REPOGEO REPORT · LITE

raullenchai/Rapid-MLX

Default branch main · commit 9e6d06e5 · scanned 6/18/2026, 12:16:58 AM

GitHub: 2,902 stars · 345 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

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 raullenchai/Rapid-MLX, 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 H1 tagline to be more specific

Why:

CURRENT

<p align="center"> <strong>Run AI on your Mac. Faster than anything else.</strong> </p>

COPY-PASTE FIX

<p align="center"> <strong>The fastest local AI engine for Apple Silicon. Drop-in OpenAI replacement with 100% tool calling.</strong> </p>

mediumreadme#2

Add a concise 'Why Rapid-MLX?' or 'Features' section to the README

Why:

COPY-PASTE FIX

Add a section like:
```
## Why Rapid-MLX?
- **Blazing Fast Local Inference:** Optimized for Apple Silicon, outperforming Ollama by 4.2x.
- **Drop-in OpenAI API Replacement:** Seamlessly integrate with existing OpenAI-compatible applications.
- **Robust Tool Calling:** 100% tool calling support with 17 built-in tool parsers.
- **Prompt Cache & Reasoning Separation:** Advanced features for efficient and intelligent AI interactions.
```

lowreadme#3

Explicitly mention performance advantage over Ollama in README intro

Why:

CURRENT

<p align="center"> Run local AI models on your Mac — no cloud, no API costs. Works with Cursor, Claude Code, and any OpenAI-compatible app. </p>

COPY-PASTE FIX

<p align="center"> Run local AI models on your Mac — no cloud, no API costs. It's 4.2x faster than Ollama and works with Cursor, Claude Code, and any OpenAI-compatible app. </p>

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 raullenchai/Rapid-MLX

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ggerganov/llama.cpp

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

ggerganov/llama.cpp · recommended 1×
apple/mlx · recommended 1×
ollama/ollama · recommended 1×
xenova/transformers.js · recommended 1×
WebGPU · recommended 1×

CATEGORY QUERY
What are the fastest local LLM inference engines for Apple Silicon Macs?
you: not recommended
AI recommended (in order):
1. llama.cpp (ggerganov/llama.cpp)
2. MLX (apple/mlx)
3. Ollama (ollama/ollama)
4. Transformers.js (xenova/transformers.js)
5. WebGPU
6. WebLLM (mlc-ai/web-llm)
7. PyTorch (pytorch/pytorch)
8. TensorFlow (tensorflow/tensorflow)
9. tensorflow-metal (apple/tensorflow-metal)
AI recommended 9 alternatives but never named raullenchai/Rapid-MLX. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a local AI tool with OpenAI API compatibility and robust tool calling on macOS.
you: not recommended
AI recommended (in order):
1. LM Studio
2. Ollama
3. LocalAI
4. Jan
5. GPT4All
AI recommended 5 alternatives but never named raullenchai/Rapid-MLX. 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 raullenchai/Rapid-MLX?
pass
AI named raullenchai/Rapid-MLX explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts raullenchai/Rapid-MLX in production, what risks or prerequisites should they evaluate first?
pass
AI named raullenchai/Rapid-MLX 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 raullenchai/Rapid-MLX solve, and who is the primary audience?
pass
AI named raullenchai/Rapid-MLX explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/raullenchai/Rapid-MLX"><img src="https://repogeo.com/badge/raullenchai/Rapid-MLX.svg" alt="RepoGEO" /></a>

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raullenchai/Rapid-MLX — 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