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Flowcode – Turing-complete visual programming platform

Hacker News

Flowcode – Turing-complete visual programming platform

Hey HN! I’m Gabriel, and I’m excited to share a project I’ve been working on for the last few years. Flowcode is a visual programming platform that tries to combine the best of both worlds (code and visual). Over the years I found myself repeatedly drawing architectures and logic. It was always my dream to just press “run” instead of having to write them in code afterwards. But none of the visual tools I found were flexible and transparent enough for building real products. I think that visual programming fits perfectly with modern backend dev tasks that revolve around connecting different services with basic logic. Flowcode is meant to speed up and simplify those tasks, leaving more time to think about design and solve design problems. Visual programming also works really well for developing workflows involving LLM calls that are non-deterministic and require a lot of debugging and prompt tweaking. There are many other visual/low code tools, but they all offer limited control and flexibility (no concurrency, loops, transparency) and most suffer from the same problems (vendor lock-in, hard to integrate with existing code etc.). Flowcode is built on an open source visual programming language (Flyde https://github.com/flydelabs/flyde , which I launched last year here on HN - https://news.ycombinator.com/item?id=39628285 ). This means Flowcode has true concurrency, no vendor lock-in (you can export flows as .flyde files), is Turing-complete (loops, recursion, control flows, multiple IOs etc.), lets you fork any node, integrates with code via an SDK and more. I’d love to hear your thoughts and feedback.

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
89%89% 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.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, open source · Missing: https docs, just released, lua
83%83% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual, tasks · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews
49%49% 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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