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Ginormous News, daily global news briefings from radio

Hacker News

Ginormous News, daily global news briefings from radio

Hey everyone! The idea behind Ginormous News is pretty simple. I have set up agents to listen to and analyze 20 different radio stations from around the world, and distill down the most globally significant stories into a free daily briefing. I’ve been working on Ginormous News for the past few months out of a desire to be more informed about global events. I felt that I was being siloed into only seeing specific news stories due to my consumption of primarily English language media, and I decided to build something to help tackle this issue. The system operates using Whisper for multilingual transcription and a combination of frontier LLMs. I make an effort to have the stories be as grounded as possible in original quotes from the broadcasts, in an effort to reduce hallucinations, and avoid errors due to the model’s lack of recent world state due to knowledge cutoff date. These are the very early stages for this product, but I’ve been learning about events in the world I wouldn’t otherwise have heard about. One question I am often asked is, why radio? Radio is a particularly interesting medium for a few reasons. For one, radio is extremely accessible. While news sites have become more and more restricted with paywalls and sign up demands, radio is broadcast globally, for free. Second, compared to the endless feeds and SEO-boosting articles of today’s online news cycle, radio has comparatively little information output at any given moment. If you think about it, since the advent of radio, the effective mental bandwidth that it can use has been fixed. There are only so many tokens of information that can be fit into a 24 news stream that is comfortable to listen to as a humans. Given the fewer tokens per minute, and the linear nature of radio’s storytelling, this means that the broadcasters must be talking about what is most important at any given moment. This means that it is a source of news that is much easier to process, and has much less noise to signal. This was built out of a personal need, and as such, I am completely fine paying out of pocket to receive this level of detailed briefing every morning. Given that it is a negligible extra cost to share it out to additional users, I plan to keep this briefing free for all for the foreseeable future. Take a look and let me know what you think!

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
91%91% 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 · Strong signals: agents, agent, model · Missing: mac, macos, cursor
76%76% 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
51%51% 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, month, users · Missing: mobile apps, ios, entrepreneurs
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
38%38% 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
14%14% 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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