American Code is an independent AI lab — the kind the open models in your Ollama library come from, aimed at the layer around them: distributed AI, on‑device intelligence, hardware, and developer tools. A lot is already shipped. A lot more is coming.
Hardware, apps, distributed AI, developer tools — a slice of what we’ve shipped. It’s a running list, not a finished one.
A global inference fabric made of consumer Macs.
An Apple MLX–based mesh network that turns idle Apple Silicon into a distributed AI inference grid. No cluster to rent, no queue to wait in — the machines find each other and get to work. Open source, open protocol, AGPL.
mlxmesh.net ↗A physical control surface for your AI agents.
A custom RP2040 hardware macropad paired with a Tauri/Rust desktop app that orchestrates coding agents across Claude, Cursor, Windsurf, and VS Code. Auto‑commits, codebase analysis, security checks, multi‑agent monitoring — and a real red emergency‑stop key.
ai-vibedeck.com ↗An IDE that runs your models, not the cloud’s.
An open‑source Ollama model IDE built on the VS Code platform: multi‑model orchestration, device‑specific model recommendations, and Ralph‑based code / test / repair loops that keep grinding until it’s green. Local by default. AGPL.
github.com/american-code ↗An NVIDIA DGX cluster, in your pocket.
A native iOS app for monitoring and commanding an on‑prem NVIDIA DGX GPU cluster — live utilization, job control, and anomaly checks routed through an on‑prem LLM. The whole rack, from your phone.
Native iOS · on-premCore ML models for anyone, from raw data.
A native macOS app that let anyone build Core ML models straight from their own data — vision, semantics, and numeric prediction — no ML degree, no pipeline, no data science team. Train on your machine, ship on your machine.
macOS · c. 2018Full-spectrum RF scanner, in your pocket.
A native iPadOS app that turns a tablet and a commodity RTL‑SDR dongle into a spectrum analyzer and automatic emitter classifier — 24 MHz to 1.766 GHz. Full DSP chain in Swift/Accelerate, Metal waterfall display, band‑plan‑aware classification, and BLE coverage out to 2.4 GHz.
Active developmentNeural TTS for research papers. On your device. No cloud.
An iPad app that reads academic PDFs aloud using Kokoro 82M — a StyleTTS2 model reimplemented in MLX Swift — at inference time with zero network calls. Complete synthesis pipeline in Swift: G2P frontend, PLBERT prosody encoder, iSTFT vocoder. Concept map extracted alongside.
Active developmentOpenAI-compatible inference server. Pure Swift. No cloud.
A production‑grade Swift 6 inference server built on Hummingbird 2 and MLX Swift — exposes the OpenAI API over local models on Apple Silicon. Prefix‑trie KV cache, deadline‑bounded batch assembler, LRU model pool with lazy loading from Hugging Face. No Python, no Docker, no cloud.
Active developmentVantage (Swift remote desktop), ScanForge (iPad LiDAR → 3D mesh), QuantForge (LLM quantization UI) — and more not yet named. Always something in the forge.
Follow along ↗The same kind of lab the open models in your Ollama library come from — pointed at the layer around the model. Distributed inference on consumer silicon. A hardware deck for driving AI agents. An IDE that runs your own models. A DGX cluster you command from your phone. Different problems, same instinct: pull it apart, understand it, and wire it up in a way no one bothered to before.
Every project starts as a question and ends as a paper. Twelve technical white papers and four academic research papers — on federated inference, on‑device AI, model interpretability, mechanistic interpretability, and the tooling around all of it.
Read the papers →We like hard problems and people who’d rather build the future than wait for it. The channel is always open.