About 9 months ago I launched bragdoc.ai, an AI tool that helps software engineers keep track of their work and turn it into useful documents for performance reviews, weekly updates, and resume sections. I wrote about how I built it in 3 weeks using AI tooling, shipped it, and then... let it sit there while I worked on other things.

But I came back to it recently and gave it a complete overhaul. The core idea remains the same - automatically track your meaningful contributions from git repos and turn them into documents - but pretty much everything else got rebuilt from the ground up.

What changed

The original version worked, but it had some issues. The UI was built around a chatbot interface because, well, that's what the Vercel Chat template gave me and I was moving fast. It worked fine but it always felt a bit clunky for what is fundamentally a data management and document generation problem.

Another issue was privacy. Bragdoc doesn't require you to link to github in any way - most employers wouldn't want some random third party app to have access to their code. Previously, the CLI would extract data from your git repos and send it up to bragdoc.ai's servers, where OpenAI would process it. That's fine for a lot of use cases, but if you're working on proprietary code at a company with strict data policies, it's not so great.

So I rebuilt it with three main goals:

Privacy first: The CLI now sends git data directly to the LLM of your choice, completely bypassing bragdoc.ai's servers. Your code stays on your machine. Always.

Configurable extraction: You get four levels of data extraction to choose from - commit messages only, diff stats, truncated diffs, or full diffs. Pick what makes sense for your privacy requirements and LLM budget.

Better UX: The UI got completely rewritten. No more chatbot pretending to be a web app. It's now a proper application that happens to use AI under the hood to do useful things.

How it works now

The workflow is pretty straightforward:

1. Install the CLI: npm install -g @bragdoc/cli
2. Point it at a git repo and tell it which LLM you want to use
3. It analyzes your commits locally and extracts achievements
4. Only the extracted achievements (not your code) sync to the cloud
5. Use the web UI to organize, tag, and generate documents

You can use OpenAI, Anthropic, or even run everything locally with Ollama if you want zero external dependencies. The system is designed to be flexible about where the AI processing happens.

Why this matters

Most engineers I know keep some version of an achievements.txt file, or they don't keep track at all and scramble when performance review season rolls around. Six months of work compressed into "worked on various features and bug fixes" because you genuinely can't remember the details.

Bragdoc solves that by making the tracking automatic. Point it at your repos, let it run, and you've got a searchable, organized record of what you actually did. When it's time to write that self-review or update your resume, you've got real data to work from.

The AI document generation is still in beta, but the basic workflow of "turn a quarter's worth of git commits into a coherent narrative" is working well enough that I'm using it myself.

Open source, with a hosted option

The whole thing is open source on GitHub. If you want to run your own instance, go for it. It's a Next.js app with a Postgres database - nothing exotic.

For folks who don't want to deal with hosting, there's a paid tier at $3.75/month that gives you the full feature set. But it's currently free during beta, and anyone who signs up during beta gets a year free when it launches.

I'm still building it in public and will keep posting updates here and on the bragdoc blog. There's more coming - better document templates, impact tracking improvements, team features - but the core is solid now.

If you're a software engineer who's ever struggled to remember what you did last quarter, or who wants a better system than a text file, give it a try. And if you're interested in seeing one take on how a production Next.js app with AI capabilities can be built, the source code is all there.