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

Codex pretty much the only tool I use now

I use OpenCode with a three agent combo (architect, developer, reviewer), as I've found it's crucial that different models write the code vs review it.

More details here:

https://www.stavros.io/posts/how-i-write-software-with-llms/

I'm a bit of a fanboy, but exe.dev + their Shelley web agent is pretty great

I’m already doing this with my school (givedirection.com) and you’re gonna have a hard time nailing this down because there’s no two similar set ups

Especially along the range of newbie to expert it’s extremely variable and you’re not gonna be able to pick one that rules them all

I would suggest you revamp your approach and have different courses for different types of people I had to split my course into a basic and an advanced and they are extremely different

Even within the advanced course fairly simple stuff like hosting your own LLMs seems to really be a stretch for a lot of people

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.

1) OpenCode. Despite its many shortcomings and constant bugs, it's good enough for basic work. Use the web interface so you can switch around sessions easily, keep it running in the background (on a VPS for example). Add to it MCP servers for other systems you use, like AWS Docs MCP, Atlassian MCP, Linear MCP, SearXNG MCP, Playwright MCP, etc, which basically multiplies the AI's usefulness. Tune config to block all use of /tmp/ because the AGENTS.md instructions to not use that dir are always ignored. Set API tokens in env vars for most services and modern AI models will figure out how to use them (cli, api, etc).

2) OpenCode Go subscription, backstopped by Chutes Plus subscription, OpenRouter with $10 in credit (for very rare use of SOTA models), and various free providers (options: https://codeberg.org/mutablecc/calculate-ai-cost/src/branch/...). I almost never run out of the OpenCode Go subscription so I'm not a heavy user. Use Kimi K2.6 or GLM 5.1 to develop plans or do complex work, DeepSeek V4 Pro for simpler planning or less complex work, DeepSeek V4 Flash for implementing plans or doing simple tasks.

3) Some kind of 'ticketing' system for the AI. At work we use Linear, at home I tried Beads but I didn't like how bloated it got, so I made my own (https://codeberg.org/mutablecc/dingles). Important to have a way to plan work, persist the plan, and work on each item til they're done. In general you're going to use your AI coding agent's Plan Mode to first build a plan around anything you do, and tell it to ask you questions to align on the solution and use question-asking tools for convenience. Then when the plan is correct, have it make all the tickets. If your context window is nearing halfway full, start a new session to begin working on the tickets, and have it commit and close them as it goes.

4) Craft an AGENTS.md (or find somebody else's) that explicitly uses TDD to craft tests. You write the test first, and verify it looks like it will actually check for the expected results; do not continue until your tests look valid to you, the human. Commit them when they look good. Then have the agent write the code to make the tests pass. If you don't do this, it will churn out tests that pass but don't actually identify when things break. You also need end-to-end tests to actually run the app and verify it works, via Playwright, screenshots, running CLIs in Docker containers, etc. This is much harder to do correctly than just generate seemingly-working code.

5) OpenCode does a decent job at balance between not asking you for permissions, and gating things outside the repo. But it's not really "safe". Your best bet is to run a VM (colima) with Docker container (Ubuntu) and run all your AI stuff in the container. This way you can use "yolo mode" to have the AI churn without you and the only thing it can destroy is the Git repos you volume-mount into the VM & Docker container. (I have some of that setup in code here: https://codeberg.org/mutablecc/ai-agent-coding)

6) If you start letting the AI do remote things (like manage remote Git repo, push, make PRs, etc) it is more likely it will do something destructive (like force-push Git repos with destructive changes, create/destroy cloud resources, SSH into boxes and destroy those boxes, etc). So be very careful not to instruct the AI to do anything remote, unless you have set up read-only credentials for the AI, and it can't somehow gain access to the read-write credentials. This is another reason VMs/Docker are good, you can make sure to only volume-mount the credentials you want it to have access to.

7) There is a full walkthrough of AI coding here that is very thorough and battle-tested (https://www.youtube.com/watch?v=-QFHIoCo-Ko). Watch the whole thing (yes it's long) to save yourself a lot of trial-and-error later.

So, you don’t have any experience in it but want to run a workshop?

I am working on a project/essay/thoughtsphere that is beautifully illustrated by this thread. My project is to help automatically take your patches/workflows and package and rebase on top of upstream using quilt so I can get the latest greatest fixes while keeping my notafork. Forks are expensive, patches are easy.

That I think we're going to see much much more variation in design, software and interfaces as the labor to produce them become trivial. Everyone can patch software to do what they want. Yesterday I had claude rewrite xrdp to allow me to remote into my desktop session without having to deal with x11vnc, it lets me drop in, pick :0 or :1, auth's with PAM and gets me in. What I have always wanted with xrdp that never worked quite right. I have patches for i3, and for vim, and for xpdf, and bash, and mocp, and all sorts of tools and scripts I wrote.

Anyway, here's the site essay I am working up but yeah:

Right now, programming is rapidly becoming not expert work. Soon we could all be running (i think this unironically) practically our own distros if we want. Total customization of the stack.

I really feel that one positive thing AI can do is drive labor costs down enough to allow personal choice in the software we use. We have open source software, but it's channelized and controlled by a few companies who fund projects! That might change too!

AI can simply One Shot a lot of small problems i have. Like reading unfamiliar codebases, finding the relevant function, and writing the delta. The gap between "I want bash to do X" and "here's a patch" is shrinking fast. When that gap closes, a lot more people are going to start customizing their software - but we don't have a great wrapper for it yet.

