0 } ] }, { "tStartMs": 633910, "dDurationMs": 2650, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 633920, "dDurationMs": 5760, "wWinId": 1, "segs": [ { "utf8": "instance,", "acAsrConf": 0 }, { "utf8": " via", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 1280, "acAsrConf": 0 }, { "utf8": " classic", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " Windows", "tOffsetMs": 1919, "acAsrConf": 0 }, { "utf8": " audio", "tOffsetMs": 2320, "acAsrConf": 0 } ] }, { "tStartMs": 636550, "dDurationMs": 3130, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 636560, "dDurationMs": 6160, "wWinId": 1, "segs": [ { "utf8": "path.", "acAsrConf": 0 }, { "utf8": " So,", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " it", "tOffsetMs": 1440, "acAsrConf": 0 }, { "utf8": " would", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " also", "tOffsetMs": 1920, "acAsrConf": 0 }, { "utf8": " happily", "tOffsetMs": 2240, "acAsrConf": 0 }, { "utf8": " accept", "tOffsetMs": 2640, "acAsrConf": 0 } ] }, { "tStartMs": 639670, "dDurationMs": 3050, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 639680, "dDurationMs": 4399, "wWinId": 1, "segs": [ { "utf8": "low", "acAsrConf": 0 }, { "utf8": " latency", "tOffsetMs": 320, "acAsrConf": 0 }, { "utf8": " game", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " audio", "tOffsetMs": 1120, "acAsrConf": 0 }, { "utf8": " via", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " Azio", "tOffsetMs": 2000, "acAsrConf": 0 }, { "utf8": " where", "tOffsetMs": 2800, "acAsrConf": 0 }, { "utf8": " I", "tOffsetMs": 2960, "acAsrConf": 0 } ] }, { "tStartMs": 642710, "dDurationMs": 1369, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 642720, "dDurationMs": 3119, "wWinId": 1, "segs": [ { "utf8": "could", "acAsrConf": 0 }, { "utf8": " still", "tOffsetMs": 160, "acAsrConf": 0 }, { "utf8": " hear", "tOffsetMs": 400, "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 559, "acAsrConf": 0 }, { "utf8": " Discord", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " chat", "tOffsetMs": 1119, "acAsrConf": 0 } ] }, { "tStartMs": 644069, "dDurationMs": 1770, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 644079, "dDurationMs": 4721, "wWinId": 1, "segs": [ { "utf8": "through", "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 241, "acAsrConf": 0 }, { "utf8": " normal", "tOffsetMs": 401, "acAsrConf": 0 }, { "utf8": " Windows", "tOffsetMs": 721, "acAsrConf": 0 }, { "utf8": " route.", "tOffsetMs": 1121, "acAsrConf": 0 }, { "utf8": " So,", "tOffsetMs": 1601, "acAsrConf": 0 } ] }, { "tStartMs": 645829, "dDurationMs": 2971, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 645839, "dDurationMs": 5601, "wWinId": 1, "segs": [ { "utf8": "the", "acAsrConf": 0 }, { "utf8": " fix", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " already", "tOffsetMs": 721, "acAsrConf": 0 }, { "utf8": " exists.", "tOffsetMs": 1120, "acAsrConf": 0 }, { "utf8": " The", "tOffsetMs": 1841, "acAsrConf": 0 }, { "utf8": " music", "tOffsetMs": 2161, "acAsrConf": 0 }, { "utf8": " world", "tOffsetMs": 2481, "acAsrConf": 0 } ] }, { "tStartMs": 648790, "dDurationMs": 2650, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 648800, "dDurationMs": 5120, "wWinId": 1, "segs": [ { "utf8": "solved", "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 479, "acAsrConf": 0 }, { "utf8": " latency", "tOffsetMs": 640, "acAsrConf": 0 }, { "utf8": " problem", "tOffsetMs": 1120, "acAsrConf": 0 }, { "utf8": " decades", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " ago,", "tOffsetMs": 1920, "acAsrConf": 0 } ] }, { "tStartMs": 651430, "dDurationMs": 2490, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 651440, "dDurationMs": 3839, "wWinId": 1, "segs": [ { "utf8": "but", "acAsrConf": 0 }, { "utf8": " for", "tOffsetMs": 160, "acAsrConf": 0 }, { "utf8": " gamers,", "tOffsetMs": 399, "acAsrConf": 0 }, { "utf8": " it's", "tOffsetMs": 959, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 1280, "acAsrConf": 0 }, { "utf8": " locked", "tOffsetMs": 1519, "acAsrConf": 0 }, { "utf8": " door.", "tOffsetMs": 1839, "acAsrConf": 0 }, { "utf8": " And", "tOffsetMs": 2240, "acAsrConf": 