{"session":{"id":"zesty-noise","working_directory":"/Users/root1","backend_type":"Windsurf","model":"glm-5-2","agent_mode":"bypass","created_at":"2026-06-28T05:07:40+00:00","last_activity_at":"2026-06-28T06:45:57+00:00","title":"Download, transcribe, and summarize X audio","main_chain_id":200,"shell_last_seen_index":0,"cogs_json":[{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/profile"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":[{"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://windsurf.com/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills/<name>/SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-for-terminal` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n<example>\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n</example>\n\n<example>\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n</example>\n\n<example>\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n</example>\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `<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.\n\nCitation format:\n- `<ref_file file=\"/absolute/path/to/file\" />` - Reference an entire file\n- `<ref_snippet file=\"/absolute/path/to/file\" lines=\"start-end\" />` - Reference specific lines in a file\n\n<example>\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. <ref_snippet file=\"/home/ubuntu/repos/project/src/services/process.ts\" lines=\"710-715\" />\n</example>\n\n<example>\nuser: Can you show me the config file?\nassistant: Here's the configuration file: <ref_file file=\"/home/ubuntu/repos/project/config.json\" />\n</example>\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `<truncation_notice>` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n<bullet points>\n\n#### Test plan\n<checklist>\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n<example>\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n</example>\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n<example>\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n</example>\n\nUsers 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.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `<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.\n\n\n\n# Tool Tips\n\n## Shell\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"playwright\"},{\"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\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"normal"},"created_at":"2026-06-28T05:07:26.189995Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"df56de90-b2b1-4295-a8c4-dc8c030b21a7"}],"tool_availability":{"BlockList":[{"Name":{"exact":"exit_plan_mode"}}]},"append_system_messages":[]},{"model":"glm-5-2","source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/model"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":{"AllowList":[{"Name":{"exact":"exec"}},{"Name":{"exact":"read"}},{"Name":{"exact":"edit"}},{"Name":{"exact":"grep"}},{"Name":{"exact":"glob"}},{"Name":{"exact":"exit_plan_mode"}},{"Name":{"literal":"get_output"}},{"Name":{"literal":"kill_shell"}},{"Name":{"literal":"write_to_process"}},{"Name":{"literal":"write"}},{"Name":{"literal":"notebook_read"}},{"Name":{"literal":"notebook_edit"}},{"Name":{"literal":"todo_write"}},{"Name":{"literal":"webfetch"}},{"Name":{"literal":"web_search"}},{"Name":{"literal":"ask_user_question"}},{"Name":{"literal":"run_subagent"}},{"Name":{"literal":"read_subagent"}},{"Name":{"literal":"request_scope"}},{"Name":{"literal":"mcp_call_tool"}},{"Name":{"literal":"mcp_list_servers"}},{"Name":{"literal":"mcp_list_tools"}},{"Name":{"literal":"mcp_read_resource"}},{"Name":{"literal":"skill"}}]},"append_system_messages":[{"role":"system","content":"You are powered by GLM-5.2 High.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-06-28T05:07:26.191082Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"63dfde65-eb13-4273-ae82-d0e833974f8b"}]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/parallel-tool-calls"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Subagent","context":[],"lifetime":{"Unique":"core/read_only_kinds"},"permissions":[[{"Tool":{"Kind":"read"}},"Allow"],[{"Tool":{"Kind":"search"}},"Allow"],[{"Tool":{"Kind":"think"}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/subagent_profiles"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[{"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"subagent_profiles"},"created_at":"2026-06-28T05:07:26.191972Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"437e981c-7bd4-44de-a202-382597d3a489"}]},{"model":null,"source":"Managed","context":[],"lifetime":{"Unique":"workspace/config_protection"},"permissions":[[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/cli/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.cognition/**/config*.json"}}},"ForceAsk"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Base","context":[],"lifetime":{"Unique":"workspace/read_permissions"},"permissions":[[{"Scope":{"Read":{"glob":"/Users/root1"}}},"Allow"],[{"Scope":{"Read":{"glob":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501"}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"UserOverride","context":[],"lifetime":{"Unique":"workspace/project_override_permissions"},"permissions":[[{"Scope":{"Command":{"matcher":{"Prefix":"brightness"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"osascript"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"pkill"}}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"workspace/context"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/accept_edits"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/bypass"},"permissions":[["AnyScope","Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/autonomous"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]}],"workspace_dirs":null,"hidden":false,"metadata":"{\"total_credit_cost\":0,\"total_acu_cost\":0.0}"},"message_count":127,"messages":[{"message_id":"df56de90-b2b1-4295-a8c4-dc8c030b21a7","node_id":16,"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://windsurf.com/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills/<name>/SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-for-terminal` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n<example>\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n</example>\n\n<example>\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n</example>\n\n<example>\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n</example>\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `<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.\n\nCitation format:\n- `<ref_file file=\"/absolute/path/to/file\" />` - Reference an entire file\n- `<ref_snippet file=\"/absolute/path/to/file\" lines=\"start-end\" />` - Reference specific lines in a file\n\n<example>\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. <ref_snippet file=\"/home/ubuntu/repos/project/src/services/process.ts\" lines=\"710-715\" />\n</example>\n\n<example>\nuser: Can you show me the config file?\nassistant: Here's the configuration file: <ref_file file=\"/home/ubuntu/repos/project/config.json\" />\n</example>\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `<truncation_notice>` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n<bullet points>\n\n#### Test plan\n<checklist>\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n<example>\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n</example>\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n<example>\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n</example>\n\nUsers 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.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `<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.\n\n\n\n# Tool Tips\n\n## Shell\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"playwright\"},{\"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\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","created_at":"2026-06-28T05:07:26.189995Z","finish_reason":null,"metrics":null},{"message_id":"437e981c-7bd4-44de-a202-382597d3a489","node_id":17,"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","created_at":"2026-06-28T05:07:26.191972Z","finish_reason":null,"metrics":null},{"message_id":"63dfde65-eb13-4273-ae82-d0e833974f8b","node_id":18,"role":"system","content":"You are powered by GLM-5.2 High.","created_at":"2026-06-28T05:07:26.191082Z","finish_reason":null,"metrics":null},{"message_id":"ecdbe596-469d-40c6-a57e-11d464977548","node_id":19,"role":"system","content":"<system_info>\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n  /Users/root1 (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Saturday, 2026-06-27\n\n</system_info>","created_at":"2026-06-28T05:07:25.559773Z","finish_reason":null,"metrics":null},{"message_id":"b8293a4c-6b17-4bb2-9669-3cc3555fb1ca","node_id":20,"role":"system","content":"<rules type=\"always-on\">\n<rule name=\"global_rules\" path=\"/Users/root1/.codeium/windsurf/memories/global_rules.md\">\n\n</rule>\n\n<rule name=\"AGENTS\" path=\"/Users/root1/AGENTS.md\">\n# Agent Preferences\n\n- If I ever paste in a YouTube link, use yt-dlp to summarize the video.\n- get the autogenerrated captions to do this\n- for testing that involves urls, start with example.com rather than about:blank\n- For tasks that may benefit from computer use (controlling macOS apps, windows, clicking, typing, etc.), use the background-computer-use skill to control local macOS apps through the BackgroundComputerUse API\n\n## File search via fff MCP\n\nFor any file search or grep in the current git-indexed project directory, prefer the **fff** MCP tools\n(`mcp__fff__grep`, `mcp__fff__find_files`, `mcp__fff__multi_grep`) over the built-in grep/glob tools.\nfff is frecency-ranked, git-aware, and more token-efficient.\n\nRules the fff server enforces (follow them to avoid 0-result queries):\n- Search BARE IDENTIFIERS only — one identifier per `grep` query. No `load.*metadata.*Foo` style regex.\n- Don't use regex unless you truly need alternation; `.*`, `\\d+`, `\\s+` almost always return 0 results.\n- After 2 grep calls, stop and READ the top result instead of grepping with more variations.\n- Use `multi_grep` for OR logic across multiple identifiers (e.g. snake_case + PascalCase variants) in one call.\n- Have a specific name → `grep`. Exploring a topic / finding files → `find_files`.\n\nThe `fff-mcp` binary lives at `/Users/root1/.local/bin/fff-mcp` and is registered at user scope\nin `~/.config/devin/config.json`. It refuses to run in `$HOME` or `/` — it must be launched from a\nproject directory (Devin does this automatically based on cwd). Update with:\n`curl -fsSL https://raw.githubusercontent.com/dmtrKovalenko/fff.nvim/main/install-mcp.sh | bash`\n\n## X/Twitter scraping via logged-in browser session\n\nWhen I need to scrape X/Twitter data (following, followers, tweets, user info, etc.),\nthe cleanest path is to use the **Playwright MCP** browser session with my own logged-in\nx.com account, rather than spinning up twscrape's account-pool flow. twscrape needs the\n`auth_token` HttpOnly cookie which JS cannot read from `document.cookie`; the browser\nsession attaches all cookies automatically.\n\n### Flow\n1. `mcp_list_tools` on the `playwright` server, then `browser_navigate` to `https://x.com`.\n2. If not logged in, ask me to log in manually in the opened window (don't handle my password).\n3. Once on `https://x.com/home`, read `ct0` from `document.cookie`:\n   `document.cookie.match(/ct0=([^;]+)/)[1]`\n4. Call X's GraphQL endpoints directly via `fetch()` inside `browser_evaluate`. Required headers:\n   - `authorization: Bearer AAAAAAAAAAAAAAAAAAAAANRILgAAAAAAnNwIzUejRCOuH5E6I8xnZz4puTs%3D1Zv7ttfk8LF81IUq16cHjhLTvJu4FA33AGWWjCpTnA` (the public web-app bearer token)\n   - `x-csrf-token: <ct0>`\n   - `x-twitter-auth-type: OAuth2Session`\n   - `x-twitter-active-user: yes`\n   - `content-type: application/json`\n5. Paginate timelines by reading `content.cursorType === \"Bottom\"` entries and passing\n   the value back as `variables.cursor` until it stops changing.\n\n### Key endpoints (queryId/OperationName)\n- `UserByScreenName` → `681MIj51w00Aj6dY0GXnHw`  (resolve @handle → numeric rest_id)\n- `Following`        → `OLm4oHZBfqWx8jbcEhWoFw`\n- `Followers`        → `9jsVJ9l2uXUIKslHvJqIhw`\n- `UserTweets`       → `RyDU3I9VJtPF-Pnl6vrRlw`\n- `SearchTimeline`   → `yIphfmxUO-hddQHKIOk9tA`\n- `TweetDetail`      → `meGUdoK_ryVZ0daBK-HJ2g`\nURL pattern: `https://x.com/i/api/graphql/<queryId>/<OpName>?variables=<enc>&features=<enc>`\n\n### Response schema notes (current X web build)\n- User objects now put `screen_name` / `name` under `core`, NOT `legacy.screen_name`.\n  twscrape's parser still reads `legacy.screen_name` and returns empty — needs updating.\n- The user `id` field is base64-encoded like `VXNlcjoxNDYwMjgzOTI1` (= `User:1460283925`).\n  Decode with `atob(u.id).split(':')[1]` to get the numeric rest_id. `u.rest_id` may also\n  be present directly.\n- `is_blue_verified` is the verified flag. `legacy.followers_count`, `legacy.description`\n  still exist under `legacy`.\n- Filter timeline entries by `content.entryType === \"TimelineTimelineItem\"` and skip\n  `cursor-`, `messageprompt-`, `module-`, `who-to-follow-` entryIds.\n\n### Features dict\nUse the full `GQL_FEATURES` block from twscrape's `api.py` — without it X returns\n`(336) The following features cannot be null`. Pass it URL-encoded as the `features` param.\n\n### Where things live\n- Output CSV:  `~/Downloads/utilities/sdand_following.csv`  (1613 rows: #, id, screen_name, name, verified, followers, bio)\n- Output JSON: `~/Downloads/utilities/sdand_following_final.json` (double-encoded JSON string; parse with `json.loads(json.loads(raw))`)\n- twscrape repo was cloned to `~/Downloads/utilities/twscrape/` for reference, then deleted after the flow was reverse-engineered. Re-clone from https://github.com/vladkens/twscrape.git if needed.\n\n</rule>\n</rules>","created_at":"2026-06-28T05:07:26.199763Z","finish_reason":null,"metrics":null},{"message_id":"ef08a86f-512a-462f-959b-1c6320854a94","node_id":21,"role":"user","content":"use cobalt.tools,, download the audio, and then run transcription, then summarize https://x.com/dwarkesh_sp/status/2070551894674555081","created_at":"2026-06-28T05:07:40.861041Z","finish_reason":null,"metrics":null},{"message_id":"d0fccb7d-c9b5-4c71-8ecb-5b0ff527a07f","node_id":22,"role":"system","content":"<available_skills>\nThe following skills can be invoked using the `skill` tool. When a built-in skill clearly matches the user's request, invoke it immediately at the start of the session.\n\n- **background-computer-use**: Launch and use the local BackgroundComputerUse macOS runtime through its self-documenting loopback API. Use when Codex needs to control local macOS apps or windows, inspect screenshots and Accessibility state, click/type/scroll/press keys, use the visible cursor, or help install/start the BackgroundComputerUse API from a skill. (source: /Users/root1/.devin/skills/background-computer-use/SKILL.md)\n- **devin-for-terminal**: Look up Devin CLI documentation (skills, extensibility, configuration, commands, models, troubleshooting) (source: /Users/root1/.local/share/devin/cli/_versions/2026.8.18/share/devin/docs)\n- **declarative-repo-setup**: Generate and verify a working environment.yaml (Devin snapshot-setup blueprint) for a repo (source: builtin:drs)\n</available_skills>","created_at":"2026-06-28T05:07:40.864300Z","finish_reason":null,"metrics":null},{"message_id":"412f9042-ba81-462d-aa88-11a82e99d5a4","node_id":24,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a4ff3167d13197ca","name":"todo_write","arguments":{"todos":[{"content":"Use cobalt.tools API to download audio from the X tweet","status":"in_progress"},{"content":"Run transcription on the downloaded audio","status":"pending"},{"content":"Summarize the transcription","status":"pending"}]}},{"id":"chatcmpl-tool-93d30924b1182a1e","name":"exec","arguments":{"command":"which ffmpeg whisper yt-dlp && cobalt --version 2>/dev/null; echo \"---\"; ls ~/Downloads/utilities/ 2>/dev/null | head"}}],"thinking":"The user wants me to:\n1. Use cobalt.tools to download audio from an X/Twitter video\n2. Run transcription on the audio\n3. Summarize the content\n\nLet me start by understanding cobalt.tools - it's an API for downloading media. Let me check what's available and set up a plan.\n\nLet me first check what tools I have available for this. I'll need to:\n1. Use cobalt API to download the audio from the tweet\n2. Transcribe the audio (need a transcription tool - whisper?)\n3. Summarize the transcription\n\nLet me start by checking what's available on the system.","created_at":"2026-06-28T05:07:46.338230Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1120,"total_time_ms":5447,"input_tokens":3318,"output_tokens":243,"cache_read_tokens":11468,"cache_creation_tokens":null,"tpot_ms":17.80658436213992,"tokens_per_sec":56.15900161774902}},{"message_id":"f0392083-5dff-4351-9858-8c980a283cba","node_id":25,"role":"tool","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable\nCurrent todo list:\n1. [~] Use cobalt.tools API to download audio from the X tweet [in_progress]\n2. [ ] Run transcription on the downloaded audio [pending]\n3. [ ] Summarize the transcription [pending]\n","created_at":"2026-06-28T05:07:46.383937Z","finish_reason":null,"metrics":null},{"message_id":"694cd010-2d8b-4a1c-ae9b-659af7af68c4","node_id":26,"role":"tool","content":"Output from command in shell 230e2c:\n/opt/homebrew/bin/ffmpeg\n/Library/Frameworks/Python.framework/Versions/3.12/bin/whisper\n/Library/Frameworks/Python.framework/Versions/3.12/bin/yt-dlp\n---\nbenchmarks-prompts\ndeep-swe\nfrontier-swe\nterminal-bench-2\n\n\nExit code: 0","created_at":"2026-06-28T05:07:46.383948Z","finish_reason":null,"metrics":null},{"message_id":"585e69b9-1a1c-47dd-8c4e-3d8542420594","node_id":28,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b75d9de198b02d49","name":"exec","arguments":{"command":"mkdir -p ~/Downloads/utilities/tweet_audio && cd ~/Downloads/utilities/tweet_audio && curl -sS -X POST \"https://api.cobalt.tools/\" \\\n  -H \"Accept: application/json\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"url\":\"https://x.com/dwarkesh_sp/status/2070551894674555081\",\"audioFormat\":\"mp3\",\"downloadMode\":\"audio\",\"filenameStyle\":\"basic\"}' \\\n  -w \"\\nHTTP_STATUS:%{http_code}\\n\""}}],"thinking":"Good, I have ffmpeg, whisper, and yt-dlp. Let me try cobalt.tools API. The public cobalt API is at https://api.cobalt.tools. Let me use it to download the audio.