{"session":{"id":"positive-lotus","working_directory":"/Users/root1","backend_type":"Windsurf","model":"swe-1-7-lightning","agent_mode":"bypass","created_at":"2026-07-17T12:43:45+00:00","last_activity_at":"2026-07-17T12:44:01+00:00","title":"Identifying fonts from HTML","main_chain_id":51,"shell_last_seen_index":0,"cogs_json":[{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/profile"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":[{"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://devin.ai/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-cli` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"playwright-microsoft\"},{\"name\":\"cloudflare-builds\"},{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"},{\"name\":\"cloudflare-docs\"},{\"name\":\"cloudflare\"},{\"name\":\"playwright\"},{\"name\":\"cloudflare-bindings\"},{\"name\":\"cloudflare-observability\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"normal"},"created_at":"2026-07-17T12:43:40.345706Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"923ca20c-7213-4991-bbb9-ac309aad8988"}],"tool_availability":{"BlockList":[{"Name":{"exact":"exit_plan_mode"}}]},"append_system_messages":[]},{"model":"swe-1-7-lightning","source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/model"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":{"AllowList":[{"Name":{"exact":"exec"}},{"Name":{"exact":"read"}},{"Name":{"exact":"edit"}},{"Name":{"exact":"grep"}},{"Name":{"exact":"glob"}},{"Name":{"exact":"exit_plan_mode"}},{"Name":{"literal":"get_output"}},{"Name":{"literal":"kill_shell"}},{"Name":{"literal":"write_to_process"}},{"Name":{"literal":"write"}},{"Name":{"literal":"notebook_read"}},{"Name":{"literal":"notebook_edit"}},{"Name":{"literal":"todo_write"}},{"Name":{"literal":"webfetch"}},{"Name":{"literal":"web_search"}},{"Name":{"literal":"ask_user_question"}},{"Name":{"literal":"run_subagent"}},{"Name":{"literal":"read_subagent"}},{"Name":{"literal":"request_scope"}},{"Name":{"literal":"mcp_call_tool"}},{"Name":{"literal":"mcp_list_servers"}},{"Name":{"literal":"mcp_list_tools"}},{"Name":{"literal":"mcp_read_resource"}},{"Name":{"literal":"skill"}}]},"append_system_messages":[{"role":"system","content":"You are powered by SWE-1.7 Lightning.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-07-17T12:43:40.345989Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"dcd07860-5d97-430e-bc0c-eda722994318"}]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/parallel-tool-calls"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[{"role":"system","content":"## Parallel tool calls\n\n- You have the capability to call multiple tools in a single response--when multiple independent pieces of information are requested, batch your tool calls together for optimal performance.\n- For example, if you need to run `git status` and `git diff`, return an array of all the arguments of the 2 read-only tool calls to run the calls in parallel.\n- Always run parallel tool calls extensively when doing independent actions, especially when reading files, analyzing directories, searching on the web, grepping and searching across the codebase.\n- Never perform dependent terminal commands or writes in parallel.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-07-17T12:43:40.346005Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"193e83b5-ffff-4f76-a1f1-1986050c365f"}]},{"model":null,"source":"Subagent","context":[],"lifetime":{"Unique":"core/read_only_kinds"},"permissions":[[{"Tool":{"Kind":"read"}},"Allow"],[{"Tool":{"Kind":"search"}},"Allow"],[{"Tool":{"Kind":"think"}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/subagent_profiles"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[{"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"subagent_profiles"},"created_at":"2026-07-17T12:43:40.346017Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"15c139ec-be54-4281-95a1-b3cdb205b71f"}]},{"model":null,"source":"Managed","context":[],"lifetime":{"Unique":"workspace/config_protection"},"permissions":[[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/cli/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.cognition/**/config*.json"}}},"ForceAsk"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Base","context":[],"lifetime":{"Unique":"workspace/read_permissions"},"permissions":[[{"Scope":{"Read":{"glob":"/Users/root1"}}},"Allow"],[{"Scope":{"Read":{"glob":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501"}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"UserOverride","context":[],"lifetime":{"Unique":"workspace/project_override_permissions"},"permissions":[[{"Scope":{"Command":{"matcher":{"Prefix":"brightness"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"osascript"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"pkill"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"yt-dlp"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"ls"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"python3"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"mkdir"}}}},"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":27,"messages":[{"message_id":"923ca20c-7213-4991-bbb9-ac309aad8988","node_id":18,"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://devin.ai/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-cli` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"playwright-microsoft\"},{\"name\":\"cloudflare-builds\"},{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"},{\"name\":\"cloudflare-docs\"},{\"name\":\"cloudflare\"},{\"name\":\"playwright\"},{\"name\":\"cloudflare-bindings\"},{\"name\":\"cloudflare-observability\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","created_at":"2026-07-17T12:43:40.345706Z","finish_reason":null,"metrics":null},{"message_id":"15c139ec-be54-4281-95a1-b3cdb205b71f","node_id":19,"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","created_at":"2026-07-17T12:43:40.346017Z","finish_reason":null,"metrics":null},{"message_id":"193e83b5-ffff-4f76-a1f1-1986050c365f","node_id":20,"role":"system","content":"## Parallel tool calls\n\n- You have the capability to call multiple tools in a single response--when multiple independent pieces of information are requested, batch your tool calls together for optimal performance.\n- For example, if you need to run `git status` and `git diff`, return an array of all the arguments of the 2 read-only tool calls to run the calls in parallel.\n- Always run parallel tool calls extensively when doing independent actions, especially when reading files, analyzing directories, searching on the web, grepping and searching across the codebase.\n- Never perform dependent terminal commands or writes in parallel.","created_at":"2026-07-17T12:43:40.346005Z","finish_reason":null,"metrics":null},{"message_id":"dcd07860-5d97-430e-bc0c-eda722994318","node_id":21,"role":"system","content":"You are powered by SWE-1.7 Lightning.","created_at":"2026-07-17T12:43:40.345989Z","finish_reason":null,"metrics":null},{"message_id":"3b1f3725-3818-420a-897b-d3c039f64101","node_id":22,"role":"system","content":"\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n /Users/root1 (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Friday, 2026-07-17\n\n","created_at":"2026-07-17T12:43:39.780239Z","finish_reason":null,"metrics":null},{"message_id":"959b8ea9-a7dd-407e-8007-3f5768b1a16c","node_id":23,"role":"system","content":"\n\n\n\n\n\n# Agent Preferences\n\n- If I ever paste in a YouTube link, use yt-dlp to summarize the video.\n- get the autogenerrated captions to do this\n- if asked to summarize a YouTube video, do not name the session until after reading and understanding the full YouTube video transcript\n- for testing that involves urls, start with example.com rather than about:blank\n- For tasks that may benefit from computer use (controlling macOS apps, windows, clicking, typing, etc.), use the background-computer-use skill to control local macOS apps through the BackgroundComputerUse API\n- Secrets/tokens live in `~/.env` (e.g. `HF_TOKEN` for Hugging Face). Source it before use: `set -a; . ~/.env; set +a`\n\n## Cloudflare DNS management\n\nFor Cloudflare DNS management (adding/editing/deleting DNS records), use the **`cf` CLI** instead of `wrangler`.\nWrangler does not have DNS management capabilities, and its OAuth token doesn't work with the Cloudflare REST API for DNS operations.\n\n### Usage\n```bash\n# Check authentication status\ncf auth whoami\n\n# List DNS records for a zone\ncf dns records list -z aidenhuang.com\n\n# Add a DNS record\ncf dns records create -z aidenhuang.com --type CNAME --name devin --content \"target.example.com\" --proxied false\n\n# Delete a DNS record\ncf dns records delete -z aidenhuang.com \n```\n\nThe `cf` CLI uses the same OAuth authentication as `wrangler` and has proper DNS record permissions.\n\n## File search via fff MCP\n\nFor any file search or grep in the current git-indexed project directory, prefer the **fff** MCP tools\n(`mcp__fff__grep`, `mcp__fff__find_files`, `mcp__fff__multi_grep`) over the built-in grep/glob tools.\nfff is frecency-ranked, git-aware, and more token-efficient.\n\nRules the fff server enforces (follow them to avoid 0-result queries):\n- Search BARE IDENTIFIERS only — one identifier per `grep` query. No `load.*metadata.*Foo` style regex.\n- Don't use regex unless you truly need alternation; `.*`, `\\d+`, `\\s+` almost always return 0 results.\n- After 2 grep calls, stop and READ the top result instead of grepping with more variations.\n- Use `multi_grep` for OR logic across multiple identifiers (e.g. snake_case + PascalCase variants) in one call.\n- Have a specific name → `grep`. Exploring a topic / finding files → `find_files`.\n\nThe `fff-mcp` binary lives at `/Users/root1/.local/bin/fff-mcp` and is registered at user scope\nin `~/.config/devin/config.json`. It refuses to run in `$HOME` or `/` — it must be launched from a\nproject directory (Devin does this automatically based on cwd). Update with:\n`curl -fsSL https://raw.githubusercontent.com/dmtrKovalenko/fff.nvim/main/install-mcp.sh | bash`\n\n## X/Twitter scraping via logged-in browser session\n\nWhen I need to scrape X/Twitter data (following, followers, tweets, user info, etc.),\nthe cleanest path is to use the **Playwright MCP** browser session with my own logged-in\nx.com account, rather than spinning up twscrape's account-pool flow. twscrape needs the\n`auth_token` HttpOnly cookie which JS cannot read from `document.cookie`; the browser\nsession attaches all cookies automatically.\n\n### Flow\n1. `mcp_list_tools` on the `playwright` server, then `browser_navigate` to `https://x.com`.\n2. If not logged in, ask me to log in manually in the opened window (don't handle my password).\n3. Once on `https://x.com/home`, read `ct0` from `document.cookie`:\n `document.cookie.match(/ct0=([^;]+)/)[1]`\n4. Call X's GraphQL endpoints directly via `fetch()` inside `browser_evaluate`. Required headers:\n - `authorization: Bearer AAAAAAAAAAAAAAAAAAAAANRILgAAAAAAnNwIzUejRCOuH5E6I8xnZz4puTs%3D1Zv7ttfk8LF81IUq16cHjhLTvJu4FA33AGWWjCpTnA` (the public web-app bearer token)\n - `x-csrf-token: `\n - `x-twitter-auth-type: OAuth2Session`\n - `x-twitter-active-user: yes`\n - `content-type: application/json`\n5. Paginate timelines by reading `content.cursorType === \"Bottom\"` entries and passing\n the value back as `variables.cursor` until it stops changing.\n\n### Key endpoints (queryId/OperationName)\n- `UserByScreenName` → `681MIj51w00Aj6dY0GXnHw` (resolve @handle → numeric rest_id)\n- `Following` → `OLm4oHZBfqWx8jbcEhWoFw`\n- `Followers` → `9jsVJ9l2uXUIKslHvJqIhw`\n- `UserTweets` → `RyDU3I9VJtPF-Pnl6vrRlw`\n- `SearchTimeline` → `yIphfmxUO-hddQHKIOk9tA`\n- `TweetDetail` → `meGUdoK_ryVZ0daBK-HJ2g`\nURL pattern: `https://x.com/i/api/graphql//?variables=&features=`\n\n### Response schema notes (current X web build)\n- User objects now put `screen_name` / `name` under `core`, NOT `legacy.screen_name`.\n twscrape's parser still reads `legacy.screen_name` and returns empty — needs updating.\n- The user `id` field is base64-encoded like `VXNlcjoxNDYwMjgzOTI1` (= `User:1460283925`).\n Decode with `atob(u.id).split(':')[1]` to get the numeric rest_id. `u.rest_id` may also\n be present directly.\n- `is_blue_verified` is the verified flag. `legacy.followers_count`, `legacy.description`\n still exist under `legacy`.\n- Filter timeline entries by `content.entryType === \"TimelineTimelineItem\"` and skip\n `cursor-`, `messageprompt-`, `module-`, `who-to-follow-` entryIds.\n\n### Features dict\nUse the full `GQL_FEATURES` block from twscrape's `api.py` — without it X returns\n`(336) The following features cannot be null`. Pass it URL-encoded as the `features` param.\n\n### Where things live\n- Output CSV: `~/Downloads/utilities/sdand_following.csv` (1613 rows: #, id, screen_name, name, verified, followers, bio)\n- Output JSON: `~/Downloads/utilities/sdand_following_final.json` (double-encoded JSON string; parse with `json.loads(json.loads(raw))`)\n- twscrape repo was cloned to `~/Downloads/utilities/twscrape/` for reference, then deleted after the flow was reverse-engineered. Re-clone from https://github.com/vladkens/twscrape.git if needed.\n\n## Fast Whisper transcription on Modal (A10G)\n\nFor transcribing long-form audio/video (interviews, podcasts, X/Twitter videos), use the\nutility at `~/Downloads/utilities/whisper_x/whisper_transcribe.py`. It does the full\npipeline: URL → yt-dlp download → ffmpeg audio extract → Modal volume upload →\nfaster-whisper on A10G → JSON + TXT output. Validated at **2.3 min wall clock for 65 min\nof audio** (no caching at any layer).\n\n### Usage\nShell alias (defined in `~/.zshrc`): `whisper`\n```bash\n# Transcribe an X/Twitter video (picks first playlist item)\nwhisper \"https://x.com/.../status/123\"\n\n# Pick a specific playlist item, use a smaller model\nwhisper \"https://x.com/...\" --playlist-item 2 --model-size medium\n\n# Transcribe a local audio file\nwhisper /path/to/audio.mp3 --name my-podcast\n\n# Custom output dir + keep downloaded source\nwhisper \"https://...\" --outdir ./transcripts --keep-source\n```\nTranscript text goes to stdout (pipe with `| pbcopy`); structured JSON + readable TXT\nsaved to `/.json` and `/.txt`.\n\n### Key optimizations (vs naive T4 run that took 11.7 min)\n- **A10G GPU** (~8x fp16 throughput vs T4; Modal ~$0.60/hr vs ~$0.16/hr — pennies for short jobs)\n- **`BatchedInferencePipeline`** with `batch_size=16` — batches encoder/decoder across chunks (2-4x)\n- **`beam_size=1`** (greedy) — ~2x faster, negligible WER increase for conversational speech\n- **`vad_filter=True`** — skips silence segments\n- **`compute_type=\"float16\"`** — halves memory bandwidth\n- **No caching**: `force_build=True` on apt/pip steps + unique `download_root` per run forces\n fresh image rebuild + fresh HF model download every time\n\n### Pinned versions (must match)\n- `faster-whisper==1.1.1` (provides `BatchedInferencePipeline`)\n- `ctranslate2==4.8.0`\n- Base image: `nvidia/cuda:12.6.3-cudnn-runtime-ubuntu22.04` (provides `libcublas.so.12`;\n `debian_slim` fails with `RuntimeError: Library libcublas.so.12 is not found`)\n\n### Audio prep (done automatically by the utility)\n```bash\nffmpeg -y -i input.mp4 -vn -ac 1 -ar 16000 -c:a aac -b:a 64k audio.m4a\n```\nMono 16kHz 64kbps AAC — a 65-min video (151 MB stream) becomes ~35 MB audio.\n\n### X/Twitter download notes\n- Tweet URLs can contain **playlists** (multiple videos). Use `--playlist-item N` to pick one.\n- Always use `-f bestaudio/best` to avoid downloading multi-GB high-bitrate video streams.\n- A 65-min interview's video variant can be 2.8+ GB; audio-only is ~63 MB (128 kbps).\n\n### Where things live\n- Utility: `~/Downloads/utilities/whisper_x/whisper_transcribe.py`\n- Strategy doc: `~/Downloads/utilities/whisper_x/STRATEGY.md` (full optimization breakdown)\n- Modal app (standalone): `~/Downloads/utilities/whisper_x/transcribe_fast.py`\n- Modal volume: `whisper-audio` (created automatically; holds uploaded audio files)\n- Modal profile: `aidenhuang-personal` (workspace with GPU access)\n\n\n","created_at":"2026-07-17T12:43:40.346394Z","finish_reason":null,"metrics":null},{"message_id":"a883c921-de51-4ed0-957c-8a210f081ba2","node_id":24,"role":"user","content":"what fonts are they using? \nAPI PlaygroundTinker

Playground

Chats are never stored.

