Minimal AI-based news aggregator for AI news!

Hacker News

I built a minimalist AI news aggregator while teaching myself NLP. It's a single static HTML page that updates every 4-6 hours with clustered AI news stories - no accounts, no cookies, no JS frameworks. Technical approach: - Python scraper for AI/tech sources - Custom NLP pipeline with BERT embeddings to filter for AI-specific content - Hierarchical clustering to group related stories - ChatGPT API for generating cluster titles and short summaries - Served as a static HTML via Cloudflare Pages - Lightweight analytics with GoatCounter and Umami (understanding these two frameworks to choose one over the other) - Experimental JSON-based search (considering proper search if this scales) The project started when I realized I was wasting hours daily checking multiple sources as a PM trying to track AI developments. Built this over 3 months between work commitments. Interesting challenges: - Finding the right threshold for story similarity (still tuning this) - Balancing comprehensive coverage with noise filtering - Keeping the page lightweight while maintaining content density Would appreciate feedback on clustering accuracy, false positive/negative rates, and overall UX. Link: https://currentai.news

Visit product View on Hacker NewsApr 22, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, chatgpt, single · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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