RawStr Notes – The missing notebook for developers

Hacker News

Hi HN, I built RawStr Notes(https://rawstr.com/) as a replacement for Google Keep, Apple Notes, Evernote, etc.. It's an executable notebook based on VSCode's Monaco editor where everything can be scripted. 1. My typical workflow is copying text out of $NOTE_TAKING_APP to VS Code(for Vim) and back. RawStr saves the roundtrip by integrating VSCode's Monaco editor in the browser. 2. I save a list of restaurants to try on $NOTE_TAKING_APP. However, I get major decision fatigue trying to pick one at random. So I made this tool to help me run random.choice(restaurants) So I built RawStr Notes. Please check it out here: https://rawstr.com/

Visit product View on Hacker NewsAug 16, 2021

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

82%82% 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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

44%44% 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 · Missing: plus, platform, intuitive

26%26% 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.

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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