REPOGEO REPORT · LITE

vaaaaanquish/Awesome-Rust-MachineLearning

Default branch main · commit 45d42d89 · scanned 6/19/2026, 12:17:34 AM

GitHub: 2,256 stars · 126 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

27 /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

1 / 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 vaaaaanquish/Awesome-Rust-MachineLearning, 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 explicitly state 'Awesome List'

Why:

CURRENT

This repository is a list of machine learning libraries written in Rust.
It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers.
This repository is targeted at people who are thinking of migrating from Python. 🦀🐍

COPY-PASTE FIX

This **Awesome List** is a comprehensive compilation of machine learning libraries and resources written in Rust. It includes GitHub repositories, blogs, books, movies, discussions, and papers, and is particularly targeted at people thinking of migrating from Python. 🦀🐍

mediumtopics#2

Add 'awesome-list' topic and correct 'awasome' typo

Why:

CURRENT

awasome, deep-learning, image-processing, machine-learning, machine-learning-library, natural-language-processing, rust, rust-library

COPY-PASTE FIX

awesome, awesome-list, deep-learning, image-processing, machine-learning, machine-learning-library, natural-language-processing, rust, rust-library

lowabout#3

Update the repository description to include 'Awesome List'

Why:

CURRENT

This repository is a list of machine learning libraries written in Rust. It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers, etc. 🦀

COPY-PASTE FIX

This **Awesome List** is a compilation of machine learning libraries and resources written in Rust, including GitHub repositories, blogs, books, movies, discussions, and papers. 🦀

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 vaaaaanquish/Awesome-Rust-MachineLearning

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LaurentMazare/tch-rs

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

LaurentMazare/tch-rs · recommended 1×
coreylowman/dfdx · recommended 1×
huggingface/candle · recommended 1×
rust-ml/linfa · recommended 1×
huggingface/rust-bert · recommended 1×

CATEGORY QUERY
What are the recommended machine learning libraries and frameworks for Rust developers?
you: not recommended
AI recommended (in order):
1. tch-rs (LaurentMazare/tch-rs)
2. dfdx (coreylowman/dfdx)
3. candle (huggingface/candle)
4. linfa (rust-ml/linfa)
5. rust-bert (huggingface/rust-bert)
6. tract (sonos/tract)
7. ndarray (rust-ndarray/ndarray)
AI recommended 7 alternatives but never named vaaaaanquish/Awesome-Rust-MachineLearning. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a comprehensive guide to machine learning tools in Rust as a Python alternative.
you: not recommended
AI recommended (in order):
1. tch-rs
2. Burn
3. dfdx
4. Linfa
5. ndarray
6. Rust-GSL
7. Polars
AI recommended 7 alternatives but never named vaaaaanquish/Awesome-Rust-MachineLearning. 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 vaaaaanquish/Awesome-Rust-MachineLearning?
pass
AI did not name vaaaaanquish/Awesome-Rust-MachineLearning — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts vaaaaanquish/Awesome-Rust-MachineLearning in production, what risks or prerequisites should they evaluate first?
pass
AI named vaaaaanquish/Awesome-Rust-MachineLearning 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 vaaaaanquish/Awesome-Rust-MachineLearning solve, and who is the primary audience?
pass
AI did not name vaaaaanquish/Awesome-Rust-MachineLearning — likely talking about a different project
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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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/vaaaaanquish/Awesome-Rust-MachineLearning.svg)](https://repogeo.com/en/r/vaaaaanquish/Awesome-Rust-MachineLearning)

HTML

<a href="https://repogeo.com/en/r/vaaaaanquish/Awesome-Rust-MachineLearning"><img src="https://repogeo.com/badge/vaaaaanquish/Awesome-Rust-MachineLearning.svg" alt="RepoGEO" /></a>

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vaaaaanquish/Awesome-Rust-MachineLearning — 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