Analyzing top HN posts with language models

Hacker News

Hi HN, I spent a few weeks looking at the top HN posts of all time. This included exploration, clustering, creating visualizations, and zooming in on what (to me personally) seems like some of the best discussions on here. Three things in this post: 1- The interesting groups of HN posts 2- The interactive visualizations that you can explore in your browser 3- The data from this exploration -- this includes CSV of the titles as well as the text embeddings of 3,000 Ask HN articles. Blog post about this whole process here: [1] ============ 1- The interesting groups of HN posts From the exploration, Ask HN proved the most interesting. These are the top four groups of topics I found insightful. Each group contains about 400 posts. - Life experiences and advice threads [2] - Technical and personal development [3] - Software career insights, advice, and discussions [4] - General content recommendations (blogs/podcasts) [5] ============ 2- The interactive visualizations that you can explore in your browser - Top 10,000 Hacker News articles of all time [6] - Top 3,000 posts in Ask HN [7] ============ 3- The data from this exploration CSV file of top 3K Ask HN posts: [8] The sentence embeddings of the titles of those posts: [9] This is a colab notebook containing the code examples (including loading these two data files): [10] ============ If you've ever wanted to get into language models, this is a good place to start. Happy to answer any questions

Visit product View on Hacker NewsJun 10, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news, 000, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

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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