TLDRticle – Save text and images from the web in a click

Hacker News

Kyle and Eric here, the co-founders of TLDRticle. We built a chrome plugin that lets you highlight, comment and save text and images from web pages into Google Docs. Try it out: 1) Install the chrome plugin & activate using your gmail address: https://chrome.google.com/webstore/detail/tldrticle/kjmhgdbbnjnbbbbgjibhaegfmkmmnfdp 2) Browse online & select text or hover over images to save them. 3) Right click to open the TLDR menu. You can create new google docs associated with your gmail, and highlights will be saved there.

Visit product View on Hacker NewsOct 12, 2020

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

71%71% 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.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

26%26% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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