RRepoGEO

REPOGEO REPORT · LITE

ezelikman/quiet-star

Default branch main · commit 892446b1 · scanned 6/8/2026, 4:12:56 PM

GitHub: 739 stars · 92 forks

AI VISIBILITY SCORE
35 /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
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 ezelikman/quiet-star, 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
  • highabout#1
    Update the repository's 'About' description

    Why:

    CURRENT
    Code for Quiet-STaR
    COPY-PASTE FIX
    Code for Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking.
  • mediumreadme#2
    Strengthen the README's opening paragraph to emphasize the core problem and solution

    Why:

    CURRENT
    This project is implemented by simply patching the base Mistral implementation in Huggingface `transformers` using a new `modeling_mistral.py` and a new `configuration_mistral.py` and otherwise applying standard `transformers` features (e.g. the default Trainer).
    COPY-PASTE FIX
    Quiet-STaR implements a novel approach where Language Models Can Teach Themselves to Think Before Speaking, significantly improving reasoning capabilities. This project provides the code for this method, implemented as a simple patch to the base Mistral implementation in Huggingface `transformers`.

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 ezelikman/quiet-star
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Chain-of-Thought (CoT) Prompting
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Chain-of-Thought (CoT) Prompting · recommended 1×
  2. Self-Consistency · recommended 1×
  3. Tree-of-Thought (ToT) Prompting · recommended 1×
  4. ReAct (Reasoning and Acting) · recommended 1×
  5. Program-Aided Language Models (PAL) · recommended 1×
  • CATEGORY QUERY
    How can I improve large language model reasoning capabilities with internal thought processes?
    you: not recommended
    AI recommended (in order):
    1. Chain-of-Thought (CoT) Prompting
    2. Self-Consistency
    3. Tree-of-Thought (ToT) Prompting
    4. ReAct (Reasoning and Acting)
    5. Program-Aided Language Models (PAL)
    6. Auto-GPT
    7. BabyAGI
    8. Retrieval-Augmented Generation (RAG)

    AI recommended 8 alternatives but never named ezelikman/quiet-star. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow adding a "think before speaking" mechanism to existing LLMs?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Guidance
    5. PromptFlow
    6. Semantic Kernel

    AI recommended 6 alternatives but never named ezelikman/quiet-star. 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 ezelikman/quiet-star?
    pass
    AI named ezelikman/quiet-star explicitly

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

  • If a team adopts ezelikman/quiet-star in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ezelikman/quiet-star 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 ezelikman/quiet-star solve, and who is the primary audience?
    pass
    AI named ezelikman/quiet-star 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 ezelikman/quiet-star. 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/ezelikman/quiet-star.svg)](https://repogeo.com/en/r/ezelikman/quiet-star)
HTML
<a href="https://repogeo.com/en/r/ezelikman/quiet-star"><img src="https://repogeo.com/badge/ezelikman/quiet-star.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

ezelikman/quiet-star — 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