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InsideStack – Find curated tech articles with semantic search

Hacker News

InsideStack – Find curated tech articles with semantic search

I built InsideStack to make it easier to find high-quality technical and software articles. Why? - The web is flooded with AI-generated content - Businesses are publishing tons of articles with biased content - Search results are often driven by engagement rather than quality. - AI-generated summaries of articles don’t drive traffic back to the original creators InsideStack lets you: - Search across curated RSS feeds with semantic search - Subscribe, bookmark, and follow topics or authors Currently, only a small set of feeds is included, but I am adding more every day. Suggestions for high-quality RSS feeds and any feedback are very welcome!

Visit product View on Hacker NewsDec 27, 2025

Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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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.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

56%56% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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