A drag and drop GUI builder for CustomTkinter

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Hi everyone, I’ve been building a visual GUI builder for CustomTkinter over the past few weeks. The idea was to experiment with a low-code interface for generating real Python GUI applications while still allowing full code access. Some technical highlights: Drag & drop widget system Multi-page project structure Visual IF/ELSE logic system JSON-based project state management Built-in code editor with syntax highlighting Export system (including standalone executable builds) One of the more interesting challenges was designing the logic system so that UI actions could be translated into structured JSON and then converted back into executable Python code. I’m mainly looking for technical feedback: Does this approach make sense architecturally? What would you improve in a GUI builder like this? Is there something fundamentally missing? Appreciate any thoughts.

Visit product View on Hacker NewsFeb 26, 2026

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

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

63%63% 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 · Strong signals: interface, builder · 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting

48%48% 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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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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