We killed our AI stock analysis and kept the news feed (API and MCP)

Hacker News

I run AlphaAI, it is a news feed for trading agents, REST plus MCP. Before it was "AI stock analysis" site. That version demoed great and made no money. i was paying for market data, users was not paying me. Nobody buys prompt output, everyone already has own ChatGPT or Claude and own prompts. The only thing people were watching was the news feed with ratings, so I built a new product around this feature and it turned out to be really useful. What's under the hood: GDELT parsing, SEC EDGAR, google public RSS and several other sources. Everything goes through a deduplication and enrichment pipeline. Only a small part of the really useful stuff gets into the feed, it's the filtering that's valuable to the user. Question to people who wire news into agents: what metadata you missing usually? For me this is main thing i want to learn from this post.

Visit product View on Hacker NewsJul 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

85%85% 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 · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google, users, trading · Missing: mobile apps, ios, personal

52%52% 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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

25%25% 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 · Strong signals: plus, users · Missing: platform, intuitive, reviews

22%22% predicted probability of success on AppSumo, 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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