Static-allocation MLP inference in ANSI C using a 2-slot ring buffer

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I've been experimenting since 2019 with ways to minimize RAM usage for tiny MLP inference on microcontrollers. [0] This project is the result of that exploration: a fully static-allocation approach to MLP inference in ANSI C, using a simple 2-slot ring buffer to keep memory usage predictable and extremely low, while at the same time fast. I believe this is close to the practical lower bound for RAM usage in general-purpose CPU MLP inference without sacrificing speed or introducing runtime complexity. A more aggressive approach I've previously used is allocating and freeing memory per layer-to-layer pair during inference, but that introduces overhead and fragmentation if not used carefully. [1] Curious how it compares to other minimal inference implementations people have seen (or built). Feedback and edge cases welcome. Hope you like it. Have fun. <3 [0]: https://github.com/GiorgosXou/NeuralNetworks#-research [1]: look for REDUCE_RAM_DELETE_OUTPUTS in the source of [0]

Visit product View on Hacker NewsMay 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: tiny, using · Missing: mac, agents, macos

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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