The part that doesn't get easier is everything after. How many 'forks' exist on github but people havent had time to maintain, or worse, are being used in production with bugs? How much code have we lost out because of that? Do forks really help us? I don't know. Does everyone want to use shitlab? I don't know.

Building the package. Getting it on your machine or out to the fleet. Keeping it there when upstream ships a security fix.

That's an infrastructure problem, not an AI problem I needed a way to solve it now

________ is that little bit of software infrastructure i need . built now, for the world where i am right about my bet.

One is the sword (claude code) one is the shield (codex)

The simplest mainstream options for tools:

1) Claude Desktop which includes Claude Code for Anthropic: https://claude.com/product/claude-code (alternatively the terminal based version; either way get the subscription)

2) Codex for OpenAI: https://developers.openai.com/codex/app (same as above, subscription preferred instead of paying per token)

3) OpenCode for a variety of models: https://opencode.ai/ (they also have a subscription, but this in particular also makes it really easy to connect to OpenRouter)

4) KiloCode is essentially the above, but for VSC derived editors: https://kilo.ai/ (I personally liked RooCode more, but that got retired)

More niche tooling options:

1) Zed is pretty good, though I saw some issues with their LSP Edits and found that connecting them to OpenCode through ACP worked better, still a cool editor: https://zed.dev/

2) If you have to pay for tokens and can't get subscriptions, look at DeepSeek as a provider (V4 Pro with Max reasoning): https://api-docs.deepseek.com/quick_start/pricing

3) I'm also writing a launcher to make running Claude Code with 3rd party providers earlier, early days still: https://ccode.kronis.dev/

Note: for anyone on Windows, if you install the terminal versions of the tools (Claude Code, Codex, OpenCode, ...), you probably want them inside of WSL so there's less confusion with file paths etc. that some models have.

In regards to actually using the tech:

  - version control and maybe worktrees
  - sub-agents are pretty nice to have, Claude Code also introduced support for longer running workflows
  - throw as much tooling as possible at the project, like Oxlint, Oxfmt etc., for Python it might be Ruff and ty or Pyright or whatever
  - throw as much testing as possible at the project, maybe require certain coverage or just have CLAUDE.md that nudges the models to write and run tests
  - throw as many additional scripts at the project as you want, e.g. how you want the architecture to be laid out, max file length limits etc., whatever common tools don't cover
  - some tools also support LSP, use those when possible
  - pretty much all models will still output slop, though making fresh instances (even of the same model) review its output, e.g. 3 parallel sub-agents looking for critical/serious issues works pretty well, I just have a review loop that I make the models run before commits
  - ideally you'd also test local instances of whatever you build (e.g. real PostgreSQL instance etc.), just so the dev loops are tighter and faster

Tldr; just teach them to talk to Codex and show Pi to ambitious ones.

Raw Codex, both app and cli. On windows 11 which is horrible (sometimes in WSL, which sometimes crashes for no reason when I copy a lot of data around and somehow reverts contents of a mounted vhd virtual drive to previous state from long ago, after a crash). I'd love to switch to linux but I'm an avid gamer. I installed a linux on my old box though and some of my AI jobs run there. GPU there is two generations back but it still has 24GB of VRAM.

Bare Pi if I have a cool idea on how to extend the harness. I don't use any skill in Codex but I ask to create some for Pi to go with the extension for Pi I am building at any given moment.

I used to have a kanban skill for Codex (and others) to build large amount of features afk in a spec driven development manner, but recently Codex is doing fine without it. And the last time I used kanban it built diligently a completely wrong thing that I, it turned out, underspecified.

Zed if I'm really inclined to view any files. But basically it's my text file viewer because it's marginally faster than the modern Notepad.

About 5 different web browsers, because they all suck. All crammed with tabs going back months.

Language, whatever. I bounce around between Python, Rust, C#, TypeScript. Maybe I should try something exotic.

Gpt-5.5 xtra high, Glm-5.1 (not recently because it's not as good, I used to like Kilocode with it, in previous major version 5(?), most recent Kilocode is streamlined into mediocrity, although you can still intall old version). Gemma4 on local ollama for specific non-coding tasks. Openai api proxy connected to my Codex sub, for cases where Gemma4 doesn't do that well.

I'm having immense fun by making programs for ad hoc tasks like transcribing a conversation I had this morning in a language I barely know. Or extending my old program that searches proofs in domain of axiomtic logic. Or adding feature to a charting "app" I built few years ago I was too lazy to add back then. Those 3 I did just today.

I can conclude that Gpt-5.5 is a better developer than I ever was or even could ever be (in all aspects) after being a programmer for two decades and being considered pretty good by my peers.

When I need a prompt for something, I ask codex to write it. If results are unsatisfactory I ask it to tweak it. It works very well.

I do image generation with chatgpt-image-2 although I think I'll need to build some tooling around it at some point, like a basic photoshop, mostly LLM controlled. The model itself is not good in basic composing and keeping track of different versions of the same sub-image

Sometimes I go to chat.com and ask for deep research on some subject an put the result in my project dir for Codex to find and learn from.

I don't use skills or MCPs. I always --yolo.

I release nothing. Even if I build something that might have wider appeal, I firmly believe that anyone could build it as well. And effort needed to find what I built and check if it fits somebody's need exceeds the effort they would need to extend to build it themselves exaclty as they want it. That's my experience. Human accessible internet, including Google is 50% dead for me already. I delegate the drudgery of browsing it to Codex or chat.

All I do is mostly for my own amusement. I have as much fun with it as with playing games. Possibly even more.

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