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REPOGEO REPORT · LITE

thinking-machines-lab/tinker-cookbook

Default branch main · commit 9f4852f7 · scanned 7/1/2026, 12:07:24 AM

GitHub: 3,525 stars · 448 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, large-language-models, fine-tuning, machine-learning, deep-learning, nlp, tinker, examples, recipes, cookbook
  • highabout#2
    Update the repository's 'About' description for clarity

    Why:

    CURRENT
    Post-training with Tinker
    COPY-PASTE FIX
    Practical examples and recipes for fine-tuning large language models 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. Keras · recommended 2×
  3. OpenAI API · recommended 2×
  4. Ludwig · recommended 2×
  5. PyTorch Lightning · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune large language models for specific downstream tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. Keras
    4. PEFT
    5. OpenAI API
    6. DeepSpeed
    7. FSDP
    8. Ludwig

    AI recommended 8 alternatives but never named thinking-machines-lab/tinker-cookbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for customizing pre-trained language models with my own data?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Ludwig
    3. OpenAI API
    4. Google Cloud Vertex AI
    5. Amazon SageMaker
    6. Keras

    AI recommended 6 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 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?

  • 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 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?

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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