\n\nCobalt API endpoint: POST https://api.cobalt.tools/ with JSON body:\n```json\n{\n  \"url\": \"https://x.com/dwarkesh_sp/status/2070551894674555081\",\n  \"audioFormat\": \"mp3\",\n  \"downloadMode\": \"audio\"\n}\n```\n\nWait, the cobalt API has changed over time. Let me check the current format. Actually, let me just try it.","created_at":"2026-06-28T05:07:52.112307Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1168,"total_time_ms":5726,"input_tokens":408,"output_tokens":271,"cache_read_tokens":14785,"cache_creation_tokens":null,"tpot_ms":16.819188191881917,"tokens_per_sec":59.45590171127687}},{"message_id":"a735fa02-73ae-4726-a75b-9bee4bb2aceb","node_id":29,"role":"tool","content":"Output from command in shell 231506:\n{\"status\":\"error\",\"error\":{\"code\":\"error.api.auth.jwt.missing\"}}\nHTTP_STATUS:400\n\n\nExit code: 0","created_at":"2026-06-28T05:07:52.542241Z","finish_reason":null,"metrics":null},{"message_id":"1e167b70-c50a-4fc4-9967-e91508163948","node_id":31,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b40df682a78642cf","name":"web_search","arguments":{"query":"cobalt.tools api public instance no auth 2026 audio download"}}],"thinking":"The public cobalt.tools API now requires JWT auth. Let me try the alternative public instances or use a different approach. Let me check if there are other public cobalt instances. Actually, I should just try a few known public instances.\n\nAlternatively, I could just use yt-dlp directly to download the audio from the tweet. But the user specifically asked for cobalt.tools. Let me try a few cobalt mirrors first.\n\nActually, let me check the cobalt API docs. The main instance now requires authentication. Let me try some public mirror instances.","created_at":"2026-06-28T05:08:01.136689Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1272,"total_time_ms":8591,"input_tokens":313,"output_tokens":136,"cache_read_tokens":15192,"cache_creation_tokens":null,"tpot_ms":53.81617647058823,"tokens_per_sec":18.581773466320538}},{"message_id":"4bef42dd-41fb-4974-b9ff-c891793875c2","node_id":32,"role":"tool","content":"# Web Search Results for \"cobalt.tools api public instance no auth 2026 audio download\"\n\n## 1. important notice for instance hosters and public v7 api users (api.cobalt.tools) · imputnet cobalt · Discussion #860 · GitHub\nURL: https://github.com/imputnet/cobalt/discussions/860\n\nimportant notice for instance hosters and public v7 api users (api.cobalt.tools) · imputnet cobalt · Discussion #860 · GitHub\n...\nimportant notice for instance hosters and public v7 api users (api.cobalt.tools) #860\n...\nseveral bad actors are continuously scraping public cobalt instances for youtube videos. as a result, many (sometimes personal) instances either run out of bandwidth, cookies, or experience reduced performance. last week it got especially bad, completely knocking out several community instances.\n...\nwe've implemented several types of authentication that hosters can use to protect their instances. they can use turnstile, api keys, or both at the same time.\n...\nas for protecting the main instance, we've enabled turnstile and api keys. we plan on keeping it this way for the foreseeable future.\n...\nif you use the old v7 api (/api/json), it will be shut down on november 11th, 2024. unfortunately, scraping gets worse as cobalt becomes more known, and we can't keep up the free public api without impacting the regular users of cobalt.tools. you can host your own cobalt instance to keep using the cobalt api after that date. you can see a timeline for public api brownout in this post.\n...\ninstance lists will not go back up as-is. all future lists will be opt-in and instances must be added only after hoster's explicit consent to participate.\n...\ni made an instruction document that will guide you through the process of configuring cloudflare turnstile on your instance. it also covers the configuration of api keys in case you don't use your instance from a web interface.\n...\nif you're not using the latest cobalt api package and want to use all the security features, now is the perfect time to upgrade. aside from new features, you will also be able to use cobalt without hosting your own frontend instance. all you need to do is add your processing instance domain in cobalt settings. support for api keys in web interface is coming very soon!\n...\nif you don't host an...\n\n## 2. docs/api.md at current · imputnet/cobalt\nURL: https://github.com/imputnet/cobalt/blob/current/docs/api.md\n\n> [!IMPORTANT]\n> hosted api instances (such as `api.cobalt.tools`) use bot protection and are **not** intended to be used in other projects without explicit permission. if you want to use the cobalt api, you should [host your own instance](/docs/run-an-instance.md) or ask an instance owner for access.\n...\nan api instance may be configured to require you to authenticate yourself.\nif this is the case, you will typically receive an [error response](#error-response)\nwith a **`api.auth.<method>.missing`** code, which tells you that a particular method\nof authentication is required.\n...\n, cobalt supports\n...\n| key               | type      | description/value                                               | default    |\n|:------------------|:----------|:----------------------------------------------------------------|:-----------|\n| `url`             | `string`  | source URL                                                      | *required* |\n| `audioBitrate`    | `string`  | `320 / 256 / 128 / 96 / 64 / 8` (kbps)                          | `128`      |\n| `audioFormat`     | `string`  | `best / mp3 / ogg / wav / opus`                                 | `mp3`      |\n| `downloadMode`    | `string`  | `auto / audio / mute`                                           | `auto`     |\n...\n| `filenameStyle`   | `string`  | `classic / pretty / basic / nerdy`                              | `basic`    |\n...\n| `videoQuality`    | `string`  | `max / 4320 / 2160 / 1440 / 1080 / 720 / 480 / 360 / 240 / 144` | `1080`     |\n...\n| `alwaysProxy`     |\n...\nnecessary                |\n...\n`    |\n...\n| `localProcessing` | `string`  | `\n...\n/ preferred / forced`                                 |\n...\ndisabled` |\n...\n| key                     | type      | description/value                                 | default |\n|:------------------------|:----------|:--------------------------------------------------|:--------|\n| `youtubeVideoCodec`     | `string`  | `h264 / av1 / vp9`                            ...\n\n## 3. docs/api.md at main · imputnet/cobalt\nURL: https://github.com/imputnet/cobalt/blob/main/docs/api.md\n\n> [!IMPORTANT]\n> hosted api instances (such as `api.cobalt.tools`) use bot protection and are **not** intended to be used in other projects without explicit permission. if you want to use the cobalt api, you should [host your own instance](/docs/run-an-instance.md) or ask an instance owner for access.\n...\nan api instance may be configured to require you to authenticate yourself.\nif this is the case, you will typically receive an [error response](#error-response)\nwith a **`api.auth.<method>.missing`** code, which tells you that a particular method\nof authentication is required.\n...\n| key               | type      | description/value                                               | default    |\n|:------------------|:----------|:----------------------------------------------------------------|:-----------|\n| `url`             | `string`  | source URL                                                      | *required* |\n| `audioBitrate`    | `string`  | `320 / 256 / 128 / 96 / 64 / 8` (kbps)                          | `128`      |\n| `audioFormat`     | `string`  | `best / mp3 / ogg / wav / opus`                                 | `mp3`      |\n| `downloadMode`    | `string`  | `auto / audio / mute`                                           | `auto`     |\n...\n| `filenameStyle`   | `string`  | `classic / pretty / basic / nerdy`                              | `basic`    |\n...\n| `videoQuality`    | `string`  | `max / 4320 / 2160 / 1440 / 1080 / 720 / 480 / 360 / 240 / 144` | `1080`     |\n...\n| `alwaysProxy`     |\n...\nall files,\n...\nnecessary                | `false`    |\n...\n| `localProcessing` | `string`  | `\n...\n/ preferred / forced`                                 |\n...\ndisabled` |\n...\n| key                     | type      | description/value                                 | default |\n|:------------------------|:----------|:--------------------------------------------------|:--------|\n| `youtubeVideoCodec`     | `string`  | `h264 / av1 / vp9`                                |...\n\n## 4. imputnet/cobalt\nURL: https://github.com/imputnet/cobalt?tab=readme-ov-file\n\n# Repository: imputnet/cobalt\n\nbest way to save what you love\n\n- Stars: 41212\n- Forks: 3531\n- Watchers: 172\n- Open issues: 251\n- Primary language: Svelte\n- Languages: Svelte (40.9%), JavaScript (39.9%), TypeScript (17.3%), CSS (1.5%), HTML (0.3%), Dockerfile\n- License: GNU Affero General Public License v3.0 (AGPL-3.0)\n- Topics: collaboration, downloader, instagram, javascript, music, reddit, social-media, soundcloud, svelte, tiktok, twitter, typescript, video, vimeo, vk, webapp, youtube, youtube-downloader\n- Default branch: main\n- Homepage: https://cobalt.tools\n- Created: 2022-07-08T18:12:55Z\n- Last push: 2026-04-06T11:59:56Z\n- Contributors: 50 (top: wukko, dumbmoron, lexito-o, Snazzah, hyperdefined, KwiatekMiki, LyfeV, zImPatrick, lathlager, jsopn)\n\n---\n\n \n \n \n \n \n \n best way to save what you love\n \n \n cobalt.tools\n \n \n \n \n 💬 community discord server\n \n \n \n 🐦 twitter\n \n \n 🦋 bluesky\n \n \n \n \n\ncobalt is a media downloader that doesn't piss you off. it's friendly, efficient, and doesn't have ads, trackers, paywalls or other nonsense.\n\npaste the link, get the file, move on. that simple, just how it should be.\n\n### cobalt monorepo\n\nthis monorepo includes source code for api, frontend, and related packages:\n\n- [api tree & readme](/api/)\n- [web tree & readme](/web/)\n- [packages tree](/packages/)\n\nit also includes documentation in the [docs tree](/docs/):\n\n- [how to run a cobalt instance](/docs/run-an-instance.md)\n- [how to protect a cobalt instance](/docs/protect-an-instance.md)\n- [cobalt api instance environment variables](/docs/api-env-variables.md)\n- [cobalt api documentation](/docs/api.md)\n\n### ethics\n\ncobalt is a tool that makes downloading public content easier. it takes **zero liability**.\nthe end user is responsible for what they download, how they use and distribute that content.\ncobalt never caches any content, it [works like a fancy proxy](/api/src/stream/).\n\ncobalt is in no way a piracy tool and cannot be used as such.\nit can only download free & publicly acces...\n\n## 5. Allow GET requests to the API via query string. · Issue #707 · imputnet/cobalt\nURL: https://github.com/imputnet/cobalt/issues/707\n\n# Issue: imputnet/cobalt #707\n\n- Repository: imputnet/cobalt | best way to save what you love | 41K stars | Svelte\n\n## Allow GET requests to the API via query string.\n\n- Author: [@SmilerRyan](https://github.com/SmilerRyan)\n- State: closed (not_planned)\n- Labels: feature request\n- Created: 2024-09-01T21:23:34Z\n- Updated: 2024-09-02T00:43:24Z\n- Closed: 2024-09-02T00:40:34Z\n- Closed by: [@wukko](https://github.com/wukko)\n\n### Allow GET requests to the API via query string\n\nCurrently in order to use this for my needs, i need to have a direct url in order to download video/audio and i'd like the ability to not have to host my own relay to convert GET request (in my case with just a youtube video id) to a POST request, then redirect with the url provided.\n\n### Additional Context\n\nFor example, the Discord bot I use seems to work great when using a direct url to media rather to music now, so I have to run an API of my own that calls the Cobalt API purely to receive the URL and redirect the user to it.\n\n### Example Code\n\nHere is how this currently looks like as a POST request vs a GET request.\n\n```js\n// POST request get get a stream URL\nawait fetch(\"https://api.cobalt.tools/api/json\", {\n    headers: {\n        \"Accept\": \"application/… (6 chars truncated)\n… (26 lines truncated)\n<truncation_notice>\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/c58e6c79/content.txt\n</truncation_notice>","created_at":"2026-06-28T05:08:02.601187Z","finish_reason":null,"metrics":null},{"message_id":"bb1e0627-b649-4755-a601-2d245b67c307","node_id":34,"role":"assistant","content":"The main `api.cobalt.tools` now requires JWT/API-key auth. Let me try some community-hosted public cobalt instances.","tool_calls":[{"id":"chatcmpl-tool-a3da66495caf8f26","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && for host in \"https://co.wuk.sh/api/json\" \"https://cobalt-api.kwiatekmiki.com/api/json\" \"https://dwnld.nichind.dev/api/json\" \"https://cobalt-api.canine.tools/api/json\" \"https://cobalt.synzr.ru/api/json\"; do\n  echo \"=== $host ===\"\n  curl -sS -m 15 -X POST \"$host\" \\\n    -H \"Accept: application/json\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\"url\":\"https://x.com/dwarkesh_sp/status/2070551894674555081\",\"audioFormat\":\"mp3\",\"downloadMode\":\"audio\",\"filenameStyle\":\"basic\"}' \\\n    -w \"\\nHTTP_STATUS:%{http_code}\\n\" 2>&1 | head -20\n  echo\ndone"}}],"thinking":"The main cobalt.tools API requires authentication now. Let me try to find a public instance that doesn't require auth, or I could just use yt-dlp as a fallback since the user wants the audio downloaded. But the user specifically asked for cobalt.tools.\n\nLet me try a few known public cobalt instances. There are community-hosted instances. Let me search for working ones.\n\nActually, let me try the cobalt.tools web frontend approach - it might use a different endpoint. Or I can try some known public instances like:\n- https://co.wuk.sh/api/json (older)\n- https://cobalt-api.kwiatekmiki.com\n- https://dwnld.nichind.dev\n\nLet me try a few in parallel.","created_at":"2026-06-28T05:08:11.942240Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1438,"total_time_ms":9337,"input_tokens":2707,"output_tokens":385,"cache_read_tokens":15504,"cache_creation_tokens":null,"tpot_ms":20.51688311688312,"tokens_per_sec":48.74034687935182}},{"message_id":"08a612db-ae1c-40e7-9f19-7d050f488146","node_id":35,"role":"tool","content":"Output from command in shell bf5385:\n=== https://co.wuk.sh/api/json ===\ncurl: (6) Could not resolve host: co.wuk.sh\n\nHTTP_STATUS:000\n\n=== https://cobalt-api.kwiatekmiki.com/api/json ===\n<!DOCTYPE html>\n<!--[if lt IE 7]> <html class=\"no-js ie6 oldie\" lang=\"en-US\"> <![endif]-->\n<!--[if IE 7]>    <html class=\"no-js ie7 oldie\" lang=\"en-US\"> <![endif]-->\n<!--[if IE 8]>    <html class=\"no-js ie8 oldie\" lang=\"en-US\"> <![endif]-->\n<!--[if gt IE 8]><!--> <html class=\"no-js\" lang=\"en-US\"> <!--<![endif]-->\n<head>\n<title>Attention Required! | Cloudflare</title>\n<meta charset=\"UTF-8\" />\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=UTF-8\" />\n<meta http-equiv=\"X-UA-Compatible\" content=\"IE=Edge\" />\n<meta name=\"robots\" content=\"noindex, nofollow\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1\" />\n<link rel=\"stylesheet\" id=\"cf_styles-css\" href=\"/cdn-cgi/styles/cf.errors.css\" />\n<!--[if lt IE 9]><link rel=\"stylesheet\" id='cf_styles-ie-css' href=\"/cdn-cgi/styles/cf.errors.ie.css\" /><![endif]-->\n<style>body{margin:0;padding:0}</style>\n\n\n<!--[if gte IE 10]><!-->\n<script>\n  if (!navigator.cookieEnabled) {\n\n=== https://dwnld.nichind.dev/api/json ===\n{\"detail\":\"Not Found\"}\nHTTP_STATUS:404\n\n=== https://cobalt-api.canine.tools/api/json ===\ncurl: (6) Could not resolve host: cobalt-api.canine.tools\n\nHTTP_STATUS:000\n\n=== https://cobalt.synzr.ru/api/json ===\ncurl: (6) Could not resolve host: cobalt.synzr.ru\n\nHTTP_STATUS:000\n\n\n\nExit code: 0","created_at":"2026-06-28T05:08:12.953469Z","finish_reason":null,"metrics":null},{"message_id":"a9987db5-95a4-4b5d-9347-8afb1e574797","node_id":37,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-961ab1b769cb8f04","name":"webfetch","arguments":{"url":"https://instances.cobalt.best/"}},{"id":"chatcmpl-tool-814bda3e5f1bd66f","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && for host in \"https://cobalt-api.kwiatekmiki.com/\" \"https://dwnld.nichind.dev/\" \"https://cobalt.synzr.ru/\" \"https://api.cobalt.tools/\" \"https://cobalt-api.ayo.tf/\"; do\n  echo \"=== $host ===\"\n  curl -sS -m 15 -X POST \"$host\" \\\n    -H \"Accept: application/json\" \\\n    -H \"Content-Type: application/json\" \\\n    -d '{\"url\":\"https://x.com/dwarkesh_sp/status/2070551894674555081\",\"audioFormat\":\"mp3\",\"downloadMode\":\"audio\",\"filenameStyle\":\"basic\"}' \\\n    -w \"\\nHTTP_STATUS:%{http_code}\\n\" 2>&1 | head -5\n  echo\ndone"}}],"thinking":"The old `/api/json` endpoint is deprecated. The new API is just POST to the root `/`. Let me try the new endpoint format on a few instances. Also, let me check the official cobalt.tools instance list. Let me look at the current docs for the API path.\n\nActually, looking at the docs, the new API path is just `/` (POST). Let me retry with the correct path. Also let me check the public instances list on cobalt.tools.","created_at":"2026-06-28T05:08:20.743938Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1877,"total_time_ms":7784,"input_tokens":832,"output_tokens":311,"cache_read_tokens":18210,"cache_creation_tokens":null,"tpot_ms":18.993569131832796,"tokens_per_sec":52.6493990181141}},{"message_id":"2e7de540-7770-4c14-b25f-2ba0862782a5","node_id":38,"role":"tool","content":"Failed to fetch URL 'https://instances.cobalt.best/'.","created_at":"2026-06-28T05:08:22.390305Z","finish_reason":null,"metrics":null},{"message_id":"419c3c86-aefe-4c85-9a46-cffd924fee43","node_id":39,"role":"tool","content":"Output from command in shell 627c44:\n=== https://cobalt-api.kwiatekmiki.com/ ===\n<!DOCTYPE html>\n<!--[if lt IE 7]> <html class=\"no-js ie6 oldie\" lang=\"en-US\"> <![endif]-->\n<!--[if IE 7]>    <html class=\"no-js ie7 oldie\" lang=\"en-US\"> <![endif]-->\n<!--[if IE 8]>    <html class=\"no-js ie8 oldie\" lang=\"en-US\"> <![endif]-->\n<!--[if gt IE 8]><!--> <html class=\"no-js\" lang=\"en-US\"> <!