REPOGEO REPORT · LITE

HazyResearch/ThunderKittens

Default branch main · commit 41f4c2a7 · scanned 5/17/2026, 5:33:03 AM

GitHub: 3,359 stars · 280 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

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 HazyResearch/ThunderKittens, 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 H1/opening paragraph for AI-specific kernel development

Why:

CURRENT

# ThunderKittens

<div align="center" >
    <br/>
    <em>ThunderKittens: Tile primitives for speedy kernels</em><br/><br/>
</div>

**ThunderKittens** is a framework to make it easy to write fast deep learning kernels in CUDA.

COPY-PASTE FIX

# ThunderKittens: A CUDA Framework for High-Performance AI Kernels

<div align="center" >
    <br/>
    <em>ThunderKittens: Tile primitives for speedy kernels</em><br/><br/>
</div>

**ThunderKittens** is a specialized framework designed to simplify and accelerate the development of high-performance deep learning kernels for NVIDIA GPUs, enabling production-scale AI training and inference.

hightopics#2
Add specific topics for AI GPU kernel development
Why:
CURRENT
```
(none)
```
COPY-PASTE FIX
```
cuda, gpu, deep-learning, ai, kernels, high-performance, machine-learning, nvidia, flashattention
```
mediumhomepage#3
Add a homepage URL to the repository
Why:
CURRENT
```
(none)
```
COPY-PASTE FIX
```
https://hazyresearch.stanford.edu/
```

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 HazyResearch/ThunderKittens

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenCL

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenCL · recommended 2×
pytorch/pytorch · recommended 1×
google/jax · recommended 1×
openai/triton · recommended 1×
CUDA C++ / HIP C++ · recommended 1×

CATEGORY QUERY
Need a framework to simplify writing efficient GPU compute kernels for AI workloads.
you: not recommended
AI recommended (in order):
1. PyTorch (pytorch/pytorch)
2. JAX (google/jax)
3. Triton (openai/triton)
4. CUDA C++ / HIP C++
5. OpenCL
AI recommended 5 alternatives but never named HazyResearch/ThunderKittens. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking tools for high-performance, low-level GPU kernel development in deep learning.
you: not recommended
AI recommended (in order):
1. CUDA C++
2. HIP
3. OpenCL
4. ROCm
5. SYCL
6. TVM
AI recommended 6 alternatives but never named HazyResearch/ThunderKittens. 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 HazyResearch/ThunderKittens?
pass
AI named HazyResearch/ThunderKittens explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts HazyResearch/ThunderKittens in production, what risks or prerequisites should they evaluate first?
pass
AI named HazyResearch/ThunderKittens 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 HazyResearch/ThunderKittens solve, and who is the primary audience?
pass
AI named HazyResearch/ThunderKittens 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/HazyResearch/ThunderKittens"><img src="https://repogeo.com/badge/HazyResearch/ThunderKittens.svg" alt="RepoGEO" /></a>

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HazyResearch/ThunderKittens — 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