Alternative HN Front End

Hacker News

Hey folks, http://dstill.ai/hackernews is alternative frontend for Hacker News, with some features on top: (1) AI powered summarizations. Summaries are generated for the link, for the post and for the discussion. Summaries are automatically generated for selected top stories, but you can also generate them on demand — currently this requires supplying your own OpenAI api key (which gets stored in your browser in localStorage). When you generate a summary, everyone else can see it and benefits from it as well. Here are some examples: Summary of a long discussion: https://dstill.ai/hackernews/item/36580192 Summary of a subthread: https://dstill.ai/hackernews/item/36582568 Summary of a PDF: https://dstill.ai/hackernews/item/36543284 Business insider: https://dstill.ai/hackernews/item/36706138 Wall Street Journal: https://dstill.ai/hackernews/item/36571407 New York Times: https://dstill.ai/hackernews/item/36653874 Reuters: https://dstill.ai/hackernews/item/36753032 It’s surprisingly not easy to get LLMs to produce summaries that aren’t vague, and I am not satisfied with the current quality just yet. E.g. the summaries might contain “the benefits of X are also discussed”, instead of “the benefits of X are A, B, C”. (2) You can view top / best / most active stories from previous days. I use “Top Yesterday” ( https://dstill.ai/hackernews/list/top/yesterday ) as my bookmark, this way I avoid the habit of refreshing the front page (expecting new stories to pop up), and refreshing each story (waiting for new comments to pop up :)). (3) You can highlight usernames and save notes about users. This way you can make sure you don’t miss posts and comments from people that you care about, and you can save notes for future reference also, like “This is the CEO of Cloudflare”, etc. (4) You can mute users. Posts from muted users will be hidden, and their comments will be collapsed by default — actually, I would love feedback on this part, as I am not sure that collapsing the comments is the best approach (better alternative might be hiding the comment’s content and author name). (5) Formatting for blockquotes and for code snippets. ## Why did I build this? I am using Hacker News as one of the more important data sources for my upcoming project (that I hope to show off soon). This requires me to keep a near real-time mirror of the HN database. Given how much I use and benefit from Hacker News as a user and as a resource for my project, I thought this would be a nice way to give something back. As a HN user, the above features were added in order to make my use of HN more effective, and I am eager to hear your feedback and feature requests :)

Visit product View on Hacker NewsJul 17, 2023

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Launch Intel predictions

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

87%87% 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, new, openai · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

62%62% 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: users, way · Missing: mobile apps, ios, personal

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: soon, users · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

Correct prediction on native model

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