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Build apps with 500 models locally. No tracking, no cloud, just code

Details

External ID
46347704
Source
HN
Company
—
Product
Build apps with 500 models locally. No tracking, no cloud, just code
Website domain
github.com
Launched
Dec. 21, 2025
Cohort
—
Upvotes
6
Upvotes percentile
0.2652671755725191
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Description

I built CodinIT because I wanted that "Bolt-like" experience, but on my own terms.100% Open SourceThe core idea: You should be able to prompt a full-stack application into existence, but the environment should be local, the models should be swappable (Ollama/LM Studio support was a priority), and the output should be standard code you actually own.A few things I focused on:Context Management: One of the hardest parts was figuring out how to feed the right file context back to the LLM without blowing out the token limit. I’ve implemented a custom indexing approach to keep the "vibe coding" flow snappy.The "Local" Win: Since it runs locally, you can use it on a plane or in a coffee shop without burning through a data plan or worrying about API latency.Stack: It’s primarily focused on the Node.js ecosystem right now, as that's where I found the most consistent generation results.It’s definitely a work in progress. Specifically, I'm still fine-tuning how it handles large-scale refactors across multiple files, which is where most AI coders tend to trip up.I’m really curious to hear from this community—do you actually prefer using local models for this kind of work, or is the "intelligence gap" between a local Llama 3 and Claude 4.5 Sonnet still too big for your daily use?Site: https://codinit.dev

Enrichment

Theme
lightweight and on-device AI runtimes
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Commercial product
Normalized one-liner
local model inference platform
Manually corrected
False

Could you build this?

Partial The chat UI and prompting interface are straightforward, but building an in-browser or local containerized full-stack execution sandbox (like WebContainers or Docker runtime orchestrator) with multi-model local LLM integration is non-trivial.

What it would actually take: The system requires a desktop or web orchestrator (Node.js/Go/Electron) that interacts with local LLM runtimes (Ollama/LM Studio via standard OpenAI-compatible endpoints) and a sandboxed execution environment (Docker or WebContainers via Node runtime in the browser) capable of installing NPM/Pip packages, running dev servers, hot-reloading, and handling file system diffs reliably. The hardest part is building resilient agentic file-editing loops that handle hallucinations when using smaller local models.

Discussion

1 comment analyzed.

Competitors

Other products that read as similar to this one — 138 launches clear the similarity bar, closest 8 shown.

Attention rank: #112 of 139 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 52 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a model & infra tool for Fintech yet.