Test our new model, Inkling, right here — or learn more about it.

Inkling is AI and can make mistakes. Your use must follow our Use must follow our Acceptable Use Policy.

\n","created_at":"2026-07-17T12:43:45.414519Z","finish_reason":null,"metrics":null},{"message_id":"78a456c9-6bcb-411e-97ba-6667326b344b","node_id":25,"role":"system","content":"\nThe following skills can be invoked using the `skill` tool. When ANY skill — built-in OR repository — clearly matches the user's request or the current task, invoke it with the `skill` tool immediately at the start of the session. If more than one skill matches, invoke ALL of them (issue the `skill` calls in parallel) — do not stop at the single most obvious one.\n\n- **cloudflare-one-migrations**: Plans migrations from Zscaler ZIA/ZPA, Palo Alto, legacy VPN, SWG, or SASE stacks to Cloudflare One. Use for migration assessments, policy mapping, rollout plans, and parity/gap analysis. (source: /Users/root1/.config/devin/skills/cloudflare-one-migrations/SKILL.md)\n- **cloudflare-email-service**: Send and receive transactional emails with Cloudflare Email Service (Email Sending + Email Routing). Use when building email sending (Workers binding or REST API), email routing, Agents SDK email handling, or integrating email into any app — Workers, Node.js, Python, Go, etc. Also use for email deliverability, SPF/DKIM/DMARC, wrangler email setup, MCP email tools, or when a coding agent needs to send emails. Even for simple requests like \"add email to my Worker\" — this skill has critical config details. (source: /Users/root1/.agents/skills/cloudflare-email-service/SKILL.md)\n- **durable-objects**: Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/durable-objects/SKILL.md)\n- **workers-best-practices**: Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/workers-best-practices/SKILL.md)\n- **web-perf**: Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge. (source: /Users/root1/.agents/skills/web-perf/SKILL.md)\n- **agents-sdk**: Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, chat applications, voice agents, or browser automation. Covers Agent class, state management, callable RPC, Workflows, durable execution, queues, retries, observability, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/agents-sdk/SKILL.md)\n- **turnstile-spin**: Set up Cloudflare Turnstile end-to-end in a project — scan the codebase, create the widget via the Cloudflare API, deploy the managed siteverify Worker, write the frontend snippets, validate, and persist the skill. Load this when a user asks to add Turnstile, set up CAPTCHA, protect a form from bots, or fix a Turnstile integration. Mirrors developers.cloudflare.com/turnstile/spin. (source: /Users/root1/.claude/skills/turnstile-spin/SKILL.md)\n- **wrangler**: Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.claude/skills/wrangler/SKILL.md)\n- **background-computer-use**: Launch and use the local BackgroundComputerUse macOS runtime through its self-documenting loopback API. Use when Codex needs to control local macOS apps or windows, inspect screenshots and Accessibility state, click/type/scroll/press keys, use the visible cursor, or help install/start the BackgroundComputerUse API from a skill. (source: /Users/root1/.devin/skills/background-computer-use/SKILL.md)\n- **cloudflare-one**: Guides Cloudflare One Zero Trust and SASE work across Access, Gateway, WARP, Tunnel, Cloudflare WAN, DLP, CASB, device posture, and identity. Use when designing, configuring, troubleshooting, or reviewing Cloudflare One deployments. Retrieval-first: use current Cloudflare docs/API schemas instead of embedded product docs. (source: /Users/root1/.config/devin/skills/cloudflare-one/SKILL.md)\n- **cloudflare**: Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.claude/skills/cloudflare/SKILL.md)\n- **sandbox-sdk**: Build sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.claude/skills/sandbox-sdk/SKILL.md)\n- **workers-best-practices**: Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/workers-best-practices/SKILL.md)\n- **find-skills**: Helps users discover and install agent skills when they ask questions like \"how do I do X\", \"find a skill for X\", \"is there a skill that can...\", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill. (source: /Users/root1/.agents/skills/find-skills/SKILL.md)\n- **cloudflare-agent-setup**: (source: /Users/root1/.devin/skills/cloudflare-agent-setup/SKILL.md)\n- **agents-sdk**: Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, chat applications, voice agents, or browser automation. Covers Agent class, state management, callable RPC, Workflows, durable execution, queues, retries, observability, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.agents/skills/agents-sdk/SKILL.md)\n- **cloudflare-one**: Guides Cloudflare One Zero Trust and SASE work across Access, Gateway, WARP, Tunnel, Cloudflare WAN, DLP, CASB, device posture, and identity. Use when designing, configuring, troubleshooting, or reviewing Cloudflare One deployments. Retrieval-first: use current Cloudflare docs/API schemas instead of embedded product docs. (source: /Users/root1/.claude/skills/cloudflare-one/SKILL.md)\n- **cloudflare-one-migrations**: Plans migrations from Zscaler ZIA/ZPA, Palo Alto, legacy VPN, SWG, or SASE stacks to Cloudflare One. Use for migration assessments, policy mapping, rollout plans, and parity/gap analysis. (source: /Users/root1/.agents/skills/cloudflare-one-migrations/SKILL.md)\n- **cloudflare**: Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), feature flags (Flagship), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/cloudflare/SKILL.md)\n- **sandbox-sdk**: Build sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/sandbox-sdk/SKILL.md)\n- **wrangler**: Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/wrangler/SKILL.md)\n- **cloudflare-email-service**: Send and receive transactional emails with Cloudflare Email Service (Email Sending + Email Routing). Use when building email sending (Workers binding or REST API), email routing, Agents SDK email handling, or integrating email into any app — Workers, Node.js, Python, Go, etc. Also use for email deliverability, SPF/DKIM/DMARC, wrangler email setup, MCP email tools, or when a coding agent needs to send emails. Even for simple requests like \"add email to my Worker\" — this skill has critical config details. (source: /Users/root1/.config/devin/skills/cloudflare-email-service/SKILL.md)\n- **turnstile-spin**: Set up Cloudflare Turnstile end-to-end in a project — scan the codebase, create the widget via the Cloudflare API, deploy the managed siteverify Worker, write the frontend snippets, validate, and persist the skill. Load this when a user asks to add Turnstile, set up CAPTCHA, protect a form from bots, or fix a Turnstile integration. Mirrors developers.cloudflare.com/turnstile/spin. (source: /Users/root1/.config/devin/skills/turnstile-spin/SKILL.md)\n- **web-perf**: Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/web-perf/SKILL.md)\n- **durable-objects**: Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge. (source: /Users/root1/.config/devin/skills/durable-objects/SKILL.md)\n- **devin-cli**: Look up Devin CLI documentation (skills, extensibility, configuration, commands, models, troubleshooting) (source: /Users/root1/.local/share/devin/cli/_versions/3000.1.27/share/devin/docs)\n- **declarative-repo-setup**: Generate and verify a working environment.yaml (Devin snapshot-setup blueprint) for a repo (source: builtin:drs)\n","created_at":"2026-07-17T12:43:45.415152Z","finish_reason":null,"metrics":null},{"message_id":"b6bf507e-6f2a-40ef-ba7a-fff3dbf44bb3","node_id":27,"role":"assistant","content":"","tool_calls":[{"id":"functions.web_search:0","name":"web_search","arguments":{"query":"Tinker Thinking Machines font family aura design system","num_results":10}},{"id":"functions.web_search:1","name":"web_search","arguments":{"query":"thinkingmachines.ai Tinker font sans mono","num_results":10}}],"thinking":"The user pasted a bunch of HTML (from tinker / thinking machines / API Playground) and asks \"what fonts are they using?\".\n\nWe need identify fonts from the HTML snippet. The snippet references CSS classes like text-aura-body-regular, text-aura-title-2-regular, text-aura-callout-regular, text-aura-subheadline-regular, text-aura-title-3-emphasized, text-aura-caption-1-regular, font-sans, font-mono. The actual font-family likely defined in CSS files root-CkN1nwxO.css etc. We only have HTML; classes refer to 'aura' design system. Maybe fonts are specified in CSS variables? Not visible in snippet. We can search the web for thinking machines / Tinker font? Or inspect if these are known.\n\nWe need answer what fonts they are using. We could fetch the linked CSS files? The assets are relative to /: root-CkN1nwxO.css. But user likely expects from the snippet. We can parse. The snippet is from a web app served at some domain (perhaps tinker.thinkingmachines.ai). The CSS files contain the fonts. The HTML alone has classes like 'font-sans' and 'font-mono'. In Tailwind default, font-sans = ui-sans-serif, system-ui, sans-serif, font-mono = ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas. But custom design system likely defines aura fonts. We can maybe search the internet for \"aura font thinking machines\" or \"Tinker thinking machines fonts\". Also the title says API Playground/Tinker. The system prompt mentions Thinking Machines Lab (maybe OpenAI? Actually Thinking Machines Lab is a new name for OpenAI? Wait: OpenAI changed to Thinking Machines? There's \"Thinking Machines\" as a model provider. Tinker is likely their platform. Maybe they use custom typeface \"TM Sans\"? Let's investigate.\n\nWe need use tools. We can web search. Also maybe fetch CSS. But the URLs are relative; we don't know base domain. The HTML appears to be from \"thinkingmachines.dev\"? The system prompt mentions https://tinker-docs.thinkingmachines.dev/tinker/ and https://thinkingmachines.ai/news/introducing-inkling, so domain is thinkingmachines.ai. The app might be at tinker.thinkingmachines.ai or thinkingmachines.ai/tinker. Could fetch root CSS.\n\nMaybe from the CSS class names like \"text-aura-body-regular\" — indicates \"Aura\" design system. There may be \"Aura\" fonts like \"Inter\", \"Geist\", etc. Let's search the web.","created_at":"2026-07-17T12:43:48.509544Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1369,"total_time_ms":2863,"input_tokens":27788,"output_tokens":610,"cache_read_tokens":10624,"cache_creation_tokens":null,"tpot_ms":2.4491803278688526,"tokens_per_sec":408.29986613119144}},{"message_id":"43b70a4d-45dc-4190-baf0-839a690f67b5","node_id":28,"role":"tool","content":"# Web Search Results for \"Tinker Thinking Machines font family aura design system\"\n\n## 1. Typography - Cognite Docs\nURL: https://docs.cognite.com/aura-design-system/foundations/typography\n\n> Aura typefaces, type scale, and how typography tokens map to Inter and Source Code Pro.\n...\nTypography is foundational to creating clear, consistent, and accessible interfaces. Our system uses design tokens and a well-defined set of styles to ensure type is applied consistently across products, platforms, and teams.\n...\nTypography tokens are predefined values that represent font properties like font family, size, weight, letter spacing and line height. Rather than using arbitrary values (e.g., setting font-size: 17px), tokens standardize how typography is applied across components and layouts.\n...\n- Ensures consistency and scalability across the design system\n- Makes it easier to implement updates or refinements system-wide\n- Supports design–developer alignment through shared, meaningful naming conventions\n...\n## Typefaces\n...\n| Token | Font | Use |\n| --- | --- | --- |\n| `--font-sans` / `--font-inter` | Inter | Main product copy — UI text, labels, and dense interfaces |\n| `--font-mono` | Source Code Pro | Code blocks, technical strings, terminal output |\n...\n## Type scale\n...\nStyles are grouped by role: Text for product UI copy and Code for monospace technical content.\n...\nInter — main product typography.\n...\nSource Code Pro — code blocks, snippets, and technical strings.\n\n## 2. Tinker - Thinking Machines Lab\nURL: https://thinkingmachines.ai/tinker/\n\nTinker - Thinking Machines Lab\n...\n### THINKING MACHINES\n\n## 3. Taste - Aura Design System\nURL: https://auradesignsystem.com/docs/taste\n\nTaste - Aura Design System\n...\nDiscover the design tokens for spacing, colors, and typography that define the Aura aesthetic.\n...\nThe following sections outline our approach to foundational design tokens that give Aura its distinct identity.\n...\n@theme inline {\n --spacing: 13px;\n...\n--font-sans: Inter, sans-serif;\n --info: #e8ebfe;\n --info-contrast: #0927ec;\n --success: #e8fef7;\n --success-contrast: #\n...\n45d3c;\n --danger: #feefe8;\n --danger-contrast\n...\n#8e3106;\n --warning: #fefbe8;\n --warning-contrast:\n...\n5d510\n...\n;\n --radius-sm: calc(var(--spacing\n...\n5);\n...\ncalc(var(--\n...\n* 1);\n...\nlg: calc(var(--spacing\n...\n1.5);\n...\n--radius-xl\n...\ncalc(var(--\n...\n--aura\n...\n-contrast: var(--\n...\nff;\n --gray-a4: #d7e4ff;\n --gray-a5: #ccdcff;\n --gray-a6: #c2d4ff;\n --gray-a7: #b4c8ff;\n --gray-a8: #99b3ff;\n --gray-a9: #0029b791;\n --gray-a10: #0025a19a;\n --gray-a11: #021a72b4;\n --gray-a12: #000a3aed;\n\n --gray-contrast: #ffffff;\n --gray-surface: #ffffffcc;\n --gray-indicator: #6d84d5;\n --gray-track: #6d84d5;\n\n --primary: var(--accent-9);\n --primary-foreground: var(--accent-contrast);\n --secundary: var(--accent-8);\n --secundary-foreground: var(--accent-contrast);\n\n /* Aura Design System - Root Tokens */\n --aura: 13px;\n --aura-font-stack: \"Inter\", -apple-system, BlinkMacSystemFont, \"Segoe UI\",\n \"Roboto\", \"Oxygen\", \"Ubuntu\", \"Cantarell\", \"Fira Sans\", \"Droid Sans\",\n \"Helvetica Neue\", sans-serif;\n --aura-font-quotes: serif;\n\n /* Aura Background & Text - Mapped to gray scale */\n --aura-bg: var(--gray-1);\n --aura-text-primary: var(--accent-12);\n --aura-text-primary-inverse: var(--gray-contrast);\n\n --aura-accents-primary: var(--accent-9);\n --aura-accents-secondary: var(--accent-9);\n\n /* Aura Component Tokens - Mapped to 12-step system */\n --aura-input-radius: 6.5px;\n --aura-button-radius: 6.5px;\n --aura-input-bg: var(--accent-2);\n --aura-input-placeholder-color: var(--gray-11);\n --aura-radius: 0.5rem;\n --aura-opacity: 0.5;\n --aura-outli...