Ask HN: What is your (AI) dev tech stack / workflow?

Hello, happy Friday!

I am looking to do some in-person "developer boot-up" workshops, and seek your suggestions for "modern tooling".

The background of the participants range from motivated newbie ("I heard you can make your own app with AI!") to existing software developers who want to get up to speed on modern development for the purposes of building stuff, and getting jobs where AI tools are being used.

For those who have been doing software development & "tech" lately using AI tools, and feel they have a great setup & flow - I would love to hear what your dev setup is, what tools you're using and what workflow has been working best for you (and your team).

// My Background

I have been programming / building for 20+ years, but have not been using AI tools much (aside from hitting up LLM APIs on a few projects).

I value open-source, and aim for long-term quality and supportability. Techniques like test-driven development (TDD), using proven / well documented tools, customer-centric development (often pairing with clients), make it easy to do the right thing. If you are familiar with Pivotal Labs, agile & XP - that's the style.

These are some of the Upcoming uses-cases for the workshop, and my own personal "IT backlog":

- Create a static "one pager" personal/professional website

- Setup a Blog / Static site generator (Pelican), create a simple but stylish theme

- Create a simple web app / backend API (FastAPI) tool - form-based calculator, convert X data to PDFs, etc.

- Figure out how to have SyncThing autosync the home folder of 3 Linux computers in the house

- Backup & archive the photos & video from my iPhone

// Tech stack I am currently using:

- Operating system: Linux Mint Debian (LMDE)

- Editor: VSCodium

- Code: Python, HTML/CSS

- Server platform: Amazon AWS

I am guessing that most workshop participants will be using MacBooks & Windows computers - but a few are on Linux, as I recently did a "Linux install party".

I haven't used any "AI harnesses", agents or anything like that - but curious what's a good starting point to take best advantage of these tools.

Thanks for sharing the knowledge!

// JRO

QA background here, recently building a security tool (accguard) with heavy AI assistance. My stack:

Claude and ChatGPT in parallel — I describe the same problem to both, compare the answers, push back on both. The disagreements are where the learning happens. Claude Code for longer sessions where context needs to persist across files TryHackMe for structured security learning alongside the building GitHub Actions for CI — AI helped me write the workflow, I understand it now because I had to debug it

The shift that actually changed my workflow: stopped asking AI to write code for me, started asking it to explain what broke and why. The understanding compounds faster that way. For your workshop participants coming from zero: the most valuable thing isn't the tool, it's learning to describe problems precisely. That skill transfers whether the AI gets better or worse.

OpenCode + their Go subscription.

Start with a nice batteries included setup, read anthropic's knowledge share, play and iterate, stay human in the loop.

Check out Dax Raad (behind OC) on the Pragmatic Engineer podcast, I think you will like his philosophies, I sure do.

Lead Dev for a Security Company with a very strict AI policy.

Mostly Hand coded, using an agent in the browser (Claude / Corporate ChatGPT account) when necessary. I am aware we will fall behind using this methodology and have advocated for change, but I suppose it comes with the territory.

I feel it’s important that this should be mentioned at least once in a thread like this: none. I choose to program the old-fashioned way, and do not anticipate this changing in the foreseeable future, and believe that I’ll cope just fine in my niche; and if it becomes commercially unviable, well, I may no longer be interested in the field anyway.

I won’t go into any details on why here, because that would make it too much about me. There have been plenty of discussions of reasons, trade-offs, &c. Plenty of people are rejecting this stuff, for a wide variety of reasons.

But one thing I will say: if I were teaching someone to program, I would actively discourage them entirely from using AI stuff, even though it will seem to help. (I mean someone that wants to learn programming, not someone that just wants results and is not interested in programming as such.)