Made a bookmarklet to pluck things from the internet into PSQL

Hacker News

Howdy HN, I previously did a "Show HN" ( https://news.ycombinator.com/item?id=40966268 ) when I got my custom database to a point where you could query the internet like a structured database by treating CSS selectors/URLs as valid identifiers ie the following: ``` SELECT span.titleline > a AS post_title, span.titleline > a@href AS post_link FROM https://news.ycombinator.com; ``` And now have a bookmarklet that makes it easy to "pluck" details from pages in order to have in a exportable format such as text, CSV, or a code-gen PSQL script to populate a table! Watch the video on my current landing page to see how the end-to-end flow looks at the moment :)

Visit product View on Hacker NewsJul 22, 2024

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Did not reach leaderboard

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

73%73% 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.

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

66%66% 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: video · Missing: mobile apps, ios, personal

33%33% 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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

22%22% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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