0 } ] }, { "tStartMs": 653910, "dDurationMs": 1369, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 653920, "dDurationMs": 3680, "wWinId": 1, "segs": [ { "utf8": "you", "acAsrConf": 0 }, { "utf8": " know,", "tOffsetMs": 80, "acAsrConf": 0 }, { "utf8": " it's", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 400, "acAsrConf": 0 }, { "utf8": " bit", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " of", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 800, "acAsrConf": 0 }, { "utf8": " dilemma", "tOffsetMs": 960, "acAsrConf": 0 } ] }, { "tStartMs": 655269, "dDurationMs": 2331, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 655279, "dDurationMs": 4081, "wWinId": 1, "segs": [ { "utf8": "really.", "acAsrConf": 0 }, { "utf8": " Humans", "tOffsetMs": 321, "acAsrConf": 0 }, { "utf8": " react", "tOffsetMs": 721, "acAsrConf": 0 }, { "utf8": " so", "tOffsetMs": 1201, "acAsrConf": 0 }, { "utf8": " much", "tOffsetMs": 1521, "acAsrConf": 0 }, { "utf8": " faster", "tOffsetMs": 1680, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 2081, "acAsrConf": 0 } ] }, { "tStartMs": 657590, "dDurationMs": 1770, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 657600, "dDurationMs": 3919, "wWinId": 1, "segs": [ { "utf8": "sound.", "acAsrConf": 0 }, { "utf8": " And", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " yet,", "tOffsetMs": 720, "acAsrConf": 0 }, { "utf8": " we've", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " been", "tOffsetMs": 1280, "acAsrConf": 0 }, { "utf8": " totally", "tOffsetMs": 1440, "acAsrConf": 0 } ] }, { "tStartMs": 659350, "dDurationMs": 2169, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 659360, "dDurationMs": 3840, "wWinId": 1, "segs": [ { "utf8": "neglecting", "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " audio", "tOffsetMs": 800, "acAsrConf": 0 }, { "utf8": " latency.", "tOffsetMs": 1120, "acAsrConf": 0 }, { "utf8": " We've", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " been", "tOffsetMs": 2000, "acAsrConf": 0 } ] }, { "tStartMs": 661509, "dDurationMs": 1691, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 661519, "dDurationMs": 4241, "wWinId": 1, "segs": [ { "utf8": "getting", "acAsrConf": 0 }, { "utf8": " closer", "tOffsetMs": 320, "acAsrConf": 0 }, { "utf8": " and", "tOffsetMs": 641, "acAsrConf": 0 }, { "utf8": " closer", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 1121, "acAsrConf": 0 }, { "utf8": " near", "tOffsetMs": 1361, "acAsrConf": 0 } ] }, { "tStartMs": 663190, "dDurationMs": 2570, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 663200, "dDurationMs": 4480, "wWinId": 1, "segs": [ { "utf8": "instant", "acAsrConf": 0 }, { "utf8": " visual", "tOffsetMs": 879, "acAsrConf": 0 }, { "utf8": " pipeline", "tOffsetMs": 1360, "acAsrConf": 0 }, { "utf8": " over", "tOffsetMs": 1920, "acAsrConf": 0 }, { "utf8": " the", "tOffsetMs": 2160, "acAsrConf": 0 }, { "utf8": " last", "tOffsetMs": 2400, "acAsrConf": 0 } ] }, { "tStartMs": 665750, "dDurationMs": 1930, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 665760, "dDurationMs": 4400, "wWinId": 1, "segs": [ { "utf8": "few", "acAsrConf": 0 }, { "utf8": " years", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " thanks", "tOffsetMs": 639, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " super", "tOffsetMs": 1040, "acAsrConf": 0 }, { "utf8": " high", "tOffsetMs": 1360, "acAsrConf": 0 }, { "utf8": " refresh", "tOffsetMs": 1600, "acAsrConf": 0 } ] }, { "tStartMs": 667670, "dDurationMs": 2490, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 667680, "dDurationMs": 5120, "wWinId": 1, "segs": [ { "utf8": "rate", "acAsrConf": 0 }, { "utf8": " monitors,", "tOffsetMs": 240, "acAsrConf": 0 }, { "utf8": " crazy", "tOffsetMs": 1040, "acAsrConf": 0 }, { "utf8": " fast", "tOffsetMs": 1440, "acAsrConf": 0 }, { "utf8": " mice,", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " and", "tOffsetMs": 2159, "acAsrConf": 0 } ] }, { "tStartMs": 670150, "dDurationMs": 2650, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 670160, "dDurationMs": 4960, "wWinId": 1, "segs": [ { "utf8": "powerful", "acAsrConf": 0 }, { "utf8": " PC", "tOffsetMs": 