Let Me Summarize That for You

Hacker News

TL;DR paste an article URL in https://letmesummarizethatforyou.com/ and get a brief summary in a shareable link. Hey HN, Machine generated summaries have become pretty good, and I still often find myself staring at a long article I'm interested in but don't have enough time/energy at the moment to read it. Copy pasting to Gemini/ChatGPT and asking it to summarize works, but I sometimes find it a little tedious. I had a 6 hour flight and I used it to put together this simple web app, all it does is fetch the URL, extract the text (if it's an HTML/PDF), ask an LLM to summarize it nicely, and produce a shareable link with the summary. I actually found myself using this quite a bit to get summaries for HN posts, for example here's the summary for the "Reverse engineering Ticketmaster's rotating barcodes" [1] article that was posted recently: https://letmesummarizethatforyou.com/url/?url=https%3A%2F%2F... [1] original HN post - https://news.ycombinator.com/item?id=40906148

Visit product View on Hacker NewsJul 14, 2024

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Launch Intel predictions

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

65%65% 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 · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

58%58% 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

55%55% 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 · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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