\n\n## 4. tinker.com Design Tokens - Colors, Fonts & CSS | FontOfWeb\nURL: https://www.fontofweb.com/tokens/tinker.com\n\ntinker.com Design Tokens - Colors, Fonts & CSS | FontOfWeb\n...\nFonts, colors, and design tokens of\n...\n# tinker.com\n...\nFoundation: Radius\n...\n### What is the tinker.com design system?\n...\nThe tinker.com design system includes 30 extracted brand colors, 1 typography styles, and various CSS design tokens used across 7 analyzed pages.\n...\n### How many colors does tinker\n...\ncom use?\n...\ntinker.com uses approximately 30 distinct colors across its web properties. These include primary brand colors, neutral palettes, and semantic colors extracted from stylesheets.\n\n## 5. docs/brand/DESIGN_SYSTEM.md\nURL: https://github.com/Josue19-08/Aura/blob/main/docs/brand/DESIGN_SYSTEM.md\n\n### Font Families\n...\n```css\n/* Display - Headlines & Titles */\nfont-family: 'Space Grotesk', sans-serif;\nfont-weight: 700 (Bold), 600 (Semi-bold);\n...\n/* Interface - UI Elements & Body */\nfont-family: 'DM Sans', sans-serif;\nfont-weight: 400 (Regular), 500 (Medium), 600 (Semi-bold);\n...\n/* Monospace - Code, Stats, Technical */\nfont-family: 'IBM Plex Mono', monospace;\nfont-weight: 500 (Medium);\n```\n\n## 6. Tinker - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/\n\nTinker - Tinker Documentation\n...\n## Train LLMs withthe Tinker API\n...\nA cloud training API that gives you full control over your data, algorithms, and models. Write training loops locally — Tinker handles the GPUs.\n\n## 7. thinking-machines-lab/tinker\nURL: https://github.com/thinking-machines-lab/tinker\n\n# Repository: thinking-machines-lab/tinker\n...\nTraining API and CLI\n...\n---\n\n Tinker Python SDK \n \n \n\nDocumentation:\n tinker-docs.thinkingmachines.ai\n\n## 8. Typography\nURL: https://tink.com/brand/our-identity/visual-identity/typography\n\nLota Grotesque is a typeface that reflects the values of our brand and is part of the Tink personality. It is available in a wide range of weights and styles, but in order to keep things simple and consistent Tink uses three of these.\n...\n## Three weights\n...\nHeadlinesLota Grotesque Bold\n...\nSub-headlinesLota Grotesque SemiBold\n...\nBody copyLota Grotesque Regular\n...\nStrive for a few sizes in the same layout, ideally never more than three. Use sizes evenly divisible by four.\n...\nGrids and columns help us create dynamic and asymmetric compositions.\n...\n## Fallback typography\n...\nIn instances such as Google Slides, where Lota is not applicable we use Helvetica Neue.\n...\nHelvetica Neue Bold Helvetica Neue Regular\n\n## 9. NBC Tinker | Capacity Studios\nURL: https://capacity.gg/project/nbc-tinker/\n\nNBC Universal : Bespoke Typeface\n...\nSome of the things we value most as a boutique studio are our range of creative specialties and our personalized, super-customized approach to each project. We were thrilled to have a chance to flex some of our typographic muscles for this bespoke type family that we designed exclusively for NBC, which also includes some fun little custom features that we built into the working fonts.\n...\nCapacity StudiosClient: NBC UniversalProject focus: type design + font creation\n\n## 10. Tinker Sans - Laura Meseguer Studio\nURL: https://www.laurameseguer.com/case/tinkersans-kent-custom-typeface-sans-serif-branding/\n\nA custom typeface typeface family for the branding of KENT, an American brand of cigarrettes.\n...\nTinker Sans is a contemporary sans-serif typeface family inspired by the KENT logo’s four letters. It artfully merges the timeless aesthetics of a classic geometric sans-serif with a twist that embraces beauty in evolution. This meticulously crafted typeface family embodies the intersection of design and technology, where beauty thrives. These complementary aspects synergize, blurring the lines between aesthetics and purpose. Featuring three weights and two sets of alternate characters, Tinker Sans adds layers to packaging and will serve for the brand communication at a global level, enhancing their visual appeal and giving more layers to the packaging and communication campaigns.\n","created_at":"2026-07-17T12:43:49.027404Z","finish_reason":null,"metrics":null},{"message_id":"09d8f9f3-ef5f-4253-b31f-e168c9058e18","node_id":29,"role":"tool","content":"# Web Search Results for \"thinkingmachines.ai Tinker font sans mono\"\n\n## 1. Tinker - Thinking Machines Lab\nURL: https://thinkingmachines.ai/tinker/\n\nTinker - Thinking Machines Lab\n...\n# Tinker is a training API for researchers\n...\n## Supported models\n...\n### THINKING MACHINES\n\n## 2. Tinker: a training API for researchers and developers - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tinker/\n\nTinker: a training API for researchers and developers - Tinker Documentation\n...\n# Tinker: a training API for researchers and developers\n...\nTinker lets you focus on what matters in LLM post-training — your data and algorithms — while we handle the heavy lifting of distributed training.\n...\nYou write a simple loop that runs on your CPU-only machine, including the data or environment and the loss function. We figure out how to make the training work on a bunch of GPUs, doing the exact computation you specified, efficiently. To change the model you're working with, you only need to change a single string in your code.\n...\nTinker gives you full control over the training loop and all the algorithmic details. It's not a magic black box that makes fine-tuning \"easy\". It's a clean abstraction that shields you from the complexity of distributed training while preserving your control.\n\n## 3. thinking-machines-lab/tinker\nURL: https://github.com/thinking-machines-lab/tinker\n\n# Repository: thinking-machines-lab/tinker\n...\nTraining API and CLI\n...\n---\n\n Tinker Python SDK \n \n \n\nDocumentation:\n tinker-docs.thinkingmachines.ai\n\n## 4. Get Started - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tinker/quickstart/\n\nGet Started - Tinker Documentation\n...\n# Quick Start\n...\nThis gives you the Python SDK (`import tinker`) and the CLI (`tinker run list`,`tinker checkpoint download`).\n...\nThis page walks through the two main LLM fine-tuning workflows — supervised fine-tuning (SFT) and reinforcement learning (RL) — showing how each step maps to Tinker SDK calls.\n...\n## API Cheatsheet\n...\n# Entry point — reads TINKER_API_KEY from environment\nservice_client = tinker.ServiceClient()\n...\n# Training client (LoRA fine-tuning)\ntraining_client = service_client.create_lora_training_client(\n base_model=\"Qwen/Qwen3-8B\", rank=32\n)\n...\n# Sampling client (text generation)\nsampling_client = service_client.create_sampling_client(\n base_model=\"Qwen/Qwen3-8B\"\n)\n...\n# Tokenizer\ntokenizer\n...\ntraining_client.get_tokenizer\n...\n`SamplingClient` is picklable — you can pass it to other\n...\nfor parallel sampling. If your training loop has CPU-heavy work (grading, environment logic), set\n...\nTINKER_SUB\n...\n_SAMPLING\n...\n1` to\n...\nsample()` and`compute_logprobs()` in a\n...\n, preventing GIL contention.\n\n## 5. tinker v0.23.0\nURL: https://pypi.org/project/tinker/\n\n# tinker v0.23.0\n...\nThe official Python SDK for the tinker API\n...\n- Email: Tinker authors \n- License: Apache-2.0\n- Python: >=3.11\n- Keywords: machine learning, tinker\n- PyPI: https://pypi.org/project/tinker/\n...\n- Documentation: https://tinker-docs.thinkingmachines.ai/\n- Homepage: https://thinkingmachines.ai/tinker\n- Repository: https://github.com/thinking-machines-lab/tinker\n...\n## Readme\n\n Tinker Python SDK \n \n \n\nDocumentation:\n tinker-docs.thinkingmachines.ai \n \n\n## Dependencies\n\n## 6. thinking-machines-lab/tinker-cookbook\nURL: https://github.com/thinking-machines-lab/tinker-cookbook/\n\n- `tinker` is a training SDK for researchers and developers to fine-tune language models. You send API requests to us and we handle the complexities of distributed training.\n- `tinker-cookbook` includes realistic examples of fine-tuning language models. It builds on the Tinker API and provides common abstractions to fine-tune language models.\n...\nHere we introduce a few Tinker primitives — the basic components to fine-tune LLMs (see the [quickstart guide](https://tinker-docs.thinkingmachines.ai/tinker/quickstart/) for more details):\n...\n### Tinker Cookbook\n...\nBesides these primitives, we also offer **Tinker Cookbook** (a.k.a. this repo), a library of a wide range of abstractions to help you customize training environments.\n...\n[`tinker_cookbook/recipes/sl_basic.py`](tinker_cookbook/recipes/sl_basic.py) and [`tinker_cookbook/recipes/rl_basic.py`](tinker_cookbook/recipes/rl_basic.py) contain minimal examples to configure supervised learning and reinforcement learning.\n...\nWe also include more complete examples in the [`tinker_cookbook/recipes/`](tinker_cookbook/recipes/) folder:\n...\n](tinker\n...\ntinker_\n...\nThe [recipes README](tinker_cookbook/recipes/README.md) covers all available recipes, including Harbor RL, rubric-based grading, VLM classification, and SDFT. Each recipe includes a `README.md` with implementation details, launch commands, and expected results.\n...\nFor the full Tinker documentation, visit [tinker-docs.thinkingmachines.ai](https://tinker-docs.thinkingmachines.ai).\n\n## 7. 101: Hello Tinker - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tutorials/basics/hello-tinker/\n\n101: Hello Tinker - Tinker Documentation\n...\n101:\n...\nTinker is a remote GPU service for LLM training and inference. You write training loops in Python on your local machine; Tinker executes the heavy GPU operations (forward passes, backpropagation, sampling) on remote workers.\n...\n## The client hierarchy\n...\nThe entry point to Tinker is the ServiceClient. From it, you create specialized clients:\n...\n- SamplingClient -- generates text from a model (inference)\n- TrainingClient -- runs forward/backward passes and optimizer steps (training)\n...\n## Sampling from a model\n...\nLet's create a SamplingClient to generate text. We will use`Qwen/Qwen3.5-9B-Base`, a base (non-chat-tuned) model, so we can feed it raw tokens and get a plain text completion -- no chat template required.\n...\nCreate a`SamplingClient` with a base model name\n...\nEncode your prompt into tokens using the model's tokenizer\n...\nCall`sample()` with the prompt and sampling parameters\n...\nreturned tokens back into text\n...\n## What about training?\n...\nSo far we have only done inference. The real power of Tinker is training -- running forward/backward passes and optimizer steps on remote GPUs while you control the training loop locally.\n...\nThe workflow looks like this:\n...\n1. Create a TrainingClient with`service_client.create_lora_training_client()`\n2. Prepare training data as`Datum` objects (input tokens + loss targets)\n3. Call`training_client.forward_backward()` to compute gradients\n4. Call`training_client.optim_step()` to update weights\n5. Save weights and create a SamplingClient to evaluate the trained model\n\n## 8. Tutorials - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tutorials/\n\nTutorials - Tinker Documentation\n...\nA progressive learning path from first API call to advanced training techniques. All tutorials are marimo notebooks — reactive Python notebooks stored as`.py` files. Read them here or run them interactively.\n...\nInstall the SDK together with the cookbook's`[tutorials]` extra, then set your API key:\n...\nThe`[tutorials]` extra pulls in`marimo`(to open the notebooks),`matplotlib`(for the loss-curve plots in a few tutorials), and the`[math-rl]` dependencies (`sympy`,`pylatexenc`,`math-verify`) that some RL tutorials import.\n...\nClone the cookbook and open any notebook in marimo's interactive editor:\n...\n```\ngit clone https://github.com/thinking-machines-lab/tinker-cookbook.git\ncd tinker-cookbook\nmarimo edit tutorials/101_hello_tinker.py\n...\nYou can also\n...\nthe notebooks online in molab without installing anything — the cookbook tutorials README links each notebook to its\n...\nab launcher.\n...\nRun the tutorials against a recent cookbook that matches them — an editable clone of`main` or a nightly build. Stable PyPI`tinker-\n...\n` paired with newer tutorial files can drift.\n\n## 9. Changelog - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/changelog/\n\nChangelog - Tinker Documentation\n...\nNotable updates to the Tinker SDK, the Tinker Cookbook, and the Tinker Console. SDK and Cookbook entries are dated by PyPI release, Console entries by launch date. For model availability and retirements, see Models & Pricing and Model Deprecations.\n\n## 10. Tinker: General Availability and Vision Input - Thinking Machines Lab\nURL: https://thinkingmachines.ai/news/tinker-general-availability/\n\nTinker: General Availability and Vision Input - Thinking Machines Lab\n...\n# Tinker: General Availability and Vision Input\n...\nThe waitlist is over! Everybody can use Tinker now; sign up here to get started. See the Tinker homepage for available models and pricing, and check out the Tinker cookbook for code examples.\n...\nWe’ve added two vision models to Tinker: Qwen3-VL-30B-A3B-Instruct and Qwen3-VL-235B-A22B-Instruct. With these, users can process pictures, screenshots, and diagrams for a variety of applications.