480, "acAsrConf": 0 }, { "utf8": " hardware.", "tOffsetMs": 880, "acAsrConf": 0 }, { "utf8": " And", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " I'm", "tOffsetMs": 1679, "acAsrConf": 0 }, { "utf8": " all", "tOffsetMs": 2000, "acAsrConf": 0 }, { "utf8": " for", "tOffsetMs": 2239, "acAsrConf": 0 }, { "utf8": " it", "tOffsetMs": 2400, "acAsrConf": 0 } ] }, { "tStartMs": 672790, "dDurationMs": 2330, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 672800, "dDurationMs": 4560, "wWinId": 1, "segs": [ { "utf8": "really.", "acAsrConf": 0 }, { "utf8": " But", "tOffsetMs": 560, "acAsrConf": 0 }, { "utf8": " that", "tOffsetMs": 719, "acAsrConf": 0 }, { "utf8": " success", "tOffsetMs": 1039, "acAsrConf": 0 }, { "utf8": " has", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " created", "tOffsetMs": 1840, "acAsrConf": 0 }, { "utf8": " a", "tOffsetMs": 2159, "acAsrConf": 0 } ] }, { "tStartMs": 675110, "dDurationMs": 2250, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 675120, "dDurationMs": 6560, "wWinId": 1, "segs": [ { "utf8": "massive", "acAsrConf": 0 }, { "utf8": " imbalance.", "tOffsetMs": 399, "acAsrConf": 0 }, { "utf8": " It", "tOffsetMs": 1279, "acAsrConf": 0 }, { "utf8": " really", "tOffsetMs": 1600, "acAsrConf": 0 }, { "utf8": " is", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " about", "tOffsetMs": 1920, "acAsrConf": 0 } ] }, { "tStartMs": 677350, "dDurationMs": 4330, "wWinId": 1, "aAppend": 1, "segs": [ { "utf8": "\n" } ] }, { "tStartMs": 677360, "dDurationMs": 4320, "wWinId": 1, "segs": [ { "utf8": "time", "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 159, "acAsrConf": 0 }, { "utf8": " bring", "tOffsetMs": 479, "acAsrConf": 0 }, { "utf8": " our", "tOffsetMs": 640, "acAsrConf": 0 }, { "utf8": " audio", "tOffsetMs": 960, "acAsrConf": 0 }, { "utf8": " up", "tOffsetMs": 1520, "acAsrConf": 0 }, { "utf8": " to", "tOffsetMs": 1760, "acAsrConf": 0 }, { "utf8": " speed.", "tOffsetMs": 1919, "acAsrConf": 0 } ] } ] } Exit code: 0 <truncation_notice> Full output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/61d96fee/content.txt </truncation_notice>

{ "file_path": "/Users/root1/parse_captions.py", "content": "import json\nimport sys\n\n# Read the JSON3 file\nwith open('Why PC Gaming Audio is Broken (and how it\\'s holding you back) [okIpbu1tp_A].en.json3', 'r') as f:\n data = json.load(f)\n\n# Extract all text segments\nfull_text = \"\"\nfor event in data.get('events', []):\n if 'segs' in event:\n for seg in event['segs']:\n if 'utf8' in seg:\n full_text += seg['utf8']\n\nprint(full_text)" }

Output from command in shell 63df42: Traceback (most recent call last): File "/Users/root1/parse_captions.py", line 5, in <module> with open('Why PC Gaming Audio is Broken (and how it\'s holding you back) [okIpbu1tp_A].en.json3', 'r') as f: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: "Why PC Gaming Audio is Broken (and how it's holding you back) [okIpbu1tp_A].en.json3" Exit code: 1

{ "file_path": "/Users/root1/parse_captions.py", "old_string": "# Read the JSON3 file\nwith open('Why PC Gaming Audio is Broken (and how it\\'s holding you back) [okIpbu1tp_A].en.json3', 'r') as f:\n data = json.load(f)", "new_string": "# Read the JSON3 file\nimport glob\nmatching_files = glob.glob('*okIpbu1tp_A*.json3')\nif not matching_files:\n print(\"No matching file found\")\n sys.exit(1)\n \nwith open(matching_files[0], 'r') as f:\n data = json.load(f)" }

Output from command in shell 18878a: This must be one of the most legendary clips in esports history. >> But it is too late. Garite is going to be waiting. Heo charges in. Doesn't check. >> What? >> Oh my god. Inhuman reactions. >> Inhuman reactions. It looks like it shouldn't be possible to react this fast, but that's what you get when you react based on sound instead of visuals. I don't want to downplay how insane Kiko's reaction and aim were in this clip, but it's a simple biological fact. Human beings react faster to sound than they do to a visual stimulus. And this can make clips like this look a bit uncanny or inhuman even. Most of the studies conclude that humans react roughly 50 milliseconds