Knowstory – API to turn unstructured text into structured data

Hacker News

Hey Hacker News! We’re excited to share Knowstory, an API to turn unstructured text and documents into structured data. As we’ve built various projects in the past, we learned that our users were actually trying to use our previous services to extract data from their text - so we thought let’s just build an API for this. Knowstory was developed out of a desire to provide a simple way for anyone to extract data from documents. We're looking for thoughts from the community to guide our development so we’d love for you to try it on desktop or laptop (we’re still ironing out our mobile views). Your feedback is especially valuable to us so thank you for being an important piece of our journey! If you would like to integrate this into your app feel free to reach out to me at pradhit@knowstory.io. I’m happy to give you a pricing discount and help you onboard.

Visit product View on Hacker NewsAug 29, 2023

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

86%86% 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 · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

16%16% 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.

Incorrect prediction on native model

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