Hi DEV, I'm Ankan, a CS student. I build whole projects end to end — the code that runs on a tiny chip, the server behind it, and the website on top. One habit I've picked up: I like models I can understand. If I can't look at what a model learned and explain it in plain words, I don't trust it enough to put it in front of people. I also keep a list of the things my projects can't do. A missing feature is fine. A feature that looks real but is actually made up is not.
This year's Hacktoberfest theme is AI Belongs to Everyone — open-source AI. That hits the one gap I have. I do train models, but small ones, the kind I can read line by line and check myself. Every time I added an AI feature to something, I just sent a request to someone else's API and hoped the answer was right. That's the opposite of how I write everything else. So this month I want to run an open-weight model on my own machine and test it like any other part of my backend: how much memory it needs, how slow it gets, when it breaks under load, and how I can tell when it's wrong. If you know good issues for someone like me — open-weight tools, inference servers, ML on small devices — send them my way. And if you've run a model yourself and hit a problem no tutorial warned you about, I'd really like to hear it before I run into it at 3am. 🎃
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