faster to sound than they do to light. So, you can literally react faster than your eyes can see. And that 50 millisecond advantage is a lot when you consider that the visual reaction time of most pro players is around the 150 millisecond mark. Being able to react about 30% faster is not a small feat. And players that have learned to use this to their advantage are responsible for some of the most legendary clips in esports history. I'm going to guard. >> Last player standing. >> One enemy remaining. >> These insane flick shots when possible because these players saw their opponents. They obviously did not. They won these fights because they were able to react based on sound. So, you would think, given how important reaction times and sound are in competitive firstperson shooters, that high-end gaming PCs would also be pretty good at low latency audio. But actually, they're not. For the last decade, the entire industry has been in an arms race over a different sense entirely. We got monitors with higher and higher refresh rates, faster GPUs, and technologies like Nvidia Reflex. And don't get me wrong, I'm all for it, but it feels like every new product is trying to shave off yet another fraction of a millisecond between your click and the pixels changing on screen, while audio latency has been mostly neglected. See, this is CS2 running uncapped on a 240 Hz monitor. I have a pretty decent gaming PC. Not the latest and greatest hardware anymore, but I'm getting pretty low lag. Roughly 9 milliseconds of endto-end latency, video latency, that is. See, this little elder device here that's attached to the monitor measures the time it takes from the mouse click up until something changes on screen. And typically that happens pretty fast. Today's harden software is well optimized for this kind of lag. We're talking singledigit latency numbers for modern gaming peripherals. Like a good 240 Hz monitor has a latency in the range of 2 to 3 milliseconds. Good gaming mice typically only have one or 2 milliseconds of latency. And the nice hall effect keyboard can detect your key press in just a millisecond or even faster. Not to mention the super fast render times you can get with high-end GPUs and CPUs in optimized games. But watch this. This is the audio latency I've measured. Why is nobody talking about this? Everyone's obsessed with upgrading their PCs, monitors, and mice for tiny sub millisecond latency improvements. And yet we're getting audio latency that's magnitudes higher. So, how can we fix this? Is there even a fix? What about fancy gaming decks like this? And wireless headsets? These are even worse for latency, right? Okay, let's actually test a few devices. Starting with what probably most people are using, the audio ports on the PC straight from the mainboard. We can measure the audio lag with the elder device as well. Just needs to be connected to the audio output so that it can hear the gunshot sound. I've also added an amplifier for better signal quality. And of course, I've already tested and confirmed that it doesn't add any additional lag. So, in CS2, my mainboard audio comes in roughly 95 milliseconds too late. Not good. Let's see if an external sound card like this can help. After all, it's built with gaming in mind. But, well, actually, it's ever so slightly slower than the mainboard audio. differences this small don't really matter when we're talking about latencies in the 100 millisecond range but it certainly doesn't bring down the latency and yes I've tested the optical input too. So what about wireless then? I happen to have this headset lying around. So we might as well test it and see how terrible wireless actually is. Or maybe isn't. That's pretty close to wired actually. And keep in mind that this is not some crazy three $400 wireless headsets. It's one of the most affordable 2.4 GHz headsets on the market. So yeah, good to see that you don't need to go out and buy a fancy new gaming deck or ditch your 2.4 GHz headset. You're getting terrible latency either way. No matter what, we're talking about audio lag in the 100 millisecond range. But, you know, it's no