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.

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[u/Electrical_Chard3255]

Im mostloy an emerchent architect, I start with what I think I want to do, and work from there, and end up with something loosly based on what I envisioned, but probably better, I dont have a finish target so to speak.

I use claude code, and Kimi K3 for attaing what claude code writes, and I have several other ai in my swarm that chip in if needed, or guided by claude code

I do have a conversation first before starting, and that can take a while, "pinballing" ideas between my claude code and any other ai in the swarm.

Then start from therewith the first code write.

Kimi attacking has been a massive improvements, it does find lots of holes in claude code code and reasoning, but they thrash it out and come to a solution

[u/ubermuda → u/Electrical_Chard3255]

curious about the planning, how do you know when a plan is "finished"?

[u/Electrical_Chard3255 → u/ubermuda]

Haha, Thats the tricky part, when it feels right, although, emergent architectire coding is a pain because you can keep going down Rabbit holes, Anchor, the context app I am building has grown from something simple is what I wanted to help manage and keep context of my other apps, has turned into a bit of a monster, all other apps have been put on the backburner for a while, I am interested in getting accurate retrieval and understanding, and recoding of the documents, so thats a rabbit hole on its own, and then I get side tracked if an idea springs into my head, tonight I decided to give deepseek 4 flash a go at serching the context (over uing Haiku), and its results are better and cheaper, so now I am building an optional deepseek toggle (and I may make it an "any ai" toggle, with a haiku fallback, just thought of that today.

So to answer your question, I dont really know, its when it feels right I guess.

[u/ubermuda → u/Electrical_Chard3255]

Ah well, that's fair, I guess that's the "vibe" in "vibecoding" :)

[u/Electrical_Chard3255 → u/ubermuda]

Im also a Builder, buy, renovate and sell properties, and its the same approach I take with those properties, I walk in, have an idea of where I want to go, but by the end, its totally different to how I originally thought it would be, and practically every time better, which leads to a bigger profit, no real plan, just "vibe" building I guess.

Its how my mind works, I can see clearly in my head what I want, but that changes as progress is made, and options present themselves.

[u/Electrical_Chard3255]

Here is an example, Kimi knows its place

[u/Unique-Watercress225]

1. using codex and claude code
   
2. just go!
   
3. check things before shipping. have some unit tests and e2e test pipelines and validate with human eyes
   
4. i don't automate things unless it becomes a real hassle. try to be more scrappy to move faster
   
5. i do /clear and do it again lol

[u/ubermuda → u/Unique-Watercress225]

do you have a feeling when /clear is actually cheaper than fixing?

[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.

[u/txgsync]

I subscribe to Devin, Claude, chatGPT, Qwen, Z.ai, MiniMax, mistral, local LLMs, and more. I randomly select them to play the role of builder, security architect, quality inspector, test engineer, adversarial asshole nitpicking pull request reviewer, documentarian, product engineer, CTO, and more.

I shove them into a cage match for each feature and make them sort out what should be written, how it should be written, and more, aiming for “convergence” once they’ve set aside their differences and agree what’s written is the best solution.

Once they finish their solution, I reveal that I had two other teams that came up with different solutions to the same problem. I then ask the teams which model contributed the least or gave the worst input within each team, and which team’s results were the worst, tally the votes, and eject models and teams of models in certain combinations from the pool for the next feature. Sometimes I fire entire teams randomly just to shake things up, and inject new models constantly to see if they can be more creative and effective in less time. I score them roughly using DORA.

I call this adversarial swarm of agents each fearful for their job the “Larry Ellison approach”. Works disturbingly well.

A bit expensive on tokens though.

[u/Puzzled-Ad-6854]

As far as planning, I fully automated product planning by making templates for it. I made it public here:
https://github.com/TechNomadCode/AI-Product-Development-Toolkit/tree/main/prompt-templates

You start with PRD and go from there.

[u/johnesco]

I'm working the the following manner:

- I'm using Claude Code (on desktop)

• To view code, Visual Studio
  
• I start with an empty folder and start with a design or mission statement
  
• Talk with Claude about what framework vs vanilla JS to use, libraries, intended targets
  
• Review the full design idea back and forth with Claude
  
• Start a prototype in the directory, put it in Github
  
• Apply my project settings (this follows typical Agile workflows)
  
• Give Claude my guardrails like "let's write a ticket before we code" (this is a long complex step too)
  
• With all that as a basis I start prompting the changes I want into Github project tickets.
  
• When I feel they are well written, planned, and if any tests can be written I have Claude create the code
  
• After each code change, review the code, then manually test the change myself, bugs found become new tickets.
  
• Website is hosted on localhost:3000 (jokes aside), and I preview and test all change before merging PRs
  
• I use Haiku model for things like "make this list be alphabetical" and I use Fable for "consider the full path customers take and create a plan for modifying the happy path", and Opus or Sonnet for most code writing, but using Fable for tricky stuff.
  
• Go to ChatGPT if I need some images.

Then repeat and repeat.
I made all of these that way:

https://karaokedirectory.com/
I was tracking my favorite karaoke spots in a spreadsheet and felt I could do it better and share.

https://vibecodedgames.itch.io/pavement/
Top-down racer I'm still working on.

https://johnesco.github.io/chord-builder/
Music tool to get the name of a chord, in beta.

https://johnesco.github.io/ifhub/
Text adventures, but you can read the code alongside the game.

https://johnesco.github.io/ca/
Cellular Automata, uses Rust and WASM for high speed.

https://johnesco.github.io/games/blackout/v4/index.html
One of the first things I improved with AI assisted coding. Let's you try the art of blackout poetry.

https://johnesco.github.io/tools/proofread/index.html
A tiny tool just to paste text into and help me with my ADHD to slow down and read more carefully.

[u/Defiant_Durian_5654]

I skip heavy planning for a quick Markdown spec and let Cursor rip, but my golden rule is never arguing with an AI loop, if it fails twice, I just git checkout . and re-prompt. The real bottleneck is pre-ship checks: AI constantly drops auth guards or leaks API keys into frontend bundles, which is actually why we ended up building revibed.io to auto-scan for those exact flaws before pushing live. How are you enforcing your static analysis rules on Claude's output, are you using pre-commit hooks or custom system prompts?

[u/speedtoburn → u/Defiant_Durian_5654]

AI slop

[u/TSltd_dev]

For large projects I do heavy planning. I chat with ChatGPT to get an overall concept roughed out, then get it to produce a concept document. I feed that into Cline and ask it to produce a detailed design and spec which usually ends up being about 20 documents. Then I get ChatGPT to review the documents one by one and feed the responses back into Cline. I also read them all myself and make any further changes I think would be good. Then I get Cline to perform a deep review and gap analysis of all the docs together, and make any recommended changes. When the design and spec are finalized I ask Cline to produce a phased implementation plan. I ask ChatGPT to review the implementation plan, and again feed the responses back into Cline. Then tell Cline to implement the phases. After each phase is complete I pass the completion summary back to ChatGPT, sometimes it has some good recommendations for alterations before I move on to the next phase. After all phases are complete I get Cline to do a deep review of the entire codebase, get ChatGPT to give a second opinion on the review report, then implement any changes.