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Heynote – A dedicated scratchpad for developers

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Heynote – A dedicated scratchpad for developers

Hey! I made Heynote entirely for my own use case. For many years, I always had an Emacs instance running with the scratch buffer open, even long after I had abandoned Emacs as my programming editor in favor of more recent IDE:s. The simplicity of having just one big scratch buffer appeals to me, but I still want to separate the different things I jot down somehow (without using tabs or similar). Previously, my solution was to insert a bunch of blank lines between the notes, but hitting C-A would still select the entire buffer. That's why I came up with the concept of "blocks", which turned out really well for my use cases. I decided to release Heynote, thinking it might be useful to others.

Visit productView on Hacker NewsDec 22, 2023
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
80%80% 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: para · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, using, notes · Missing: agents, macos, agent
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
43%43% 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
13%13% 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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