RRepoGEO

REPOGEO REPORT · LITE

srush/awesome-o1

Default branch main · commit 7e86916d · scanned 6/29/2026, 3:22:54 PM

GitHub: 1,213 stars · 51 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
23 /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
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 srush/awesome-o1, 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 H1 to clarify 'o1' refers to OpenAI's model

    Why:

    CURRENT
    # awesome-o1
    COPY-PASTE FIX
    # awesome-o1: A bibliography and survey of papers related to OpenAI's o1 model for LLM reasoning
  • hightopics#2
    Add specific topics related to LLMs, reasoning, and reinforcement learning

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm-reasoning, reinforcement-learning, chain-of-thought, openai, o1-model, bibliography, research-papers
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root. For a bibliography, a permissive license like MIT or CC-BY-4.0 is often suitable. For example, add a file named 'LICENSE' containing the text of the MIT License.

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 srush/awesome-o1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/trl
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/trl · recommended 1×
  2. microsoft/DeepSpeed · recommended 1×
  3. CarperAI/trlx · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How to improve large language model reasoning and problem-solving with data-efficient reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face TRL (huggingface/trl)
    2. DeepSpeed-Chat (microsoft/DeepSpeed)
    3. TRLX (CarperAI/trlx)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Guidance (microsoft/guidance)
    7. OpenAI API
    8. Anthropic API
    9. d3rlpy (takuseno/d3rlpy)
    10. Stable Baselines3 (DLR-RM/stable-baselines3)
    11. Python
    12. PyTorch (pytorch/pytorch)
    13. TensorFlow (tensorflow/tensorflow)

    AI recommended 13 alternatives but never named srush/awesome-o1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What techniques exist for training AI models to self-correct and refine their thought process?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT
    2. InstructGPT
    3. Auto-GPT
    4. BabyAGI
    5. Model-Agnostic Meta-Learning (MAML)
    6. AlphaCode
    7. modAL
    8. Generative Adversarial Networks (GANs)
    9. TensorFlow
    10. PyTorch

    AI recommended 10 alternatives but never named srush/awesome-o1. 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 srush/awesome-o1?
    pass
    AI did not name srush/awesome-o1 — 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 srush/awesome-o1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named srush/awesome-o1 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 srush/awesome-o1 solve, and who is the primary audience?
    pass
    AI named srush/awesome-o1 explicitly

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

Embed your GEO score

Drop this badge into the README of srush/awesome-o1. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/srush/awesome-o1.svg)](https://repogeo.com/en/r/srush/awesome-o1)
HTML
<a href="https://repogeo.com/en/r/srush/awesome-o1"><img src="https://repogeo.com/badge/srush/awesome-o1.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

srush/awesome-o1 — 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