\n","created_at":"2026-07-17T12:43:49.027411Z","finish_reason":null,"metrics":null},{"message_id":"325ee8af-ba58-43cd-886c-dcce57f90243","node_id":31,"role":"assistant","content":"","tool_calls":[{"id":"functions.web_search:2","name":"web_search","arguments":{"query":"\"root-CkN1nwxO.css\"","num_results":10}},{"id":"functions.web_search:3","name":"web_search","arguments":{"query":"\"playground-AlZIgwfc.css\"","num_results":10}},{"id":"functions.web_search:4","name":"web_search","arguments":{"query":"\"tinker-playground-chat-text\" font","num_results":10}}],"thinking":"Search results not specifically giving fonts. The 'Aura Design System' by Cognite uses Inter and Source Code Pro. But the app might use an 'Aura' design system specific to Thinking Machines? The classes use 'aura' prefix. There may be a design system named Aura within Thinking Machines/Tinker. We can inspect CSS by fetching from the app. The HTML base URL unknown. The assets paths are /assets/... and the page seems served from root domain. We can try fetching CSS from thinkingmachines.ai? Actually the app is maybe https://thinkingmachines.ai/tinker? Let's find the actual playground URL. The page title \"API Playground\" / \"Tinker\". The page has href links to /, /projects, /sessions, /playground, etc. Likely the base is \"https://tinker.thinkingmachines.ai\" or \"https://console.tinker.thinkingmachines.ai\"? Let's search for the playground URL or assets file names. Could search web for \"playground-AlZIgwfc.css\" etc. Also the root CSS file names are unique hashes, web search might find them. Let's search for an asset name.","created_at":"2026-07-17T12:43:50.567819Z","finish_reason":"tool_calls","metrics":{"ttft_ms":800,"total_time_ms":1539,"input_tokens":4716,"output_tokens":325,"cache_read_tokens":38400,"cache_creation_tokens":null,"tpot_ms":2.273846153846154,"tokens_per_sec":439.7834912043302}},{"message_id":"7f31a856-fc5b-462c-bf23-c4e8981b1f94","node_id":32,"role":"tool","content":"# Web Search Results for \"\"root-CkN1nwxO.css\"\"\r\n\r\n## 1. Design system reference | Root developer documentation\r\nURL: https://docs.rootapp.com/docs/app-docs/develop/client/design-system-reference/\r\n\r\nStyle your apps to match the Root visual style using CSS color variables, spacing, typography, and component patterns provided by the Root SDK.\r\n...\r\nThe Root design system is a set of CSS custom properties (variables) prefixed with`--rootsdk-` that the Root hosting environment injects into your App's client at runtime, plus conventions for spacing, radii, typography, shadows, and component patterns that match Root's first-party UI.\r\n...\r\nThe CSS variables hold the current theme's color values, allowing your App to automatically adapt when the user switches between light and dark mode. Root updates the variables automatically when the theme changes, so your App does not need to know the actual color values. Just reference the variables and they will adapt.\r\n...\r\n| Token | CSS Variable | Description |\r\n| --- | --- | --- |\r\n| brandPrimary | `--rootsdk-brand-primary` | Primary brand color, used for primary buttons and key interactive elements |\r\n| brandSecondary | `--rootsdk-brand-secondary` | Secondary brand color, used for success states and accents |\r\n| brandTertiary | `--rootsdk-brand-tertiary` | Tertiary brand color, used for additional accents |\r\n...\r\n| Token | CSS Variable | Description |\r\n| --- | --- | --- |\r\n| textPrimary | `--rootsdk-text-primary` | Primary text color for headings and body text |\r\n| textSecondary | `--rootsdk-text-secondary` | Secondary text color for less prominent text |\r\n| textTertiary | `--rootsdk-text-tertiary` | Tertiary text color for disabled or placeholder text |\r\n| textWhite | `--rootsdk-text-white` | White text color, used on dark backgrounds regardless of theme |\r\n...\r\n| Token | CSS Variable | Description |\r\n| --- | --- | --- |\r\n| backgroundPrimary | `--rootsdk-background-primary` | Primary background color\r\n...\r\nmain content areas |\r\n| backgroundSecondary | `--rootsdk-background-secondary` | Secondary background color for cards and panels |\r\n| backgroundTertiary | `--rootsdk-background-tertiary` | Tertiary background color\r\n...\r\nnested elements and dropdow...\r\n\r\n## 2. rootcf/rootie-css\r\nURL: https://github.com/rootcf/rootie-css\r\n\r\nA static css file to use in every web project.\r\n...\r\n# rootie-css\r\n...\r\nA static css file to use in every web project. \r\nYou can use this link to add rootie-css to your project; \r\n https://raw.githack.com/rootcf/rootie-css/main/rootie-.css \r\n \r\n\r\n# Documentation\r\n\r\n## 3. CssVarsRoot should be respected in @layer reset, base and tokens to prevent global style leakage\r\nURL: https://github.com/chakra-ui/chakra-ui/issues/9256\r\n\r\n# CssVarsRoot should be respected in @layer reset, base and tokens to prevent global style leakage\r\n...\r\nI am using Chakra-ui in a subsystem as part of a huge project. By default, Chakra overrides some of the global styles, so I tried the solution mentioned in [#2145](https://github.com/chakra-ui/chakra-ui/issues/2145), using shadow dom and `CacheProvider`. \r\n \r\nHowever, some Chakra styles do not inherit correctly. After investigating for days, I found the following rules to be involved: \r\n \r\n| css layer | rule(selector) | configuration entry to override | file | \r\n| ------- | --------------------------- | ------------------------------- | ------------------------------- | \r\n| @reset | html | preflight: {scope: ...} | (not found, possibly in panda css) | \r\n| @base | html | delete defaultConfig.globalCss.html; globalCss:{xxx:{...}} | `packages/react/src/theme/global-css.ts` | \r\n| @tokens | :where(html,.chakra-themes) | cssVarsRoot | `packages/react/src/theme/index.ts` | \r\n| @tokens | :root, .light | condition:{light:...} | `packages/react/src/preset-base.ts` | \r\n \r\nOverriding the above configuration entries will fix most of them: \r\n```typescript\r\nconst myCssVarsRoot = \":host\";\r\nconst config = defineConfig({\r\n cssVarsRoot: myCssVarsRoot,\r\n conditions: {\r\n light: `${myCssVarsRoot}, .light`,\r\n },\r\n preflight: { scope: myCssVarsRoot },\r\n globalCss: {\r\n [myCssVarsRoot]: defaultConfig.globalCss.html,\r\n },\r\n});\r\nconst system = createSystem(defaultConfig, config);\r\n```\r\n \r\nIn this way we can prevent Chakra styles from leaking without shadow dom by setting `myCssVarsRoot` to the target element. A full implementation of this `ContainedProvider` is available in this [codesandbox](https://codesandbox.io/p/sandbox/wispy-leaf-h99sgr) (and `ShadowProvider` if shadow dom is still needed). \r\n \r\n(Note: Drawer and Dialog use a built-in portal that defaults to `docuemnt.body`. To use it in a contained situation, either set `portalled={false}` on `DrawerContent` or specify `portalRef` manuall...\r\n\r\n## 4. 37dece8 perf(css): merge top-level :root blocks in built component CSS\r\nURL: https://github.com/core-ds/core-components/commit/37dece8a98ea024e39191782a7fee78d55c119eb\r\n\r\n# 37dece8 perf(css): merge top-level :root blocks in built component CSS\r\n...\r\nperf(css): merge top-level :root blocks in built component CSS\r\n...\r\nEach component inlines base tokens plus its own aliases as separate :root\r\nblocks, so a consumer's bundled CSS ends up with hundreds of :root blocks.\r\nThey inherit into every node, and Chrome DevTools re-renders each one per\r\nselected/hovered node, freezing the Styles panel (matches primevue#8309).\r\n...\r\nAdd a postcss-merge-roots plugin (next to postcss-remove-empty-root) that\r\ncollapses all top-level :root rules in each emitted file into one deduped\r\nblock, preserving source order so no resolved value changes. Cuts per-file\r\n:root blocks (e.g. button 21 -> 9). Top-level only; @media-scoped :root is\r\nleft intact. Applies to every build variant via the shared postcss chain.\r\n\r\n## 5. ckeditor5.css | Documentation\r\nURL: https://docs.backdropcms.org/api/backdrop/core%21modules%21ckeditor5%21lib%21ckeditor5%21dist%21browser%21ckeditor5.css/1\r\n\r\n-focus:#6cb5f9;--ck-color-base-text:#333;--ck-color-base-active:#2977ff;--ck-color-base-active-focus:#0d65ff;--ck-color-base-error:#db3700;--ck-color-focus-border-coordinates:218, 81.8%, 56.9%;--ck-color-focus-border:hsl(var(--ck-color-focus-border-coordinates));--ck-color-focus-outer-shadow:#cae1fc;--ck-color-focus-disabled-shadow:#77baf84d;--ck-color-focus-error-shadow:#ff401f4d;--ck-color-text:var(--ck-color-base-text);--ck-color-shadow-drop:#00000026;--ck-color-shadow-drop-active:#0003;--ck-color-shadow-inner:#0000001a;--ck-color-button-default-background:transparent;--ck-color-button-default-hover-background:#f0f0f0;--ck-color-button-default-active-background:#f0f0f0;--ck-color-button-default-disabled-background:transparent;--ck-color-button-on-background:#f0f7ff;--ck-color-button-on-hover-background:#dbecff;--ck-color-button-on-active-background:#dbecff;--ck-color-button-on-disabled-background:#f0f2f4;--ck-color-button-on-color:#2977ff;--ck-color-button-action-background:var(--ck-color-base-action);--ck-color-button-action-hover-background:#4d9d30;--ck-color-button-action-active-background:#4d9d30;--ck-color-button-action-disabled-background:#7ec365;--ck-color-button-action-text:var(--ck-color-base-background);--ck-color-button-save:#008a00;--ck-color-button-cancel:#db3700;--ck-color-switch-button-off-background:#939393;--ck-color-switch-button-off-hover-background:#7d7d7d;--ck-color-switch-button-on-background:var(--ck-color-button-action-background);--ck-color-switch-button-on-hover-background:#4d9d30;--ck-color-switch-button-inner-background:var(--ck-color-base-background);--ck-color-switch-button-inner-shadow:#0000001a;--ck-color-dropdown-panel-background:var(--ck-color-base-background);--ck-color-dropdown-panel-border:var(--ck-color-base-border);--ck-color-dialog-background:var(--ck-custom-background);--ck-color-dialog-form-header-border:var(--ck-custom-border);--ck-color-input-background:var(--ck-color-base-background);--ck-color-input-border:var(--ck-co...\r\n\r\n## 6. knightedcodemonkey/css\r\nURL: https://github.com/knightedcodemonkey/css/\r\n\r\n`@knighted/css` is a bundler-optional CSS pipeline for JavaScript and TypeScript projects. Use it standalone or plug it into your bundler via the `?knighted-css` loader query—it walks the graph, compiles every CSS-like dependency (CSS, Sass/SCSS, Less, vanilla-extract), and hands back both a concatenated stylesheet string and optional `.knighted-css.*` selector manifests for type-safe loaders.\r\n...\r\n- **Graph walking**: Follows `import` trees the same way Node does (tsconfig `paths`, package `exports`/`imports`, hash specifiers, etc.) using [`oxc-resolver`](https://github.com/oxc-project/oxc-resolver).\r\n...\r\n- **Multi-dialect compilation**: Runs Sass, Less, Lightning CSS, or vanilla-extract integrations on demand so every dependency ends up as plain CSS.\r\n...\r\n- **Attribute-aware imports**: Honors static `with { type: \"css\" }\r\n...\r\ngets pulled into\r\n...\r\nwhen extensions aren’t present.\r\n...\r\n- **Loader + CLI**: Ship CSS at runtime via `?knighted-css` loader queries or ahead of time via the `css()` API and the `knighted-css-generate-types` command.\r\n...\r\n- **Shadow\r\n...\r\ncomponents, or keep\r\n...\r\n—all without wiring\r\n...\r\n| Workspace | NPM Name | What it contains |\r\n| --- | --- | --- |\r\n| `packages/css` | [`@knighted/css`](./packages/css/README.md) | The production library: graph walker, compilation pipeline, loader helpers, CLI, and docs. Published to npm and meant for real builds. |\r\n| `packages/playwright` | [`@knighted/css-playwright-fixture`](./packages/playwright/README.md) | The end-to-end demo + regression suite. Playwright drives Lit + React examples, hash-import workspace scenarios, and SSR checks to ensure\r\n...\r\ncore package keeps working across bundlers. |\r\n...\r\n```ts\r\nimport { css } from '@knighted/css'\r\n...\r\nconst sheet = await css('./src/entry.tsx', {\r\n cwd: process.cwd(),\r\n lightningcss: { minify: true },\r\n})\r\n\r\nconsole.log(sheet) // use during SSR, static builds, or to inline Shadow DOM styles\r\n...\r\n- Need runtime imports? See [docs/loader.md](./\r\n...\r\n/loader.md).\r\n- Want strong selecto...\r\n\r\n## 7. Index of /files/style\r\nURL: https://root.cern/files/style/\r\n\r\nIndex of /fi… (9 chars truncated)\r\n… (178 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/173d3e20/content.txt\n","created_at":"2026-07-17T12:43:51.317124Z","finish_reason":null,"metrics":null},{"message_id":"02b45dcc-e6fb-4019-9d88-1e9c68ecdff2","node_id":33,"role":"tool","content":"# Web Search Results for \"\"playground-AlZIgwfc.css\"\"\n\n## 1. CSS Playground — Edit & Test Demos Live | CodeFronts\nURL: https://codefronts.com/playground/\n\nCSS Playground — Edit & Test Demos Live | CodeFronts\n...\n# CSS Playground — Edit, Test, and Style Demos Live\n...\nA free browser-based CSS playground — pick a curated demo (buttons, cards, forms, loaders), edit HTML, CSS, and JavaScript live in a sandboxed preview, apply one-click variants (darker, lighter, glow, brutalist, glass, neon), run accessibility checks, and copy production-ready code.\n...\nLive HTML / CSS / JS editor\n...\nEdit code in tabbed panels and watch the sandboxed iframe preview update instantly. No build step, no setup.\n...\nApply darker, lighter, glow, brutalist, glass, or neon transforms to the current CSS — preview, then keep or discard.\n...\nEvery snippet is checked for aria-label coverage, focus state rules, and WCAG colour contrast — issues flagged in real\n...\nYes. The playground is completely free with no sign-up, no rate limits, no premium tier, and no tracking. Everything runs in your browser.\n...\nYour edits run inside a sandboxed iframe in your own browser. Nothing is sent to a server — code never leaves your machine, and your edits are not saved between sessions.\n...\nYes. Every demo is pure HTML, CSS, and (optionally) plain JavaScript with no framework dependencies. Click \"Copy all\" to grab a self-contained snippet you can drop into any project.\n...\n### What CSS\n...\nThe built-in variants are Darker, Lighter, Glow, Brutalist, Glass, and Neon. Each rewrites the current CSS in place — preview the change, then click Keep to commit or Discard to revert.\n...\nThe checker scans your HTML and CSS for common accessibility issues: missing aria-label on buttons, unlabeled form inputs, decorative SVGs without aria-hidden, missing focus styles, and WCAG AA colour contrast (4.5:1 minimum) between the first two colours in your CSS.