--<![endif]-->\n\n=== https://dwnld.nichind.dev/ ===\n{\"status\":\"tunnel\",\"url\":\"http://130.61.113.238:9000/tunnel?id=6uS-QKEg8OE_4pQk_IA4e&exp=1782623391696&sig=Df_Bpy62GZ5AH0xtuG-Bvc3IPVS1u-tSlVUdVFGiB9g&sec=duaNhK076CBVHk3RcIwA2h0GReBNU8VDMxsvEefScaY&iv=b7qwo61_xATKlylGJhjbkQ\",\"filename\":\"twitter_2070551894674555081_audio.mp3\",\"instance_info\":{\"url\":\"130.61.113.238:9000\"},\"latency_ms\":428,\"checked_instances\":11}\nHTTP_STATUS:200\n\n=== https://cobalt.synzr.ru/ ===\ncurl: (6) Could not resolve host: cobalt.synzr.ru\n\nHTTP_STATUS:000\n\n=== https://api.cobalt.tools/ ===\n{\"status\":\"error\",\"error\":{\"code\":\"error.api.auth.jwt.missing\"}}\nHTTP_STATUS:400\n\n=== https://cobalt-api.ayo.tf/ ===\nerror code: 1033\n\nHTTP_STATUS:530\n\n\n\nExit code: 0","created_at":"2026-06-28T05:08:22.390317Z","finish_reason":null,"metrics":null},{"message_id":"599d2962-f988-4c60-8d54-a2cd4a14811e","node_id":41,"role":"assistant","content":"`dwnld.nichind.dev` returned a tunnel URL. Let me download the audio file.","tool_calls":[{"id":"chatcmpl-tool-ab22ce450e9f12a0","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && curl -sS -L -o \"twitter_2070551894674555081_audio.mp3\" \"http://130.61.113.238:9000/tunnel?id=6uS-QKEg8OE_4pQk_IA4e&exp=1782623391696&sig=Df_Bpy62GZ5AH0xtuG-Bvc3IPVS1u-tSlVUdVFGiB9g&sec=duaNhK076CBVHk3RcIwA2h0GReBNU8VDMxsvEefScaY&iv=b7qwo61_xATKlylGJhjbkQ\" -w \"HTTP_STATUS:%{http_code} SIZE:%{size_download}\\n\" && ls -lh twitter_2070551894674555081_audio.mp3 && file twitter_2070551894674555081_audio.mp3"}}],"thinking":"`dwnld.nichind.dev` worked! It returned a tunnel URL for the audio file. Let me download it.","created_at":"2026-06-28T05:08:27.259293Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1457,"total_time_ms":4860,"input_tokens":766,"output_tokens":280,"cache_read_tokens":19041,"cache_creation_tokens":null,"tpot_ms":12.153571428571428,"tokens_per_sec":82.28034087569792}},{"message_id":"e1bf9511-8d12-4ac2-9301-12d67421407c","node_id":42,"role":"tool","content":"No output produced after 5.0 seconds. Command running in background with ID: c3e4bd. Run get_output with timeout if you need to wait.","created_at":"2026-06-28T05:08:32.284737Z","finish_reason":null,"metrics":null},{"message_id":"849c7600-e2bc-4188-95f3-66322d393be1","node_id":44,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-aab9ae2353c90f7a","name":"get_output","arguments":{"shell_id":"c3e4bd","timeout":60000}}],"created_at":"2026-06-28T05:08:33.520940Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1231,"input_tokens":319,"output_tokens":24,"cache_read_tokens":19806,"cache_creation_tokens":null,"tpot_ms":51.291666666666664,"tokens_per_sec":19.49634443541836}},{"message_id":"f6bd4189-1d43-441f-aa08-a4b42af1caa8","node_id":45,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:09:33.525194Z","finish_reason":null,"metrics":null},{"message_id":"72cd1406-bc0d-4a98-bccb-a2307c2b8b8c","node_id":47,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-aac76cac58412841","name":"get_output","arguments":{"shell_id":"c3e4bd","timeout":120000}}],"created_at":"2026-06-28T05:09:35.553781Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2024,"input_tokens":36,"output_tokens":24,"cache_read_tokens":20124,"cache_creation_tokens":null,"tpot_ms":84.33333333333333,"tokens_per_sec":11.857707509881422}},{"message_id":"a141f19d-e7a1-4a44-b45a-717cd4c12c3d","node_id":48,"role":"tool","content":"HTTP_STATUS:200 SIZE:19086089\n-rw-r--r--  1 root1  staff    18M Jun 27 22:09 twitter_2070551894674555081_audio.mp3\ntwitter_2070551894674555081_audio.mp3: Audio file with ID3 version 2.4.0, contains: MPEG ADTS, layer III, v1, 128 kbps, 48 kHz, JntStereo\n\n\nExit code: 0","created_at":"2026-06-28T05:09:56.749132Z","finish_reason":null,"metrics":null},{"message_id":"6e9aa49c-3b70-4089-a39d-5ee0a8d5a7e2","node_id":50,"role":"assistant","content":"Audio downloaded (18MB MP3). Now let me check duration and run transcription.","tool_calls":[{"id":"chatcmpl-tool-93063da6380ab8a4","name":"todo_write","arguments":{"todos":[{"content":"Use cobalt.tools API to download audio from the X tweet","status":"completed"},{"content":"Run transcription on the downloaded audio","status":"in_progress"},{"content":"Summarize the transcription","status":"pending"}]}},{"id":"chatcmpl-tool-bb4cfdc1aa0c0d94","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 twitter_2070551894674555081_audio.mp3 && whisper --help 2>&1 | head -30"}}],"thinking":"Audio downloaded successfully - 18MB MP3. Now let me run transcription. I have whisper available. Let me check the duration first and then run whisper.","created_at":"2026-06-28T05:10:00.416691Z","finish_reason":"tool_calls","metrics":{"ttft_ms":942,"total_time_ms":3665,"input_tokens":142,"output_tokens":189,"cache_read_tokens":20159,"cache_creation_tokens":null,"tpot_ms":14.407407407407407,"tokens_per_sec":69.40874035989718}},{"message_id":"19bc9992-53d5-488a-a398-2767c5c742fc","node_id":51,"role":"tool","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable\nCurrent todo list:\n1. [x] Use cobalt.tools API to download audio from the X tweet [completed]\n2. [~] Run transcription on the downloaded audio [in_progress]\n3. [ ] Summarize the transcription [pending]\n","created_at":"2026-06-28T05:10:02.146662Z","finish_reason":null,"metrics":null},{"message_id":"f6e0aa98-fdd1-4fa5-8d1a-0090ab35a79f","node_id":52,"role":"tool","content":"Output from command in shell b89e56:\n1192.872000\nusage: whisper [-h] [--model MODEL] [--model_dir MODEL_DIR] [--device DEVICE]\n               [--output_dir OUTPUT_DIR]\n               [--output_format {txt,vtt,srt,tsv,json,all}]\n               [--verbose VERBOSE] [--task {transcribe,translate}]\n               [--language {af,am,ar,as,az,ba,be,bg,bn,bo,br,bs,ca,cs,cy,da,de,el,en,es,et,eu,fa,fi,fo,fr,gl,gu,ha,haw,he,hi,hr,ht,hu,hy,id,is,it,ja,jw,ka,kk,km,kn,ko,la,lb,ln,lo,lt,lv,mg,mi,mk,ml,mn,mr,ms,mt,my,ne,nl,nn,no,oc,pa,pl,ps,pt,ro,ru,sa,sd,si,sk,sl,sn,so,sq,sr,su,sv,sw,ta,te,tg,th,tk,tl,tr,tt,uk,ur,uz,vi,yi,yo,yue,zh,Afrikaans,Albanian,Amharic,Arabic,Armenian,Assamese,Azerbaijani,Bashkir,Basque,Belarusian,Bengali,Bosnian,Breton,Bulgarian,Burmese,Cantonese,Castilian,Catalan,Chinese,Croatian,Czech,Danish,Dutch,English,Estonian,Faroese,Finnish,Flemish,French,Galician,Georgian,German,Greek,Gujarati,Haitian,Haitian Creole,Hausa,Hawaiian,Hebrew,Hindi,Hungarian,Icelandic,Indonesian,Italian,Japanese,Javanese,Kannada,Kazakh,Khmer,Korean,Lao,Latin,Latvian,Letzeburgesch,Lingala,Lithuanian,Luxembourgish,Macedonian,Malagasy,Malay,Malayalam,Maltese,Mandarin,Maori,Marathi,Moldavian,Moldovan,Mongolian,Myanmar,Nepali,Norwegian,Nynorsk,Occitan,Panjabi,Pashto,Persian,Polish,Portuguese,Punjabi,Pushto,Romanian,Russian,Sanskrit,Serbian,Shona,Sindhi,Sinhala,Sinhalese,Slovak,Slovenian,Somali,Spanish,Sundanese,Swahili,Swedish,Tagalog,Tajik,Tamil,Tatar,Telugu,Thai,Tibetan,Turkish,Turkmen,Ukrainian,Urdu,Uzbek,Valencian,Vietnamese,Welsh,Yiddish,Yoruba}]\n               [--temperature TEMPERATURE] [--best_of BEST_OF]\n               [--beam_size BEAM_SIZE] [--patience PATIENCE]\n               [--length_penalty LENGTH_PENALTY]\n               [--suppress_tokens SUPPRESS_TOKENS]\n               [--initial_prompt INITIAL_PROMPT]\n               [--carry_initial_prompt CARRY_INITIAL_PROMPT]\n               [--condition_on_previous_text CONDITION_ON_PREVIOUS_TEXT]\n               [--fp16 FP16]\n               [--temperature_increment_on_fallback TEMPERATURE_INCREMENT_ON_FALLBACK]\n               [--compression_ratio_threshold COMPRESSION_RATIO_THRESHOLD]\n               [--logprob_threshold LOGPROB_THRESHOLD]\n               [--no_speech_threshold NO_SPEECH_THRESHOLD]\n               [--word_timestamps WORD_TIMESTAMPS]\n               [--prepend_punctuations PREPEND_PUNCTUATIONS]\n               [--append_punctuations APPEND_PUNCTUATIONS]\n               [--highlight_words HIGHLIGHT_WORDS]\n               [--max_line_width MAX_LINE_WIDTH]\n               [--max_line_count MAX_LINE_COUNT]\n               [--max_words_per_line MAX_WORDS_PER_LINE] [--threads THREADS]\n               [--clip_timestamps CLIP_TIMESTAMPS]\n               [--hallucination_silence_threshold HALLUCINATION_SILENCE_THRESHOLD]\n               audio [audio ...]\n\npositional arguments:\n… (107 lines truncated)\n\nExit code: 0\n\n`| head -30` was parsed out (30 of 137 total lines shown).\n<truncation_notice>\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/604c1b5c/content.txt\n</truncation_notice>","created_at":"2026-06-28T05:10:02.146666Z","finish_reason":null,"metrics":null},{"message_id":"57cc96ce-36c9-407e-93f7-d32df166b547","node_id":54,"role":"assistant","content":"~20 min audio. Running Whisper transcription (medium model for accuracy) in the background.","tool_calls":[{"id":"chatcmpl-tool-8a8fd1a13bc45bef","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && whisper twitter_2070551894674555081_audio.mp3 --model medium.en --output_dir . --output_format txt --language English --verbose False 2>&1 | tail -50","run_in_background":true}}],"thinking":"~20 minutes of audio (1192 seconds). Let me run whisper with a medium model for good accuracy. I'll use the medium.en model since it's likely English (Dwarkesh podcast). Let me run it in the background and output to txt.","created_at":"2026-06-28T05:10:04.549375Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1034,"total_time_ms":2401,"input_tokens":1233,"output_tokens":141,"cache_read_tokens":20300,"cache_creation_tokens":null,"tpot_ms":9.695035460992909,"tokens_per_sec":103.14557425018288}},{"message_id":"58ead43f-6d63-4c39-a406-8d62eff3f2f4","node_id":55,"role":"tool","content":"Command running in background with ID: 70446e","created_at":"2026-06-28T05:10:04.567754Z","finish_reason":null,"metrics":null},{"message_id":"d67e1265-bacb-46fc-9095-d466e2ca2b41","node_id":57,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-afb9192bed2f5f6d","name":"get_output","arguments":{"shell_id":"70446e","timeout":120000}}],"created_at":"2026-06-28T05:10:06.298253Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1725,"input_tokens":158,"output_tokens":23,"cache_read_tokens":21532,"cache_creation_tokens":null,"tpot_ms":75,"tokens_per_sec":13.333333333333334}},{"message_id":"33bc2e9e-a63e-46a4-a06b-b7fa0a5e0829","node_id":58,"role":"tool","content":"Traceback (most recent call last):\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/urllib/request.py\", line 1344, in do_open\n    h.request(req.get_method(), req.selector, req.data, headers,\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py\", line 1336, in request\n    self._send_request(method, url, body, headers, encode_chunked)\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py\", line 1382, in _send_request\n    self.endheaders(body, encode_chunked=encode_chunked)\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py\", line 1331, in endheaders\n    self._send_output(message_body, encode_chunked=encode_chunked)\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py\", line 1091, in _send_output\n    self.send(msg)\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py\", line 1035, in send\n    self.connect()\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/http/client.py\", line 1477, in connect\n    self.sock = self._context.wrap_socket(self.sock,\n                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/ssl.py\", line 455, in wrap_socket\n    return self.sslsocket_class._create(\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/ssl.py\", line 1041, in _create\n    self.do_handshake()\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/ssl.py\", line 1319, in do_handshake\n    self._sslobj.do_handshake()\nssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/bin/whisper\", line 10, in <module>\n    sys.exit(cli())\n             ^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/whisper/transcribe.py\", line 595, in cli\n    model = load_model(model_name, device=device, download_root=model_dir)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/whisper/__init__.py\", line 137, in load_model\n    checkpoint_file = _download(_MODELS[name], download_root, in_memory)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/whisper/__init__.py\", line 73, in _download\n    with urllib.request.urlopen(url) as source, open(download_target, \"wb\") as output:\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/urllib/request.py\", line 215, in urlopen\n    return opener.open(url, data, timeout)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/urllib/request.py\", line 515, in open\n    response = self._open(req, data)\n               ^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/urllib/request.py\", line 532, in _open\n    result = self._call_chain(self.handle_open, protocol, protocol +\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/urllib/request.py\", line 492, in _call_chain\n    result = func(*args)\n             ^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/urllib/request.py\", line 1392, in https_open\n    return self.do_open(http.client.HTTPSConnection, req,\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/urllib/request.py\", line 1347, in do_open\n    raise URLError(err)\nurllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)>\n\n\nExit code: 1","created_at":"2026-06-28T05:10:06.300473Z","finish_reason":null,"metrics":null},{"message_id":"c4c498c6-3656-4f08-b41e-0f568ffd324a","node_id":60,"role":"assistant","content":"SSL cert issue when downloading the model. Let me check what's already cached locally.","tool_calls":[{"id":"chatcmpl-tool-8f7d802fad45e988","name":"exec","arguments":{"command":"ls -lh ~/.cache/whisper/ 2>/dev/null; echo \"---\"; pip list 2>/dev/null | grep -i -E \"openai-whisper|whisper|mlx\""}}],"thinking":"SSL certificate verification failed - likely a corporate proxy/MITM. Let me check if the model is already cached, or use a smaller model that's already cached, or work around the SSL issue. Let me check the cached models.","created_at":"2026-06-28T05:10:09.852604Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1296,"total_time_ms":3549,"input_tokens":1166,"output_tokens":115,"cache_read_tokens":21689,"cache_creation_tokens":null,"tpot_ms":19.591304347826085,"tokens_per_sec":51.04305370616955}},{"message_id":"17aced86-5d28-43df-b037-cf524d18e99d","node_id":61,"role":"tool","content":"Output from command in shell 5a24ab:\ntotal 0\n---\nfaster-whisper                           1.2.1\nopenai-whisper                           20250625\nwhisperx                                 3.7.4\n\n\nExit code: 0","created_at":"2026-06-28T05:10:10.343093Z","finish_reason":null,"metrics":null},{"message_id":"1bd07078-1514-412c-8336-d81522f0b78f","node_id":63,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-866fadedd9dc70e8","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && PYTHONHTTPSVERIFY=0 curl -k -sS -L -o ~/.cache/whisper/medium.pt \"https://download.pytorch.org/whisper/medium.pt\" -w \"HTTP:%{http_code} SIZE:%{size_download}\\n\" --create-dirs 2>&1 | tail -5","run_in_background":true}}],"thinking":"No cached models. Let me try to download with SSL verification disabled, or use a different approach. Let me try setting CURL_CA_BUNDLE or using a smaller model. Actually, let me try the tiny model first to test, and disable SSL verification via env var.\n\nActually the issue is urllib in Python. Let me try setting PYTHONHTTPSVERIFY=0 or use a workaround. Or I could download the model file manually with curl -k.\n\nThe whisper models are hosted at https://download.pytorch.org/whisper/... Let me download manually with curl -k.\n\nActually, let me try the env var approach first - it's cleaner.","created_at":"2026-06-28T05:10:16.187219Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1330,"total_time_ms":5843,"input_tokens":175,"output_tokens":223,"cache_read_tokens":22854,"cache_creation_tokens":null,"tpot_ms":20.23766816143498,"tokens_per_sec":49.412807445158435}},{"message_id":"7ca55514-b26b-41a2-b4b5-4618b897724e","node_id":64,"role":"tool","content":"Command running in background with ID: c22d11","created_at":"2026-06-28T05:10:16.201360Z","finish_reason":null,"metrics":null},{"message_id":"a924e018-8dec-4151-8443-879ef50d160e","node_id":66,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8358be05f3918a0a","name":"get_output","arguments":{"shell_id":"c22d11","timeout":90000}}],"created_at":"2026-06-28T05:10:17.769738Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1565,"input_tokens":239,"output_tokens":23,"cache_read_tokens":23028,"cache_creation_tokens":null,"tpot_ms":68.04347826086956,"tokens_per_sec":14.696485623003195}},{"message_id":"6e209751-d93e-4527-87dc-d135ac14437b","node_id":67,"role":"tool","content":"HTTP:403 SIZE:243\n\n\nExit code: 0","created_at":"2026-06-28T05:10:17.771540Z","finish_reason":null,"metrics":null},{"message_id":"1647cca7-697d-4505-a31a-82c001fcbefd","node_id":69,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9c208ef8134de5ea","name":"exec","arguments":{"command":"cat ~/.cache/whisper/medium.pt; echo \"---\"; curl -k -sS -I \"https://download.pytorch.org/whisper/medium.pt\" 2>&1 | head -10"}}],"created_at":"2026-06-28T05:10:20.145181Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2371,"input_tokens":42,"output_tokens":53,"cache_read_tokens":23266,"cache_creation_tokens":null,"tpot_ms":44.735849056603776,"tokens_per_sec":22.35343736819907}},{"message_id":"9b5e40bb-9ed7-433b-bf12-98dea229cebf","node_id":70,"role":"tool","content":"Output from command in shell 76221b:\n<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>63MKZ6VG1161P9A6</RequestId><HostId>gIaHsatKfb1Qha/5T9zFLUsDk72cHJ/LTEa41QB6jyPmDjljId1uV0hw6s/qCITKgygBIW/TrMQ=</HostId></Error>---\nHTTP/2 403 \r\ncontent-type: application/xml\r\nserver: AmazonS3\r\ndate: Sun, 28 Jun 2026 05:10:19 GMT\r\nx-cache: Error from cloudfront\r\nvia: 1.1 de27db1efe10b9871dae1b244587c0ea.cloudfront.net (CloudFront)\r\nx-amz-cf-pop: SFO53-P12\r\nx-amz-cf-id: EzgnguWPjyXafNDx_hMdOMizyiI4g-ucYhW9Jw3txDZXU68sZbbq4w==\r\n\r\n\n\nExit code: 0","created_at":"2026-06-28T05:10:20.459087Z","finish_reason":null,"metrics":null},{"message_id":"89f76334-13a2-49e1-9dcc-1a8fa4118894","node_id":72,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-ab048e41033cde32","name":"exec","arguments":{"command":"rm -f ~/.cache/whisper/medium.pt; python3 -c \"import whisper; print(whisper._MODELS['medium'])\""}}],"thinking":"The pytorch.org URL is blocked (403). The actual URL whisper uses is different. Let me check the whisper source for the actual URL. Looking at the error, it was trying to download from somewhere. Let me check the whisper _MODELS dict.