Show HN: Product as Code – YAML-based product management for AI coding workflows

Show HN (score: 6)
Found: July 24, 2025
ID: 512

Description

Other
Show HN: Product as Code – YAML-based product management for AI coding workflows I have worked with AI assistants for a while, and while I am really excited about it, there were times when I felt really frustrated. LLM would end up in a loop with no end.

This is when I started experimenting with creating work plans. Initially simple todo list, but it felt like "product vibing", so a bit more sophisticated later on. I started seeing value in it.

The one day it hit me. Why not use the same GitOps principles to managing product tickets? I started playing with that and really liked the way it worked.

After a chat with a friend of mine, I realised that a standard or a spec would be something really useful. You could then create all sort of tooling around this.

I took an inspiration from the way kubernetes yaml are used, cause I find it quite neat.

You can view examples here: https://spec.productascode.org/draft/#sec-Epic-Example-YAML-

Key design decisions so far:

1. YAML over JSON: Human-readable, git-diff friendly, excellent tooling ecosystem 2. Hierarchical structure: Epics → Tickets → Tasks (matches development workflow) 3. Atomic tickets: Each ticket = one branch = one PR (prevents scope creep) 4. ISO 8601 timestamps/durations: Machine-parseable time data

If I manage to create an epic with a bunch of tickets in backlog, then my favourite part of work is to tell Claude Code: "Close the current ticket, and start another one".

Here is the link to the post which has links to draft spec and GitHub repository.

Currently working on v0.1.0 and I would love to hear your thoughts.

https://mantcz.com/blog/introducing-product-as-code/

More from Show

Show HN: Ragnerock, an AI data analysis tool

Show HN: Ragnerock, an AI data analysis tool Hi HN, I’m Matt Mahowald, and together with my cofounder John, we’re launching the public beta of Ragnerock today.<p>As a data scientist, you spend the majority of your time wrangling data. Even though you might have a set of techniques and tricks you like to use, how exactly you treat a particular source of data tends to be fairly bespoke, so you end up writing custom logic each time.<p>Ragnerock was born from the observation that modern LLMs can be used to automate a lot of the grunt work involved in this process, while still allowing for fully customizable pipelines. What’s more, by leveraging techniques like constrained decoding, it’s possible to provide a unified query interface regardless of the data source - bridging raw data sources like text and images with your existing structured data living in your databases.<p>Ragnerock has four main components:<p>- A workflow designer that lets you build LLM-driven data processing and analysis pipelines<p>- A job orchestration layer that runs those workflows<p>- A query interface which lets you inspect the results of those workflows with plain SQL<p>- A notebook system which is 100% API-compatible with Jupyter and runs on your existing kernels, so you can easily pull data into your existing environments and analyses<p>Ragnerock also supports bring-your-own AI (OpenAI, Anthropic, and Google APIs), databases, and blob storage, so you can join with your existing datasets and have all outputs flow to your data lake. We’re particularly excited about our web crawling feature, which allows you to scrape websites and trigger workflows on updates: for example, you might point Ragnerock at your favorite blog and run a workflow to assess posts for topics and sentiment.<p>You can try it out at <a href="https:&#x2F;&#x2F;www.ragnerock.com" rel="nofollow">https:&#x2F;&#x2F;www.ragnerock.com</a> ; no credit card needed and the first 20 hours of compute are free. It’s an early-stage product so we’re especially interested in feedback.<p>Happy to answer any questions - John and I will be around in the comments today.

No other tools from this source yet.