\n...\nNo. The playground is intentionally session-only — refreshing the page or selecting a different demo resets the editor. Use \"Copy all\" to keep your changes.\n\n## 2. CSS Playground\nURL: https://www.cssplayground.dev/?lang=en-US\n\nCSS Playground \n\nClick on each concept to view details and practice\n\n## 3. HTML Playground + CSS Playground + JS Playground\nURL: https://code-editor-playground.vercel.app/\n\nHTML Playground + CSS Playground + JS Playground\n...\n##### ![CSS](https://code-editor-playground.vercel.app/assets/css-bLrZJUBe.png) CSS\n\n## 4. joshwcomeau/playground\nURL: https://github.com/joshwcomeau/playground\n\nA simple playground for HTML, CSS and JavaScript supporting module imports.\n...\n## README\n\n \n Playground \n \n A simple playground for HTML, CSS and JavaScript supporting module imports. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n**Playground** is a HTML, CSS and JS demonstration component that runs entirely in the browser and does not require loading any third party sites.\n...\n```\nnpm install styled-components @agney/playground\n# OR\nyarn add styled-components @agney/playground\n```\n...\nPlayground uses [styled-components](https://styled-components.com/) for styling.\n...\n```jsx\nimport Playground from \"@agney/playground\";\n...\n. Allows bare ES package imports\n...\ncan add imports for\n...\nsupports ES Module resolution and\n...\n| Props | description | default | required |\n| :--------------: | :-------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: | :------: |\n| id | a unique identifier for the iFrame | | true |\n| initialSnippet | Initial code to be displayed | | true |\n| defaultEditorTab | Initial editor tab to be displyed. Possible values: \"markup\", \"css\", \"javascript\" | \"markup\" | false |\n| defaultResultTab | Initial tab on result panel to be displayed. Possible values: \"console\", \"result\" | \"result\" | false |\n| transformJs | Transform the JavaScript using Babel. This is required if you want bare module imports | false | false |\n| presets | Array of presets you want Babel to transform. This works only if transformJs is true. Eg. `[\"react\", \"es2015\"]` | | false |\n| theme | Pass in the theme variables to customise the appearance | [Our Theme](https://github.com/agneym/playground/blob/master/playground/src/utils/theme.ts) | false |\n...\n```jsx\nimport Playground from \"@agney/playground\";\n...\nconst App = () => {\n const snippet = {\n markup: `
`,\n css: ``,\n javascript: `import React, { createElement } from \"https://cdn....\n\n## 5. ⚡️ Lightning CSS Playground\nURL: https://lightningcss.dev/playground/\n\n⚡️ Lightning CSS Playground\n...\n⚡️ Lightning CSS Playground\n...\n# Lightning CSS Playground\n...\n[ GitHub ](https://github.com/parcel-bundler/lightningcss)\n...\nMinify CSS modules Analyze dependencies Visitor ### Draft syntax\n...\nCustom media queries ### Targets\n...\nChrome: Firefox: Safari: Opera: Edge: IE: iOS: Android: Samsung:\n\n## 6. Code Playground - HTML CSS and JS Compiler\nURL: https://codeplayground.workzio.io/\n\nCode Playground - HTML CSS and JS Compiler\n\nHTML Editor\n\n## 7. cssnext Playground\nURL: https://cssnext.github.io/playground/\n\ncssnext Playground\n...\n/* custom properties */ :root\n...\n--fontSize: 1rem; --mainColor: #12345678; --highlightColor: hwb(190, 35%, 20%); } /* custom properties set & @apply rule */ :root\n...\n--centered: { display: flex; align-items: center; justify-content: center; }; } .centered\n...\ncentered; } /* custom media queries */ @custom-media --viewport-medium (width <= 50rem); /* some var() & calc() */ body { color: var(--mainColor); font-size: var(--fontSize); line-height: calc(var(--fontSize) * 1.5); padding: calc((var(--fontSize) / 2) + 1px); } /* custom media query usage */ @media (--viewport-medium) { body { font-size: calc(var(--fontSize) * 1.2); } } /* custom selectors */ @custom-selector :--heading h1, h2, h3, h4, h5, h6; :--heading { margin-top:\n...\n0 } /* image-set function */ .foo { background-image: image-set( url(img/test.png) 1x, url(img/test-2x.png) 2x ); } /* colors stuff */ a { color: var(--highlightColor); transition\n...\nautoprefixed\n...\n{ color: gray(255, 50%)\n...\ncolor: rebeccapurple }\n...\nfocus { background-color: rgb(255 153 0 / 33\n...\nsolid hsl(1turn 60% 50\n...\n: color(var\n...\nblackness(+20%)) }\n...\nsmall-caps\n...\nlining-nums\n\n## 8. itsjavi/css-grid-playground\nURL: https://github.com/itsjavi/css-grid-playground\n\n# Repository: itsjavi/css-grid-playground\n...\nInteractive CSS Grid Layout tool: Edit, view real-time changes, and share prototypes. Includes some helpful presets to get started.\n...\n# CSS Grid Playground [![Build Status\n...\nhttps://github.com/itsjavi/css-grid-playground/actions/workflows/quality.yml/badge.svg)](https://github.com/itsjavi/css-grid-playground/actions/workflows/quality.yml)\n...\n**CSS Grid Playground** lets you visually play with the [CSS Grid Layout](https://www.w3.org/TR/css3-grid-layout/)\nelements in the browser to quickly sketch up a layout for your site.\n...\nThe layout, which provides a basic way of editing a container and its direct children, renders changes instantly as you\nupdate the corresponding CSS giving you instant feedback.\n...\nafter cloning this repository\n\n## 9. tailwindlabs/tailwindcss-playground\nURL: https://www.github.com/tailwindcss/playground\n\n# Tailwind CSS Playground\n...\nA simple starter project for playing around with Tailwind in a proper PostCSS environment.\n...\nBuilding for production\n...\nEven though this isn't necessarily a starter kit for a proper project, we've included an example of setting up [cssnano](https://cssnano.co/) to optimize your CSS for production.\n...\nAfter that's done, check out `./public/build/tailwind.css` to see the optimized output.\n\n## 10. Tailwind CSS Playground\nURL: https://tail-wind-css-play-ground.vercel.app/\n\nTailwind CSS Playground\n...\n# Tailwind CSS Playground\n...\nExperiment with Tailwind CSS utility classes and see live results\n...\nBlue ButtonCard LayoutGradient BackgroundFlex CenterText StylesBorder & Shadow\n...\nTailwind Classes\n...\nBoxButtonText\n","created_at":"2026-07-17T12:43:51.317129Z","finish_reason":null,"metrics":null},{"message_id":"6a74d60f-5998-41d9-9097-8fd751c950ea","node_id":34,"role":"tool","content":"# Web Search Results for \"\"tinker-playground-chat-text\" font\"\n\n## 1. Open Aiplayground Fun Starts\nURL: https://chatmemo.com/open-aiplayground-fun-starts\n\nplayground and felt like you walked\n...\ns garage full\n...\nblinking LEDs and half\n...\nfinished coffee cups? You\n...\nalone. Unlike the slick,\n...\nfront porch of ChatGPT, the OpenAI Playground is more like the basement lab where\n...\nreal magic—and mess—happens. It\n...\n’s got sliders for “temperature\n...\nand “top_p” that sound like they belong on a synth, not\n...\nAI interface. But\n...\nkicker: if you\n...\nthinks, speaks, or even stumbles\n...\nhaiku, the open\n...\nplayground is where\n...\ngod—with parameters.\n...\neven embeddings—without\n...\nspicy). You get\n...\nmax tokens, presence penalty, and frequency penalty like you\n...\ntuning a guitar. Want your\n...\nto be more creative\n...\naiplayground gives you the knobs; you decide\n...\nEasy as pie: just head to https://platform.openai.com/playground—not chat.openai.com, not some sketchy .net clone, but the real-deal platform subdomain. Log in with your OpenAI account (yes, you still need one), and boom—you’re in. No app install, no CLI, just your browser and curiosity. Pro tip: bookmark it. And maybe clear your cache if it loads slower than a dial-up modem in ‘99. Once inside, you’ll see dropdowns for models, text boxes for prompts, and enough sliders to make a sound engineer jealous. Welcome to the open aiplayground—where prompts become playgrounds.\n...\nWhether you’re tweakin’ prompts in the open aiplayground or runnin’ models off your laptop, knowledge is your compass. Start by bookmarkin’ the Chat Memo homepage—we drop fresh, no-BS guides every week. Then, wander over to the Explore section for deep dives that don’t kiss corporate boots. And if you’re hungry for more underground tricks, our piece on Open AI GTB Secrets Exposed reveals how power users bend AI to their will—no login required. Because in this wild digital frontier, the best tools aren’t always the shiniest… they’re the freest.\n...\nThe OpenAI Playground is a web-based interface that allows users to interact directly with OpenAI’s language models, adjust parameters like ...\n\n## 2. Chat GPT Playground: A Beginner’s Guide to Creating Your Chatbot\nURL: https://supersparrow.org/chat-gpt-playground/\n\n3. Now that you’re connected, it’s time to customize your chatbot. You can start by setting up automated responses for certain keywords or phrases so that when a user types in those particular words or phrases, the bot will respond with an appropriate response. You can also customize conversation topics, adding questions related to what you want your chatbot to discuss with users. Finally, add some personality to your bot by changing its font size and color scheme, as well as any other customizations you’d like it to have.\n\n## 3. How to add a custom prompt before form input? - API - OpenAI Developer Community\nURL: https://community.openai.com/t/how-to-add-a-custom-prompt-before-form-input/692623/5\n\nfor chat, you have a messages array. You can\n...\nthat array before sending it off to OpenAI.\n...\nI’ve been playing around with the Vercel SDK and\n...\n. Specifically,\n...\n’ve been\n...\nthe chat mode feature\n...\nVercel\n...\ntheir documentation.\n...\n’ve been tinkering\n...\nIf you haven’t tried yet, a good testbench to get started is the playground:\n...\nthere you can manually test how well your prompt works.\n...\nAnd if you’re a little lost on where to start with prompting, here’s the prompting guide from openai: https://platform.openai.com/docs/guides/prompt-engineering It contains a bunch of basic tricks to help you get started.\n\n## 4. Text Generation Web UI Review: Is This the Easiest Way to Run Local LLMs?\nURL: https://sider.ai/blog/ai-tools/text-generation-web-ui-review-is-this-the-easiest-way-to-run-local-llms\n\nIf you’ve ever wished you could run GPT‑style models on your own machine—no API keys, no data leaving your desk—there’s a good chance you’ve heard of Text Generation Web UI. It’s a popular, open-source front end that puts a clean, configurable interface on top of powerful local LLM backends like Transformers, llama.cpp, and ExLlama. In this Text Generation Web UI review, we’ll unpack what it does well, where it stumbles, and how it compares to other options—so you can decide if it’s the right cockpit for your local AI setup.\n...\nText Generation Web UI (often abbreviated as “TGU” or “textgen webui”) is an open‑source, browser‑based interface for running local large language models. Think of it as an all‑in-one control panel that:\n...\n* **Exposes a feature‑rich chat and text generation UI** with history, presets, and parameter controls.\n...\n* **Adds power tools** like LoRA loading, prompt templates, character cards, RAG extensions, and generation streaming.\n...\n* **Chat and Instruct modes:** Switch between role‑playing or assistant‑style prompting. Character cards and system prompts help simulate personas reliably.\n...\n* **Parameter panel in plain sight:** Temperature, top‑p, top‑k, repetition penalty, `min\\_p`, `typical\\_p`, and stop sequences are all one click away. You can save and share presets.\n...\nText Generation Web UI is a browser-based interface for running local LLMs. It lets you load models, tweak generation parameters, and chat or generate text privately on your own hardware.\n\n## 5. FastChat Alternatives: Smarter, Simpler Ways to Run Your Own AI Chat\nURL: https://sider.ai/blog/ai-tools/fastchat-alternatives-smarter-simpler-ways-to-run-your-own-ai-chat\n\n1. Text-generation web UI and friends: the tinkerers’ playground\n...\nWhat it is: GUIs that make running local models feel like a modded video game—sliders for temperature, dropdowns for quant sizes. Not exactly production, but great for learning and spot testing.\n...\nBest for: Hobbyists, benchmarkers, and “just show me the settings” folks.\n\n## 6. TilmanGriesel/chipper: ✨ AI interface for tinkerers (Ollama, Haystack RA...\nURL: http://gitmemories.com/index.php/TilmanGriesel/chipper\n\nChipper provides a web interface, CLI, and a modular, hackable, and lightweight architecture for RAG pipelines, document splitting, web scraping, and query workflows, enhancing generative AI models with advanced information retrieval capabilities. It can also function as a proxy between an Ollama client, such as Enchanted or Open WebUI, and an Ollama instance. Built with Haystack, Ollama, Hugging Face, Docker, TailwindCSS, and ElasticSearch, it runs as a fully containerized service.\n...\nThis project wants\n...\nbe more than just a technical foundation, for educators, it provides a\n...\nto teach AI concepts in\n...\nmanageable and practical way.\n...\nexplorers, tinkerers and companies, it offers a\n...\nupon a versatile\n...\nUse Chipper's built-in web interface to set up and customize RAG pipelines with ease. Built with vanilla JavaScript and TailwindCSS, it works offline and doesn't require any framework-specific knowledge. Run the`/help` command to learn how to switch models, update the embeddings index, and more.\n...\n- [x] Mirror Ollama Chat API (Enable Chipper as a drop-in middleware)\n- [x] Haystack Chat Generators (`ChatPromptBuilder`&`OllamaChatGenerator`)\n- [x] Distributed Processing (Chain multiple Chipper instances for workload distribution)\n...\n- [ ] React-Based Web Application (Modernized UI/UX)\n\n## 7. Everything About OpenAI's ChatGPT\nURL: https://tech-vise.com/about-openais-chatgpt/\n\nityAI\n- For sales and marketing: ChatSpot\n- For messaging: Personal AI\n- For tinkering: OpenAI playground, DeepAI Chat\n- For fun: Character.AI\n- For serious: Armageddon (kidding)\n- On social media: Snapchat My AI\n...