# Vector : local code summariser

> Source: <https://dev.to/amit_sinha_2a6ee473012f2d/vector-local-code-summariser-1k63>
> Published: 2026-10-25 16:07:11+00:00

*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady](https://dev.to/challenges/hacktoberfest-week3-2026-10-19)*

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."