The Summarized Times: A Web App for Concise News Summaries

Hacker News

I built a web application called The Summarized Times, which provides concise summaries of the top news articles using the NewsAPI and OpenAI's GPT-4 API. The app fetches the latest headlines, generates short summaries with key bullet points, and displays them in a clean, newspaper-like layout. The app is designed to help users quickly grasp the main points of current news without wading through lengthy articles. Deployed on Fly.io, the app ensures seamless access and performance. It uses SQLite for caching summaries to optimize API usage and improve response times. Check it out and get your news summaries at a glance.

Visit product View on Hacker NewsJun 20, 2024

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

77%77% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

46%46% 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: users · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

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

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

Correct prediction on native model

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