FlowCode – Visual Flowcharts That Generate and Execute Python

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I built FlowCode, a browser-based tool for teaching programming logic. You drag flowchart blocks, connect them, and: the flowchart executes step-by-step variables update live in a watch window Python code is generated automatically loops (for / while) and if / elif / else are auto-detected you can save/load as JSON no backend — everything runs client-side Demo: https://southernadd-cmyk.github.io/flowCode/ It’s aimed at: computing / CS teachers students learning sequencing & control flow anyone who thinks in diagrams first A few things it currently supports: Start/End, Process, Variable, Decision, Input, Output nodes live execution highlighting variable trace table zoom / pan canvas import/export example diagrams loop pattern detection (including basic for inference) I’d really value feedback on: UI clarity for students whether execution semantics feel intuitive missing blocks you’d expect (functions & arrays maybe coming soon) browser performance with larger charts anything confusing or obviously wrong I’m especially interested in teacher perspectives using this with classes. It’s a pure HTML/JS project — no accounts, no tracking, no ads.

Visit product View on Hacker NewsDec 28, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, including · Missing: reddit linkedin, podcasting, created

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, soon · Missing: plus, platform, reviews

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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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