RRepoGEO

REPOGEO REPORT · LITE

dCaples/AutoDidact

Default branch main · commit c4f7c787 · scanned 6/10/2026, 12:53:40 AM

GitHub: 689 stars · 63 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 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 dCaples/AutoDidact, 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
  • highreadme#1
    Reposition the README's opening to clarify it trains LLMs, not humans

    Why:

    CURRENT
    # **AutoDidact: Bootstrapping Search Through Self-VerificationResearch exploring how small LLMs can autonomously enhance their own research and reasoning capabilities by generating, researching, and answering self-created question-answer pairs, learning agentic search via reinforcement learning. All running on a single RTX 4090!Credits:** This project was built using Unsloth's Efficient GRPO code, and adds support for function calling and agentic loops.
    COPY-PASTE FIX
    # **AutoDidact: Autonomously Train Research-Agent LLMs with Self-Verification & Reinforcement Learning**
    A framework for small LLMs to autonomously enhance their own research and reasoning capabilities by generating, researching, and answering self-created question-answer pairs, learning agentic search via reinforcement learning. All running on a single RTX 4090!
    
    **Credits:** This project was built using Unsloth's Efficient GRPO code, and adds support for function calling and agentic loops.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a standard open-source LICENSE file (e.g., MIT, Apache-2.0, GPL-3.0) to the repository root.

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 dCaples/AutoDidact
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. AutoTrain · recommended 1×
  3. huggingface/trl · recommended 1×
  4. huggingface/datasets · recommended 1×
  5. huggingface/accelerate · recommended 1×
  • CATEGORY QUERY
    How to autonomously train small LLMs on custom data using self-verification?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. AutoTrain
    3. TRL (huggingface/trl)
    4. datasets (huggingface/datasets)
    5. accelerate (huggingface/accelerate)
    6. OpenAI API
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)
    9. Ray Tune (ray-project/ray)
    10. sentence-transformers (UKPLab/sentence-transformers)
    11. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 11 alternatives but never named dCaples/AutoDidact. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for local LLM agentic search and research using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset/haystack)
    2. LlamaIndex
    3. LangChain
    4. Ray RLlib
    5. Stable Baselines3

    AI recommended 5 alternatives but never named dCaples/AutoDidact. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 dCaples/AutoDidact?
    pass
    AI named dCaples/AutoDidact explicitly

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

  • If a team adopts dCaples/AutoDidact in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dCaples/AutoDidact 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 dCaples/AutoDidact solve, and who is the primary audience?
    pass
    AI named dCaples/AutoDidact explicitly

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

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dCaples/AutoDidact — 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