Launch HN: Magnitude (YC S25) – Self-optimizing inference engine for agents
Hey HN, Anders and Tom here. We're building Magnitude, an inference engine for agents that optimizes itself to run as fast as possible on your hardware. It works on Mac, Linux, and Windows on any hardware and is up to 2x faster than llama.cpp. We're both software engineers and previously built an open source browser agent to 4k+ GH stars and 100k+ downloads. We increasingly wanted to run it on local models, but found that no inference engine worked for our use case. Inference engines today all m
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Source record
Recorded metrics
points · 194 votes
2026-10-24 00:30:29.156Z
cumulative since publication
Research analysis
Project record
Repository: magnitudedev/magnitude. Source account: Hey HN, Anders and Tom here. We're building Magnitude, an inference engine for agents that optimizes itself to run as fast as possible on your hardware. It works on Mac, Linux, and Windows on any hardware and is up to 2x faster than llama.cpp. We're both software engineers and previously built an open source browser agent to 4k+ GH stars and 100k+ downloads. We increasingly wanted to run it on local models, but found that no inference engine worked for our use case. Inference engines today all m
Observed adoption & comparisons
Source votes: 194 votes (Oct 24, 2026 UTC; cumulative since publication); previous same-scope observation: 194 (Oct 23, 2026 UTC). These observations do not establish demand growth. Recent records to compare: “google/skills” (GitHub Trending, Nov 5, 2026); “mattpocock/skills” (GitHub Trending, Nov 5, 2026). Shared themes are navigation hints; they have not been verified as the same need or independent corroboration.
Adoption decision
Evaluate it against the tool or manual workflow it would replace. The stored record supports discovery; it does not yet establish reliability, lower operating cost or commercial adoption.
Unverified requirements
Not verified: license permissions, required hardware, installation steps, maintenance status and a representative task with its failure cases.
Next test
Choose one real task and a current alternative. Record the commit, environment, input, output, elapsed time and failure behavior; retain the result before recommending adoption.