RRepoGEO

REPOGEO REPORT · LITE

thinking-machines-lab/tinker-cookbook

Default branch main · commit 9ca34da1 · scanned 5/19/2026, 2:41:55 PM

GitHub: 3,318 stars · 420 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface thinking-machines-lab/tinker-cookbook, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to clearly state LLM fine-tuning examples

    Why:

    CURRENT
    We provide two libraries for the broader community to customize their language models: `tinker` and `tinker-cookbook`.
    COPY-PASTE FIX
    The Tinker Cookbook provides practical, realistic examples and recipes for fine-tuning large language models (LLMs) using the Tinker API. It offers common abstractions to simplify the complexities of customizing LLMs for various applications.
  • hightopics#2
    Add specific topics for LLM fine-tuning and examples

    Why:

    COPY-PASTE FIX
    llm, large-language-models, fine-tuning, machine-learning, deep-learning, nlp, examples, recipes, tinker
  • mediumabout#3
    Update the repository description for clarity

    Why:

    CURRENT
    Post-training with Tinker
    COPY-PASTE FIX
    Practical examples and recipes for fine-tuning large language models (LLMs) using the Tinker API.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface thinking-machines-lab/tinker-cookbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PEFT · recommended 1×
  3. TRL · recommended 1×
  4. Ludwig · recommended 1×
  5. OpenAI Fine-tuning API · recommended 1×
  • CATEGORY QUERY
    How can I easily fine-tune large language models without managing complex distributed training?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. TRL
    4. Ludwig
    5. OpenAI Fine-tuning API
    6. Google Cloud Vertex AI
    7. Model Garden
    8. Vertex AI Custom Training
    9. RunPod
    10. Vast.ai
    11. Replicate

    AI recommended 11 alternatives but never named thinking-machines-lab/tinker-cookbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find practical examples and recipes for customizing pre-trained language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Kaggle
    3. PyTorch
    4. TensorFlow
    5. Papers With Code
    6. OpenAI Cookbook
    7. LangChain

    AI recommended 7 alternatives but never named thinking-machines-lab/tinker-cookbook. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of thinking-machines-lab/tinker-cookbook?
    pass
    AI named thinking-machines-lab/tinker-cookbook explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts thinking-machines-lab/tinker-cookbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thinking-machines-lab/tinker-cookbook explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo thinking-machines-lab/tinker-cookbook solve, and who is the primary audience?
    pass
    AI did not name thinking-machines-lab/tinker-cookbook — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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thinking-machines-lab/tinker-cookbook — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite