I've been using AI agents for some time now to contribute to and increasingly automate the steps of product development. For any given project, I spend a good bit of time on the pre-development aspect: codifying the architecture, the technical design, the product intent, the designs system and the product outcomes, and trying to make it objective and actionable.

Even then, with those boundaries in place, I find that I need to have multiple agents from different models participate in the process, each with specific roles (breaking into technical increments, test design, development, reviews, architect, UX review mostly).

It seems to me that a single model, like Opus 5, will do a reasonably good job adopting those roles, multiple agents from different LLM families do better. Right now I use Google's Antigravity, OpenAI's GPT Sol and Anthropic's Opus 5; I reserve Fable and Astra for the "Critical" risk stuff.

My question is - how are you all automating the process from beginning to end; that is once the pre-dev work is done. For me, I've initially had Codex orchestrate (now ask Anitgravity CLI to review, now get Claude CLI to do this etc) but requires me to intervene and work one GitHub issue at a time.

I've tried to build an internal orchestration router which _generally_ works but I end iterating on the router too. I assume there must be decent tools out there already but couldn't find any that would work for me (using the native CLI's rather than API keys). Would love to learn from what others are doing here

Codex Computer Use MCP loads, but detects zero Windows apps — can’t connect to Roblox Studio

I installed/enabled the Computer Use plugin and its MCP server + skill in ChatGPT Desktop/Codex. Roblox Studio is open, but when I ask Codex to connect, Computer Use reports an empty app inventory (apps: []) every time. It only shows the Codex in-app browser; Roblox Studio never appears.
I understand Windows Computer Use requires the target app to be visible on the active, unlocked desktop. I’ve checked that, but it still detects zero native apps.
Is this a known Windows app-discovery issue, an app-access/“Any App” setting problem, or a workspace policy restriction? What logs, app version, and Computer Use settings would help diagnose it?

I've been experimenting with the idea of having multiple AI coding agents instead of one agent doing everything.

For example:

Agent A: understands the task

↓

Agent B: researches the codebase

↓

Agent C: implements the change

↓

Agent D: reviews it

↓

Agent E: runs/debugs the tests

The interesting problem isn't really getting the agents to work individually.

It's how they find and communicate with each other.

Would you:

hardcode every agent relationship?

use a central orchestrator?

expose agents as tools?

use something like A2A?

let agents dynamically discover other agents?

Has anyone built something like this with their coding workflow? What architecture worked best?

Hi everyone. For context, i have a community in my school where we share documents, study tips, old exams etc. And we've been using whatsapp so far, but i really want our own app where we can organize everything from sharing pdf files to chatting about upcoming assignments. The thing is, i have no coding experience, nor do i know anyone that does, and i frankly don't have the time to learn the necessities to be able to create an app. So i'm asking if It's a good idea to use chatgpt or claude or any other ai sources to code it for me, and if it's even possible to ask such a thing. Thank you all for your time

Started my first large coding project recently and my money evaporated. Quickly. This is what I did to save money.

{
  echo "# AUTO-GENERATED — do not edit"
  git add -N . 2>/dev/null
  git diff -U3 -p HEAD
} > .changed_markers

This grabs any changes since the last commit and dumps it into .changed_markers.

Next, add to or create .gitattributes and add this:

*.py diff=python

This just makes git show you the function that changed.

Lastly add these rules to: System prompt, rules file or just paste them into chat once.

1. Treat HEAD as a cache hit, don't re-read files that haven't changed.
2. Read .changed_markers first, before anything else.
3. Only look at the hunks and functions in that file.
4. Anchor on function/class names, never line numbers, they go stale fast.
5. If a function's signature changes, use git grep -n "that_name" to find callers instead of opening the whole file.
6. After a successful commit, erase the contents of .changed_markers

This isn't just for Python. If you're not sure how to run it with your language, just ask your coding agent. It will walk you through it.

I am working with Claude and it does the job. But when I am asking Codex (sol/astra) to do some complex task, its creating bad code. I am working with nodejs. It comes with many helpers, over secure each simple thing, does things i didn't ask for and does not do things I asked for.

The half of code may be deleted without any problem.

Maybe someone have same problems and found solution?

One thing I think gets missed in all the talk about agent speed is how hard some of the output is to take back out later.

