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Compile English specs into 22 MB neural functions that run locally

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We built ProgramAsWeights (PAW) — https://programasweights.comYou describe a function in English — like "classify if this message is urgent" — and PAW compiles it into a tiny neural program (22 MB) that runs locally like a normal Python function. No API keys, no internet after compilation, deterministic output.It's for tasks that are easy to describe but hard to code with rules: urgency triage, JSON repair, log filtering, tool routing for agents. pip install programasweights import programasweights as paw f = paw.compile_and_load("Classify if this is urgent or not.") f("Need your signature by EOD") # "urgent" Compilation takes a few seconds on our server. After that, everything runs on your machine. Each program is a LoRA adapter + text instructions that adapt a fixed pretrained interpreter (Qwen3 0.6B). The model itself is unchanged — all task behavior comes from the compiled program.On our evaluation, this 0.6B interpreter with PAW reaches 73% accuracy. Prompting the same 0.6B directly gets 10%. Even prompting Qwen3 32B only gets 69%.Also runs in the browser (GPT-2 124M, WebAssembly): https://programasweights.com/browserYou can also use it in your AI agents by copying the prompt here: https://programasweights.com/agentsSource: https://github.com/programasweightsTry it out: https://programasweights.com

HN · launched Apr 15, 2026 · 11 upvotes (top 38%) · 1 view

Niche: machine learning developer tools · 303 launches, 11 in the last 7 days ▼ -45% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Commercial product

Source listing ↗

Could you build this?

No Compiling natural language specifications into ultra-small, deterministic 22 MB executable neural network weights requires novel ML research in weight generation, meta-learning, and model distillation.

What it would actually take: This architecture likely utilizes a meta-model or hypernetwork trained to emit model weights directly from text specifications, or an automated pipeline combining synthetic dataset generation, architecture search, and aggressive quantization/distillation into tiny sub-20M parameter architectures (e.g., heavily quantized MobileNet/tiny Transformer backbones). Achieving deterministic inference within a tiny binary without external dependencies requires deep ML systems engineering, custom C++/Rust inference runtimes (like ONNX or llama.cpp bindings), and massive GPU compute for training the compiler model.

Discussion

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Competitors

Other products that read as similar to this one : 36 launches clear the similarity bar, closest 8 shown.

Attention rank: #16 of 37 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 157 days after the earliest competitor.

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