REPOGEO REPORT · LITE

SharpAI/SwiftLM

Default branch main · commit d5a9d118 · scanned 6/6/2026, 3:07:03 AM

GitHub: 684 stars · 37 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 SharpAI/SwiftLM, 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 intro to explicitly state competitive advantage

Why:

CURRENT

A blazingly fast, native Swift inference server that serves MLX models with a strict **OpenAI-compatible API**.

COPY-PASTE FIX

A blazingly fast, native Swift inference server that serves MLX models with a strict **OpenAI-compatible API**. For Swift developers on Apple Silicon, SwiftLM offers a no-Python, bare-metal alternative to solutions like `llama.cpp` or `Ollama`.

mediumreadme#2

Add a dedicated 'Features' section to highlight unique capabilities

Why:

COPY-PASTE FIX

Add a 'Key Features' section, including: 
- **SSD Streaming:** Efficiently run 100B+ MoE models by streaming expert layers directly from NVMe SSD, bypassing macOS virtual memory swapping.
- **TurboQuant KV Cache Compression:** Optimize memory usage for large models.

lowabout#3
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://github.com/SharpAI/SwiftLM
```

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 SharpAI/SwiftLM

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Llama.cpp

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Llama.cpp · recommended 1×
Ollama · recommended 1×
MLX · recommended 1×
Hugging Face `transformers` · recommended 1×
OpenAI API · recommended 1×

CATEGORY QUERY
How to run large language models efficiently on Apple Silicon with an OpenAI API?
you: not recommended
AI recommended (in order):
1. Llama.cpp
2. Ollama
3. MLX
4. Hugging Face `transformers`
5. OpenAI API
AI recommended 5 alternatives but never named SharpAI/SwiftLM. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a native Swift solution for on-device LLM inference without Python runtime overhead.
you: not recommended
AI recommended (in order):
1. Core ML
2. llama.cpp (ggerganov/llama.cpp)
3. ONNX Runtime (microsoft/onnxruntime)
4. TensorFlow Lite (tensorflow/tensorflow)
5. PyTorch Mobile (pytorch/pytorch)
AI recommended 5 alternatives but never named SharpAI/SwiftLM. 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 SharpAI/SwiftLM?
pass
AI named SharpAI/SwiftLM explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts SharpAI/SwiftLM in production, what risks or prerequisites should they evaluate first?
pass
AI named SharpAI/SwiftLM 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 SharpAI/SwiftLM solve, and who is the primary audience?
pass
AI named SharpAI/SwiftLM explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/SharpAI/SwiftLM.svg)](https://repogeo.com/en/r/SharpAI/SwiftLM)

HTML

<a href="https://repogeo.com/en/r/SharpAI/SwiftLM"><img src="https://repogeo.com/badge/SharpAI/SwiftLM.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite