REPOGEO REPORT · LITE

KomputeProject/kompute

Default branch master · commit 6160e788 · scanned 5/12/2026, 7:11:53 AM

GitHub: 2,501 stars · 193 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 KomputeProject/kompute, 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

Add explicit positioning against CUDA alternatives in README intro

Why:

CURRENT

Blazing fast, mobile-enabled, asynchronous, and optimized for advanced GPU acceleration usecases.

COPY-PASTE FIX

Blazing fast, mobile-enabled, asynchronous, and optimized for advanced GPU acceleration usecases. Ideal for cross-platform and mobile AI/ML applications where CUDA is not available or desired, offering a high-level API for Vulkan-based GPU compute.

mediumabout#2

Refine 'About' description to highlight CUDA-free, mobile-first positioning

Why:

CURRENT

General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.

COPY-PASTE FIX

High-level, general-purpose GPU compute framework built on Vulkan, ideal for cross-platform and mobile AI/ML applications where CUDA is not available or desired. Supports 1000s of cross-vendor graphics cards (AMD, Qualcomm, NVIDIA & friends) with blazing fast, asynchronous processing. Backed by the Linux Foundation.

lowreadme#3

Add a 'Comparison to Alternatives' section in README

Why:

COPY-PASTE FIX

## Comparison to Alternatives

Kompute differentiates itself from other GPU compute solutions like OpenCL, SYCL, or even higher-level mobile ML frameworks (e.g., TFLite, MNN) by providing a lightweight, high-level API for general-purpose GPU compute specifically leveraging Vulkan. This makes it particularly suitable for mobile-first and cross-platform AI/ML applications where CUDA is not available or desired, offering a more accessible entry point to Vulkan compute than raw API calls, while remaining flexible for custom use cases.

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 KomputeProject/kompute

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenCL

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenCL · recommended 1×
SYCL · recommended 1×
Intel's oneAPI DPC++ · recommended 1×
Codeplay's ComputeCpp · recommended 1×
Rocm · recommended 1×

CATEGORY QUERY
Seeking a fast, cross-vendor GPU compute framework for general-purpose data processing tasks.
you: not recommended
AI recommended (in order):
1. OpenCL
2. SYCL
3. Intel's oneAPI DPC++
4. Codeplay's ComputeCpp
5. Rocm
6. HIP
7. ROCm-OpenCL
8. CUDA
9. OpenMP
10. Vulkan Compute
11. TensorFlow
12. PyTorch
AI recommended 12 alternatives but never named KomputeProject/kompute. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks enable deep learning model acceleration on mobile GPUs using Vulkan?
you: not recommended
AI recommended (in order):
1. TFLite (TensorFlow Lite)
2. MNN (Mobile Neural Network)
3. NCNN
4. PyTorch Mobile
5. ONNX Runtime
AI recommended 5 alternatives but never named KomputeProject/kompute. 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 KomputeProject/kompute?
pass
AI named KomputeProject/kompute explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts KomputeProject/kompute in production, what risks or prerequisites should they evaluate first?
pass
AI named KomputeProject/kompute 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 KomputeProject/kompute solve, and who is the primary audience?
pass
AI named KomputeProject/kompute 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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MARKDOWN (README)

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HTML

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

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KomputeProject/kompute — 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