local-ai-code-assistant

CodeLoom's design premise is worth taking seriously: most local coding assistants lock you into one model family, forcing a choice between a fast small model and a slow capable one. CodeLoom refuses that trade-off by treating models as threads — a weft path of 1–3B models answering in under 200ms for autocomplete and linting, and a warp path of 7–70B models for refactoring and architectural reasoning. The Multi-Weave architecture puts up to five sessions in one workspace, each with its own context window, history and parameters, so you can watch two or three models attack the same function and pick the answer you trust. Contextual Thread Fusion is the feature that separates it from a plain multi-chat window: output from a small drafting model is passed into a larger model for expansion without copy-paste, which is a genuine engineering step beyond prompt wrapping. Native File Looming indexes up to 100k tokens of your project and splits it intelligently so each thread sees a relevant slice, and Intelligent Prompt Looms turn that into reusable templates — the documented Code Review loom dispatches one instruction to three models for security, performance and style in a single click. The privacy posture is the strongest part of the offer: no telemetry, no cloud relays, no user accounts, models pulled from verified Hugging Face repositories, and inference executed through llama.cpp, ExLlama or MLX. A local WebSocket adds LAN-only collaborative editing, which is rare for an offline tool and useful for pairing desks in the same office. The weaknesses are equally concrete. Documented minimums of 16 GB RAM and 6 GB VRAM (12 GB+ recommended) rule out most thin-and-light laptops. Setup is technical — you download and quantize models yourself, and the Universal Model Manager only automates the quantization choice, not the whole pipeline. There is no API, so it cannot be wired into your CI or editor tooling. Collaboration is LAN-only, so remote teammates are out. And the project is small: 122 stars and a single maintainer mean you should treat it as a promising open-source bet rather than a supported product with an SLA. The right comparison is Continue.dev: choose Continue when you want a mature plugin ecosystem and cloud models, choose CodeLoom when running and comparing several local models is the point.