Briff – Site Summarizer

Hacker News

I read HN everyday and some posts are way too long for my short attention span, so I've been wondering about summarizing long articles using AI and as I use Brave it comes with an AI sidebar tool that has a Summarize option, which is cool but... it doesn't work in Venezuela So I built this little tool and thought I'd share with you all. It's just a summarizer, enter a URL and AI (DeepSeek) writes an executive summary in less than 100 lines plus some key bullet points. Enough to get an understanding of the topic covered so I can decide to read the whole article or not I hope you all enjoy it and please don't abuse it as it cost tokens (1 cent per summary). I may think on adding ads in the future but I hate ads, so token consumption will decide. Perhaps limiting it to 3 daily summaries per user capping at 100 per month? Time will tell Feedback is highly appreciated https:/briff.site

Visit product View on Hacker NewsSep 9, 2026

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

57%57% 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: user, using · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

50%50% predicted probability of success on AppSumo, 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

40%40% 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, way · Missing: mobile apps, ios, personal

31%31% 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

20%20% 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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