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Chisel – GPU development through MCP

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Chisel – GPU development through MCP

We've been running lots of experiments on AMD MI300Xs. The price/performance is compelling compared to NVIDIA, and ROCm is finally usable. But, no local hardware means constant SSH juggling. Upload code, compile remotely, run rocprof, download results, repeat. We were spending more time managing infrastructure than optimizing kernels. We're currently competing in the GPU mode kernel optimization competition. Our goal is to build the fastest AMD kernels in the world. We spend a good chunk of our time setting up the infra to profile these kernels. So we built Chisel internally to make GPU development feel local to us. One command spins up a droplet, syncs your code, runs profiling, and pulls results back. It handles the SSH, rsync, and teardown automatically. The profiling integration was the killer feature for us. Since building Chisel, I have Claude constantly running experiments seamlessly and in parallel. I feel so much more productive when exploring different optimization strategies for these kernels. Available on PyPI: pip install chisel-cli Source: https://github.com/Herdora/chisel Would love feedback and thoughts from the community, especially from people doing GPU work and exploring AMD alternatives to NVIDIA. If you're interested in contributing, we'd welcome any help in making chisel better.

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, mcp, code · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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