RRepoGEO

REPOGEO REPORT · LITE

walter-grace/mac-code

Default branch main · commit 07401cb1 · scanned 6/13/2026, 5:02:51 AM

GitHub: 997 stars · 108 forks

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 walter-grace/mac-code, 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 the repo's core value proposition at the top of the README

    Why:

    CURRENT
    # mac code

**Run models that don't fit in RAM on your Mac. $0/month.**
    COPY-PASTE FIX
    # mac code

**Claude Code, but it runs on your Mac for free. A 35B AI agent at 30 tok/s via Apple Silicon flash-paging. $0/month.**
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, apple-silicon, local-llm, ai-agent, macos, large-language-models, flash-paging, on-device-ai
  • 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 root of the repository with the text of your chosen license (e.g., 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 walter-grace/mac-code
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 1×
  2. LM Studio · recommended 1×
  3. Jan · recommended 1×
  4. llama.cpp · recommended 1×
  5. MLC LLM · recommended 1×
  • CATEGORY QUERY
    How can I run large language models on my Apple Silicon Mac with limited RAM?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan
    4. llama.cpp
    5. MLC LLM

    AI recommended 5 alternatives but never named walter-grace/mac-code. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a free way to run powerful AI models locally on my Mac without cloud costs.
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. LM Studio (lmstudio-ai/lmstudio)
    3. Jan (janhq/jan)
    4. LocalAI (mudler/LocalAI)
    5. llama.cpp (ggerganov/llama.cpp)
    6. llama-cpp-python (abetlen/llama-cpp-python)

    AI recommended 6 alternatives but never named walter-grace/mac-code. 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 walter-grace/mac-code?
    pass
    AI named walter-grace/mac-code explicitly

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

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

Subscribe to Pro for deep diagnoses

walter-grace/mac-code — 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