coincidence that the three devices we've tested so far all ended up around the 100 millisecond mark. To really get to the bottom of this, it's time to bring out the big gun, the RME80i-24 ProSE, a $2,000 professional audio interface. It'll help us understand what's going on here. See, low latency is extremely important for music production. So, this thing is actually built with a low latency in mind. So, watch this. In a music production program, the latency with this RME interface is absolutely tiny. The sound arrives not even 3 milliseconds after the mouse click. That's magnitudes lower than everything we've seen so far. However, watch what happens when we switch back to CS. As soon as we're testing in game, we're getting more or less the same latency as we got with all the other devices. What's causing all this latency is not really the audio device itself or even the USB connection. It's the game, the Windows audio engine, and the audio API. Every step of the audio chain claims itself a little safety margin and a little audio buffer that's a few milliseconds long. And these buffers do add up. See, when testing outside of CS2 directly through the Elder application, we can get an idea of how much latency the game itself is adding. In the Elder application, we're getting almost 60 milliseconds less latency than in game. So, a big chunk of the overall latency comes from the game alone. Of course, not every game is the same. Some have more and some have less audio than others. Here's the latency I've calculated for CS, Overwatch, and Valerant. And as you can see, each of these games take their fair time to process the audio before it even reaches the Windows audio stack. Also, the differences between the games are pretty wild. Like Overwatch is taking more than twice as long as Valerant to process the audio. But typically, there is nothing we can do about the game's built-in audio latency. Games usually don't have a setting to change the audio buffer except Counter Strike. CS has a console command to adjust the mix ahead, which allows users to change the size of the audio buffer. So, you could play around to find the smallest number that still works on your machine before breaking the audio. Back in the days, Battle Nonsense was able to demonstrate nicely how the setting can reduce the audio latency, but this was in CS:GO. The command is still there in CS2 today, but according to my testing, it doesn't have an effect anymore. CS2 supposedly uses the most optimal setting already, but seeing how much worse the audio lag is in CS2 than it is in Valerant, I'm not so sure about that. In comparison to the game's built-in audio latency, the latency that the audio device itself is causing is often negligible. Every audio device has its own buffer, too, but typically it's small. The RE interface allows us to choose the size of this audio buffer, but for buffer sizes up to 256 samples, the effect is so small that it gets buried in the deviation. For some context, the 32 sample buffer I've been using for most of these tests equates to just 0.7 milliseconds of lag at 84 kHz. But then at 512 samples, something strange happens. There's a sudden roughly 43 millisecond latency increase. However, it's not the audio device itself that's responsible for this jump. 512 samples only equate to less than 11 milliseconds of lag. The 43 millisecond jump is actually caused by the way games process audio. I really don't want to get into the nitty-gritty of how this all works. It's pretty boring. But the practical takeaway here really is that if you have the choice, use buffer sizes of 256 samples or smaller. And the other takeaway is that the audio device itself only contributes a small amount to the overall audio latency. The game's own audio processing is a much bigger factor. But thing is, even if all game developers were using the smallest audio buffers possible, there's still a good amount of audio lag caused by the Windows audio stack. See, when testing through the Elder application, we are still getting roughly 36 milliseconds of latency best case. And most of this latency can be attributed to