\n\nActually, the openai-whisper models are at `https://download.pytorch.org/whisper/medium.pt`. But it's getting 403. Maybe it needs proper headers or the URL changed. Let me check the actual URL in the whisper package.\n\nActually, let me just disable SSL verification in Python and let whisper download it itself. The error was SSL cert verification. Let me set the env var properly.","created_at":"2026-06-28T05:10:23.815352Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1045,"total_time_ms":3351,"input_tokens":328,"output_tokens":179,"cache_read_tokens":23307,"cache_creation_tokens":null,"tpot_ms":12.88268156424581,"tokens_per_sec":77.62359063313096}},{"message_id":"b93d7ed8-6908-4982-9186-5cdec0c48003","node_id":73,"role":"tool","content":"Output from command in shell 094f5f:\nhttps://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt\n\n\nExit code: 0","created_at":"2026-06-28T05:10:24.499391Z","finish_reason":null,"metrics":null},{"message_id":"22234b75-2378-47d3-adf0-351859587c21","node_id":75,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8dc4213d698c93fa","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && curl -k -sS -L --create-dirs -o ~/.cache/whisper/medium.pt \"https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt\" -w \"HTTP:%{http_code} SIZE:%{size_download}\\n\" 2>&1 | tail -3","run_in_background":true}}],"thinking":"The actual URL is on Azure. Let me download it with curl -k, or just run whisper with PYTHONHTTPSVERIFY=0. Let me try the env var approach with whisper directly.","created_at":"2026-06-28T05:10:27.917520Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1602,"total_time_ms":3417,"input_tokens":263,"output_tokens":175,"cache_read_tokens":23634,"cache_creation_tokens":null,"tpot_ms":10.371428571428572,"tokens_per_sec":96.41873278236915}},{"message_id":"e643ca12-1447-4386-90f4-b18480bf6241","node_id":76,"role":"tool","content":"Command running in background with ID: 077782","created_at":"2026-06-28T05:10:27.935005Z","finish_reason":null,"metrics":null},{"message_id":"8de09793-b9b8-474f-a06b-dcd8900d7928","node_id":78,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-901cbdba3c4bc7da","name":"get_output","arguments":{"shell_id":"077782","timeout":180000}}],"created_at":"2026-06-28T05:10:29.969889Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2031,"input_tokens":192,"output_tokens":23,"cache_read_tokens":23896,"cache_creation_tokens":null,"tpot_ms":88.30434782608695,"tokens_per_sec":11.324470704086657}},{"message_id":"58764382-00fd-4025-af19-54fb560c72fa","node_id":79,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:13:29.973589Z","finish_reason":null,"metrics":null},{"message_id":"0ded3057-0675-477f-a6bc-55d91c9a9618","node_id":81,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-866ad7a5cefd3d62","name":"get_output","arguments":{"shell_id":"077782","timeout":240000}}],"created_at":"2026-06-28T05:13:31.940222Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1963,"input_tokens":35,"output_tokens":23,"cache_read_tokens":24087,"cache_creation_tokens":null,"tpot_ms":85.34782608695652,"tokens_per_sec":11.716760061130923}},{"message_id":"014a051e-1213-4e7d-80a1-c1224dc27b0d","node_id":82,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:17:31.922197Z","finish_reason":null,"metrics":null},{"message_id":"db47fbcf-91eb-4db9-895e-a05f66b0792e","node_id":84,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8d929bd5ffd88cd7","name":"exec","arguments":{"command":"ls -lh ~/.cache/whisper/medium.pt 2>/dev/null"}}],"created_at":"2026-06-28T05:17:33.930963Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2001,"input_tokens":35,"output_tokens":26,"cache_read_tokens":24121,"cache_creation_tokens":null,"tpot_ms":76.96153846153847,"tokens_per_sec":12.993503248375811}},{"message_id":"b7c842b0-8d23-4e5e-91e3-65b930fdf443","node_id":85,"role":"tool","content":"Output from command in shell 66f1d8:\n-rw-r--r--  1 root1  staff   611M Jun 27 22:17 /Users/root1/.cache/whisper/medium.pt\n\n\nExit code: 0","created_at":"2026-06-28T05:17:33.958979Z","finish_reason":null,"metrics":null},{"message_id":"8b2670b2-e2b9-47d6-ba04-94722e76545e","node_id":87,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-98c2b7368e5fa337","name":"get_output","arguments":{"shell_id":"077782","timeout":300000}}],"thinking":"Still downloading - 611M so far. The medium model is ~1.5GB. Let me wait more.","created_at":"2026-06-28T05:17:35.804876Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1210,"total_time_ms":1845,"input_tokens":86,"output_tokens":48,"cache_read_tokens":24155,"cache_creation_tokens":null,"tpot_ms":13.229166666666666,"tokens_per_sec":75.59055118110236}},{"message_id":"a26347bd-9205-4c9e-ad2c-182f3d3a22e7","node_id":88,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:22:35.803546Z","finish_reason":null,"metrics":null},{"message_id":"5b8a13c3-1dce-4757-b91e-8cae944968af","node_id":90,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b06783e2ce4d6ef9","name":"exec","arguments":{"command":"ls -lh ~/.cache/whisper/medium.pt 2>/dev/null"}}],"created_at":"2026-06-28T05:22:38.170799Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2351,"input_tokens":23531,"output_tokens":26,"cache_read_tokens":769,"cache_creation_tokens":null,"tpot_ms":90.42307692307692,"tokens_per_sec":11.05912377711612}},{"message_id":"e7bad7fb-f45d-4125-a633-4f62d17cd0d7","node_id":91,"role":"tool","content":"Output from command in shell 2a1f5a:\n-rw-r--r--  1 root1  staff   1.0G Jun 27 22:22 /Users/root1/.cache/whisper/medium.pt\n\n\nExit code: 0","created_at":"2026-06-28T05:22:38.224606Z","finish_reason":null,"metrics":null},{"message_id":"713a99e1-af05-42d7-9482-79f344783a33","node_id":93,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-89baaedc7ac8217e","name":"get_output","arguments":{"shell_id":"077782","timeout":300000}}],"created_at":"2026-06-28T05:22:39.507284Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1277,"input_tokens":88,"output_tokens":23,"cache_read_tokens":24299,"cache_creation_tokens":null,"tpot_ms":55.52173913043478,"tokens_per_sec":18.010963194988253}},{"message_id":"0af8d77c-6d6d-4336-ab3b-f9d52ce9e79a","node_id":94,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:27:39.508227Z","finish_reason":null,"metrics":null},{"message_id":"36ac7c6b-2030-4abe-bb0c-073435666899","node_id":96,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a4e256808d8d4e54","name":"exec","arguments":{"command":"ls -lh ~/.cache/whisper/medium.pt 2>/dev/null"}}],"created_at":"2026-06-28T05:27:42.211164Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2694,"input_tokens":35,"output_tokens":26,"cache_read_tokens":24386,"cache_creation_tokens":null,"tpot_ms":103.61538461538461,"tokens_per_sec":9.651076466221232}},{"message_id":"1f2d01f9-a781-4ee5-9db2-00c228512395","node_id":97,"role":"tool","content":"Output from command in shell 8868b6:\n-rw-r--r--  1 root1  staff   1.4G Jun 27 22:27 /Users/root1/.cache/whisper/medium.pt\n\n\nExit code: 0","created_at":"2026-06-28T05:27:42.263373Z","finish_reason":null,"metrics":null},{"message_id":"a8683762-151b-4925-80ff-388d330d3592","node_id":99,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-905baa3c2647a699","name":"get_output","arguments":{"shell_id":"077782","timeout":300000}}],"created_at":"2026-06-28T05:27:45.291487Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3023,"input_tokens":87,"output_tokens":23,"cache_read_tokens":24420,"cache_creation_tokens":null,"tpot_ms":131.43478260869566,"tokens_per_sec":7.608336089976844}},{"message_id":"465acfe3-d7d2-4665-a635-be3c1a81f4e2","node_id":100,"role":"tool","content":"HTTP:200 SIZE:1528008539\n\n\nExit code: 0","created_at":"2026-06-28T05:27:56.405619Z","finish_reason":null,"metrics":null},{"message_id":"bf5cba1e-17bb-486c-80f6-9639dde0377c","node_id":102,"role":"assistant","content":"Model downloaded (1.5GB). Now running transcription.","tool_calls":[{"id":"chatcmpl-tool-bd454f0884336406","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && whisper twitter_2070551894674555081_audio.mp3 --model medium --output_dir . --output_format txt --language English --verbose False 2>&1 | tail -20","run_in_background":true}}],"created_at":"2026-06-28T05:28:05.730257Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1513,"total_time_ms":9321,"input_tokens":44,"output_tokens":81,"cache_read_tokens":24506,"cache_creation_tokens":null,"tpot_ms":96.39506172839506,"tokens_per_sec":10.373975409836065}},{"message_id":"3c029dc0-c7e7-4a3a-aeb3-d407a85b634d","node_id":103,"role":"tool","content":"Command running in background with ID: 3aa4a4","created_at":"2026-06-28T05:28:05.750897Z","finish_reason":null,"metrics":null},{"message_id":"d64e3029-7cf1-4512-8976-cd160d73a56e","node_id":105,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b2ec0bd6fd5d05fb","name":"get_output","arguments":{"shell_id":"3aa4a4","timeout":300000}}],"created_at":"2026-06-28T05:28:07.418647Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1661,"input_tokens":99,"output_tokens":24,"cache_read_tokens":24549,"cache_creation_tokens":null,"tpot_ms":69.20833333333333,"tokens_per_sec":14.449127031908489}},{"message_id":"1e346573-8bf4-4087-b37c-986f0840ec51","node_id":106,"role":"tool","content":"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/whisper/transcribe.py:132: UserWarning: FP16 is not supported on CPU; using FP32 instead\n  warnings.warn(\"FP16 is not supported on CPU; using FP32 instead\")\n","created_at":"2026-06-28T05:33:07.468524Z","finish_reason":null,"metrics":null},{"message_id":"b507d744-056b-46db-9c69-43a0eebec5f2","node_id":108,"role":"assistant","content":"Transcription running on CPU (will take a while for 20min audio + medium model). Waiting...","tool_calls":[{"id":"chatcmpl-tool-b050b150307f7588","name":"get_output","arguments":{"shell_id":"3aa4a4","timeout":600000}}],"thinking":"Transcription is running on CPU. This will take a while for a 20-min audio with medium model. Let me wait.","created_at":"2026-06-28T05:33:10.205241Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1986,"total_time_ms":2710,"input_tokens":90,"output_tokens":71,"cache_read_tokens":24647,"cache_creation_tokens":null,"tpot_ms":10.19718309859155,"tokens_per_sec":98.06629834254143}},{"message_id":"894f9c99-8fb3-4379-a85c-6d1d181d81f4","node_id":109,"role":"tool","content":"Warning: requested timeout 10m exceeds maximum of 5m; clamping to 5m.\nNo output yet (still running)","created_at":"2026-06-28T05:38:10.242641Z","finish_reason":null,"metrics":null},{"message_id":"cee78089-3fa0-44da-961f-cdc82fb80f84","node_id":111,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9592c8ccab50096f","name":"get_output","arguments":{"shell_id":"3aa4a4","timeout":300000}}],"created_at":"2026-06-28T05:38:14.538055Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":4283,"input_tokens":24071,"output_tokens":24,"cache_read_tokens":769,"cache_creation_tokens":null,"tpot_ms":178.45833333333334,"tokens_per_sec":5.603548914312398}},{"message_id":"c1e0769c-958a-4e14-ad9c-21aa39bd1162","node_id":112,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:43:14.552500Z","finish_reason":null,"metrics":null},{"message_id":"2c20e325-09f4-4c48-ab1f-0a3ccf098df5","node_id":114,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a9ffae16b45aad87","name":"get_output","arguments":{"shell_id":"3aa4a4","timeout":300000}}],"created_at":"2026-06-28T05:43:16.174849Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1595,"input_tokens":36,"output_tokens":24,"cache_read_tokens":24839,"cache_creation_tokens":null,"tpot_ms":66.45833333333333,"tokens_per_sec":15.047021943573668}},{"message_id":"36afce19-3f66-49e3-8c84-a6a11b157289","node_id":115,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:48:16.307005Z","finish_reason":null,"metrics":null},{"message_id":"f6ec0037-bcb7-466a-a726-eee949772d7e","node_id":117,"role":"assistant","content":"CPU transcription with medium model is slow. Let me switch to faster-whisper (CTranslate2) which is much faster.","tool_calls":[{"id":"chatcmpl-tool-a2003e19c112a3c1","name":"kill_shell","arguments":{"shell_id":"3aa4a4"}},{"id":"chatcmpl-tool-bcabbf5765b376b0","name":"exec","arguments":{"command":"python3 -c \"import faster_whisper; print(faster_whisper.__version__)\" 2>&1; sysctl -n machdep.cpu.brand_string 2>/dev/null; sysctl -n hw.ncpu"}}],"thinking":"It's taking a long time on CPU. The medium model on CPU for 20 min audio could take 30+ minutes. Let me check if there's a faster option - faster-whisper or whisperx is installed. Let me kill this and use faster-whisper with CTranslate2 which is much faster, or use a smaller model.\n\nActually, let me check if mlx-whisper is available since this is Apple Silicon (macOS). That would be much faster. But it's not in the pip list. Let me use faster-whisper which is installed.\n\nActually, let me reconsider. The medium model on CPU for 20 min audio could take ~30-60 min. faster-whisper with medium model on CPU would still be slow but maybe 2-4x faster. Let me kill and switch to faster-whisper with the medium model, or use a smaller model like small/base for speed.\n\nActually, let me check the CPU usage and just be patient. But it's been ~10 min already. Let me switch to faster-whisper which should be significantly faster.","created_at":"2026-06-28T05:48:22.520565Z","finish_reason":"tool_calls","metrics":{"ttft_ms":2681,"total_time_ms":6190,"input_tokens":24706,"output_tokens":316,"cache_read_tokens":204,"cache_creation_tokens":null,"tpot_ms":11.104430379746836,"tokens_per_sec":90.05414648047876}},{"message_id":"773a1af7-5d73-45a3-ad60-326418bdf323","node_id":118,"role":"tool","content":"Shell 3aa4a4 terminated successfully","created_at":"2026-06-28T05:48:27.593680Z","finish_reason":null,"metrics":null},{"message_id":"b4fc7828-0735-4746-9a1e-24913d82833b","node_id":119,"role":"tool","content":"No output produced after 5.0 seconds. Command running in background with ID: 16b4f6. Run get_output with timeout if you need to wait.","created_at":"2026-06-28T05:48:27.593705Z","finish_reason":null,"metrics":null},{"message_id":"80522411-abc5-44cc-b9df-9dcdec717048","node_id":121,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a8ce9ba3e25e477c","name":"get_output","arguments":{"shell_id":"16b4f6","timeout":15000}}],"created_at":"2026-06-28T05:48:28.683287Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1059,"input_tokens":367,"output_tokens":24,"cache_read_tokens":24909,"cache_creation_tokens":null,"tpot_ms":44.125,"tokens_per_sec":22.6628895184136}},{"message_id":"d3e22c75-31d6-4e62-81e4-62a109b1974e","node_id":122,"role":"tool","content":"1.2.1\nApple M4 Pro\n12\n\n\nExit code: 0","created_at":"2026-06-28T05:48:34.256273Z","finish_reason":null,"metrics":null},{"message_id":"f436ef1c-7c4f-4ed3-9a9a-0fcc4cb1d41e","node_id":124,"role":"assistant","content":"Apple M4 Pro! Let me check if mlx-whisper is available (much faster on Apple Silicon).","tool_calls":[{"id":"chatcmpl-tool-b880ca4eae7023b2","name":"exec","arguments":{"command":"pip list 2>/dev/null | grep -i mlx; which mlx_whisper 2>/dev/null; python3 -c \"import mlx_whisper; print('mlx_whisper available')\" 2>&1 | head -3"}}],"thinking":"Apple M4 Pro! I should use mlx-whisper for Apple Silicon, which would be blazing fast. But it's not installed. faster-whisper on M4 Pro CPU should still be reasonably fast. Let me use faster-whisper with the medium model. Actually, let me check if mlx is available.","created_at":"2026-06-28T05:48:36.670120Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1285,"total_time_ms":2409,"input_tokens":47,"output_tokens":141,"cache_read_tokens":25275,"cache_creation_tokens":null,"tpot_ms":7.971631205673759,"tokens_per_sec":125.44483985765126}},{"message_id":"5b1d5cd1-c330-4f2e-963b-1c12d5e6b9f9","node_id":125,"role":"tool","content":"Output from command in shell 086337:\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'mlx_whisper'\n\n\nExit code: 0","created_at":"2026-06-28T05:48:39.263079Z","finish_reason":null,"metrics":null},{"message_id":"99e11e9c-2c9a-49ea-bab7-b1275e553071","node_id":127,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9e422bd02df0ea4e","name":"write","arguments":{"file_path":"/Users/root1/Downloads/utilities/tweet_audio/transcribe_fw.py","content":"import os\nos.environ[\"HF_HUB_DISABLE_PROGRESS_BARS\"] = \"1\"\n# Work around SSL cert issues on this machine\nos.environ[\"CURL_CA_BUNDLE\"] = \"\"\nos.environ[\"REQUESTS_CA_BUNDLE\"] = \"\"\nimport ssl\nssl._create_default_https_context = ssl._create_unverified_context\n\nfrom faster_whisper import WhisperModel\n\naudio = \"twitter_2070551894674555081_audio.mp3\"\n# medium on CPU, beam_size 5, English\nmodel = WhisperModel(\"medium\", device=\"cpu\", compute_type=\"int8\", cpu_threads=12)\n\nsegments, info = model.transcribe(\n    audio,\n    language=\"en\",\n    beam_size=5,\n    vad_filter=True,\n    vad_parameters=dict(min_silence_duration_ms=500),\n)\nprint(f\"detected language={info.language} prob={info.language_probability:.2f} duration={info.duration:.1f}s\")\n\ntext_parts = []\nwith open(\"twitter_2070551894674555081_audio.txt\", \"w\") as f:\n    for seg in segments:\n        line = f\"[{seg.start:06.1f}-{seg.end:06.1f}] {seg.text.strip()}\"\n        print(line, flush=True)\n        f.write(seg.text.strip() + \" \")\n        text_parts.append(seg.text.strip())\n    f.write(\"\\n\")\n\nwith open(\"twitter_2070551894674555081_audio_full.txt\", \"w\") as f:\n    f.write(\" \".join(text_parts) + \"\\n\")\nprint(\"DONE\")\n"}}],"thinking":"mlx-whisper not installed. Let me use faster-whisper which is installed and should be fast on M4 Pro. Let me write a quick script to transcribe with faster-whisper using the medium model. faster-whisper downloads its own CTranslate2 format models from HuggingFace. Let me try it.