\n- For learning: Khan Academy\n...\ns Khanmigo\n...\nmobile: Chatty Butler, Chat\n\n## 8. What are the Best AI Chatbots in 2024?\nURL: https://www.webtechpanda.com/what-are-the-best-ai-chatbots-in-2024/\n\n- Overall Best Chatbot: ChatGPT\n- Integration with Google Apps: Google Bard\n- Microsoft Bing AI\n- Tinkering: OpenAI Playground\n- Building Your Chatbot: Zapier AI Chatbot\n- Learning: Khan Academy’s Khanmigo\n- DeepAI Chat\n- Longest Conversation Memory: Claude\n- Multiple AI Models: Poe\n- Internet Deep Dives: Perplexity\n- Content Writing: Jasper Chat,\n- Chat by Copy.ai\n- Chatsonic\n- ZenoChat\n- Web Search: YouChat\n...\n### OpenAI Playground\n...\nOpenAI’s playground allows you to delve deeper into GPT models beyond simple prompts. It empowers you to adjust various settings:\n...\n- Temperature influences the unpredictability of the generated text.\n- Maximum length determines the size of the output.\n- Stop sequences guide the model on when to conclude the text generation.\n...\nThis spot is perfect for exploring what GPT can do. You can try improving your prompts, grasp how AI language models function, or assess whether an AI-powered app idea could work using an OpenAI chatbot. While it’s not exactly like chatting with a chatbot, it’s still pretty user-friendly and accessible.\n\n## 9. Your next deck is one prompt away. And it'll actually look like your brand. | Prompt, Tinker, Innovate | GenEdge | GenEdge\nURL: https://genedge.co/newsletter/your-next-deck-is-one-prompt-away\n\nThe leading AI tools now have a built-in skill for building PowerPoint files, and for the first time, they work pretty well. Describe your presentation—topic, audience, brand colors, fonts—and they generate a properly structured .pptx you can download directly from the chat or open in Google Slides.\n...\nThe quality depends almost entirely on how much brand context you give upfront. Hex codes instead of color names. The exact font name instead of \"something modern.\" And as much of the copy as you have ready.\n...\nThe prompt below gives you two paths. Path 1 is the fast version—just your colors and font. Path 2 is the full brand brief, which gets you closer on the first try with less cleanup after.\n...\nBuild me a PowerPoint presentation (.pptx) I can download.… (898 chars truncated)\n… (13 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/012c47e6/content.txt\n","created_at":"2026-07-17T12:43:51.317132Z","finish_reason":null,"metrics":null},{"message_id":"11090caa-c39f-46de-8afc-a83391a6b735","node_id":36,"role":"assistant","content":"","tool_calls":[{"id":"functions.web_search:5","name":"web_search","arguments":{"query":"site:thinkingmachines.ai Tinker console playground","num_results":10}},{"id":"functions.web_search:6","name":"web_search","arguments":{"query":"https://tinker.thinkingmachines.ai playground","num_results":10}}],"thinking":"Web search didn't return direct. Could try fetch actual CSS from a guessed URL. But user only asked \"what fonts are they using?\" given an HTML snippet. They likely want us to infer from the classes and CSS. Let's inspect snippet for font-family references. Search within text for \"font\" maybe no explicit. In the HTML classes: font-sans, font-mono. The CSS will map to variables. We can maybe fetch the CSS files if we know the domain. The HTML is from an app served at some base. We can look at the script tags: it sets `window.__reactRouterContext` and `window.__reactRouterManifest`. The manifest references paths /assets/... It uses React Router. The app may be Tinker console at https://console.tinker.thinkingmachines.ai? Let's search for \"tinker console\" URLs. Also we can identify the domain from the user? Maybe the page is from `https://tinker.thinkingmachines.ai` or `https://platform.thinkingmachines.ai`. Let's search.","created_at":"2026-07-17T12:43:52.917468Z","finish_reason":"tool_calls","metrics":{"ttft_ms":983,"total_time_ms":1599,"input_tokens":7369,"output_tokens":278,"cache_read_tokens":43008,"cache_creation_tokens":null,"tpot_ms":2.2158273381294964,"tokens_per_sec":451.2987012987013}},{"message_id":"4663d1c0-24a0-4b00-92d9-dd717a4424eb","node_id":37,"role":"tool","content":"# Web Search Results for \"site:thinkingmachines.ai Tinker console playground\"\n\n## 1. Inkling: Our Open-Weights Model - Thinking Machines Lab\nURL: https://thinkingmachines.ai/news/introducing-inkling/\n\n[Try on Tinker](https://tinker.thinkingmachines.ai/playground?utm_source=blog&utm_campaign=inkling_model_release) [Model card](https://thinkingmachines.ai/model-card/inkling/) [Hugging Face](https://huggingface.co/thinkingmachines/inkling)\n...\nWe want to make customization accessible for more use cases, so Inkling is available for fine-tuning on Tinker today. Picking the right base model to fine-tune is a qualitative judgment that combines measurable benchmarks with the unique feel of a model that comes from playing with it. To enable the latter we’re adding the Inkling Playground in the Tinker console: a developer-facing interface for chatting with Inkling.\n\n## 2. Changelog - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/changelog/\n\nNotable updates to the Tinker SDK, the Tinker Cookbook, and the Tinker Console. SDK and Cookbook entries are dated by PyPI release, Console entries by launch date. For model availability and retirements, see Models & Pricing and Model Deprecations.\n...\n| Console | July 6, 2026 | Added a Session Metrics view to every session page, rolling out to all organizations. Time-series panels show what your training and sampling traffic did over time — active sample requests, training utilization, and average training, sampling, and prefill throughput — with drag-to-zoom, a time-range picker, and a plain-language explanation of each metric. Added a Download Perfetto button to the session page that exports the full session timeline as a`.pftrace` you can open at ui.perfetto.dev. The trace goes deeper than the console panels: per-request training and sampling spans, plus counter tracks for concurrency, throughput, per-request sizes, latency percentiles (p50/p90/p99), running totals, and rate-limit events. Re-exporting picks up new session activity. See the session metrics docs for details. |\n...\n| Console | June 17, 2026 | Reorganized navigation around sessions: the Training Runs list is replaced by a Sessions list (session ID, created time, owner, project — with filtering and pagination), and each session page shows its training runs. Listing is index-backed, so large projects load much faster. |\n\n## 3. Using Inkling - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/cookbook/inkling/\n\nUsing Inkling - Tinker Documentation\n...\n# Using Inkling\n...\nInkling is Thinking Machines Lab's model tailored for Tinker. It is a general-purpose model that can code, reason, and call tools. Model effort can be controlled by a float. Image and audio inputs are also supported.\n...\nUse tml-renderers to construct all of these inputs.\n...\nMessages can be OpenAI-compatible Cookbook dictionaries or typed`tml_renderers.chat` objects. Use the format that matches your application.\n...\nInstall the Cookbook with the`inkling` extra, which pulls in the`tml_renderers` package that Inkling's renderer and scripts depend on:\n...\nin your existing\n...\nWe recommend sweeping learning rate and thinking effort to adapt Inkling to your use case. Use sample_reasoning.py to compare standard effort values.\n...\nThe Cookbook selects Inkling's recommended renderer automatically. Each guide links to its corresponding runnable example.\n...\n- tml-renderers— Message formats, tools, rendering, and training.\n- Thinking effort— Effort controls, recommended sweep values, and scaling results.\n- Audio and Images— Multimodal input requirements.\n- Rendering tutorial— How renderers bridge messages and model tokens.\n- Tinker sampling quickstart— The lower-level sampling flow.\n\n## 4. Get Started - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tinker/quickstart/\n\nGet Started - Tinker Documentation\n...\n# Quick Start\n...\nSet your API key (get one from the Tinker Console):\n...\n```\nexport TINKER_API_KEY=\"your-api-key-here\"\n\n```\n...\nThis gives you the Python SDK (`import tinker`) and the CLI (`tinker run list`,`tinker checkpoint download`).\n...\nThis page walks through the two main LLM fine-tuning workflows — supervised fine-tuning (SFT) and reinforcement learning (RL) — showing how each step maps to Tinker SDK calls.\n...\n## API Cheatsheet\n...\n```\nimport tinker\nfrom\n...\n# Entry point — reads TINKER_API_KEY from environment\nservice_client = tinker.ServiceClient()\n...\n# Training client (LoRA fine-tuning)\ntraining_client = service_client.create_lora_training_client(\n base_model=\"Qwen/Qwen3-8B\", rank=32\n)\n...\n# Sampling client (text generation)\nsampling_client = service_client.create_sampling_client(\n base_model=\"Qwen/Qwen3-8B\"\n)\n...\n# Tokenizer\ntokenizer = training_client.get_tokenizer()\n...\n`SamplingClient` is picklable — you can pass it\n...\nother processes for parallel sampling. If your training loop has CPU-heavy work (grading, environment logic), set`T\n...\nER_SUBPROCESS_SAMPLING=1` to run`sample()` and`compute_logprobs()` in a dedicated subprocess, preventing GIL contention.\n\n## 5. Navigating these docs – Tinker API\nURL: https://tinker-docs.thinkingmachines.ai/docs-outline\n\n- Development tips\n- Installation explains how to install both`tinker` and`tinker-cookbook`, and points you to the Tinker Console for your API key.\n\n## 6. Tinker - Thinking Machines Lab\nURL: https://thinkingmachines.ai/tinker/\n\nTinker - Thinking Machines Lab\n...\n# Tinker is a training API for researchers\n...\nControl every aspect of model training and fine-tuning while we handle the infrastructure.\n...\n#### Tinker cookbook\n...\nExplore realistic examples and common abstractions for fine-tuning language models with the Tinker API.\n...\nTinker is a flexible API for efficiently fine-tuning open source models with LoRA. It's designed for researchers and developers who want flexibility and full control of their data and algorithms without worrying about infrastructure management.\n...\nTinker handles scheduling, tuning, resource management, and infrastructure reliability so you can focus on the training data and algorithms. Behind the scenes, Tinker orchestrates distributed training on powerful GPU clusters for efficient utilization.\n...\nA dataset of supervised learning examples or reinforcement learning environments. After picking a base model to train on, the Tinker API provides simple functions to compute gradients, update the weights, and sample outputs from the trained model. See our cookbook for examples to get started.\n\n## 7. Session Metrics - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tinker/session-metrics/\n\nSession metrics provide critical insight into your session's activity, allowing you to monitor usage and debug performance bottlenecks. The Session Metrics view shows exactly what your training and sampling traffic did over time, including throughput, training utilization, and in-flight requests. You can read the summary panels in the console to track general resource utilization, or export the full timeline to Perfetto for a detailed, per-request view.\n...\nThe Session Metrics view in the console.\n...\nThe console panels are a subset of the trace: In-flight sample requests (the console's \"Active sample requests\"), Training utilization, and the Training, Generation, and Prefill tokens/sec rates. The exported Perfetto trace is a strict superset, adding per-request sizes, latency percentiles, totals, rate-limit events, and the individual request spans.\n...\nfull picture, every request and counter at fine resolution,\n...\na Perfetto trace:\n...\nOpen trace file.\n...\ngrouped: Con\n...\n## Metric Details\n...\n- In-flight sample requests (`requests`): sampling requests running at each moment (the console's \"Active sample requests\"). Split by weights version: each colored band is a different generation (e.g. a newer checkpoint), stacking to the total, so you see the checkpoint mix in flight.\n...\n- Training utilization (`%`): the percentage of wall-clock time the trainer was actively running forward/backward steps in each interval. Intervals are 1 minute long in Perfetto and can be longer in the console. It will not always be 100%; treat any value above 0% as a sign that training work was happening in that interval.\n...\n- Rate-limit events/sec (`events/s`): how often the session hit a rate or capacity limit, per second.\n...\n- Training tokens/sec and Generation tokens/sec (`tok/s`): total training and generated tokens per second. Each request's tokens are spread evenly across the time that request was active (from when it started running to when it finished), instead of being credite...\n\n## 8. 504: OpenCode - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tutorials/deployment/opencode/\n\n504: OpenCode - Tinker Documentation\n...\n# Coding with a tinker model using OpenCode\n...\n- A saved Tinker checkpoint (see Weights Management)\n- OpenCode installed\n- A`TINKER_API_KEY`(get one from the Tinker Console)\n...\nOpenCode can talk to any OpenAI-compatible endpoint. Tinker exposes one, so you can chat or code with a fine-tuned checkpoint directly from your terminal — no export or download needed.\n...\n## Step 2: Add a provider to opencode.json\n...\nCreate or edit`opencode.json` in your project root:\n...