how Windows handles audio. As some of you might have suspected, the super low sub 3 millisecond latency number I was able to get with the RME interface earlier is only possible when using Azio. This basically circumvents the traditional audio path that games are using. Azio allows the application to talk to the audio device directly, bypassing most of the stuff that it's latency. But games don't support Ezio. Instead, they route their audio through the entire Windows audio stack, which is well known to be laggy. So, why do they do that? Well, convenience probably. Virtually every device works with Windows audio, but not every device supports Azio. And traditionally, Ezio is single client, meaning that if a game was using your audio device via Azio, no other application could. No Discord, no Spotify. You're just going to get game audio. However, multiclient Azure drivers already exist. My RME interface has those and happily accepts playback from a door via Azio where I can still play something else through Chrome, for instance, via the classic Windows audio path. So, it would also happily accept low latency game audio via Azio where I could still hear the Discord chat through the normal Windows route. So, the fix already exists. The music world solved the latency problem decades ago, but for gamers, it's a locked door. And you know, it's a bit of a dilemma really. Humans react so much faster to sound. And yet, we've been totally neglecting the audio latency. We've been getting closer and closer to near instant visual pipeline over the last few years thanks to super high refresh rate monitors, crazy fast mice, and powerful PC hardware. And I'm all for it really. But that success has created a massive imbalance. It really is about time to bring our audio up to speed. Exit code: 0

Summary: "Why PC Gaming Audio is Broken (and how it's holding you back)"

Main Topic: The video explores how audio latency in PC gaming is significantly worse than visual latency, creating a massive performance imbalance that holds gamers back.

Key Points:

Sound vs. Visual Reaction Times: Humans react roughly 50 milliseconds faster to sound than to visual stimuli. This is a biological fact that gives sound-based reactions a significant advantage in competitive gaming.
The Gaming Industry's Focus: For the past decade, the industry has been obsessed with reducing visual latency - higher refresh rate monitors, faster GPUs, Nvidia Reflex, etc. - while largely neglecting audio latency.
Current Audio Latency Problems:
- Visual latency can be as low as 9ms end-to-end
- Audio latency typically ranges around 100ms regardless of the device used
- This creates a massive imbalance where visual systems are near-instant but audio systems are significantly laggy
Testing Results: The creator tested various audio devices:
- Motherboard audio: ~95ms latency
- External gaming sound card: Slightly slower than motherboard
- 2.4GHz wireless headset: Similar to wired (surprisingly good)
- $2,000 professional RME audio interface: Still ~100ms in games
Root Causes:
- The audio device itself isn't the main culprit
- The game's audio processing adds significant latency
- Windows audio stack adds substantial latency
- Each step in the audio chain adds safety margins and buffers
Game Comparison: Different games have vastly different audio processing times:
- CS2: Higher latency
- Overwatch: More than twice as long as Valorant
- Valorant: Better audio performance
The Solution Exists: The music production world solved latency problems decades ago using ASIO (Audio Stream Input/Output), which allows applications to talk directly to audio devices, bypassing Windows' laggy audio stack. However:
- Games don't support ASIO
- ASIO was traditionally single-client (no Discord/Spotify simultaneously)
- Multi-client ASIO drivers already exist for professional interfaces

Conclusion: The technology to fix gaming audio latency already exists, but it's locked behind a door that game developers haven't opened. The video calls for bringing audio latency down to match the incredible advancements in visual latency that have occurred in recent years.