Fi

Find Hidden Gems on HN

Hacker News

Find Hidden Gems on HN

Hey HN. I created this website. https://pj4533.com/hn-overlooked/ It's just a simple web app that discovers overlooked posts on Hacker News. I created it because I was often coming to Hacker News and realizing that I was missing a lot of stuff, and there just didn't seem to be an easy way to surface content that was interesting to me but just didn't bubble up to the top of the page. So I built this. I got the idea a while back, one night when I was recording (you can watch it here, it's pretty funny: https://youtu.be/FDyDb4sX30w?si=E3rby-DaGWA6gy0R ). But I never really did anything with the idea. So I decided just to make it into a little single-page web app. The Hacker News API is pretty cool because it doesn't require an API key, so you can just vibe code against it super easy. I just loaded up Claude Code and started talking to it. That first night when I was recording, it was just me with this repo, that I call 'thefuture' and I just put everything in there: scripts, whatever. Then i'll have Claude Code use OpenAI to talk to me and I'll just get bored and explore different APIs and see what I can come up with. That's all inside a single repo that Claude Code knows about, and just set it in YOLO mode and just go to town - it's super fun. It's kind of slow though, so that's the only downside. But if you put a script in there for Claude to talk to you, it can be pretty fun just to explore things. This website is just one idea extracted from that one session of messing around with a Claude Code last month. I open sourced it, you can look at the repo here: https://github.com/pj4533/hn-overlooked

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Actual performance

118points
19comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, new, openai · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, hacker news, ide · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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