AI Summary Helper – Summarize Web Articles – Ready for on the Go

Hacker News

Hi HN community - Today I wanted to share with you my latest open source project: When scrolling through HN every day I usually open multiple tabs. Some articles I read immediately but most of them I want to consume at a later point, preferably on my e-reader - in a format tailored to me so it provides more context sparks more inspiration. AI Summary Helper (available as Chrome Extension and Universal / browser-agnostic bookmarklet) allows you to summarize articles with a custom prompt — so it can be as tailored to your language, profession or point of view as you define it. You can also ask questions about the article. What makes it special? The summary gets injected in the content area itself. This way you can easily forward the article, including the generated summary to your Kindle device e.g. using Reabbles Send-to-Kindle tool. https://philffm.github.io/ai-summary-helper/ https://github.com/philffm/ai-summary-helper Support link: https://philwornath.gumroad.com/l/ai-summary-helper It's a very simple approach (yes, yet another OpenAI GPT 4o mini-wrapper (possibly with more integrations to come), but one of which I now use on a daily basis - thus I felt like sharing it :). Cheers, Phil

Visit product View on Hacker NewsJul 24, 2024

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

80%80% 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: including · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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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Summarize Articles into Insights in Seconds

Product Hunt+175Productivity

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