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Hacker News

Many have attempted to create niche 'HN for X' platforms, but why reinvent the wheel?. HN already has all the best content, good ranking algo, good moderation, great discussions, etc. - it's all there! However, I'm spending way too much time on the orange site, mainly because of FOMO in certain topics like AI. I wanted a tailored HN feed focusing on my personally relevant topics, so I built HackSnack, your customizable HN feed. It uses LLMs to extract, summarize, and tag the articles and classify the different perspectives in the comments. You can receive it as either a newsletter, RSS feed, or simply visit your custom feed URL. You can create a HackSnack for pretty much for any topic, e.g. AI: https://www.kadoa.com/hacksnack/485cd2ef-b689-4c61-a703-2720... Physics, Robotics, Hardware: https://www.kadoa.com/hacksnack/7111d95e-1756-401d-a0f1-fa61... Or you can simply add your own topics :) It's still WIP, but let me know what you think!

Visit product View on Hacker NewsNov 30, 2023

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Launch Intel predictions

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

64%64% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

58%58% 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: personal, way · Missing: mobile apps, ios, entrepreneurs

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

13%13% 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.

Incorrect prediction on native model

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