\n```\n{\n \"$schema\": \"https://opencode.ai/config.json\",\n \"provider\": {\n \"tinker\": {\n \"env\": [\"TINKER_API_KEY\"],\n \"npm\": \"@ai-sdk/openai-compatible\",\n \"models\": {\n \"tinker://YOUR_CHECKPOINT_PATH\": {\n \"name\": \"My Fine-Tuned Model\",\n \"attachment\": false,\n \"reasoning\": true,\n \"temperature\": true,\n \"tool_call\": true,\n \"cost\": { \"input\": 0, \"output\": 0 },\n \"limit\": {\n \"context\": 262144,\n \"output\": 8192\n },\n \"options\": {\n \"separate_reasoning\": true\n }\n }\n },\n \"options\": {\n \"baseURL\": \"https://tinker.thinkingmachines.dev/service… (26 chars truncated)\n… (86 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/39778049/content.txt\n","created_at":"2026-07-17T12:43:53.464907Z","finish_reason":null,"metrics":null},{"message_id":"f5eba6a7-959e-4b23-8c0c-570533f59444","node_id":38,"role":"tool","content":"# Web Search Results for \"https://tinker.thinkingmachines.ai playground\"\n\n## 1. Inkling: Our open-weights model - Thinking Machines Lab\nURL: https://thinkingmachines.ai/news/introducing-inkling/\n\nWe want to make customization accessible for more use cases, so Inkling is available for fine-tuning on Tinker today. Picking the right base model to fine-tune is a qualitative judgment that combines measurable benchmarks with the unique feel of a model that comes from playing with it. To enable the latter we’re adding the Inkling Playground in the Tinker console: a developer-facing interface for chatting with Inkling.\n...\nTo get a feel for the model before committing to a run, users can head to the Inkling Playground in the Tinker console. The playground offers a chat interface with integrated agentic web search, free for a limited time.\n\n## 2. Tinker - Thinking Machines Lab\nURL: https://thinkingmachines.ai/tinker/\n\nTinker - Thinking Machines Lab\n...\n# Tinker is a training API for researchers\n...\nControl every aspect of model training and fine-tuning while we handle the infrastructure.\n...\n## Supported models\n...\n### THINKING MACHINES\n...\n#### Tinker uses LoRA\n...\n#### Tinker cookbook\n...\nExplore realistic examples and common abstractions for fine-tuning language models with the Tinker API.\n...\nTinker is a flexible API for efficiently fine-tuning open source models with LoRA. It's designed for researchers and developers who want flexibility and full control of their data and algorithms without worrying about infrastructure management.\n...\nTinker handles scheduling, tuning, resource management, and infrastructure reliability so you can focus on the training data and algorithms. Behind the scenes, Tinker orchestrates distributed training on powerful GPU clusters for efficient utilization.\n...\nA dataset of supervised learning examples or reinforcement learning environments. After picking a base model to train on, the Tinker API provides simple functions to compute gradients, update the weights, and sample outputs from the trained model. See our cookbook for examples to get started.\n...\nTinker is currently available for a broad selection of open-source models, ranging from compact models like Qwen3.5-4B to large MoEs like Kimi-K2.6. We plan to expand our model lineup with even more choices soon.\n\n## 3. Tinker - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/\n\nTinker - Tinker Documentation\n...\n## Train LLMs withthe Tinker API\n...\nA cloud training API that gives you full control over your data, algorithms, and models. Write training loops locally — Tinker handles the GPUs.\n...\nGet Started → Cookbook Tutorials\n...\nModel-agnostic LoRA training from 1B to 1T+ parameters — dense and MoE architectures, text and vision.\n...\nSFT, RL (GRPO, PPO), DPO, distillation. Write your own loop or use cookbook abstractions.\n...\n### Remote GPU Clusters\n...\nDistributed training via API. Author code on a CPU machine — Tinker runs the compute.\n...\n### 15+ Recipes\n...\nEnd-to-end recipes for math RL, code RL, tool use, multi-agent training, and more.\n...\n23\n\nInteractive Tutorials\n\n## 4. Get Started - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tinker/quickstart/\n\nGet Started - Tinker Documentation\n...\n# Quick Start\n...\n```\nuv pip install tinker\n...\nSet your API key (get one from the Tinker Console):\n...\nTINKER_API_KEY=\"your-api-key-here\"\n...\nThis gives you the Python SDK (`import tinker`) and the CLI (`tinker run list`,`tinker checkpoint download`).\n...\nThis page walks through the two main LLM fine-tuning workflows — supervised fine-tuning (SFT) and reinforcement learning (RL) — showing how each step maps to Tinker SDK calls.\n...\n## Supervised Fine-Tuning (SFT)\n...\n## Reinforcement Learning (RL)\n...\n## API Cheatsheet\n...\n### Create clients\n...\n# Entry point — reads TINKER_API_KEY from environment\nservice_client = tinker.ServiceClient()\n...\n# Training client (LoRA fine-tuning)\ntraining_client = service_client.create_lora_training_client(\n base_model=\"Qwen/Qwen3-8B\", rank=32\n)\n...\n# Sampling client (text generation)\nsampling_client = service_client.create_sampling_client(\n base_model=\"Qwen/Qwen3-8B\"\n)\n...\n# Tokenizer\ntokenizer = training_client.get_tokenizer()\n...\n`SamplingClient` is picklable — you can pass it to other processes for parallel sampling. If your training loop has CPU-heavy work (grading, environment logic), set`TINKER_SUBPROCESS_SAMPLING=1` to run`sample()` and`compute_logprobs()` in a dedicated subprocess, preventing GIL contention.\n...\n### Prepare training data\n...\nA`Datum` is a single\n...\nexample. It contains\n...\ntokens, target tokens, and per-token loss weights (0 = ignore, 1 = compute loss).\n...\n(importance_\n...\n### Sample text\n...\n### Compute log-probabilities\n...\n### Forward-backward\n...\n### Optimizer step\n...\n### Save and load weights\n...\n### Concurrent requests\n\n## 5. OpenAI-Compatible API - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tinker/compatible-apis/openai/\n\nOpenAI-Compatible API - Tinker Documentation\n...\n# OpenAI API Compatible Inference (in beta)\n...\nOpenAI-compatible inference lets you interact with any model checkpoint in Tinker, using an endpoint compatible with the OpenAI Completions API. It’s designed to let you easily “poke at” your model while you're training it.\n...\nFor inference within your training runs (e.g. RL), we recommend using Tinker’s standard sampling client (see the API Reference).\n...\nCurrently, OpenAI-compatible inference is meant for testing and internal use with low internal traffic, rather than large, high-throughput, user-facing deployments. Latency and throughput may vary by model and may change without notice during\n...\nbeta. If you need higher or more stable throughput, contact the Tinker team in our Discord for guidance on larger-scale setups.\n...\n## Using OpenAI compatible inference from an OpenAI client\n...\nThe new interface exposes an OpenAI-compatible HTTP API. You can use any OpenAI SDK or HTTP client that lets you override the base URL.\n...\n1. Set the base URL of your OpenAI-compatible client\n...\n```\nhttps://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1\n...\n. Use a Tinker sampler weight path as the model name. For example:\n...\ninker://0034d8c9-0a88-52a9-b2b7-bce7cb1e6fef:train:0/sampler_weights/000080\n...\nAny valid Tinker sampler checkpoint path works here. You can keep training and sample from the same checkpoint simultaneously.\n...\n3. Authenticate with your Tinker API key, by passing the same key used for Tinker as the API key to the OpenAI client.\n...\nNote: We support both`/completions` and`/chat/completions` endpoints. For most use cases we recommend`/chat/completions`. Here's how to decide which one to use:\n...\n- `/chat/completions`— for chat-formatted prompts. You pass`messages` and the server renders them with the model’s default Hugging Face chat template, so it works out-of-the-box for checkpoints that use that template.\n- `/completions`— for raw text continua...\n\n## 6. Using Inkling - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/cookbook/inkling/\n\nUsing Inkling\n...\n# Using Inkling\n...\nInkling is Thinking Machines Lab's model tailored for Tinker. It is a general-purpose model that can code, reason, and call tools. Model effort can be controlled by a float. Image and audio inputs are also supported.\n...\nUse tml-renderers to construct all of these inputs.\n...\nMessages can be OpenAI-compatible Cookbook dictionaries or typed`tml_renderers.chat` objects. Use the format that matches your application.\n...\nInstall the Cookbook with the`inkling` extra, which pulls in the`tml_renderers` package that Inkling's renderer and scripts depend on:\n...\nuv pip install 'tinker-cookbook[ink\n...\nSet the model name in your existing Cookbook workflow:\n...\n```\nmodel_name = \"thinkingmachines/Inkling\"\n...\nWe recommend sweeping learning rate and thinking effort to adapt Inkling to your use case. Use sample_reasoning.py to compare standard effort values.\n...\nThe Cookbook selects Inkling's recommended renderer automatically. Each guide links to its corresponding runnable example.\n...\n- tml-renderers— Message formats, tools, rendering, and training.\n- Thinking effort— Effort controls, recommended sweep values, and scaling results.\n- Audio and Images— Multimodal input requirements.\n- Rendering tutorial— How renderers bridge messages and model tokens.\n- Tinker sampling quickstart— The lower-level sampling flow.\n\n## 7. thinking-machines-lab/tinker\nURL: https://github.com/thinking-machines-lab/tinker\n\n# Repository: thinking-machines-lab/tinker\n...\nTraining API and CLI\n...\n---\n\n Tinker Python SDK \n \n \n\nDocumentation:\n tinker-docs.thinkingmachines.ai\n\n## 8. 101: Hello Tinker - Tinker Documentation\nURL: https://tinker-docs.thinkingmachines.ai/tutorials/basics/hello-tinker/\n\n101: Hello Tinker - Tinker Documentation\n...\n# Tutorial 101: Hello Tinker\n...\nTinker is a remote GPU service for LLM training and inference. You write training loops in Python on your local machine; Tinker executes the heavy GPU operations (forward passes, backpropagation, sampling) on remote workers.\n...\n## The client hierarchy\n...\nThe entry point to Tinker is the ServiceClient. From it, you create specialized clients:\n...\n- SamplingClient -- generates text from a model (inference)\n- TrainingClient -- runs forward/backward passes and optimizer steps (training)\n...\nBoth talk to the same remote GPU workers. Let's start with the ServiceClient.\n...\n# Create a ServiceClient. This reads TINKER_API_KEY from your environment.\nservice_client = tinker.ServiceClient()\n...\n## Sampling from a model\n...\nLet's create a SamplingClient to generate text. We will use`Qwen/Qwen3.5-9B-Base`, a base (non-chat-tuned) model, so we can feed it raw tokens and get a plain text completion -- no chat template required.\n...\nThe sampling workflow is:\n...\n1. Create a`SamplingClient` with a base model name\n2. Encode your pro… (43 chars truncated)\n… (109 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/a2d9ac65/content.txt\n","created_at":"2026-07-17T12:43:53.464915Z","finish_reason":null,"metrics":null},{"message_id":"16834a4a-c411-48bb-bb63-ca2ab099755d","node_id":40,"role":"assistant","content":"","tool_calls":[{"id":"functions.webfetch:7","name":"webfetch","arguments":{"url":"https://tinker.thinkingmachines.ai/assets/root-CkN1nwxO.css"}},{"id":"functions.webfetch:8","name":"webfetch","arguments":{"url":"https://tinker.thinkingmachines.ai/assets/playground-AlZIgwfc.css"}},{"id":"functions.webfetch:9","name":"webfetch","arguments":{"url":"https://tinker.thinkingmachines.ai/assets/index-CfVKi3_s.css"}}],"thinking":"The console likely at https://tinker.thinkingmachines.ai/playground. We can fetch CSS from that domain: https://tinker.thinkingmachines.ai/assets/root-CkN1nwxO.css etc. Let's try webfetch those CSS files. Need use webfetch. Could fetch root CSS, playground CSS, index CSS.","created_at":"2026-07-17T12:43:54.787861Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1044,"total_time_ms":1321,"input_tokens":4982,"output_tokens":166,"cache_read_tokens":50304,"cache_creation_tokens":null,"tpot_ms":1.6686746987951808,"tokens_per_sec":599.2779783393502}},{"message_id":"4d459bdf-b63e-441a-9953-bac7d5a01801","node_id":41,"role":"tool","content":"# Web Content from https://tinker.thinkingmachines.ai/assets/root-CkN1nwxO.css\n\n/*! tailwindcss v4.2.2 | MIT License | https://tailwindcss.com */@layer properties{@supports\n(((-webkit-hyphens:none)) and (not (margin-trim:inline))) or ((-moz-orient:inline) and (not\n(color:rgb(from red r g\nb)))){*,:before,:after,::backdrop{--tw-translate-x:0;--tw-translate-y:0;--tw-translate-z:0;--tw-rota\nte-x:initial;--tw-rotate-y:initial;--tw-rotate-z:initial;--tw-skew-x:initial;--tw-skew-y:initial;--t\nw-space-y-reverse:0;--tw-divide-y-reverse:0;--tw-border-style:solid;--tw-gradient-position:initial;-\n-tw-gradient-from:#0000;--tw-gradient-via:#0000;--tw-gradient-to:#0000;--tw-gradient-stops:initial;-\n-tw-gradient-via-stops:initial;--tw-gradient-from-position:0%;--tw-gradient-via-position:50%;--tw-gr\nadient-to-position:100%;--tw-leading:initial;--tw-font-weight:initial;--tw-tracking:initial;--tw-ord\ninal:initial;--tw-slashed-zero:initial;--tw-numeric-figure:initial;--tw-numeric-spacing:initial;--tw\n-numeric-fraction:initial;--tw-shadow:0 0\n#0000;--tw-shadow-color:initial;--tw-shadow-alpha:100%;--tw-inset-shadow:0 0\n#0000;--tw-inset-shadow-color:initial;--tw-inset-shadow-alpha:100%;--tw-ring-color:initial;--tw-ring\n-shadow:0 0 #0000;--tw-inset-ring-color:initial;--tw-inset-ring-shadow:0 0\n#0000;--tw-ring-inset:initial;--tw-ring-offset-width:0px;--tw-ring-offset-color:#fff;--tw-ring-offse\nt-shadow:0 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T… (31 chars truncated)\n… (247 lines truncated)\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/f33930d1/content.txt\n","created_at":"2026-07-17T12:43:54.928074Z","finish_reason":null,"metrics":null},{"message_id":"397c3780-292c-46fb-a6bc-0827c425ede6","node_id":45,"role":"assistant","content":"","tool_calls":[{"id":"functions.grep:10","name":"grep","arguments":{"pattern":"aura-font","path":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/c35ed2d6/content.txt","output_mode":"content","max_results":100}}],"thinking":"We got CSS root with font definitions. Need parse to find custom aura fonts. The root CSS sets `--font-sans: ui-sans-serif, system-ui, sans-serif, \"Apple Color Emoji\"...`; `--font-serif: \"Iowan Old Style\"...`; `--font-mono: ui-monospace, SFMono-Regular...