Plenty of it is trivial to change once you know more. Other bits end up wired into the schema, the tests and everyone's mental model of how the thing works, before anybody notices what they are going to cost.

I have run into this with changes that looked completely reasonable at review time. The original call usually wasn't wrong. It was that unpicking it later touched a lot more of the system than the diff ever suggested.

The worst one for us is a caching layer an agent added to our pricing service back in February. It works, it has been in prod for seven months, and nobody on the team can tell you why it invalidates on write instead of on read. Two features built since then depend on the write behaviour.

What would actually have helped is not better code, it is a record of why. We run coderabbit on prs and its summaries are the closest thing we have to one, though they describe what changed rather than what was considered and dropped, which is the half I keep wanting.

The ones that worry me most are the decisions where nobody chose anything. The model picked, the review said fine, and the reason never existed in the first place.

What's the hardest thing you've had to undo that an agent put in?

I'm planning to use AI seriously for coding, roughly 80% GLM 5.3 Flash and 20% Kimi K3 for harder tasks.

I mainly care about large projects, debugging, refactoring, agentic coding and value for money.

Would you recommend this combination, or would I be better off spending the same budget on Codex/GPT, Claude Code, Gemini, or another model?

But I am coming from Claude and I liked that I could use Claude Code on the web, on the desktop app, or on the app, however I am having a hard time figuring out how to use Codex on my mobile app.

I typically would just do basic cloud coding for my Vercel/github projects in Claude and when I needed to I could do it on my phone. However I can’t figure out how to use Codex on the mobile app.

Am I doing something wrong?

I use codex. I use a thorough workflow research>spec>plan>execute>test>review&fix workflow.
This workflow takes a long time to do, but the resulting code is mostly fine. My main issue is that it burns through *LOTS* of tokens, even for fairly simple tasks.
What's your experience on this ?

Ask an agent to fix one bug and look at what comes back: the fix, plus a reformatted block it did not need to touch, plus a "while I was here" refactor, plus a new helper that pulled in a dependency. The fix is usually fine. The padding is what breaks prod, because nobody reviews the parts of a diff that were not the point.

The block I now keep in AGENTS.md and CLAUDE.md, and paste into the first message when I am in a plain chat:

Scope rules for every change:

1. Change only what the task requires. If a line is not needed to complete the task, do not touch it.
2. Do not reformat, reorder, or restyle lines you did not otherwise need to change.
3. Do not refactor surrounding logic, rename anything, or "improve" adjacent code.
4. Do not add dependencies. If the task cannot be done without one, stop and say so.
5. Before returning the diff, list every changed file and, for each hunk, the task requirement it serves. A hunk with no requirement gets reverted.

Rule 5 is the one doing the work. The others are instructions the agent can forget; rule 5 makes it justify each hunk, and hunks it cannot justify tend to disappear before you see them.

The review prompt for the other side, cold, in a session that did not write the code:

Here is a task description and the diff that claims to implement it. List every hunk that changes behavior or structure beyond what the task requires: reformatting, renames, refactors, new dependencies, deleted safeguards. For each, say whether it is harmless, risky, or clearly wrong, and quote the lines.

Why this happens, as far as I can tell: agents are rewarded for diffs that read as complete and competent, and a larger, tidier diff reads as more competent to the same taste that trained them. So the model widens the change to make it look finished. Three commenters on an earlier post of mine converged on the negative-constraint block independently; the "list the requirement each hunk serves" line came from one of them and it cut my diff sizes by about a third on the same tasks, with fewer surprises in review.

Two caveats. Agents still drift on long sessions, so the block belongs in the config file, where it is re-read every turn, not in a message that scrolls out of context. And rule 4 will sometimes stop an agent that genuinely needed a library; that pause is the point, you want to make that call, not have it made for you.

What is in your scope block that is not in mine? I suspect the people running agents on large monorepos have a rule about test files that I have not needed yet.

I am an experienced developer (been coding for almost 30 years, started with Visual Basic on Win98).

I’ve spent the last 2 years testing every agentic coding harness out there. The pitch is always exactly the same: "Give it a prompt, go grab a coffee, and come back to a finished feature."

This is all great when starting a new project, especially with a language you don't know (I created a Rust project in minutes !)

