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News Radar, an experiment on generating news with AI

Hacker News

News Radar, an experiment on generating news with AI

Hey folks! This is a project that I've been working in my free time for the past few months. It's a news aggregator that uses AI to select relevant articles and summarize them. The default sources are frameworks and libraries' updates, popular HN topics, and languages' subreddits threads that make past a certain threshold. You can run a local instance (needs an OpenAI key) and customize it with your own sources, and adjust the prompt as well. The resulting website can be browsed at https://dev-radar.com/ My next experiment will be with local news. I'm building some feeds with public information from my town (the town's hall official news, the legislators weekly meeting notes, weather reports, waze, etc), and based on that make it generate news items. The thing about it is that its sources will be (nearly) primary - it will not copy content from other journalists (apart from the official town hall news, which I will need to tell the AI that will be biased towards the current administration). When analyzing the local records, it might be able to catch shady stuff that regular journalists would not notice. Imagine feeding some purchase records from the town and asking the AI some questions like "is something illegal going on here?" or "are any of these items overpriced?".

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, openai, notes · Missing: mac, agents, macos
84%84% 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
68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% 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: month · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · 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 · Missing: web3, chat, crypto
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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