RRepoGEO

REPOGEO REPORT · LITE

Arthur-Ficial/apfel

Default branch main · commit 295fd518 · scanned 5/15/2026, 7:06:36 AM

GitHub: 5,344 stars · 206 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Arthur-Ficial/apfel, 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 README's opening to clarify core identity and competitive landscape

    Why:

    CURRENT
    # apfel

### The free AI already on your Mac.

Apple Silicon Macs ship a built-in LLM via Apple FoundationModels. `apfel` exposes it as a UNIX tool and a local OpenAI-compatible server. 100% on-device. No API keys, no cloud.
    COPY-PASTE FIX
    # apfel: Your On-Device OpenAI-Compatible LLM for macOS

### The free AI already on your Mac. No API keys, no cloud, no downloads.

`apfel` exposes the built-in Apple FoundationModels LLM on Apple Silicon Macs as a UNIX tool and a local OpenAI-compatible server, offering a 100% on-device alternative to cloud APIs and other local LLM runners like Ollama or LM Studio.
  • highreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## Comparison to Ollama, LM Studio, and LocalAI`, that outlines `apfel`'s unique selling points (e.g., built-in, no download, Apple Intelligence integration, native performance, privacy) compared to its direct competitors.
  • mediumtopics#3
    Add more descriptive topics for better categorization

    Why:

    CURRENT
    apple-intelligence, apple-silicon, cli, foundationmodels, homebrew, llm, macos, macos-26, on-device, openai-compatible, swift, tool-calling, unix
    COPY-PASTE FIX
    apple-intelligence, apple-silicon, cli, foundationmodels, homebrew, llm, macos, macos-26, on-device, openai-compatible, swift, tool-calling, unix, local-llm, ai, inference, server

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 Arthur-Ficial/apfel
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ollama/ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ollama/ollama · recommended 2×
  2. LM Studio · recommended 2×
  3. go-skynet/LocalAI · recommended 2×
  4. janhq/jan · recommended 2×
  5. oobabooga/text-generation-webui · recommended 1×
  • CATEGORY QUERY
    Looking for a self-hosted OpenAI API alternative that runs entirely on my Mac.
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. LM Studio
    3. LocalAI (go-skynet/LocalAI)
    4. Jan (janhq/jan)
    5. text-generation-webui (oobabooga/text-generation-webui)

    AI recommended 5 alternatives but never named Arthur-Ficial/apfel. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I integrate a local large language model into my macOS shell scripts?
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. LM Studio
    3. LocalAI (go-skynet/LocalAI)
    4. llama.cpp (ggerganov/llama.cpp)
    5. Jan (janhq/jan)
    6. jq (stedolan/jq)

    AI recommended 6 alternatives but never named Arthur-Ficial/apfel. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 Arthur-Ficial/apfel?
    pass
    AI named Arthur-Ficial/apfel explicitly

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

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

Subscribe to Pro for deep diagnoses

Arthur-Ficial/apfel — 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