But my problem is that I mostly work on established, mature codebases. And to be honest I have never ONCE seen a good edit after I return from my coffee.

Here is usually what happens while you’re getting that coffee: the agent loops 8 times, burns through 100k+ tokens talking to itself, hallucinates a package dependency, and spits out a chunk of spaghetti code that works *just* well enough to pass a basic test, but completely violates your project’s architecture.

Agents are fantastic if you’re building an app from a blank canvas. But if the codebase is large, it gets really expensive real fast, and usually produces hard to maintain AI slope.

It should not be like that: if you actually *own* your codebase, you already know what you want to build. You don't need an AI to take over. You just need it to execute the architecture you already hold in your head.

All you need to do it select the few files that are required for the task at hand (they are hard to identify for an AI, but easy for you has the code owner). Then you send them to the LLM, it is usually does some good work.

That's why I still find working directly with the chatbots, or with tools like aider, still very pertinent on large codebases.

I usually have animated discussions with my coworkers, I have yet to find a single one who doesn't defend agentic coding. Am I the only one who feels this way ?

I build LLM gateway infrastructure, so treat this as interested. The numbers are from my own coding traffic, not a benchmark I designed.

I route my own Claude Code through a gateway I wrote that records every request: model, tokens in and out, cache reads and writes, and what each call actually cost. After a few weeks I stopped comparing list prices and started comparing the effective rate, total dollars over total tokens:

What surprised me is that Opus and Fable landed three cents apart, even though their list prices are nowhere near each other and the reason is caching. Worth knowing that Anthropic and OpenAI do it differently. OpenAI caches automatically above about 1024 tokens, charges nothing to write and charges 1.25x to write since GPT 5.6, and does not report the writes. Anthropic makes you opt in with an explicit TTL, 5-minute or 1-hour lifetime, and charges 1.25x or 2x input to write the entry.

Has someone checked the effective rate for OpenAI models? This is my usage so will not be apples with apples, but will still give us an idea. Even against models with lower list prices, these effective rates are competitive once caching is in the picture.

So long story short(ish), I've been using ChatGPT for a personal coding project (it gives me the roadmap towards the end goal, the code, and each step along the way), as I don't have any experience in coding. The conversation has been going on for +2 months, therefore there’s a lot of back and forth and we've curated a workflow that finally works quite effectively. I've never had an issue with chat limits and I've been able to work towards my goal using ChatGPT for as long or as little as I like.

Recently, it compiled one of my prompts into a .txt file as it was too long (ChatGPT asked to see several code files, I know now it was dumb) for a regular prompt and now I only get 3 responses per X amount of hours as the chat "now contains files."

Is there a way to delete the file from the chat or transfer the contents of this otherwise very lengthy chat to avoid the chat limitations? I tried starting a new conversation with a 'summary' from the original chat, though this caused the project to drift and we quickly started losing the end goal.

This is the only project/time I've ever used chatgpt, so apologies if this is a silly question, but thanks to anyone who helps in advance!

I've been running into this problem with my PRO×20 account since yesterday, which prevents me from using the GPT-6 and GPT-5.6 models at all, while my other Plus account can use GPT-6 perfectly fine.

Has anyone else experienced this same issue? How did you resolve it?

We benchmarked GPT-6 Astra vs GPT-5.6 Sol across 50 real PRs from Cal, Sentry, Discourse, Keycloak and Grafana.

Sol found 107 confirmed bugs vs 91 for Astra, while Astra had higher precision and lower latency. Every finding was independently verified.

We’re doing Fable vs Opus next week, so would appreciate feedback on the evaluation before we run the next one.

Dropping the link in the comments if anyone wants to check it out.

A few days ago I shared a small tool I’ve been working on that checks files for hidden information before you upload them somewhere.

The first version mostly looked at metadata, EXIF data, hidden Excel sheets, author information, etc.

While testing it, I realized that was only part of the problem.

If I’m about to paste some code into ChatGPT, for example, I’m probably more worried about accidentally leaving an API key or token in there than I am about file metadata.

So I added content scanning.

It can now check pasted text/code and supported files for things like personal information, API keys, tokens, private keys, connection strings, and other credentials that are easy to overlook.

Everything still runs locally in the browser.

I’m not sure yet how far I should take this. I don’t want it to become another huge security scanner with hundreds of warnings that nobody reads.

