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DeepSelect

DeepSelect: TopK kernels for DeepSeek Sparse Attention (DSA) and Samplers

Details

External ID
1362805626
Source
GITHUB
Company
—
Product
DeepSelect
Website domain
github.com
Launched
Sept. 9, 2026
Cohort
—
Upvotes
327
Upvotes percentile
0.9795029464514476
Tags
—
Fetched at
Sept. 13, 2026, 5:56 p.m.
Updated at
Sept. 13, 2026, 5:56 p.m.

Enrichment

Theme
ai video generation and editing tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
top-k kernels for deepseek sparse attention
Manually corrected
False

Could you build this?

No Developing custom TopK GPU kernels for Sparse Attention architectures requires deep low-level CUDA, Triton, and hardware-specific GPU memory hierarchy optimization.

What it would actually take: The project requires writing optimized CUDA, CUTLASS, or Triton kernels tailored to modern GPU architectures (e.g., Hopper/Blackwell) for TopK selection and sparse attention computation. It demands expertise in warp-level primitives, shared memory bank conflicts, tensor cores, and PyTorch C++ extension bindings to match peak hardware FLOPs and memory bandwidth.

Competitors

Other products that read as similar to this one — 976 launches clear the similarity bar, closest 8 shown.

Attention rank: #24 of 977 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 314 days after the earliest competitor.

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