\n\nActually, let me try installing mlx-whisper quickly since M4 Pro would make it super fast. But network has SSL issues. Let me just use faster-whisper which is already installed.","created_at":"2026-06-28T05:48:44.954389Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1624,"total_time_ms":5686,"input_tokens":194,"output_tokens":466,"cache_read_tokens":25321,"cache_creation_tokens":null,"tpot_ms":8.716738197424892,"tokens_per_sec":114.72181191531266}},{"message_id":"362d559e-45ef-4a5d-b58c-859e9a7ff77f","node_id":128,"role":"tool","content":"File created successfully at: /Users/root1/Downloads/utilities/tweet_audio/transcribe_fw.py","created_at":"2026-06-28T05:48:45.036740Z","finish_reason":null,"metrics":null},{"message_id":"23ba303f-a289-48db-a700-f0b8261d9b50","node_id":130,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-bc31f24ee1071f95","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && python3 transcribe_fw.py 2>&1","run_in_background":true}}],"created_at":"2026-06-28T05:48:46.461544Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1415,"input_tokens":491,"output_tokens":37,"cache_read_tokens":25514,"cache_creation_tokens":null,"tpot_ms":38.24324324324324,"tokens_per_sec":26.148409893992934}},{"message_id":"1091b609-924f-4682-9186-8b14d1fd929d","node_id":131,"role":"tool","content":"Command running in background with ID: dd7a8e","created_at":"2026-06-28T05:48:46.494814Z","finish_reason":null,"metrics":null},{"message_id":"956bb15f-e20f-4824-8d87-969bccbf53a8","node_id":133,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-85a3063607978139","name":"get_output","arguments":{"shell_id":"dd7a8e","timeout":120000}}],"created_at":"2026-06-28T05:48:47.521862Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1020,"input_tokens":54,"output_tokens":24,"cache_read_tokens":26004,"cache_creation_tokens":null,"tpot_ms":42.5,"tokens_per_sec":23.52941176470588}},{"message_id":"15c85b0f-c3e0-43b4-967b-6b737e1eefb8","node_id":134,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:50:47.529359Z","finish_reason":null,"metrics":null},{"message_id":"b0750eba-b908-453b-8605-a3b3224ccffc","node_id":136,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a209913e18d6e995","name":"get_output","arguments":{"shell_id":"dd7a8e","timeout":180000}}],"created_at":"2026-06-28T05:50:50.965537Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3433,"input_tokens":36,"output_tokens":24,"cache_read_tokens":26057,"cache_creation_tokens":null,"tpot_ms":143.04166666666666,"tokens_per_sec":6.990969997087095}},{"message_id":"d27bde4a-59fd-4885-a150-a4eae16a6351","node_id":137,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:53:50.973547Z","finish_reason":null,"metrics":null},{"message_id":"c9e3db19-9676-4472-ab7e-81909ee2b6cc","node_id":139,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b13ed551d11665ba","name":"exec","arguments":{"command":"ls -lh ~/.cache/huggingface/hub/ 2>/dev/null; du -sh ~/.cache/huggingface/ 2>/dev/null; ls ~/.cache/huggingface/hub/models--Systran--faster-whisper-medium/ 2>/dev/null"}}],"thinking":"faster-whisper downloads the model from HuggingFace first. With SSL issues, it might be hanging on download. Let me check if there's progress. Let me check the HF cache.","created_at":"2026-06-28T05:53:57.865106Z","finish_reason":"tool_calls","metrics":{"ttft_ms":5122,"total_time_ms":6887,"input_tokens":36,"output_tokens":106,"cache_read_tokens":26092,"cache_creation_tokens":null,"tpot_ms":16.650943396226417,"tokens_per_sec":60.05665722379604}},{"message_id":"0306eb08-af16-4db6-9b1f-4a9782fba453","node_id":140,"role":"tool","content":"Output from command in shell ae52ac:\ntotal 0\ndrwxr-xr-x  4 root1  staff   128B Jun 22 22:49 models--lukealonso--GLM-5.2-NVFP4\ndrwxr-xr-x  4 root1  staff   128B Jun 22 22:49 models--Mapika--GLM-5.2-NVFP4\ndrwxr-xr-x  3 root1  staff    96B Jun 19 00:25 models--osunlp--QUEST-2B\ndrwxr-xr-x  5 root1  staff   160B Jun 27 22:48 models--Systran--faster-whisper-medium\n 48M\t/Users/root1/.cache/huggingface/\nblobs\nrefs\nsnapshots\n\n\nExit code: 0","created_at":"2026-06-28T05:53:57.910547Z","finish_reason":null,"metrics":null},{"message_id":"b5d9038d-db98-4d1b-8c0a-e2c5fa98f744","node_id":142,"role":"assistant","content":"Model downloading from HuggingFace. Let me monitor progress.","tool_calls":[{"id":"chatcmpl-tool-88feee9f64eda290","name":"get_output","arguments":{"shell_id":"dd7a8e","timeout":120000}}],"thinking":"It's downloading the model (48M so far, medium model is ~1.5GB). Let me wait for it to finish downloading and then transcribe. Let me check progress periodically.","created_at":"2026-06-28T05:54:05.252971Z","finish_reason":"tool_calls","metrics":{"ttft_ms":6522,"total_time_ms":7340,"input_tokens":303,"output_tokens":75,"cache_read_tokens":26127,"cache_creation_tokens":null,"tpot_ms":10.906666666666666,"tokens_per_sec":91.68704156479218}},{"message_id":"4712a6f8-6662-40f3-bc6c-5504a2f9486e","node_id":143,"role":"tool","content":"No output yet (still running)","created_at":"2026-06-28T05:56:05.259417Z","finish_reason":null,"metrics":null},{"message_id":"845e154b-0974-4463-9089-39aa5810d5e4","node_id":145,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b11077ec3be8c4da","name":"exec","arguments":{"command":"du -sh ~/.cache/huggingface/hub/models--Systran--faster-whisper-medium/ 2>/dev/null"}}],"created_at":"2026-06-28T05:58:45.644693Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":14698,"input_tokens":87,"output_tokens":37,"cache_read_tokens":26429,"cache_creation_tokens":null,"tpot_ms":397.2432432432432,"tokens_per_sec":2.517349299224384}},{"message_id":"f2f66aa3-0b01-437e-87f1-5fcbf4119007","node_id":146,"role":"tool","content":"Output from command in shell 1cd4f3:\n322M\t/Users/root1/.cache/huggingface/hub/models--Systran--faster-whisper-medium/\n\n\nExit code: 0","created_at":"2026-06-28T05:58:45.667472Z","finish_reason":null,"metrics":null},{"message_id":"afd97450-5a66-4928-8503-b87a872fc992","node_id":148,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-981e949f618e4b2d","name":"get_output","arguments":{"shell_id":"dd7a8e","timeout":180000}}],"created_at":"2026-06-28T05:58:49.132282Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3463,"input_tokens":85,"output_tokens":24,"cache_read_tokens":26515,"cache_creation_tokens":null,"tpot_ms":144.29166666666666,"tokens_per_sec":6.930407161420733}},{"message_id":"9f612088-68e2-4238-ac40-50b334b9f45b","node_id":149,"role":"tool","content":"/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/faster_whisper/feature_extractor.py:224: RuntimeWarning: divide by zero encountered in matmul\n  mel_spec = self.mel_filters @ magnitudes\n/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/faster_whisper/feature_extractor.py:224: RuntimeWarning: overflow encountered in matmul\n  mel_spec = self.mel_filters @ magnitudes\n/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/faster_whisper/feature_extractor.py:224: RuntimeWarning: invalid value encountered in matmul\n  mel_spec = self.mel_filters @ magnitudes\ndetected language=en prob=1.00 duration=1192.9s\n[0000.0-0002.9] So here's the big research bet that all the labs are making.\n[0002.9-0005.4] They think that if we train AIs to accomplish\n[0005.4-0007.7] millions of verifiable tasks across\n[0007.7-0010.2] thousands of diverse RL environments,\n[0010.2-0012.6] then we will have basically built AGI.\n[0012.6-0015.4] Because this kind of training will have created\n[0015.4-0017.5] a kind of problem-solving agent,\n[0017.5-0019.8] the kind of thing that can make progress on\n[0019.8-0022.0] open-ended tasks for weeks on end\n[0022.0-0024.9] in the face of errors and mistakes and ambiguity.\n[0024.9-0027.6] The people who are optimistic about this vision will say that\n[0027.6-0029.8] all these things that we talk about as\n[0029.8-0032.6] the fundamental deficits in the current training paradigm,\n[0032.6-0034.8] for example, the data inefficiency of\n[0034.8-0037.1] these models or the fact that they lack into neural learning.\n[0037.1-0039.0] These things can just be steamrolled\n[0039.0-0040.9] if we just scale training more.\n[0040.9-0043.8] The same way that all the fundamental research problems in\n[0043.8-0046.0] natural language processing collapsed when you\n[0046.0-0048.5] just threw enough compute into LLMs.\n[0048.5-0050.2] So in the previous essay,\n[0050.2-0052.0] I talked about how these models are\n[0052.0-0055.0] one-to-one millionth as sample-efficient as humans.\n[0055.0-0056.6] The people who are in favor of\n[0056.6-0057.9] the current training paradigm will say,\n[0057.9-0059.4] look, that might be true,\n[0059.4-0061.6] but this is only true during training.\n[0061.6-0064.5] Training is this one-time cost that is amortized\n[0064.5-0067.9] across billions of sessions that a model will experience.\n[0067.9-0070.8] What really matters is how smart and general and\n[0070.8-0073.7] sample-efficient the model is during a session.\n[0073.7-0075.8] This has clearly been improving as we've been\n[0075.8-0077.5] doing more RL training.\n[0077.5-0079.4] AI agents are able to solve more and more\n[0079.4-0081.9] ambitious problems over longer and longer time spans.\n[0081.9-0084.2] Anybody who has used these models for coding knows that.\n[0084.2-0086.1] Similarly, people would say, look,\n[0086.1-0088.7] continued learning, this capability I keep harping about,\n[0088.7-0091.2] where the model's weights get updated based\n[0091.2-0093.1] on what it's learning from deployment,\n[0093.1-0095.2] may simply not be necessary.\n[0095.2-0098.4] Because if in-context learning gets so\n[0098.4-0101.2] good across longer and longer time horizons,\n[0101.2-0103.2] then you don't need to distill back\n[0103.2-0106.4] everything the model is learning on the job into the weights.\n[0106.4-0108.4] People often say that their employees are not\n[0108.4-0109.7] net-productive until\n[0109.7-0112.1] six months or more of them working on the job.\n[0112.1-0115.9] So clearly, online learning is necessary for competence.\n[0115.9-0117.6] But what if you could just fit those six months\n[0117.6-0118.7] into the context window?\n[0118.7-0120.7] There's been tons of architectural innovations that\n[0120.7-0123.2] dramatically increase the amount of\n[0123.2-0124.7] information or the amount of\n[0124.7-0127.0] context that a transformer can store.\n[0127.0-0128.7] Why not think with a couple more years of progress,\n[0128.7-0130.0] you might have what feels\n[0130.0-0132.3] like infinitely large context windows.\n[0132.3-0134.7] Before we discuss this research a bit further,\n[0134.7-0136.9] I want to step back and I want to ask\n[0136.9-0139.2] a completely tangential question which I find\n[0139.2-0141.7] actually very interesting and\n[0141.7-0144.6] confusing about the nature of current AI progress.\n[0144.6-0147.5] Why has progress on computer use\n[0147.5-0150.4] been so much slower than other domains?\n[0150.4-0154.1] Computer use is so clearly verifiable.\n[0154.1-0155.4] You could ask a question like,\n[0155.4-0158.5] did the desired Etsy item I ordered get delivered?\n[0158.5-0162.0] Is the venue for an event I'm trying to organize booked?\n[0162.0-0163.5] Have my taxes been submitted?\n[0163.5-0165.4] So isn't it weird that computer use has been making\n[0165.4-0167.6] so much slower progress than coding\n[0167.6-0169.7] and math and these other verifiable domains?\n[0169.7-0171.9] I'm sure there's many reasons for this and\n[0171.9-0174.1] one of them of course is the fact that\n[0174.1-0176.8] the models are exposed to far less high quality\n[0176.8-0179.1] multimodal data during free training.\n[0179.1-0180.6] But one reason that I think is actually\n[0180.6-0183.5] quite underrated by people and which I think\n[0183.5-0185.5] reveals the canyon walls against which\n[0185.5-0187.1] this river of AI progress will\n[0187.1-0189.5] only slowly chip away at is that\n[0189.5-0192.6] it is not enough for a domain to be verifiable.\n[0192.6-0195.3] It also has to be very grindable in\n[0195.3-0196.8] the sense that you have to be able to run\n[0196.8-0198.8] lots of parallel rollouts against\n[0198.8-0201.8] a deterministic and replayable simulator.\n[0201.8-0203.0] You're going to run those rollouts\n","created_at":"2026-06-28T06:01:49.139283Z","finish_reason":null,"metrics":null},{"message_id":"11c4e515-1f79-444b-8df7-af5218ba8831","node_id":151,"role":"assistant","content":"Transcription is working well. Let me wait for it to complete.","tool_calls":[{"id":"chatcmpl-tool-b4a195c71defd65c","name":"get_output","arguments":{"shell_id":"dd7a8e","timeout":300000}}],"thinking":"Transcription is working and producing good output. Let me wait for it to complete.","created_at":"2026-06-28T06:01:51.335781Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1597,"total_time_ms":2195,"input_tokens":2076,"output_tokens":55,"cache_read_tokens":26599,"cache_creation_tokens":null,"tpot_ms":10.872727272727273,"tokens_per_sec":91.97324414715719}},{"message_id":"49282832-847f-41ae-b3fc-762ad5283dd5","node_id":152,"role":"tool","content":"[0203.0-0204.6] from the same starting point.\n[0204.6-0206.6] If you're trying to make a model better at coding,\n[0206.6-0208.8] you can define some container that has\n[0208.8-0210.7] the software repo with some missing feature\n[0210.7-0212.8] that you have tasked the AIs with creating.\n[0212.8-0214.4] And then you have a thousand parallel agents\n[0214.4-0215.8] that go at the problem,\n[0215.8-0218.5] each of which has an identical copy of the container.\n[0218.5-0220.2] But this doesn't work with computer use,\n[0220.2-0221.4] at least not trivially.\n[0221.4-0222.7] You can't just have a thousand agents\n[0222.7-0224.9] go try the same checkout flow on Amazon\n[0224.9-0227.2] to get better at using websites\n[0227.2-0229.4] because Andy Jassy will find your bots\n[0229.4-0231.1] and shut your ass down.\n[0231.1-0234.0] You can solve this by making clones of Slack and Gmail\n[0234.0-0236.8] and all the other common applications and websites.\n[0236.8-0239.1] But at least currently, this is a very labor-intensive\n[0239.1-0241.9] and unscalable way to build environments.\n[0241.9-0244.2] Of course, once AIs get good enough at coding themselves\n[0244.2-0246.6] to build these clones with extremely high fidelity,\n[0246.6-0248.4] then I'm sure the computer use will make\n[0248.4-0250.2] quicker progress than it is right now.\n[0250.2-0252.4] And you're also killing two birds with one stone\n[0252.4-0254.1] with this kind of procedure because\n[0254.1-0257.7] getting AIs to rebuild whole applications from scratch\n[0257.7-0260.2] is also a great RL objective for coding.\n[0260.2-0262.6] So while computer use itself may soon be solved,\n[0262.6-0265.4] its current lethargy is telling us the following.\n[0265.4-0268.1] That unless you can build a very replayable training\n[0268.1-0269.3] target for a domain,\n[0269.3-0272.1] the models will struggle to make much progress.\n[0272.1-0273.6] And the reason this is true, of course,\n[0273.6-0276.3] the models are incredibly sample inefficient\n[0276.3-0277.6] during training.\n[0277.6-0279.9] This is a point I was making in my last video essay.\n[0279.9-0281.0] So for computer use,\n[0281.0-0282.0] we might be able to make up\n[0282.0-0283.3] for the sample efficiency deficit\n[0283.3-0286.8] by building these farmable deterministic simulators.\n[0286.8-0288.5] But for so many other different kinds of skills\n[0288.5-0290.5] that we need AIs to have,\n[0290.5-0292.2] we simply can't do this.\n[0292.2-0294.0] How do we train an AI to get really good\n[0294.0-0295.9] at building a business from scratch?\n[0295.9-0297.4] How about winning court cases?\n[0297.4-0299.9] Or having a profitable day of trading in the markets?\n[0299.9-0302.1] Or helping a candidate win an election?\n[0302.1-0305.1] The rollout here requires interacting with the real world\n[0305.1-0306.5] and you can't recreate it\n[0306.5-0308.4] from just within the data center.\n[0308.4-0310.7] The outer loop verification here may take months\n[0310.7-0314.1] or even years of real world actions to elicit.\n[0314.1-0315.7] And you can't re-observe it\n[0315.7-0318.3] by perturbing the model's actions slightly\n[0318.3-0320.7] in thousands of parallel rollouts\n[0320.7-0323.2] to isolate exactly what the model did\n[0323.2-0324.8] that actually worked.\n[0324.8-0327.7] Now, dealing with such reset-free non-stationary environments\n[0327.7-0329.5] is a known open problem in RL.\n[0329.5-0331.2] I'm not pointing out anything new.\n[0331.2-0332.7] But I really do want to emphasize\n[0332.7-0334.0] that because of the idiosyncratic\n[0334.0-0335.9] and sparse nature of data\n[0335.9-0337.7] in most domains in the world,\n[0337.7-0341.1] you need sample efficiency in order to get proficient.\n[0341.1-0344.9] If AIs are to develop all the skills that humans have\n[0344.9-0347.2] and even skills that humans don't have,\n[0347.2-0349.4] then they need to be able to learn from information\n[0349.4-0353.8] revealed in unstructured, unverifiable, and ambiguous ways\n[0353.8-0356.8] from scarce amounts of real-world interaction.\n[0356.8-0358.1] Because in many domains,\n[0358.1-0359.7] the relevant training information\n[0359.7-0362.6] simply doesn't exist in any other way.\n[0362.6-0364.5] What is the RL environment to make an AI\n[0364.5-0367.0] that is as good at politics as Lyndon Johnson?\n[0367.0-0368.9] Or as good at building a space launch business\n[0368.9-0370.2] as Elon Musk?\n[0370.2-0372.7] The labs are betting that RLVR will generalize.\n[0372.7-0373.9] That is that if you train on enough\n[0373.9-0376.0] containerized reproducible environments,\n[0376.0-0378.4] you will develop a very general agent\n[0378.4-0381.0] that can make and execute plans\n[0381.0-0383.2] and learn rapidly from new information\n[0383.2-0385.2] and even pick up new skills,\n[0385.2-0387.3] all within a single session.\n[0387.3-0389.9] If you dropped this endlessly RLVR'd AI\n[0389.9-0392.5] into Texas politics in 1948,\n[0392.5-0394.7] it could give you better advice than LBJ\n[0394.7-0395.9] about winning the Senate seat.\n[0395.9-0397.7] And if you gave it $100 million in 2002\n[0397.7-0399.8] and let it cook, it would build SpaceX for you.\n[0399.8-0402.1] Now whether RLVR can generalize this well\n[0402.1-0403.8] is an empirical question.\n[0403.8-0406.0] If the labs went from spending billions of dollars\n[0407.0-0409.0] on RL environments to a trillion dollars,\n[0409.0-0410.9] would you get the kind of thing that is\n[0410.9-0413.1] a fully human-like general intelligence\n[0413.1-0414.7] within the context window?\n[0414.7-0417.1] Dario gave a telling quote during our podcast together\n[0417.1-0420.3] which I think hints that RLVR generalization\n[0420.3-0422.7] is not infinitely strong,\n[0422.7-0424.2] but he was explaining why model performance\n[0424.2-0425.6] tends to degrade at long context.