Eev and TikZ, or: how to learn TikZ using a REPL

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Hi all, I made a video that at first sight is about a way to use REPLs to explore TikZ - and TikZ is a huge (La)TeX package for drawing graphics... At second sight that video is about a series of tricks for using REPLs in Emacs, and TikZ is just an excuse to present them. As far as I know those tricks are very unusual; they implement a kind of "meta-REPL" that controls other REPLs, and they do that in a way that is much simpler, and much easier to hack, than Org's code blocks and than the cells in Jupyter notebooks. The page has lots of screenshots and links, and it has instructions for downloading the video and its subtitles, and for reading the subtitles in plain text. I tried to make everything as accessible as possible for the people who just want to watch the the first two parts of the video - "Introduction" and "Trying it" - in super-high speed. I'm especially interested in pointers to related work. Cheers, have fun, etc! =)

Visit product View on Hacker NewsNov 11, 2022

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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.

best fitHighest predicted score across all platforms for this description.

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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal

50%50% 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

41%41% 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

17%17% 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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