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

I am like you were late to the AI party, and still find it hard to give up on coding and let the AI do everything, however i learned to trust the AI a little in the past few months.

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.

Curious how many people are finding that context management has become a bigger bottleneck than model quality.

We've experimented with a few different workflows and the biggest failures usually aren't because the model can't code—they happen when the model loses track of project conventions, previous decisions, or why something was built in the first place.

Has anyone found a workflow that solves that well at scale?

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.

MacOS, Ghostty, Neovim, Pi (with a fair bit of customization to each). I'm relatively new to Pi after using Codex pretty heavily, but it's nice to be able to customize things to how I want.

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 have a vibe coded script which creates a git worktree + zellij pane with a specific layout + a virtualenv per feature. "tmuxinator" style.

The zellij layout includes panes for OpenCode, a shell, a neovim, inotify tests, etc.

I cycle through the zellij sessions during agent prefills.

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.)

Claude code + very opinionated type script. Try to push as much as possible as far left in the SDLF (types -> lint rules -> tests -> md) and try to improve the dev ex after every single PR.

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

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

I wrote my own tooling around the raw LLMs:

I can tick files in Vim, those get concatenated into a prompt. Along with a feature request. Plus an instructions file that tells the LLM how to reply. Plus my general "rules for good code" file, plus one "rules for good code" file per language involved, plus a project specific overview file. The LLM then answers with a list of changes it wants to make to the code. My tooling then applies those changes and I look at them via "git diff". If I like it, I commit. If not, I change one of the prompts and start the process again.

Instead of replying with code changes, the LLM can also decide to request more files. I wrote a little DSL for that.

I described the beginnings of this workflow last July:

https://www.gibney.org/prompt_coding

Feels like an eternity ago. I think I will write a new blog post this July and describe how the workflow has evolved over the past year.

My stack is really boring, just VSCode + Ghostty and Claude Code team plan (premium seat).

Stanford University offered the course "CS146S: The Modern Software Developer" in Fall 2025. Check it out if interested. https://themodernsoftware.dev/

Something different that other folks might not have thought of: Robust multi-environment infra deploy scripts that leverage terraform + AWS SSO

I've found that converting stuff that's previously been very ops-cli heavy into very detailed skills has worked really really well.

I use Claude Opus 4.8 + Conductor as my daily driver

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

There's lots of ways. You have to upskill through the stages IMO. Write code, write w/ agent, write w/ multi agents, write w/orchestrators.

My way is to just run a giant AI agent factory engine and make the agents full flow do everything. (plan long term, write prd, task, review).

Here's ~4000 commits in last month as an example, i have about ~10k ish including private/work stuff? https://github.com/portpowered/you-agent-factory/commits/mai...

The premise when you get to full automation generally is you go full industral engineering:

1. watch overall flow, improve process via continuous improvement

2. work via checklists and gates.

3. replace process with mechanisms as much as possible (code > agents)

4. optimal throughput is continual testing and iteration (CI, CD), coverage, full e2e tests, mock everything, general best practices really.

decent blog: https://openai.com/index/harness-engineering/

general points:

- build lots of linters

- document literally everything (arch, prd, best practices in repo)

- too many agents at the same time makes lots of code conflicts, so need to consider architecture of code how to maximize concurrency.

VS Code + Claude Code (or Gemini CLI) + GitHub + Docker + FastAPI/Python, using an AI-assisted workflow where I plan features, generate code, write tests, review/refactor everything manually, and then deploy.

Currently using Arch Linux with VsCode and as server, I am currently going for vercel for no cost.

I am using Spec Driven Development approach implemented as a Claude Code plugin since Feb for all mid + size tasks. The idea is to write detailed specs first using agent help doing research and interviewing, decompose the task into smaller subtasks, write detailed spec for each task, implement each task separately. You can restart the session after every step in the workflow and after each subtask implementation since all requirements are materialized in specs. This helps to keep session context focused on a single task at time, improve adherence, reduce cost and allow to implement bigger tasks that are hard to implement with pure plan + code.

Discussion on hn: https://news.ycombinator.com/item?id=48231575

Repo: https://github.com/sermakarevich/sddw

Slides: https://docs.google.com/presentation/d/1SjKXF7hkoqyiN9-3tBGY...

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

Self made TUI that just lists LXC containers.

I have a base container.

"A" to make a new instance.

Pi.dev when I hit enter on any container. Hot swap anthropic enterprise and openai and openrouter as needed.

Every container has the dev env already running for my current projects. Iterate, rarely use vim when needed, spec driven and have llm draft prs for me then I review.

I know the codebase in and out so what I want done is on bypass mode and then I review closer at the draft PR step before marking ready for the team.

Codex pretty much the only tool I use now

My setup has settled into three layers. Cursor for code, a persistent-memory agent for async ops (email triage, calendar prep, recurring context), and the Claude API for one-off heavy reasoning.

The persistent layer is the real unlock. Once the agent knows your open threads, recurring contacts, and meeting cadence, the per-session context tax mostly disappears. Most days I'm reviewing the agent's drafts rather than retyping the situation from scratch.

Still working out whether to consolidate providers or keep the layers purpose-separated. Consolidation simplifies billing but tends to optimize for the average use case rather than the one you actually have.

MacOS, Ghostty, Tmux, Neovim, Workmux[1], OpenCode/Claude Code, and lots of markdowns.

1 - https://github.com/raine/workmux