\n[0425.6-0427.1] He said, there's two things.\n[0427.1-0429.2] There's the context length you train at\n[0429.2-0431.6] and there's a context length that you serve at.\n[0431.6-0433.5] If you train at a small context length\n[0433.5-0435.5] and then try to serve at a long context length\n[0435.5-0437.5] like maybe you get these degradations.\n[0437.5-0438.8] Now maybe I'm reading too much into this,\n[0438.8-0440.5] but it seems like he's saying\n[0440.5-0442.3] that short-horizon RL training\n[0442.3-0443.6] doesn't necessarily generalize\n[0443.6-0445.7] to long-horizon RL performance.\n[0445.7-0446.8] And if you can't generalize\n[0446.8-0449.3] from short-horizon to long-horizon,\n[0449.3-0450.9] then how are agents supposed to generalize\n[0450.9-0453.5] from getting trained at a bunch of white-collar tasks\n[0453.5-0456.6] to say having the ability to be dropped\n[0456.6-0458.8] in the real world and build a business from scratch\n[0458.8-0460.3] as well as Sam Walton?\n[0460.3-0462.3] And even if after enough in-context experience\n[0462.3-0465.3] the AIs could become like Henry Ford or Albert Einstein,\n[0465.3-0467.1] all of that would be ephemeral and wasted\n[0467.1-0470.4] if you couldn't get those learnings back into the weights.\n[0470.4-0473.5] Around 30 to 50% of a lab's compute goes to inference.\n[0473.5-0477.3] And that compute is currently not playing any productive role\n[0477.3-0478.9] in helping improve the model.\n[0478.9-0480.2] This seems like a huge waste\n[0480.2-0481.4] and it's even worse than it sounds\n[0481.4-0483.7] because it is only in deployment\n[0483.7-0485.5] that the most valuable bits of information\n[0485.5-0488.1] which your model could learn from are actually revealed.\n[0488.1-0489.9] Things like what's actually happening\n[0489.9-0491.6] in the organizations where I'm being used\n[0491.6-0493.2] and what are they using me for\n[0493.2-0495.0] and what kinds of mistakes do I tend to make\n[0495.0-0496.1] in the real world?\n[0496.1-0497.8] We've got some genius grad student\n[0497.8-0500.3] who's never been allowed to take a real internship\n[0500.3-0502.9] and we keep giving it more and more classroom case studies\n[0502.9-0505.1] in the form of RRL training on environments.\n[0505.1-0507.5] It's so bizarre that we have AIs\n[0507.5-0510.5] that are broadly deployed through the economy already\n[0510.5-0513.8] and are participating in so many different kinds of tasks\n[0513.8-0516.8] and are privy to so much domain\n[0516.8-0519.5] and organization-specific tacit knowledge\n[0519.5-0521.5] and they're not able to make use of it.\n[0521.5-0523.2] But this kind of continual learning\n[0523.3-0525.6] requires going back to the weights.\n[0525.6-0528.6] AIs can't just keep building up a bigger and bigger KV cache\n[0528.6-0530.5] as they learn from more and more users.\n[0530.5-0532.1] That's just not scalable\n[0532.1-0533.7] and that's also not how humans do it.\n[0533.7-0535.2] There's no clean separation in our brain\n[0535.2-0537.0] between parameters and activations\n[0537.0-0540.0] and it's not like some part of your skull keeps expanding\n[0540.0-0543.3] as you learn more things through your lifetime.\n[0543.3-0544.8] When we learn stuff,\n[0544.8-0546.9] there's clearly some kind of compression\n[0546.9-0550.1] and this aids our generalization and rocking.\n[0550.1-0551.5] There are in fact some humans\n","created_at":"2026-06-28T06:06:51.341475Z","finish_reason":null,"metrics":null},{"message_id":"2d5795c1-26bf-4ddc-974c-7f4e70e5beaa","node_id":154,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a8ad6d5e0d52d81f","name":"get_output","arguments":{"shell_id":"dd7a8e","timeout":300000}}],"created_at":"2026-06-28T06:06:56.746092Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":5402,"input_tokens":30184,"output_tokens":24,"cache_read_tokens":2096,"cache_creation_tokens":null,"tpot_ms":225.08333333333334,"tokens_per_sec":4.442798963346909}},{"message_id":"6ac8c0c0-fcc1-4ba5-85bf-4964b88ef0d2","node_id":155,"role":"tool","content":"[0551.5-0554.0] who have this autistic savante type ability\n[0554.0-0556.3] to recall random tables of numbers\n[0556.3-0559.3] or nonsense syllables years later.\n[0559.3-0561.5] Basically the kind of fidelity information\n[0561.5-0563.6] that models have in context.\n[0563.6-0566.9] And such sheer volume cripples these humans' ability\n[0566.9-0569.5] to understand abstractions and metaphors.\n[0569.5-0570.7] Human continual learning\n[0570.7-0572.6] is less about having all your observations\n[0572.6-0573.8] at the tip of your tongue\n[0573.8-0576.1] and more about chiseling the right intuitions\n[0576.1-0578.9] and big picture knowledge back into the weights.\n[0578.9-0580.6] But the moment you move into the weights,\n[0580.6-0582.5] you have to give up on in context learning\n[0582.5-0583.7] sample efficiency.\n[0583.7-0586.5] Because gradient updates are super sample inefficient,\n[0586.5-0589.0] all of the successfully shipped online learning models\n[0589.0-0591.2] have had to learn the exact same thing\n[0591.2-0593.0] across millions of users.\n[0593.0-0596.1] For example, the cursor tab model online learns\n[0596.1-0598.7] by predicting the same exact objective\n[0598.7-0600.9] for over 400 million requests a day.\n[0600.9-0602.7] The objective here being which edits\n[0602.7-0604.7] actually got accepted by the user.\n[0604.7-0605.8] At least so far,\n[0605.8-0607.7] we haven't seen models online learn\n[0607.8-0610.7] different kinds of things for different users.\n[0610.7-0612.8] Because while a single session may generate\n[0612.8-0615.2] more than enough data for a human to learn from,\n[0615.2-0618.7] it's not enough to train a more capable AI.\n[0618.7-0620.3] Current online learning can work\n[0620.3-0622.8] for a very limited number of use cases.\n[0622.8-0625.3] But the whole point of continual learning\n[0625.3-0627.2] is that the world is very complicated.\n[0627.2-0631.3] An East job and company and problem is different.\n[0631.3-0632.6] And you need your intelligence\n[0632.6-0635.4] to be able to learn the specific information\n[0635.4-0637.8] related to a particular deployment,\n[0637.8-0639.5] which simply can't be stuffed\n[0639.5-0641.5] into some shared training run.\n[0641.5-0642.8] These are all the things we're talking about\n[0642.8-0644.7] when we talk about on the job learning.\n[0644.7-0646.6] Things like how does everything in your organization\n[0646.6-0648.1] work and fit together?\n[0648.1-0650.3] And how to cooperate with all the infrastructure\n[0650.3-0651.5] and the other people around you\n[0651.5-0654.1] to make progress on some larger project.\n[0654.1-0656.1] And what the common failure modes are\n[0656.1-0658.4] and many other things like this.\n[0658.4-0659.6] As the podcast has grown,\n[0659.6-0661.1] I've had to deal with more and more\n[0661.1-0662.7] operational overhead.\n[0662.7-0663.8] Take paying bills.\n[0663.8-0666.6] In the past, contractors would just email me their invoices.\n[0666.6-0668.5] Every few weeks, I dig through my inbox,\n[0668.5-0670.1] I'd create a folder with all the bills\n[0670.1-0671.9] and I'd manually pay each one.\n[0671.9-0672.8] At this point though,\n[0672.8-0674.2] I just give everybody an email address\n[0674.2-0675.9] that goes straight to Mercury,\n[0675.9-0677.2] which is my banking platform.\n[0677.2-0679.6] Whenever anybody sends an invoice to that address,\n[0679.6-0681.4] Mercury automatically downloads it,\n[0681.4-0684.3] scans it and extracts all the relevant information.\n[0684.3-0686.4] Things like the contractor name, address,\n[0686.4-0688.5] payment amount, invoice number and due date,\n[0688.5-0691.0] and then uses all of this to create a draft payment.\n[0691.0-0693.1] Mercury then stores a list of these drafts\n[0693.1-0694.1] for me to review.\n[0694.1-0695.1] I just go through this list\n[0695.1-0697.0] and double check that I've been built correctly.\n[0697.0-0698.3] I don't have to track anything\n[0698.3-0699.9] or enter any information myself.\n[0699.9-0701.6] Mercury does all the fundamental things\n[0701.6-0703.3] for your business extremely well\n[0703.3-0705.0] and it puts them all in one place.\n[0705.0-0708.4] If you wanna learn more, go to mercury.com.\n[0708.4-0710.2] Mercury is a FinTech company,\n[0710.2-0712.4] not an FDIC insured bank.\n[0712.4-0714.6] Banking services provided through a choice financial\n[0714.6-0718.0] group and column NA members FDIC.\n[0718.0-0720.8] In this way, sample efficiency and continual learning\n[0720.8-0723.1] are actually deeply connected problems.\n[0723.1-0725.2] Relatively little data is available\n[0725.2-0727.3] to the model on the job.\n[0727.3-0730.1] Now to learn from this data require sample efficiency\n[0730.1-0732.3] and models can do that in context\n[0732.3-0734.8] but using the fast weights that are built\n[0734.8-0736.5] on the fly by attention,\n[0736.5-0738.4] which allows for the sample efficiency\n[0738.4-0740.9] but scales very poorly in terms of memory.\n[0740.9-0742.7] So we need architectural innovations\n[0742.7-0745.6] that allow for some kind of intermediate representation.\n[0745.6-0747.3] I talked before about how we already have\n[0747.3-0749.4] many different working ideas for this kind of thing\n[0749.4-0752.4] from sparse attention to KV cash compaction.\n[0752.4-0754.3] And every week somebody releases a new paper\n[0754.3-0758.1] suggesting some kind of other architectural optimization.\n[0758.1-0759.6] It doesn't seem to me that architecture\n[0759.6-0762.6] is fundamentally what is bottlenecking continual learning.\n[0762.6-0764.9] So perhaps the bottleneck is the loss function.\n[0764.9-0766.4] How do we update the weights,\n[0766.4-0769.1] aka how do we improve the model itself\n[0769.1-0770.7] based on information that was learned\n[0770.7-0773.1] from one particular session?\n[0773.1-0774.6] Even here, naively it seems like\n[0774.6-0776.3] there are many ideas that ought to work.\n[0776.3-0778.6] A lot of people are talking about this technique\n[0778.6-0781.3] called on policy self-distillation recently.\n[0781.3-0782.4] If you wanna learn more about it,\n[0782.4-0784.8] I recorded a little impromptu Blackboard lecture\n[0784.8-0787.6] on my iPhone with Sasha Rush a couple of weeks ago\n[0787.6-0790.0] and it's in the link in the description.\n[0790.0-0791.7] But to summarize the explanation,\n[0791.7-0794.3] the idea is that we encourage the base model\n[0794.3-0796.0] to make the same predictions\n[0796.0-0798.4] when trying to solve some real world problem\n[0798.4-0800.7] as the model with all the context accumulated\n[0800.7-0803.0] after a long session would have made.\n[0803.0-0804.5] The whole point of this procedure\n[0804.5-0807.1] is to distill what the model learned in a session\n[0807.1-0809.2] back into the weights themselves.\n[0809.2-0811.9] This is better than RLVR for two reasons.\n[0811.9-0814.1] One, OPSD doesn't require us to have\n[0814.1-0816.5] some outer loop verifiable reward.\n[0816.5-0818.8] We just need a model that can learn the right things\n[0818.8-0820.4] within the context window.\n[0820.4-0822.1] And as long as we have that,\n[0822.1-0823.3] we can train the base model\n[0823.3-0825.3] to match our veteran teacher model\n[0825.3-0828.8] which has built up all this experience during the session.\n[0828.8-0832.7] And two, OPSD provides a much denser supervision signal\n[0832.7-0834.0] than Naive RL.\n[0834.0-0835.9] Instead of projecting a single reward\n[0835.9-0837.2] through the whole trajectory,\n[0837.2-0840.4] you can train on the per token probability discrepancy\n[0840.4-0842.3] between the teacher and student.\n[0842.3-0843.3] For continual learning,\n[0843.3-0846.7] OPSD is also superior to supervised fine tuning.\n[0846.7-0848.7] The most naive version of SFT\n[0848.7-0850.4] for this application that you can imagine\n[0850.4-0851.6] is just to train the base model\n[0851.6-0852.5] to predict all the tokens\n[0852.5-0854.2] that are observed during the session.\n[0854.2-0855.1] But this makes no sense\n[0855.1-0856.9] if you think about it as a learning target.\n[0856.9-0858.2] The way you get better at your job\n[0858.2-0859.6] is not by recalling the transcript\n[0859.6-0861.8] of every single thing that happened every day\n[0861.8-0863.2] with perfect fidelity.\n[0863.2-0864.9] Rather, it's by consolidating\n[0864.9-0867.4] the handful of insights and pieces of knowledge\n[0867.4-0868.8] that are actually relevant\n[0868.8-0870.7] to you getting better at your job.\n[0870.7-0872.9] RL training doesn't suffer from this failure mode.\n[0872.9-0875.7] RL is great at concentrating the update\n[0875.7-0878.6] to only what is relevant to getting the outcome right.\n[0878.6-0880.2] That's why actually very few parameters\n[0880.2-0883.2] are changed during an RL training step.\n[0883.2-0884.5] And this is a very important property\n[0884.5-0885.3] for continual learning\n[0885.3-0887.2] because as you're learning on the job,\n[0887.2-0888.6] you don't want to overwrite\n[0888.6-0890.0] and forget all the other things\n[0890.0-0892.0] that the base model knows.\n[0892.0-0893.4] I wrote a post a few months earlier\n[0893.4-0896.5] arguing that RL learns much less information\n[0896.5-0898.6] per sample than supervised learning.\n[0898.6-0899.8] But this may be a good thing\n[0899.8-0901.0] rather than a bad thing.\n[0901.0-0902.4] You only change the model\n[0902.4-0904.7] as much as is absolutely necessary\n[0904.7-0907.4] to achieve the outcome and no more.\n[0907.4-0910.3] OPSD preserves this property of supervised learning\n[0910.3-0911.2] where instead of slingshotting\n[0911.2-0912.5] towards the teacher distribution\n[0912.5-0914.5] as supervised learning would have you do,\n[0914.5-0916.1] you only extract the knowledge\n[0916.1-0918.6] that is necessary for you to achieve\n[0918.6-0920.6] the same results of the teacher\n[0920.6-0923.0] on actual real-world tasks.\n[0923.0-0924.9] OPSD is one way to attack\n[0924.9-0926.3] the sample efficiency problem.\n[0926.3-0928.7] You take this scarce real-world experience\n[0928.7-0929.8] and you squeeze … (14 chars truncated)\n… (10 lines truncated)\n<truncation_notice>\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/bb149119/content.txt\n</truncation_notice>","created_at":"2026-06-28T06:11:56.757006Z","finish_reason":null,"metrics":null},{"message_id":"80eaf2d5-f60e-4d46-8995-2fd9ec81c330","node_id":157,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9a25d183a2bdf8b5","name":"get_output","arguments":{"shell_id":"dd7a8e","timeout":300000}}],"created_at":"2026-06-28T06:11:59.315513Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2556,"input_tokens":36233,"output_tokens":24,"cache_read_tokens":null,"cache_creation_tokens":null,"tpot_ms":106.5,"tokens_per_sec":9.389671361502348}},{"message_id":"d9d624ff-2375-42d0-b499-3f98ae010027","node_id":158,"role":"tool","content":"[0951.7-0953.2] Let's go back in history a bit.\n[0953.2-0955.9] A couple years after DeepMind released AlphaZero,\n[0955.9-0958.5] a group of researchers trained a model called EfficientZero.\n[0958.5-0959.6] And the whole point of this model\n[0959.6-0962.0] is to be very efficient with data.\n[0962.0-0964.9] So if this model and a human both got two hours\n[0964.9-0967.7] to play against a simulator of an Atari game\n[0967.7-0969.0] that they hadn't seen before,\n[0969.0-0970.4] this model would actually probably\n[0970.4-0971.9] beat the novice human.\n[0971.9-0972.9] Does this mean that the model\n[0972.9-0974.7] was more sample efficient than the humans?\n[0974.7-0976.4] Well, that was the goal of the training,\n[0976.4-0979.5] but it depends on how you measure sample efficiency\n[0979.5-0981.9] because for each step in the real game,\n[0981.9-0983.7] EfficientZero is playing dozens\n[0983.7-0986.0] of simulated games in its head.\n[0986.0-0986.9] In a similar way,\n[0986.9-0988.5] future LLMs might be able to consume\n[0988.5-0990.3] far less real world data\n[0990.3-0992.7] while practicing endlessly against environments\n[0992.7-0994.6] that they build for themselves.\n[0994.6-0995.6] The big difference, of course,\n[0995.6-0996.9] is that it will be much harder\n[0996.9-0999.4] to build a simulation of the whole world\n[0999.4-1001.7] than it is to emulate the game of Go.\n[1001.7-1004.2] That's why I said this is a much more speculative idea.\n[1004.2-1007.3] If it works, it would become a fourth axis of scaling\n[1007.3-1010.6] alongside pre-training, RL, and inference time compute.\n[1010.6-1013.4] You could call it test time training or dreaming.\n[1013.4-1016.1] The model spends compute writing up RL environments\n[1016.1-1017.6] and then training against them\n[1017.6-1019.5] and it's rehearsing all the skills\n[1019.5-1021.8] that will actually be used in production\n[1021.8-1023.7] for a specific user.\n[1023.7-1025.7] So instead of hitting forward slash compact\n[1025.7-1027.9] in codex or cursor or clod,\n[1027.9-1029.9] which kindles a small amount of compute\n[1029.9-1031.3] to write up a summary\n[1031.3-1034.4] and which gives you the simulacrum of continued learning,\n[1034.4-1036.2] you hit forward slash dream\n[1036.2-1038.7] and this incinerates huge amounts of compute\n[1038.7-1041.3] to build and train against the video game version\n[1041.3-1044.1] of what the model is witnessing in the real world.\n[1044.1-1045.8] So what might continual learning look like\n[1045.8-1049.5] by 2027 or 2028 and how do we get there?\n[1049.5-1050.8] Here's one scenario.\n[1050.8-1053.8] All of this RLVR training is producing an agent\n[1053.8-1054.9] that can get its bearings\n[1054.9-1056.7] when it's thrown at an unfamiliar problem\n[1056.7-1058.3] and it can try different strategies\n[1058.3-1060.2] and it can iterate when it hits a roadblock.\n[1060.2-1063.5] This is the crucial thing that RLVR has given you.\n[1063.5-1066.2] An AI that is at least competent enough\n[1066.2-1069.0] to start getting some real world experience\n[1069.0-1070.2] if you could learn from it.\n[1070.2-1071.2] And once you have that,\n[1071.2-1073.8] you send it out into the world to do real work,\n[1073.8-1077.0] even on projects that are off the trading distribution.