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Products that do what Compile English specs into 22 MB neural functions that run locally does
We built ProgramAsWeights (PAW) — https://programasweights.com You 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…
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I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system). It compiles to a single ~27MB binary — no Node.js, Docker, or Python required. Key features: - Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider:…
Feb 2026 · github.com
- 2PB
2021 · github.com
- 3HT
2017 · blog.klipse.tech
- 4MC
Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them. For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior. After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x…
2023 · github.com
- 5RR
I built a single-file Python script that lets you run LLM prompts from the command line with templating, structured outputs, and the ability to chain prompts together. When I discovered Google's Dotprompt format (frontmatter + Handlebars templates), I realized it was perfect for something I'd been wanting: treating prompts as first-class programs you can pipe together Unix-style. Google uses Dotprompt in Firebase Genkit and I wanted something simpler - just run a .prompt file directly on the command line. Here's what it looks like: --- model: anthropic/claude-sonnet-4-20250514 output:…
Nov 2025 · github.com
- 6BA
I've built many interpreters over the years, and Bolt represents my attempt at building the scripting language I always wanted. This is the first public release, 0.1.0! I've felt like most embedded languages have been moving towards safety and typing over years, with things like Python type hints, the explosive popularity of typescript, and even typing in Luau, which powers one of the largest scripted evironments in the world. Bolt attempts to harness this directly in the lagnauge rather than as a preprocessing step, and reap benefits in terms of both safety and performance. I intend to be…
2025 · github.com
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2018 · github.com
- 8LD
Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - https://news.ycombinator.com/item?id=39528087 As we were building the root-cause phase of our automated debugger, we realized that we developed…
2024 · github.com
- 9SB
2015 · spacy.io
- 10PL
2017 · github.com
- 11AS
2019 · github.com
- 12CA
2015 · github.com
- 13FB
2019 · fastapi.tiangolo.com
- 14PL
2019 · github.com
- 15SS
2013 · github.com
- 16CI
2017 · treefrogframework.github.io
- 17AC
2010 · pyxc.org
- 18PP
2020 · codewithrepl.it
- 19IE
Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
Jun 2026 · github.com
- 20IW
2021 · github.com
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- 22LR
I just noticed it takes literally ~5 minutes to train millions parameters on slow CPU...but before you call Yudkowsky that "it's over", an important note: the main bottleneck is the corpus size, params are just 'cleverness' but given limited info it's powerless. Anyway, here is the project: https://github.com/bggb7781-collab/lrnnsmdds/tree/main couple of notes: 1. single C file, no dependencies. Below are literally all the "dependencies", not even custom header (copy paste from the top of the single c file): #define _POSIX_C_SOURCE 200809L #include #include…
Apr 2026 · raw.githubusercontent.com
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Hey HN! We built Achilles, a tool that automatically accelerates your Python code. It identifies performance bottlenecks, rewrites those functions in optimized C++, and seamlessly patches them into your running program—without you changing a single line of code. In CPU-intensive, loop-heavy tasks, we've observed performance improvements of 100-1000x. Achilles can be installed via pip and works with just a single command. We'd appreciate your feedback, and feel free to give us a star if you find it interesting!
2025 · github.com
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