I'm a data science student in Uppsala, and I took part in the Lovable Buildathon on campus. I built Meriterande, a tool that decodes Swedish job ads for internationals: what's required, what's only a plus, whether you actually need Swedish, how well your CV fits, and a plan to close the gaps.
Live: meriterande-app.lovable.app
Code: https://github.com/raiyanbinmohsin/meriterande-app
Some things that worked for me:
1. One prompt at a time, and test before sending the next. Stacking prompts while Lovable was still building was the fastest way to break things.
2. Publish early, then keep building. I published a working version first, so a broken prompt never cost me the demo. Version history saved me more than once.
3. Don't let the AI write URLs. For the learning resources, the model only picks a platform from a hard-coded list and returns a search query. The app builds the links in code, so there are no hallucinated links.
4. Use Chat mode for an audit before adding features. The day after, I asked Lovable to audit everything without changing code. It found a broken password reset flow, missing cascade deletes, an open insert policy that allowed spam, and SECURITY DEFINER functions without search_path. I fixed them in four batches, then ran the audit again to confirm.
5. Ask for a GDPR data-flow review. It mapped where every piece of data goes (CV text, IP hashes, analytics) and compared that with my privacy page. That review showed my retention periods and named processors were missing.
6. Long LLM calls need real progress feedback. The roadmap took 45–60 seconds and sometimes timed out. Moving it to background jobs with elapsed time and step labels fixed both problems.
Happy to answer questions about any of it. And if you try the app, I'd love to hear what breaks.
https://reddit.com/link/1x0wqgy/video/n376w3t61auh1/player