`; `--font-aura: var(--aura-font-family); --font-aura-display: var(--aura-font-display); --font-aura-mono: var(--aura-font-mono);` We need values for these aura font variables. The root CSS truncated. Could grep/search the fetched CSS for `--aura-font-family` etc. Use webfetch full content? It returned truncated. The truncation_notice path likely contains full. We can read that file. It is in /var/folders/d9/.../c35ed2d6/content.txt. Let's read. But it may be large. Use grep within root CSS for aura font definitions.","created_at":"2026-07-17T12:43:56.400675Z","finish_reason":"tool_calls","metrics":{"ttft_ms":919,"total_time_ms":1471,"input_tokens":9247,"output_tokens":274,"cache_read_tokens":55168,"cache_creation_tokens":null,"tpot_ms":2.0145985401459856,"tokens_per_sec":496.3768115942029}},{"message_id":"31985af9-8132-4df9-b517-5bd5840558e2","node_id":46,"role":"tool","content":"Found 56 match(es) for pattern 'aura-font' in /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/c35ed2d6/content.txt:\n-- 56 matches in /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/c35ed2d6/content.txt\n 80|var(--alpha-16);--font-aura:var(--aura-font-family);--font-aura-display:var(--aura-font-display);--f\n 81|ont-aura-mono:var(--aura-font-mono);--color-aura-text-default:var(--aura-text-default);--color-aura-\n 197|-sm{font-family:var(--aura-font-family);font-size:12px;font-weight:500;line-height:15px}.text-aura-b\n 198|tn-md{font-family:var(--aura-font-family);font-size:13px;font-weight:500;line-height:16px}.text-aura\n 199|-btn-lg{font-family:var(--aura-font-family);font-size:16px;font-weight:500;line-height:19px}}@layer\n1594|ura-large-title-regular{font-family:var(--aura-font-family);font-size:26px;font-weight:400;line-heig\n1595|ht:32px}.text-aura-large-title-emphasized{font-family:var(--aura-font-family);font-size:26px;font-we\n1596|ight:700;line-height:32px}.text-aura-title-1-regular{font-family:var(--aura-font-family);font-size:2\n1597|2px;font-weight:400;line-height:26px}.text-aura-title-1-emphasized{font-family:var(--aura-font-famil\n1600|ily:var(--aura-font-family);font-size:17px;font-weight:700;line-height:22px}.text-aura-title-3-regul\n1601|ar{font-family:var(--aura-font-family);font-size:15px;font-weight:400;line-height:20px}.text-aura-ti\n1602|tle-3-emphasized{font-family:var(--aura-font-family);font-size:15px;font-weight:590;line-height:20px\n1603|}.text-aura-headline-regular{font-family:var(--aura-font-family);font-size:13px;font-weight:700;line\n1604|-height:16px}.text-aura-headline-emphasized{font-family:var(--aura-font-family);font-size:13px;font-\n1605|weight:860;line-height:16px}.text-aura-body-regular{font-family:var(--aura-font-family);font-size:13\n1606|px;font-weight:400;line-height:16px}.text-aura-body-emphasized{font-family:var(--aura-font-family);f\n1609|var(--aura-font-family);font-size:12px;font-weight:590;line-height:15px}.text-aura-subheadline-regul\n1610|ar{font-family:var(--aura-font-family);font-size:11px;font-weight:400;line-height:14px}.text-aura-su\n1611|bheadline-emphasized{font-family:var(--aura-font-family);font-size:11px;font-weight:590;line-height:\n1612|14px}.text-aura-footnote-regular{font-family:var(--aura-font-family);font-size:10px;font-weight:400;\n1613|line-height:13px}.text-aura-footnote-emphasized{font-family:var(--aura-font-family);font-size:10px;f\n1614|ont-weight:590;line-height:13px}.text-aura-caption-1-regular{font-family:var(--aura-font-family);fon\n1616|gular{font-family:var(--aura-font-family);font-size:10px;font-weight:510;line-height:13px}.text-aura\n1617|-caption-2-emphasized{font-family:var(--aura-font-family);font-size:10px;font-weight:590;line-height\n1618|:13px}.text-aura-menu-item-regular{font-family:var(--aura-font-family);font-size:13px;font-weight:50\n1619|0;line-height:19px}.text-aura-menu-item-emphasized{font-family:var(--aura-font-family);font-size:13p\n1620|x;font-weight:590;line-height:19px}.font-aura-root{font-family:var(--aura-font-family)}}@property\n1697|0)}:root{--aura-font-family:-apple-system, BlinkMacSystemFont, \"SF Pro Text\", \"SF Pro Display\",\n1698|\"Helvetica Neue\", sans-serif;--aura-font-display:-apple-system, BlinkMacSystemFont, \"SF Pro\n1699|Display\", \"SF Pro Text\", \"Helvetica Neue\", sans-serif;--aura-font-mono:ui-monospace, \"SF Mono\",\n1702|var(--aura-font-family);--aura-text-large-title-emphasized:700 26px/32px\n1703|var(--aura-font-family);--aura-text-title-1-regular:400 22px/26px\n1704|var(--aura-font-family);--aura-text-title-1-emphasized:700 22px/26px\n1705|var(--aura-font-family);--aura-text-title-2-regular:400 17px/22px\n1706|var(--aura-font-family);--aura-text-title-2-emphasized:700 17px/22px\n1707|var(--aura-font-family);--aura-text-title-3-regular:400 15px/20px\n1708|var(--aura-font-family);--aura-text-title-3-emphasized:590 15px/20px\n1709|var(--aura-font-family);--aura-text-headline-regular:700 13px/16px\n1710|var(--aura-font-family);--aura-text-headline-emphasized:860 13px/16px\n1711|var(--aura-font-family);--aura-text-body-regular:400 13px/16px\n1712|var(--aura-font-family);--aura-text-body-emphasized:590 13px/16px\n1713|var(--aura-font-family);--aura-text-callout-regular:400 12px/15px\n1714|var(--aura-font-family);--aura-text-callout-emphasized:590 12px/15px\n1715|var(--aura-font-family);--aura-text-subheadline-regular:400 11px/14px\n1716|var(--aura-font-family);--aura-text-subheadline-emphasized:590 11px/14px\n1717|var(--aura-font-family);--aura-text-footnote-regular:400 10px/13px\n1718|var(--aura-font-family);--aura-text-footnote-emphasized:590 10px/13px\n1719|var(--aura-font-family);--aura-text-caption-1-regular:400 10px/13px\n1720|var(--aura-font-family);--aura-text-caption-1-emphasized:510 10px/13px\n1721|var(--aura-font-family);--aura-text-caption-2-regular:510 10px/13px\n1722|var(--aura-font-family);--aura-text-caption-2-emphasized:590 10px/13px\n1723|var(--aura-font-family);--aura-text-menu-item-regular:500 13px/19px\n1724|var(--aura-font-family);--aura-text-menu-item-emphasized:590 13px/19px\n1725|var(--aura-font-family);--aura-text-default:#1a1a1a;--aura-text-secondary:#00000080;--aura-text-tert\n1940|var(--aura-font-family);font:var(--tinker-chat-font)}.tinker-playground-chat-transcript{--foreground\n1943|var(--aura-font-family);--tinker-chat-content-gap:1rem;--tinker-chat-list-item-gap:.375rem;--tinker-","created_at":"2026-07-17T12:43:56.426680Z","finish_reason":null,"metrics":null},{"message_id":"335e4a6f-2776-4383-9cee-38cb20183a25","node_id":48,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:11","name":"read","arguments":{"file_path":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/c35ed2d6/content.txt","offset":1690,"limit":30}}],"thinking":"Great. We have font definitions. The root CSS shows:\n\n```\n:root {\n --aura-font-family: -apple-system, BlinkMacSystemFont, \"SF Pro Text\", \"SF Pro Display\", \"Helvetica Neue\", sans-serif;\n --aura-font-display: -apple-system, BlinkMacSystemFont, \"SF Pro Display\", \"SF Pro Text\", \"Helvetica Neue\", sans-serif;\n --aura-font-mono: ui-monospace, \"SF Mono\", ...;\n}\n```\n\nLet's get the exact value for aura-font-mono. Read around line 1697-1700. Also default Tailwind font-sans, etc.","created_at":"2026-07-17T12:43:57.460630Z","finish_reason":"tool_calls","metrics":{"ttft_ms":742,"total_time_ms":1032,"input_tokens":2070,"output_tokens":197,"cache_read_tokens":64384,"cache_creation_tokens":null,"tpot_ms":1.4720812182741116,"tokens_per_sec":679.3103448275863}},{"message_id":"de9dc419-3088-441f-9af3-c4ba33056da9","node_id":49,"role":"tool","content":"\n1690|0);--destructive:oklch(70.4% .191 22.216);--border:oklch(100% 0 0/.1);--input:oklch(100% 0\n1691|0/.15);--ring:oklch(55.6% 0 0);--chart-1:oklch(48.8% .243 264.376);--chart-2:oklch(69.6% .17\n1692|162.48);--chart-3:oklch(76.9% .188 70.08);--chart-4:oklch(62.7% .265 303.9);--chart-5:oklch(64.5%\n1693|.246 16.439);--sidebar:oklch(20.5% 0 0);--sidebar-foreground:oklch(98.5% 0\n1694|0);--sidebar-primary:oklch(48.8% .243 264.376);--sidebar-primary-foreground:oklch(98.5% 0\n1695|0);--sidebar-accent:oklch(26.9% 0 0);--sidebar-accent-foreground:oklch(98.5% 0\n1696|0);--sidebar-border:oklch(100% 0 0/.1);--sidebar-ring:oklch(55.6% 0\n1697|0)}:root{--aura-font-family:-apple-system, BlinkMacSystemFont, \"SF Pro Text\", \"SF Pro Display\",\n1698|\"Helvetica Neue\", sans-serif;--aura-font-display:-apple-system, BlinkMacSystemFont, \"SF Pro\n1699|Display\", \"SF Pro Text\", \"Helvetica Neue\", sans-serif;--aura-font-mono:ui-monospace, \"SF Mono\",\n1700|SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\",\n1701|monospace;--aura-text-large-title-regular:400 26px/32px\n1702|var(--aura-font-family);--aura-text-large-title-emphasized:700 26px/32px\n1703|var(--aura-font-family);--aura-text-title-1-regular:400 22px/26px\n1704|var(--aura-font-family);--aura-text-title-1-emphasized:700 22px/26px\n1705|var(--aura-font-family);--aura-text-title-2-regular:400 17px/22px\n1706|var(--aura-font-family);--aura-text-title-2-emphasized:700 17px/22px\n1707|var(--aura-font-family);--aura-text-title-3-regular:400 15px/20px\n1708|var(--aura-font-family);--aura-text-title-3-emphasized:590 15px/20px\n1709|var(--aura-font-family);--aura-text-headline-regular:700 13px/16px\n1710|var(--aura-font-family);--aura-text-headline-emphasized:860 13px/16px\n1711|var(--aura-font-family);--aura-text-body-regular:400 13px/16px\n1712|var(--aura-font-family);--aura-text-body-emphasized:590 13px/16px\n1713|var(--aura-font-family);--aura-text-callout-regular:400 12px/15px\n1714|var(--aura-font-family);--aura-text-callout-emphasized:590 12px/15px\n1715|var(--aura-font-family);--aura-text-subheadline-regular:400 11px/14px\n1716|var(--aura-font-family);--aura-text-subheadline-emphasized:590 11px/14px\n1717|var(--aura-font-family);--aura-text-footnote-regular:400 10px/13px\n1718|var(--aura-font-family);--aura-text-footnote-emphasized:590 10px/13px\n1719|var(--aura-font-family);--aura-text-caption-1-regular:400 10px/13px\n","created_at":"2026-07-17T12:43:57.466180Z","finish_reason":null,"metrics":null},{"message_id":"1bdea89d-a672-4942-a85b-ba72da5baf7c","node_id":51,"role":"assistant","content":"The fonts are defined in Tinker’s Aura/Tailwind CSS stack:\n\n- **Main UI / body text**: `-apple-system, BlinkMacSystemFont, \"SF Pro Text\", \"SF Pro Display\", \"Helvetica Neue\", sans-serif` (`--aura-font-family`)\n- **Display text**: `-apple-system, BlinkMacSystemFont, \"SF Pro Display\", \"SF Pro Text\", \"Helvetica Neue\", sans-serif` (`--aura-font-display`)\n- **Monospace / code**: `ui-monospace, \"SF Mono\", SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace` (`--aura-font-mono`)\n- **Tailwind fallbacks**:\n - `font-sans`: `ui-sans-serif, system-ui, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\"`\n - `font-serif`: `\"Iowan Old Style\", \"Iowan Old Style BT\", \"Palatino Linotype\", Palatino, Georgia, serif`\n - `font-mono`: same system-mono stack as above\n- **Math rendering**: a full set of **KaTeX** web fonts are loaded for equations (`KaTeX_Main`, `KaTeX_Math`, `KaTeX_AMS`, etc.)\n\nSo practically it renders with Apple’s **SF Pro / San Francisco** system fonts on macOS/iOS, falls back to `system-ui`/`Helvetica Neue` elsewhere, and uses **SF Mono** for code. The icons are **Lucide** SVG icons.","tool_calls":[],"thinking":"We have the answer. Additionally, there are KaTeX fonts in index-CfVKi3_s.css for math rendering. The question \"what fonts are they using?\" We can summarize: Main UI uses Apple's system fonts / San Francisco (SF Pro Text, SF Pro Display) via the Aura font stack; monospace uses SF Mono + system mono stack; Tailwind defaults: font-sans uses ui-sans-serif/system-ui/Apple Color Emoji, font-serif uses Iowan Old Style, font-mono uses ui-monospace/SFMono/Menlo/etc. Also KaTeX math fonts loaded. The HTML snippet uses classes `text-aura-body-regular`, `font-sans`, `font-mono`. The actual rendered fonts are the Aura variables. We can answer clearly with source reference.\n\nNeed cite files: root CSS from webfetch (the CSS file at https://tinker.thinkingmachines.ai/assets/root-CkN1nwxO.css) and index CSS. Use or . The file is an external URL not local. The instruction says use self-closing XML tags with absolute paths. The webfetch content came from a URL. We can cite the URL path. Might use . And . Since we extracted from overflow file, line numbers correspond to that file not necessarily original (minified). Maybe line numbers from the web content? The fetched content was written to overflow file; line numbers in that file may correspond to wrapped lines. We can cite the CSS URL with approximate lines? But the original CSS is minified single line maybe, not exact lines. Better to just reference the CSS file. The user didn't ask for citations but it's helpful. Let's include short answer with the font stacks.\n\nThe question: \"what fonts are they using?\" The user pasted HTML markup. We can say: The UI is styled by Tailwind v4 + an \"Aura\" design system. The typefaces used are:\n\n- Body/UI: `var(--aura-font-family)` = `-apple-system, BlinkMacSystemFont, \"SF Pro Text\", \"SF Pro Display\", \"Helvetica Neue\", sans-serif`\n- Display: `var(--aura-font-display)` = `-apple-system, BlinkMacSystemFont, \"SF Pro Display\", \"SF Pro Text\", \"Helvetica Neue\", sans-serif`\n- Monospace: `var(--aura-font-mono)` = `ui-monospace, \"SF Mono\", SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace`\n- Tailwind fallback `font-sans`: `ui-sans-serif, system-ui, sans-serif, \"Apple Color Emoji\", ...`\n- Tailwind `font-serif`: `\"Iowan Old Style\", ...`\n- KaTeX math fonts for equations.\n- Icons are Lucide (SVG).\n\nGiven the class names `font-sans` and `font-mono` appear on elements. But the `text-aura-*` classes map to `var(--aura-font-family)`. So main font is SF Pro / system San Francisco. On macOS, it'll use SF Pro Text/Display; on Windows etc system-ui/Helvetica Neue. Monospace is SF Mono if available. Let's include note.\n\nNeed be concise.","created_at":"2026-07-17T12:44:00.620355Z","finish_reason":"stop","metrics":{"ttft_ms":1131,"total_time_ms":3151,"input_tokens":1220,"output_tokens":1060,"cache_read_tokens":66432,"cache_creation_tokens":null,"tpot_ms":1.9056603773584906,"tokens_per_sec":524.7524752475248}}]}