What's HN Working On – A structured dataset

Hacker News

The latest Ask HN: What are you working[1] on thread just dropped. And to give my own answer, building structured datasets! I wrote a quick scraper for the HN comments. Just pulling every top level comment along with its replies as a nested object. This ended up pulling 642 top level comments with about 458 replies. I created a posrgres db with this original data set. The replies I just concatenated together in the order they came in (with an indent field to mark what level comment it was. Then stringified the json array and added it to the db. I generated the structured data using my own tool of course (OmniAI[2]). And pulled out the following values: - project_category - Enum - PERSONAL_PROJECT, STARTUP, SELF_IMPROVEMENT, OTHER - is_open_source - Boolean - github_link - String - project_industry - Enum - SOFTWARE_DEVELOPMENT, HEALTHCARE, EDUCATION, TRANSPORTATION, etc. - one_liner - String - A one line pitch for the project - tech_stack - String[] - reply_sentiment - Num - Sentiment betwee 0 and 2 for the comment replies - demo_link - String - ai_project - Boolean [1] https://news.ycombinator.com/item?id=41342017 [2] https://getomni.ai/

Visit product View on Hacker NewsAug 25, 2024

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% 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, education · Missing: mobile apps, ios, entrepreneurs

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using, open · Missing: mac, agents, macos

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

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

9%9% 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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