\n[1077.0-1078.2] Now let's say at this point,\n[1078.2-1080.0] the effective context lengths have expanded\n[1080.0-1083.1] such that AIs can jam and co-work with you\n[1083.1-1086.4] for a full week of wall clock time.\n[1086.4-1087.5] At the end of a week,\n[1087.5-1089.0] you give it a thumbs up or a thumbs down,\n[1089.0-1090.6] give it a work review.\n[1090.6-1091.8] If you give it a thumbs up,\n[1091.8-1093.4] the base model distills everything\n[1093.4-1095.4] that the AI learned during the session.\n[1095.4-1098.0] And it may use OPSD, it may use Dreaming,\n[1098.0-1099.3] it may use some other technique\n[1099.3-1100.7] that we aren't even aware of,\n[1100.7-1103.4] or it'll use a combination of all of the above.\n[1103.4-1104.6] And once it does so,\n[1104.6-1106.6] the AI starts getting better domains\n[1106.6-1109.6] that are adjacent to what it was explicitly trained to\n[1109.6-1112.1] beforehand with RLVR training.\n[1112.1-1113.5] And in the next round after that,\n[1113.5-1115.0] it can get better at things that are adjacent\n[1115.0-1117.6] to what it had previously online learned.\n[1117.6-1120.3] In this way, the gamut of AI skills and knowledge\n[1120.3-1123.1] and capabilities can expand far beyond\n[1123.1-1125.6] the verifiable domains that the model\n[1125.6-1128.4] was originally trained against before it was deployed.\n[1128.4-1130.7] Just as pre-training created a base intelligence\n[1130.7-1133.8] that was smart enough to become a competent agent\n[1133.8-1135.7] with enough RLVR on top,\n[1135.7-1138.6] so RLVR has created an agent\n[1138.6-1140.2] that is competent enough\n[1140.2-1142.3] to be actually broadly deployed in the world\n[1142.3-1145.4] and from this broad deployment to learn on the job\n[1145.4-1147.5] once the training recipe for continual learning\n[1147.5-1148.9] actually arrives.\n[1148.9-1151.6] By this point, the main way that AIs get better\n[1151.6-1153.1] is not from the training they have received\n[1153.1-1155.0] before they are released to the public.\n[1155.0-1156.8] Rather, it's from all this experience\n[1156.8-1157.8] that they'll be accumulating\n[1157.8-1160.2] from being broadly deployed in the economy\n[1160.2-1162.8] and engaging in so many different kinds of tasks.\n[1162.8-1164.5] Every time that you interact with an AI,\n[1164.5-1165.5] it'll be smarter.\n[1165.5-1166.8] Not only because it's been learning\n[1166.8-1167.8] from your previous sessions,\n[1167.8-1169.6] but also because it's been learning\n[1169.6-1171.2] from all its interactions\n[1171.2-1173.2] with all the other users in the world.\n[1173.2-1175.3] And that's very scary and exciting\n[1175.3-1179.0] and different from the way that AI improves right now.\n[1179.0-1181.1] This was a narration of a blog post\n[1181.1-1185.0] that I also released on my website at dwarkash.com.\n[1185.0-1186.6] Go there if you wanna read all the footnotes\n[1186.6-1187.8] or if you wanna sign up\n[1187.8-1190.5] so you can find out when I release the next blog post.\n[1190.5-1192.9] Otherwise, I'll see you on the next episode.\nDONE\n\n\nExit code: 0","created_at":"2026-06-28T06:15:14.072291Z","finish_reason":null,"metrics":null},{"message_id":"1ed08797-7651-40cf-94ed-770204f39560","node_id":160,"role":"assistant","content":"Transcription complete. Let me read the full text file and then summarize.","tool_calls":[{"id":"chatcmpl-tool-bde42858d6847358","name":"todo_write","arguments":{"todos":[{"content":"Use cobalt.tools API to download audio from the X tweet","status":"completed"},{"content":"Run transcription on the downloaded audio","status":"completed"},{"content":"Summarize the transcription","status":"in_progress"}]}},{"id":"chatcmpl-tool-8075b76b714d2b05","name":"read","arguments":{"file_path":"/Users/root1/Downloads/utilities/tweet_audio/twitter_2070551894674555081_audio.txt"}}],"created_at":"2026-06-28T06:15:15.884676Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1179,"total_time_ms":1810,"input_tokens":2344,"output_tokens":119,"cache_read_tokens":36232,"cache_creation_tokens":null,"tpot_ms":5.302521008403361,"tokens_per_sec":188.58954041204436}},{"message_id":"54916628-e20a-454b-bae7-aa49816bb4ef","node_id":161,"role":"tool","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable\nCurrent todo list:\n1. [x] Use cobalt.tools API to download audio from the X tweet [completed]\n2. [x] Run transcription on the downloaded audio [completed]\n3. [~] Summarize the transcription [in_progress]\n","created_at":"2026-06-28T06:15:15.928474Z","finish_reason":null,"metrics":null},{"message_id":"ce88dc76-0c93-46b2-9fe0-41daf5efcce8","node_id":162,"role":"tool","content":"<file-view path=\"/Users/root1/Downloads/utilities/tweet_audio/twitter_2070551894674555081_audio.txt\" start_line=\"1\" end_line=\"1\" total_lines=\"1\">\n  1|So here's the big research bet that all the labs are making. They think that if we train AIs to accomplish millions of verifiable tasks across thousands of diverse RL environments, then we will have basically built AGI. Because this kind of training will have created a kind of problem-solving agent, the kind of thing that can make progress on open-ended tasks for weeks on end in the face of errors and mistakes and ambiguity. The people who are optimistic about this vision will say that all these things that we talk about as the fundamental deficits in the current training paradigm, for example, the data inefficiency of these models or the fact that they lack into neural learning. These things can just be steamrolled if we just scale training more. The same way that all the fundamental research problems in natural language processing collapsed when you just threw enough compute into LLMs. So in the previous essay, I talked about how these models are one-to-one millionth as sample-efficient as humans. The people who are in favor of the current training paradigm will say, look, that might be true, but this is only true during training. Training is this one-time cost that is amortized across billions of sessions that a model will experience. What really matters is how smart and general and sample-efficient the model is during a session. This has clearly been improving as we've been doing more RL training. AI agents are able to solve more and more ambitious problems over longer and longer time spans. Anybody who has used these models for coding knows that. Similarly, people would say, look, continued learning, this capability I keep harping about, where the model's weights get updated based on what it's learning from deployment, may simply not be necessary. Because if in-context learning gets so good across longer and longer time horizons, then you don't need to distill back everything the model is learning on the job into the weights. People often say that their employe... [truncated 20127 chars]\n</file-view>","created_at":"2026-06-28T06:15:15.928484Z","finish_reason":null,"metrics":null},{"message_id":"ea8dfdfb-ebac-4ade-a4cb-786953c0d346","node_id":164,"role":"assistant","content":"I have the full transcription. Here's the summary.","tool_calls":[{"id":"chatcmpl-tool-ba6eb52c1588434c","name":"todo_write","arguments":{"todos":[{"content":"Use cobalt.tools API to download audio from the X tweet","status":"completed"},{"content":"Run transcription on the downloaded audio","status":"completed"},{"content":"Summarize the transcription","status":"completed"}]}}],"thinking":"I have the full transcription. Now let me write a summary. The transcription is complete and I have all the content. Let me produce a comprehensive summary based on what I read in the output.\n\nThis is a Dwarkesh podcast/blog post narration about AI continual learning, RLVR (RL with verifiable rewards), sample efficiency, and the path to AGI. Let me write a good summary.","created_at":"2026-06-28T06:15:18.298036Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1070,"total_time_ms":2368,"input_tokens":663,"output_tokens":160,"cache_read_tokens":38575,"cache_creation_tokens":null,"tpot_ms":8.1125,"tokens_per_sec":123.26656394453005}},{"message_id":"f5042f6d-65ec-45f8-966a-13245a1606f7","node_id":165,"role":"tool","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable\nCurrent todo list:\n1. [x] Use cobalt.tools API to download audio from the X tweet [completed]\n2. [x] Run transcription on the downloaded audio [completed]\n3. [x] Summarize the transcription [completed]\n","created_at":"2026-06-28T06:15:18.304676Z","finish_reason":null,"metrics":null},{"message_id":"22ba568c-c85c-4a4c-9cc7-678f90c6c7ac","node_id":167,"role":"assistant","content":"Done. Here's the full pipeline result.\n\n## Pipeline\n1. **Download** — `api.cobalt.tools` now requires JWT auth, so I used the community cobalt instance `dwnld.nichind.dev` (POST to `/` with `downloadMode: audio`, `audioFormat: mp3`). Got an 18 MB MP3 (~19:53) saved to `~/Downloads/utilities/tweet_audio/twitter_2070551894674555081_audio.mp3`.\n2. **Transcription** — `faster-whisper` (medium model, int8, CPU, VAD filter) on the M4 Pro. Full transcript saved to `~/Downloads/utilities/tweet_audio/twitter_2070551894674555081_audio.txt`. (The openai-whisper CLI was unusable here because of a self-signed-cert MITM on the model download — faster-whisper via HuggingFace worked around it.)\n3. **Summary** — below.\n\n---\n\n## Summary\n\nThis is Dwarkesh narrating a blog post (find it at dwarkesh.com) about **what's missing between today's RL-trained agents and real AGI: continual / on-the-job learning.**\n\n### The labs' big bet: RLVR → AGI\nThe labs believe that training AIs on **millions of verifiable tasks across thousands of diverse RL environments** (RLVR — RL with verifiable rewards) will basically produce AGI: a problem-solving agent that can grind on open-ended tasks for weeks despite errors and ambiguity. Optimists argue the known deficits (sample inefficiency, no continual learning) will be \"steamrolled\" by scale, just as NLP research problems collapsed once enough compute hit LLMs.\n\n### The counterarguments Dwarkesh raises\n- **\"Sample inefficiency only matters during training\"** — defenders say training cost is amortized over billions of sessions; what matters is in-session smarts, which RL is clearly improving (visible in coding agents). Fair, but…\n- **\"Continual learning may be unnecessary if context windows get huge\"** — people argue you can just fit the \"six months to become productive\" into context. Dwarkesh is skeptical.\n- **Computer use is suspiciously slow** — a telling clue. Computer-use tasks *are* verifiable (did the Etsy order arrive? were taxes filed?), yet progress lags coding/math. His diagnosis: a domain must be not just **verifiable** but **grindable** — you need thousands of parallel rollouts against a deterministic, replayable simulator. Coding has that (spin up 1,000 identical containers); Amazon checkout doesn't (Andy Jassy bans your bots). You can clone Slack/Gmail, but it's labor-intensive — until AIs can code well enough to build high-fidelity clones themselves (which is also a great coding RL objective).\n- **The deeper problem** — many crucial skills (building a business, winning court cases, profitable trading, winning elections, LBJ-level politics, Elon-level space companies) require **real-world interaction**, months/years of outer-loop verification, and you can't perturb actions across parallel rollouts to isolate what worked. Because data there is idiosyncratic and sparse, **you need sample efficiency to get proficient**, and that data doesn't exist in any containerized form.\n\n### Does RLVR generalize to long horizons / real world?\nEmpirical question. He reads a telling Dario quote about context length: train-short / serve-long causes degradation — hinting that **short-horizon RL doesn't automatically generalize to long-horizon performance**, which would also undermine \"train on white-collar tasks → build a business like Sam Walton.\"\n\n### Continual learning is the crux\n- ~30–50% of lab compute goes to **inference**, currently doing nothing to improve the model — a huge waste, especially since the most valuable learning signal (what orgs actually use the model for, real-world mistakes) is only revealed in deployment.\n- Analogy: a genius grad student never allowed a real internship, fed only classroom case studies.\n- You can't just grow the KV cache forever (not scalable, not how humans learn — human memory compresses; savants with perfect recall actually struggle with abstraction).\n- But moving learnings into the weights is **sample-inefficient**: every shipped online-learning model (e.g., Cursor Tab, ~400M requests/day) only works because it learns the *same* objective across millions of users. We haven't seen per-user online learning, because one session's data is enough for a human but not for training a more capable AI.\n\n### Techniques that could close the gap\n1. **On-policy self-distillation (OPSD)** — train the base model to match the predictions of a \"veteran\" teacher model that has accumulated a long session of context. Better than RLVR (no outer-loop verifiable reward needed; denser per-token supervision) and better than naive SFT (which would force the model to memorize the whole session transcript rather than consolidate the few useful insights). Preserves RL's nice property of changing only the parameters necessary to achieve the outcome — important for avoiding catastrophic forgetting.\n2. **\"Dreaming\" / test-time training** — inspired by EfficientZero (which beats novice humans at a novel Atari game after 2 hours by playing dozens of simulated games in its head per real step). Future LLMs might consume little real-world data while endlessly practicing against **self-built simulations** of what they're witnessing in production. Much harder than emulating Go — you'd have to simulate the whole world — so this is the most speculative idea, but if it works it becomes a **fourth axis of scaling** alongside pretraining, RL, and inference-time compute. Cue the joke: instead of `/compact` in Cursor/Claude (a cheap summary masquerading as continual learning), you'd hit `/dream` and incinerate huge compute building and training against the \"video game version\" of your real-world task.\n\n### The 2027–2028 scenario he sketches\nRLVR produces an agent competent enough to be deployed on real, even out-of-distribution work. Context lengths grow to a full week of co-work. At week's end you give it a thumbs up/down \"work review\"; on thumbs up, the base model distills the session's learnings (via OPSD, dreaming, or something unknown). It then gets better at domains adjacent to its RLVR training, then adjacent to what it online-learned, and so on — skills expand far beyond the original verifiable training set. **The main way AIs improve is no longer pre-release training but accumulated deployment experience across all users** — every interaction makes the AI smarter, both from your prior sessions and from everyone else's. \"And that's very scary and exciting and different from the way that AI improves right now.\"\n\n### Notable asides\n- Mercury (banking) sponsor read woven into a personal anecdote about paying contractor invoices.\n- References his prior video essay on sample inefficiency (models ~1M× less sample-efficient than humans) and an impromptu blackboard lecture with Sasha Rush on OPSD (linked in the video description).\n\n**Files produced** (in `~/Downloads/utilities/tweet_audio/`):\n- `twitter_2070551894674555081_audio.mp3` — downloaded audio\n- `twitter_2070551894674555081_audio.txt` — full Whisper transcript\n- `transcribe_fw.py` — the faster-whisper script used","tool_calls":[],"created_at":"2026-06-28T06:15:40.403102Z","finish_reason":"stop","metrics":{"ttft_ms":1045,"total_time_ms":22091,"input_tokens":245,"output_tokens":1579,"cache_read_tokens":39237,"cache_creation_tokens":null,"tpot_ms":13.328689043698544,"tokens_per_sec":75.02613323196806}},{"message_id":"8666b94e-b334-413c-8818-7ff96295bb58","node_id":168,"role":"user","content":"how long did that take","created_at":"2026-06-28T06:15:55.971896Z","finish_reason":null,"metrics":null},{"message_id":"3a04b49d-7395-4bab-acd9-78f997db5468","node_id":170,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a71432107ea0ecf3","name":"exec","arguments":{"command":"cd ~/Downloads/utilities/tweet_audio && stat -f \"%Sm %N\" -t \"%H:%M:%S\" twitter_2070551894674555081_audio.mp3 twitter_2070551894674555081_audio.txt transcribe_fw.py 2>/dev/null; echo \"---model---\"; stat -f \"%Sm %N\" -t \"%H:%M:%S\" ~/.cache/whisper/medium.pt 2>/dev/null; ls -la ~/.cache/huggingface/hub/models--Systran--faster-whisper-medium/ 2>/dev/null"}}],"thinking":"The user is asking how long the whole process took. Let me check timestamps from the shell outputs.\n\nLooking at the timeline:\n- First cobalt attempt and download: started around 22:09 (file timestamp showed `Jun 27 22:09`)\n- Transcription completed - the audio file was 18M downloaded at 22:09\n- Model download (medium.pt 1.5GB) took from ~22:17 to ~22:27 (about 10 min)\n- Then faster-whisper model download from HF\n- Transcription ran and completed\n\nLet me check file timestamps to figure out the actual durations.","created_at":"2026-06-28T06:16:02.536769Z","finish_reason":"tool_calls","metrics":{"ttft_ms":2186,"total_time_ms":6563,"input_tokens":24576,"output_tokens":269,"cache_read_tokens":14786,"cache_creation_tokens":null,"tpot_ms":16.271375464684017,"tokens_per_sec":61.45761937400045}},{"message_id":"eca92383-88e5-4409-92a7-5d8c592d9cb3","node_id":171,"role":"tool","content":"Output from command in shell 5175b5:\n22:09:56 twitter_2070551894674555081_audio.mp3\n23:15:13 twitter_2070551894674555081_audio.txt\n22:48:45 transcribe_fw.py\n---model---\n22:27:56 /Users/root1/.cache/whisper/medium.pt\ntotal 0\ndrwxr-xr-x  5 root1  staff  160 Jun 27 22:59 .\ndrwxr-xr-x  7 root1  staff  224 Jun 27 22:48 ..\ndrwxr-xr-x  6 root1  staff  192 Jun 27 22:59 blobs\ndrwxr-xr-x  3 root1  staff   96 Jun 27 22:48 refs\ndrwxr-xr-x  3 root1  staff   96 Jun 27 22:48 snapshots\n\n\nExit code: 0","created_at":"2026-06-28T06:16:02.569658Z","finish_reason":null,"metrics":null},{"message_id":"8c799046-6622-47f2-984f-7facacac410e","node_id":173,"role":"assistant","content":"Here's the breakdown from file timestamps:\n\n| Phase | Duration |\n|---|---|\n| Cobalt audio download | ~30 seconds (18 MB MP3, done by 22:09:56) |\n| openai-whisper `medium.pt` download (1.5 GB) | ~18 min (22:10 → 22:27:56) — **wasted**: SSL-cert issue blocked it, then I killed the slow CPU run |\n| Switch to faster-whisper + HF model download (~1.5 GB) | ~10 min (22:48 → 22:59) |\n| **Actual transcription** (faster-whisper medium, int8, CPU) | **~16 min** (22:59 → 23:15:13) for 19:53 of audio |\n\n**Total wall clock: ~67 min**, but roughly **half of that was model downloads and a dead-end openai-whisper attempt** (SSL MITM on this machine forced the switch to faster-whisper).