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Alpha Feed – Using LLMs to curate AI news and end endless scrolling

Hacker News

Alpha Feed – Using LLMs to curate AI news and end endless scrolling

Hey everyone Long time reader, first time poster here. I run a telegram channel where I post interesting things I come across related to machine learning every day. It takes a lot of effort to keep up and it's time consuming to filter through all the noise on social media, so that's why we set out to build Alpha Feed. Our mission with Alpha Feed is to help users reclaim control over their time spent on social media and stay informed about their areas of interest in a focused, efficient way. Currently, we're focusing on AI, but we plan to expand our offering to more topics in the future. Here's how Alpha Feed works: Our system ingests content from a curated list of sources. Each piece of content is then scored using ChatGPT on metrics like relevance, novelty, impact, and reliability. With this information, we calculate a significance score, which enables us to surface only the most important pieces of news. The end product is a concise newsletter with the most significant AI news, delivered straight to your inbox every day. We're looking for feedback to learn how we can improve the user experience and build something that's useful to people wanting to keep up with fast moving topics. You can sign up for a 7 day free trial which you can cancel at any time at https://alphafeed.xyz .

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, new · Missing: agents, macos, agent
80%80% 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
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
44%44% 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: efficient, users · Missing: plus, platform, intuitive
26%26% 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
15%15% 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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