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TikZ Editor – WYSIWYG editor for figures in LaTeX

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TikZ Editor – WYSIWYG editor for figures in LaTeX

Hi all! TikZ is a widely-used LaTeX package for drawing figures in papers. It uses commands like \draw[->] (0,0) -- (1,2); to draw lines, shapes, text, etc. Academics usually code up their figures by hand, so there is lots of twiddling around with the coordinates and recompiling until things look nice. I guess it’s a bit like SVG, but it’s more code than markup, for example it has loops with \foreach. I built an open-source WYSIWYG TikZ editor (available for web and desktop) that allows you to edit your TikZ source code visually by dragging and resizing elements. It simultaneously shows the source code and the rendered figure, and lets you edit either one while the two views stay in sync. I’m not aware of any other editors that are simultaneously source editors and WYSIWYG (even for editing SVG or HTML), and I’m quite pleased with how well the combination works. The way the app is implemented is by parsing the TikZ code, and at all times keeping track of the exact source location of each object. Thereby, when a user drags an element to a new position, the app can override just the numbers in the coordinate without changing anything else in the code (such as line breaks or indentation). This approach essentially required reimplementing a large fraction of TikZ, which is the kind of task that no human would ever want to do. I think building software that doesn’t exist yet because it would be impossibly tedious to code up is one of the great new possibilities thanks to coding agents, and it’s worth brainstorming for other examples. (This app was built almost entirely by Codex.) Implementing the app came with lots of fun side quests, including building converters from SVG / pptx / ipe to TikZ, re-implementing the LaTeX hyphenation and line-breaking algorithm to support multi-line nodes, and making a color picker that uses the red!20!black color mixing notation used in LaTeX papers.

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Indie HackersFits the IH revenue-focused audience · Strong signals: latex, including · Missing: supports, reddit linkedin, podcasting
94%94% 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: agents, agent, user · Missing: mac, macos, cursor
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
78%78% 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: way · Missing: mobile apps, ios, personal
54%54% 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
23%23% 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
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.

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