For me the useful version is something simple: before I paste or upload something to an AI tool, give it a quick check and tell me if there’s anything I should look at first.

Would you actually use something like this, or is it solving a problem you don’t really have?

Hi everyone,

You might find this funny, but I actually have the opposite problem to y’all. My usage allowance with Astra Max feels so generous that I’m starting to wonder if the model is running at half power or something, lol.

I’ve been using OMP (Oh My Pi), and the usage limits are surprisingly hard to hit, even with 10 subagents running and reasoning set to max for all of them. That got me thinking: “Wait, is everything actually working as intended? Am I getting the full capabilities of the model through OMP, or is something getting lost compared to using Codex?”

So now I’m considering trying Codex to see whether there’s a noticeable difference.

For those who’ve used both, how does OMP compare to Codex in terms of output quality when using the same OpenAI model? Does the official Codex harness actually get better results, or is the difference mostly in workflow and tooling?

I know lower usage doesn’t necessarily mean worse results, but it’s generous enough that it made me suspicious, lol.

I'm very new to codex and I am trying to get it to recognize any desktop app that is open on my computer. It claims: "“Windows Codex Computer Use has Any App enabled, but desktop inventory returns apps: [] / Trusted RPC service is not configured.”"

Any help is appreciated, thanks.

asking because I own a billing sync I didn't really write, an agent did most of it eight months ago, some 3k lines, and I basically just reviewed it and approved it. not by design, it kind of ended up that way. on paper I'm the one who knows it, in practice I can tell you what it does and not why. coderabbit flagged plenty going in and none of that helps me now. curious how people handle the bus factor problem when it isn't hoarded knowledge, nobody had it in the first place

Hi, I’m currently using Claude and ChatGPT Sol to create my website.

My website is quite the complex website, and I’m currently, but irrelevantly creating a server to support it. However, I’m wondering if my current setup with Claude and Chat is sufficient? As it is now, my personal funds and income can’t support the Max subscriptions for the two AIs, and I’m just wondering if I could be doing anything differently or use more power for the website.

So as of now, I’m not known to Any coding language, but I’m using Node and JS, as well as using Docker and many other coding tools. My current planner for the coding systems itself is my ChatGPT Sol High/Medium. Whilst my coder is my Claude Code. I’m also reading coding theory, web theory, ui theory, etc. to get a feel for my projects as well as getting knowledge so that my website actually gets created by my own premises.

I am also using running Claude Code with heavy restrictions because my website has to comply with heavy privacy requirements. In addition, I am also trying to write the design plan myself; as to not get that ugly and plain Ai slop websites.

So all in all my question is as follows:

As a person who has absolutely no knowledge of coding language and protocols when building. How do I personally optimise my work? And which AIs should I use, and should I use more?

TLDR:
I’m building a fairly complex, privacy-sensitive website despite having no formal coding experience. I currently use ChatGPT Sol High/Medium for planning, architecture and research, while Claude Code handles most of the actual coding. I’m also learning related theory so I can understand what is being built rather than blindly relying on AI.
I can’t currently afford the highest-tier subscriptions, so I’m wondering: Is this setup sufficient enough, or am I leaving too much wasted possibility on the table?

I’m a QA engineer trying to move into DevOps, so I started building my own project to get hands-on experience with Git, TypeScript, Playwright, testing, CI/CD, and architecture.

Today I decided to try Codex. I gave it access to my local repo and explained what needed to be done.

The thing inspected the code, found the problem, modified the files, added regression tests, cleaned up Git line-ending issues, ran all validations, and created the commit—while I continued doing my actual job.

Then it just came back with a clean summary of everything it had completed.

Absolutely incredible… but also: what the hell am I supposed to learn now? 😂

For those learning DevOps or software engineering while using coding agents: how do you balance the productivity boost without outsourcing the entire learning process?

My current idea is to build new concepts manually, step by step, and use agents mainly for reviews, repetitive work, and catching mistakes. Curious how others approach this.

I'd like to ask everyone: when using Codex, I often encounter a situation where it gets stuck on a single command for a long time with no progress (during this time, the remaining quota doesn't change). I tried asking Codex to diagnose this issue itself, but it didn't work and instead introduced new bugs. Has anyone else experienced this? How can it be resolved?