Show HN: Agentic CUDA Kernel Optimizer
CUDA Kernel Optimizer Contents How it works Setup Run Examples Results and limits Workflow graph An agentic CUDA kernel optimizer that turns workload descriptions into GPU implementations through an automated cycle of code generation, correctness checks, benchmarking, and refinement. Powered by LangGraph, the agent explores kernel implementations and launch configurations, queries GPU properties, and can research NVIDIA documentation for optimization guidance and inspect Nsight Compute counters to inform its next experiment. Each experiment is recorded, and the fastest validated implementation is retained.
- ▪CUDA Kernel Optimizer Contents How it works Setup Run Examples Results and limits Workflow graph An agentic CUDA kernel optimizer that turns workload descriptions into GPU implementations through an automated cycle of code generation, corre
- ▪Powered by LangGraph, the agent explores kernel implementations and launch configurations, queries GPU properties, and can research NVIDIA documentation for optimization guidance and inspect Nsight Compute counters to inform its next experi
- ▪Each experiment is recorded, and the fastest validated implementation is retained.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/bertaye/agentic-cuda-optimizer |
| Publication time | Fri, 25 Sep 2026 10:32:58 +0000 |
| Retrieval time | 2026-09-25T12:10:32.469Z |
| Last seen | 2026-09-25T12:10:32.469Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | -17TPIDpTgqi · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
CUDA Kernel Optimizer Contents How it works Setup Run Examples Results and limits Workflow graph An agentic CUDA kernel optimizer that turns workload descriptions into GPU implementations through an automated cycle of code generation, correctness checks, benchmarking, and refinement. Powered by LangGraph, the agent explores kernel implementations and launch configurations, queries GPU properties, and can research NVIDIA documentation for optimization guidance and inspect Nsight Compute counters to inform its next experiment. Each experiment is recorded, and the fastest validated implementation is retained. The model can change both kernel code and per-case launch configurations.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.