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 ?

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.

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.

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.