Fenn is an open-source Python tool for tracking machine learning training sessions — it produces .fn session logs and ships with a lightweight Flask dashboard for exploring them. The maintainer just opened a broad coordination issue (#98) inviting new contributors to help improve the dashboard, and I claimed a sub-issue to take on a small scoped piece of the work: [YOUR_SUB_ISSUE_AREA].
Posting this partly to commit publicly (the best way to make sure I actually ship the PR), partly because I think the way this issue is structured is a good template for any maintainer trying to onboard new contributors.
How the maintainer structured the work
The parent issue (#98) isn't a single task — it's a coordination hub. The flow:
Pick a small improvement.
Open a dedicated sub-issue describing what you want to improve, your proposed approach, and the affected files.
Wait for maintainer feedback.
Submit a PR linked to the sub-issue.
This is much better than the standard "find an issue, claim it in a comment, submit a PR" pattern. The sub-issue forces you to think through the change before writing code, gives the maintainer a chance to redirect you early if you're going down the wrong path, and keeps the work visible to other contributors so nothing gets duplicated.
What I'm working on
[YOUR_SUB_ISSUE_AREA — replace with 2-3 sentences describing the specific piece you claimed, why it matters, and roughly how you plan to approach it. Example for dark mode: "I'm adding dark mode support to the dashboard. The dashboard currently has a single light theme with hardcoded color values across multiple templates. My plan is to extract the color palette into CSS custom properties, add a theme toggle in the dashboard header, and persist the choice in localStorage."]
Why I think this is a good entry point for other devs
If you've been wanting to make your first open-source contribution and don't know where to start, this issue is a genuinely friendly on-ramp:
Labeled good first issue and help wanted
Active, responsive maintainer
Clear contribution guidelines (small scoped changes, isolated improvements)
A range of areas to pick from: frontend, backend, API, docs, testing
No deep ML knowledge required — the dashboard is just Flask + Python + standard frontend
Repo: https://github.com/pyfenn/fenn
Coordination issue: https://github.com/pyfenn/fenn/issues/98
I'll post a follow-up when the PR is ready. If anyone else picks up a piece of this, drop a link in the comments — happy to compare notes.
Tags: opensource, python, flask, contributing, machine-learning