A visual CI/CD system

Hacker News

Over a year ago I started Actionforge, a managed/self-managed visual CI/CD platform. Just for fun, because I like building and experimenting with dev tools. Back then I thought the user would be a human with a keyboard and a mouse. These days I rarely write pipelines. I mostly write specs, an agent dumps 500 lines of YAML, Python and a Makefile then I get my artifacts. The amount is overwhelming and a liability, and I stop trusting what I just merged. So I pivoted my CI/CD project toward more visualization, analytics and data aggregation, so engineers can see what agents and the pipeline produced, the flow, steps, data. Where it fails. With that I have a better mental model instead of a blob of code. The goal is better observability, and pipelines that explain their own failures back to the next agent. And also with a personal focus on UX and design. My project supports Perforce P4, Git/GitHub and GitLab, so quite flexible. Would love to hear your impression. I'm also very interested in your CI/CD horror stories, and how you use them these days. Any input is much appreciated! P.S. Code is public and provides GitHub Attestations.

Visit product View on Hacker NewsApr 22, 2026

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

93%93% 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 · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

66%66% 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: platform · Missing: plus, intuitive, reviews

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

19%19% 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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