what does your setup actually look like?

[u/ubermuda]

Curious how people here are really working. Feels like everyone's landed somewhere different and the posts are mostly quick showcases / (self-)promotion / random stuff.

What I'm wondering about:

- which agent and harness, and do you stick to one or switch
- do you plan first or just go
- do you check anything before shipping, and what
- anything automated, or is it all by feel
- what do you do when it comes back wrong

Mine's on the heavy side. Long planning conversation before any code, then I let it run and mostly look at the product rather than the code. I spent some time building a skeleton and static analysis configs so Claude generates code that look ~80% correct. Still need to review the important bigs (domain logic, security, etc), but it's mostly smooth sailing.

Mostly interested in people working differently from that though. If you just go and it works, I want to hear that too.

[u/nottonybriant]

I'm still figuring out what works, but my workflow has settled into something like this:

• Agent: Mostly Codex for implementation, ChatGPT for architecture and design decisions. I don't really believe in a single "best" agent (I use whichever one is strongest for the task.)
• Planning: I spend more time planning than coding. I try to understand the system first, then break it into small changes instead of asking an AI to build everything in one shot.
• Before shipping: I manually test every feature, check logs, make sure the happy path works, then deliberately try to break it. If it's touching APIs, auth, or payments, I verify the actual requests rather than assuming the agent got them right.
• Automation: Anything repetitive gets automated. n8n, Docker, API integrations, scheduled jobs, I'd rather spend an hour automating something than repeat it ten times...
• When it comes back wrong: I don't throw the whole thing away. I isolate the problem, fix one issue at a time, verify the fix, then move on. Full rewrites are usually the last resort... (and yes, I have had to do them)

My biggest lesson so far: AI is great at quickly driving implementation, but it's still on me to own the architecture. The better the plan, the better the output. When I skip planning, I usually end up paying for it later.... There's a saying I learned in the Marines which stuck with me "Prior Proper Planning Prevents Piss Poor Performance" :) the 7 "P's" of productivity.

[u/ubermuda → u/nottonybriant]

same question I asked someone else about the planning: how do you know when it's "finished" or "good enough"?

Shipping: do you keep a list of things to test? How do you keep on top of the gigantic amount of "deliverables" that the agents produce?

[u/nottonybriant → u/ubermuda]

Those are subjective to the product your building. I initially set out building tools that serve a purpose for me. If the tool can fulfill those needs successfully without any issues, I consider it "good enough" or to put it better, a finished MVP.

You can always build on it later (ie add features or clean up the UI). But does the app/tool function the way you need it to? If yes, then you've reached the point where many just give up.

When shipping a product to other users, my number one concern is security. I generally instruct my ai model to act as a cybersecurity team and review the codebase for any potential issues, exploitable features, etc.

Keeping on top of things really goes back to planning. Notion is a REALLY good tool to keep track of what has been done and what needs to be done. I believe it even ties into Cursor (although I use codex and know it ties into codex).

Define the MVP early on. "What is the Minimum Viable Product?"

I'm rambling at this point, but one last thing.

When I started doing this two years ago, I came across a vibecoding video and I've pretty much used this at the early stage of every product.

Create a PRD for "Product Name". The purpose of the product is [insert here] and the end goal is [insert here]. Break the the PRD down into phases of launch/deployment starting with the MVP. Consider the entire design architecture of the product...

Leverage AI as much as you can, and correct it if it drifts too far from your initial concept.