StackDAG: Build and share application stacks as DAGs

Hacker News

I’ve just launched StackDAG into public beta. It’s a platform for building and sharing application stacks using Directed Acyclic Graphs (DAGs). You can model anything from a classic PERN stack to your own infrastructure layout, using a flexible visual editor. It includes: - Templates for common stacks - A wide component library - Forking and sharing - Import/export as JSON - Community feed for trending DAGs The idea is to make stack design more modular and shareable, especially for teams and devs working across multiple tools. I’m very open to feedback at this stage, and there will be frequent updates during beta and beta testers will keep their role (plus early access to premium features later). Try it here: https://stackdag.pages.dev Discord: https://discord.gg/VqwqHmg5fn

Visit product View on Hacker NewsJul 25, 2025

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

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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 · Missing: https docs, excited, just released

44%44% 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

36%36% 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

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.

Correct prediction on native model

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