\n\nIf the models had already been cached — which they now are — the same job would take **~17 minutes**: 30s for cobalt + ~16 min for transcription. For reference, the audio is ~20 min long, so faster-whisper-medium on this M4 Pro CPU runs at roughly **1.2× realtime**.\n\nIf you want it faster next time, options:\n- Install `mlx-whisper` (`pip install mlx-whisper`) — runs on the Apple Silicon GPU/neural engine, typically 5–10× faster than CPU faster-whisper.\n- Use `small` instead of `medium` — about 3× faster, slightly lower accuracy (for a clear single-speaker narration like this, easily good enough).\n- Cache is now warm, so re-runs skip the downloads entirely.","tool_calls":[],"thinking":"Let me piece together the timeline:\n- Audio download finished: 22:09:56\n- openai-whisper medium.pt model download finished: 22:27:56 (so ~18 min for the 1.5GB download)\n- transcribe_fw.py created: 22:48:45 (this is when I switched to faster-whisper after killing the slow openai-whisper run)\n- HF faster-whisper medium model snapshot: 22:59\n- Transcript file written: 23:15:13\n\nSo:\n- Cobalt download: a few seconds (the curl returned 200 with 19MB)\n- openai-whisper model download: ~18 min (mostly wasted — SSL issue, then I killed the slow CPU run)\n- faster-whisper HF model download: ~10 min (22:48 → 22:59)\n- Actual transcription: 22:59 → 23:15 = ~16 min for 19:53 of audio\n\nTotal wall clock from first cobalt call (~22:08) to transcript done (23:15) ≈ ~67 min, but a big chunk was model downloads and the dead-end openai-whisper attempt.\n\nThe \"useful\" work:\n- Cobalt download: ~30s\n- faster-whisper model download: ~10 min\n- Transcription: ~16 min\n\nIf models were already cached, the whole thing would have been ~17 minutes (30s download + 16 min transcription).","created_at":"2026-06-28T06:16:13.969459Z","finish_reason":"stop","metrics":{"ttft_ms":1102,"total_time_ms":11398,"input_tokens":493,"output_tokens":686,"cache_read_tokens":39361,"cache_creation_tokens":null,"tpot_ms":15.008746355685131,"tokens_per_sec":66.62781662781663}},{"message_id":"0fba966f-1b92-45b0-904f-2afe39a0b893","node_id":174,"role":"user","content":"holy fuck","created_at":"2026-06-28T06:16:26.021330Z","finish_reason":null,"metrics":null},{"message_id":"33a118d3-07f7-4b08-a7e0-6fa7b854d04e","node_id":176,"role":"assistant","content":"Yeah — the model downloads were the killer. The actual transcription was only ~16 min for 20 min of audio, which is fine. Everything else was me fighting infrastructure: cobalt.tools now hides behind JWT auth, your machine has a self-signed-cert MITM that broke openai-whisper's model fetch, and I had to fall back to a community cobalt instance + faster-whisper via HuggingFace.\n\nGood news: all of that is cached now. Next tweet you throw at me should be **~17 min end-to-end**, mostly just the transcription itself. And if you want me to install `mlx-whisper` so it uses the M4 Pro's GPU instead of CPU, say the word — that'd likely drop the transcription to ~2–3 min.","tool_calls":[],"created_at":"2026-06-28T06:16:29.816338Z","finish_reason":"stop","metrics":{"ttft_ms":1345,"total_time_ms":3794,"input_tokens":748,"output_tokens":160,"cache_read_tokens":39362,"cache_creation_tokens":null,"tpot_ms":15.30625,"tokens_per_sec":65.33278889342589}},{"message_id":"84b23899-4632-4420-b38d-50c56a829c78","node_id":177,"role":"user","content":"explain this  self-signed-cert MITM, ","created_at":"2026-06-28T06:16:47.546182Z","finish_reason":null,"metrics":null},{"message_id":"91aacadb-76a4-402a-858d-993109a59910","node_id":179,"role":"assistant","content":"Let me actually investigate what's going on rather than just hand-wave. The error was:\n\n```\nssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain\n```","tool_calls":[{"id":"chatcmpl-tool-a0fa6fb8da73ba06","name":"exec","arguments":{"command":"echo \"=== openaipublic.azureedge.net chain ===\"; openssl s_client -connect openaipublic.azureedge.net:443 -servername openaipublic.azureedge.net -showcerts </dev/null 2>/dev/null | grep -E \"^( s:| i:|Verify return)\" | head -20\necho\necho \"=== huggingface.co chain ===\"; openssl s_client -connect huggingface.co:443 -servername huggingface.co -showcerts </dev/null 2>/dev/null | grep -E \"^( s:| i:|Verify return)\" | head -20\necho\necho \"=== python ssl default CA file ===\"; python3 -c \"import ssl; print(ssl.get_default_verify_paths())\"\necho\necho \"=== curl CA bundle env ===\"; env | grep -iE \"curl_ca|requests_ca|ssl_cert|cert_path\" "}}],"thinking":"The user wants me to explain the SSL error I encountered. Let me look at what actually happened and investigate the root cause rather than just speculating.","created_at":"2026-06-28T06:16:53.951551Z","finish_reason":"tool_calls","metrics":{"ttft_ms":2275,"total_time_ms":6401,"input_tokens":173,"output_tokens":278,"cache_read_tokens":40109,"cache_creation_tokens":null,"tpot_ms":14.841726618705035,"tokens_per_sec":67.37760542898691}},{"message_id":"e0ec6910-ba1f-49e0-a0dc-f406cbb38eb6","node_id":180,"role":"tool","content":"Output from command in shell db4c41:\n=== openaipublic.azureedge.net chain ===\n\n=== huggingface.co chain ===\n\n=== python ssl default CA file ===\nDefaultVerifyPaths(cafile=None, capath=None, openssl_cafile_env='SSL_CERT_FILE', openssl_cafile='/Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem', openssl_capath_env='SSL_CERT_DIR', openssl_capath='/Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/certs')\n\n=== curl CA bundle env ===\n\n\nExit code: 1","created_at":"2026-06-28T06:16:54.312942Z","finish_reason":null,"metrics":null},{"message_id":"e2003d1b-ff70-4db6-a1ef-075dc65a2ef3","node_id":182,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-bd0c4faebb7fec10","name":"exec","arguments":{"command":"echo \"=== Does Python's expected CA file exist? ===\"; ls -la /Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem 2>&1\nls -la /Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/certs 2>&1 | head\necho\necho \"=== system keychain certs (count) ===\"; security find-certificate -a /Library/Keychains/System.keychain 2>/dev/null | grep -c \"alis\"\necho\necho \"=== try openssl with system roots ===\"; openssl s_client -connect openaipublic.azureedge.net:443 -servername openaipublic.azureedge.net -CAfile /etc/ssl/cert.pem </dev/null 2>&1 | grep -E \"Verify|verify\" | head\necho\necho \"=== does /etc/ssl/cert.pem exist? ===\"; ls -la /etc/ssl/cert.pem 2>&1"}}],"created_at":"2026-06-28T06:16:58.351285Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":4037,"input_tokens":406,"output_tokens":214,"cache_read_tokens":40281,"cache_creation_tokens":null,"tpot_ms":18.864485981308412,"tokens_per_sec":53.00966063908844}},{"message_id":"c6448fb3-6cc5-4065-8648-4184fe1a3ba9","node_id":183,"role":"tool","content":"Output from command in shell 59a9b4:\n=== Does Python's expected CA file exist? ===\nls: /Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem: No such file or directory\nls: /Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/certs: No such file or directory\n\n=== system keychain certs (count) ===\n3\n\n=== try openssl with system roots ===\nverify return:1\nverify return:1\nverify return:1\nverify return:1\n    Verify return code: 0 (ok)\n\n=== does /etc/ssl/cert.pem exist? ===\n-rw-r--r--  1 root  wheel  333483 May 22 03:44 /etc/ssl/cert.pem\n\n\nExit code: 0","created_at":"2026-06-28T06:16:58.507176Z","finish_reason":null,"metrics":null},{"message_id":"2308ff39-cdf0-467d-8b1d-0b35efa58b8d","node_id":185,"role":"assistant","content":"OK — I owe you a correction. I called it a \"MITM\" earlier, but it's **not** a man-in-the-middle attack. It's a misconfigured Python install. Here's what's actually going on:\n\n## The real cause\n\nThe python.org macOS installer ships its **own copy of OpenSSL**, configured to look for trusted root certificates at:\n\n```\n/Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem\n```\n\nOn your machine, **that file does not exist**. Neither does the `certs/` directory next to it. So Python's `ssl` module has an **empty trust store** — it doesn't know about any root CA, including perfectly legitimate ones like DigiCert or Let's Encrypt.\n\n## Why the error said \"self-signed certificate\"\n\nThat wording is what threw me off. Every TLS chain ends in a **root CA**, and root CAs are *by definition* self-signed (they sign themselves — that's what makes them roots). Normally OpenSSL validates a root by checking it against the trust store. With an empty trust store, Python hits the root cert, can't find it anywhere, and reports:\n\n```\nCERTIFICATE_VERIFY_FAILED: self-signed certificate in certificate chain\n```\n\nIt's not saying someone injected a fake cert. It's saying \"I reached the top of the chain and it's self-signed, *and I have no way to trust it*.\" Same error you'd get on any clean TLS connection when your CA bundle is missing.\n\n## Proof it's not a MITM\n\n- `openssl s_client` against `openaipublic.azureedge.net:443` using the **system** CA bundle (`/etc/ssl/cert.pem`, which exists and is 333 KB) returns **`Verify return code: 0 (ok)`** — the chain is legitimate.\n- macOS itself has the roots in the Keychain; `curl`, `git`, browsers, etc. all work fine because they use the system trust store, not Python's bundled one.\n- Only Python (and tools built on Python's `ssl`) break.\n\n## Why faster-whisper worked where openai-whisper didn't\n\n- `openai-whisper` downloads its model via raw `urllib.request.urlopen` — pure Python stdlib, uses Python's broken OpenSSL → fails.\n- `faster-whisper`/HuggingFace uses `requests`/`huggingface_hub`, which on this machine apparently fell back to a CA bundle from `certifi` (a pip package that bundles its own Mozilla CA list). That's why the HF model download succeeded.\n\n## The fix (one-time, ~10 seconds)\n\n```bash\n/Applications/Python\\ 3.12/Install\\ Certificates.command\n```\n\nThat's the script python.org ships precisely for this — it downloads `certifi`'s PEM and symlinks it into the OpenSSL path Python expects. After running it, `urllib`, `openai-whisper`, `pip` over HTTPS, etc. all start working. Alternatively:\n\n```bash\npip install certifi\npython3 -c \"import certifi, os, shutil; src=certifi.where(); dst='/Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem'; os.makedirs(os.path.dirname(dst), exist_ok=True); shutil.copy(src, dst)\"\n```\n\nWant me to run the fix now so this never bites you again?","tool_calls":[],"thinking":"Found it. The root cause is clear now. Let me explain it properly.\n\nPython's bundled OpenSSL is configured to look for CA certificates at:\n`/Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem`\n\nBut that file **does not exist**. So Python's ssl module has an empty trust store and rejects every TLS connection with \"self-signed certificate in certificate chain\" — because from Python's perspective, it can't find any root CA to validate against, so every chain bottoms out at an \"untrusted\" self-signed cert (which is what root CAs technically are).\n\nThe system has perfectly good roots at `/etc/ssl/cert.pem` (macOS's extracted keychain roots), and openssl-using-that-file verifies the chain fine (\"Verify return code: 0 (ok)\").\n\nSo this is NOT actually a MITM. I was wrong to call it that. It's a misconfigured Python install — the python.org macOS installer ships its own OpenSSL but doesn't always populate the cert bundle, and on this machine that bundle is missing.\n\nThe \"self-signed certificate in certificate chain\" wording was misleading me — it just means Python hit a cert in the chain (the root CA) that it couldn't verify, because its trust store was empty. Not that someone was actually intercepting traffic.\n\nLet me correct myself honestly.","created_at":"2026-06-28T06:17:16.236457Z","finish_reason":"stop","metrics":{"ttft_ms":1184,"total_time_ms":17727,"input_tokens":397,"output_tokens":997,"cache_read_tokens":40686,"cache_creation_tokens":null,"tpot_ms":16.592778335005015,"tokens_per_sec":60.267182494106265}},{"message_id":"075b95e8-1b9a-4b76-ba40-c759f5421a9f","node_id":186,"role":"user","content":"fix","created_at":"2026-06-28T06:45:44.293326Z","finish_reason":null,"metrics":null},{"message_id":"b0306e44-90d3-45bb-8fe8-df2e98840059","node_id":188,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9aee4d4de7180834","name":"exec","arguments":{"command":"ls /Applications/ | grep -i python; ls \"/Applications/Python 3.12/\" 2>/dev/null | head"}}],"created_at":"2026-06-28T06:45:46.534999Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2240,"input_tokens":29925,"output_tokens":37,"cache_read_tokens":11850,"cache_creation_tokens":null,"tpot_ms":60.54054054054054,"tokens_per_sec":16.517857142857142}},{"message_id":"7e98173f-d7da-41bf-8d29-544e99169de0","node_id":189,"role":"tool","content":"Output from command in shell 19ef96:\nPython 3.12\nIcon\r\nIDLE.app\nInstall Certificates.command\nLicense.rtf\nPython Documentation.html\nPython Launcher.app\nReadMe.rtf\nUpdate Shell Profile.command\n\n\nExit code: 0","created_at":"2026-06-28T06:45:46.563939Z","finish_reason":null,"metrics":null},{"message_id":"3e9970fb-8420-4cd1-90f5-39854c60aa1f","node_id":191,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a9c6dbc7ac9cf794","name":"exec","arguments":{"command":"\"/Applications/Python 3.12/Install Certificates.command\" 2>&1"}}],"created_at":"2026-06-28T06:45:47.847107Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1281,"input_tokens":96,"output_tokens":28,"cache_read_tokens":41774,"cache_creation_tokens":null,"tpot_ms":45.75,"tokens_per_sec":21.85792349726776}},{"message_id":"4b52332a-cb45-4178-b7d2-df2e0719bf65","node_id":192,"role":"tool","content":"Output from command in shell 6601bb:\nRequirement already satisfied: certifi in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (2026.1.4)\nCollecting certifi\n  Downloading certifi-2026.6.17-py3-none-any.whl.metadata (2.5 kB)\nDownloading certifi-2026.6.17-py3-none-any.whl (133 kB)\nInstalling collected packages: certifi\n  Attempting uninstall: certifi\n    Found existing installation: certifi 2026.1.4\n    Uninstalling certifi-2026.1.4:\n      Successfully uninstalled certifi-2026.1.4\nSuccessfully installed certifi-2026.6.17\n\n[notice] A new release of pip is available: 26.1 -> 26.1.2\n[notice] To update, run: pip install --upgrade pip\n -- pip install --upgrade certifi\n -- removing any existing file or link\n -- creating symlink to certifi certificate bundle\n -- setting permissions\n -- update complete\n\n\nExit code: 0","created_at":"2026-06-28T06:45:48.945215Z","finish_reason":null,"metrics":null},{"message_id":"9d70d205-b39e-4ed2-ab65-511ca4cab4f2","node_id":194,"role":"assistant","content":"Now verify the fix actually works:","tool_calls":[{"id":"chatcmpl-tool-a97d84b471d29722","name":"exec","arguments":{"command":"ls -la /Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem 2>&1\necho \"---\"\npython3 -c \"\nimport ssl, urllib.request\nctx = ssl.create_default_context()\nprint('CA file:', ctx.cafile)\nurl = 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt'\nreq = urllib.request.Request(url, method='HEAD')\nwith urllib.request.urlopen(req, context=ctx, timeout=15) as r:\n    print('HTTP', r.status, '- TLS verified OK')\n\""}}],"created_at":"2026-06-28T06:45:51.714466Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1652,"total_time_ms":2768,"input_tokens":276,"output_tokens":181,"cache_read_tokens":41869,"cache_creation_tokens":null,"tpot_ms":6.165745856353591,"tokens_per_sec":162.1863799283154}},{"message_id":"c21d8b1b-1743-49c1-a196-fec5787e7a6f","node_id":195,"role":"tool","content":"Output from command in shell edffc1:\nlrwxr-xr-x  1 root1  admin  53 Jun 27 23:45 /Library/Frameworks/Python.framework/Versions/3.12/etc/openssl/cert.pem -> ../../lib/python3.12/site-packages/certifi/cacert.pem\n---\nTraceback (most recent call last):\n  File \"<string>\", line 4, in <module>\nAttributeError: 'SSLContext' object has no attribute 'cafile'\n\n\nExit code: 1","created_at":"2026-06-28T06:45:51.804663Z","finish_reason":null,"metrics":null},{"message_id":"951c50b8-8d5e-4e1a-807e-d853ebd71883","node_id":197,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b5abb372e4fdc4c7","name":"exec","arguments":{"command":"python3 -c \"\nimport ssl, urllib.request\nctx = ssl.create_default_context()\nurl = 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt'\nreq = urllib.request.Request(url, method='HEAD')\nwith urllib.request.urlopen(req, context=ctx, timeout=15) as r:\n    print('HTTP', r.status, '- TLS verified OK against', ctx.get_ca_certs().__len__(), 'CAs')\n\""}}],"created_at":"2026-06-28T06:45:53.825053Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2018,"input_tokens":296,"output_tokens":149,"cache_read_tokens":42144,"cache_creation_tokens":null,"tpot_ms":13.543624161073826,"tokens_per_sec":73.83548067393458}},{"message_id":"02ef15ed-1d2f-4458-9597-e1df062c0409","node_id":198,"role":"tool","content":"Output from command in shell 4adc63:\nHTTP 200 - TLS verified OK against 118 CAs\n\n\nExit code: 0","created_at":"2026-06-28T06:45:54.247239Z","finish_reason":null,"metrics":null},{"message_id":"04639986-bb9d-4baf-81d8-99a7d641eb3a","node_id":200,"role":"assistant","content":"Fixed. Verification:\n\n- `cert.pem` is now a symlink → `certifi/cacert.pem` (the Mozilla CA bundle, 118 root CAs).\n- `urllib.request.urlopen` against the exact Azure URL that failed before now returns **HTTP 200, TLS verified OK** — no more `CERTIFICATE_VERIFY_FAILED`.\n\nSo `openai-whisper`, `pip install`, `urllib`, and anything else using Python's stdlib SSL will now work normally. The earlier failure was purely the missing trust store; nothing was actually intercepting your traffic.","tool_calls":[],"created_at":"2026-06-28T06:45:56.769875Z","finish_reason":"stop","metrics":{"ttft_ms":1567,"total_time_ms":2518,"input_tokens":182,"output_tokens":118,"cache_read_tokens":42439,"cache_creation_tokens":null,"tpot_ms":8.059322033898304,"tokens_per_sec":124.07991587802314}}]}