Small Collection of Emacs Packages

Hacker News

Sometimes, I come across a handy tool or a frustrating issue in my daily workflow. If it's both, I might write a small program in a language I’m comfortable with to solve it. Except for ELisp... For some reason, it annoyed me so much that I couldn't stop writing in it. First, it was simple config tweaks, then small patches, and before I knew it, I was enjoying it. I ended up creating a few packages, and of course, with each one, I’d hit a new problem, push the old one aside, and start fresh. Just hoping I don’t forget to go back to it later. Well, I didn't and I'm slowly revisiting, refactoring or adding new things where necessary. It works for what I need. Maybe you will find it useful too :) https://github.com/KeyWeeUsr/typewriter-roll-mode https://github.com/KeyWeeUsr/imgur https://github.com/KeyWeeUsr/emacs-syncthing https://github.com/KeyWeeUsr/org-epa-gpg https://github.com/KeyWeeUsr/dbml-mode https://github.com/KeyWeeUsr/mermaid-docker-mode https://github.com/KeyWeeUsr/decor https://github.com/KeyWeeUsr/ob-base64

Visit product View on Hacker NewsDec 16, 2024

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6points

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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

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

39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

15%15% 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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