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Profile GPU Kernels with One Command, Zero GPU Setup

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Profile GPU Kernels with One Command, Zero GPU Setup

We've been doing lots of GPU kernel profiling and optimization on cloud infrastructure, but without local GPU hardware, that meant constant SSH juggling: upload code, compile remotely, profile kernels, download results, repeat. We were spending more time managing infrastructure than writing optimized kernels. So we built Chisel: one command spins up a cloud instance, syncs your GPU code, runs profiling (supports Python/PyTorch/TensorFlow and AMD rocprofv3), and automatically pulls results back. Zero local GPU hardware required. Next up: Web dashboard for visualizing results, simultaneous profiling across multiple GPU types, and automatic resource cleanup. Available via PyPI: pip install chisel-cli Github: https://github.com/Herdora/chisel We're actively developing and would love community feedback, especially from GPU devs exploring AMD alternatives to NVIDIA. Feature requests and contributions welcome!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: visual, code · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
50%50% 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
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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