This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Everyone has opened an unfamiliar repo and spent the first hour just trying to work out what it does. Vector is my lazy answer to that hour.
Vector is a local code summariser. Give it a file, a GitHub repo, or an entire project folder, and it tells you what the code is for before you read any of it. Every file gets a TL;DR, its purpose, key functions with line ranges, dependencies, and gotchas. For a whole project it also writes an overview: tech stack, architecture, a directory map, entry points, how to run it, and an ordered "start reading here" list.
The laziest part is watch mode. Point Vector at a folder, edit a file, and a fresh summary shows up on its own a few seconds later. You never have to ask.
It's for anyone who reads more code than they write: students inheriting a messy assignment, Hacktoberfest contributors landing in a new repo, and anyone who has ever said "wait, what does this module do?"
[https://vector-frontend-dxot.onrender.com](https://vector-frontend-dxot.onrender.com)
[https://github.com/amitsinha-cell/vector.git](https://github.com/amitsinha-cell/vector.git)
The open pieces
My hardware is the whole story. I built and tested this on an HP Victus laptop with an RTX 4050 (6 GB VRAM, 50W TGP). That is not a big machine, and it shaped every decision:
What went wrong. gemma4:e4b is 6.6 GB on disk and my card has about 5 GB free, so part of it runs on CPU. I added a smaller-model retry and kept E2B one dropdown away.
My favourite bug: the UI said "Ollama isn't running" while Ollama was fine. The real cause was a CORS preflight rejection. Vite had moved to port 8081 and the backend's allowlist didn't include it, so the browser never read /health. The fix was easy, but the real lesson was that "backend unreachable" and "Ollama down" are different failures and the UI should say which one it is. It does now.
Your code stays on your machine. A code summariser is a poor fit for a closed API. People paste private, unreleased, or employer-owned code into it. With local models the code never leaves the laptop. The UI shows a "100% local" badge, and a hosted fallback exists only as an opt-in setting that is off by default, with a warning before any code is sent.
Watch mode is only practical when inference is free. An assistant that re-summarises every time you save a file would run up an API bill quickly. Locally, the only cost is electricity.
Swapping models is a dropdown. I could try Gemma 4 against Qwen2.5-Coder on my own files and pick per task. A closed API doesn't let me do that.
It works offline. Once the models are pulled, Vector needs no internet except for GitHub repo mode.
Customising is possible. Because the weights are open, I can fine-tune a small Gemma on code-summary pairs next. I've fine-tuned small Gemma models before, and that path is closed with a hosted API.
Where closed still wins. Large frontier models write better summaries of huge, tangled files. For "tell me what this folder is about," a small local model with good chunking was good enough, and it was private.
For example: "A 40-file project that took me about 120 minutes to understand by reading now takes 30 minutes, and the cached re-run takes 10 seconds."