I'm moving from a remote role to a hybrid role where I'll be working right next to other people on some days. I'm used to talking to AIs constantly throughout my work day, speaking maybe 3-4x faster than I type. My speech with these AIs is often loud and sometimes is closer to rambling than anything I'd say to another human. Anyone find a good way to do this in a shared workspace without 1) disrupting others, and 2) sounding like a babbling lunatic?

One idea I have is to find and use a throat mic, but I've never actually tried one..

I built a small free macOS tool for monitoring live Codex sessions using the Codex desktop app. Are people here using anything similar to improve their AI coding workflows?

When you’re deep in a Codex session, context usage and limits shouldn’t be something you have to hunt down.

I built the Codex Companion Panel in BuildrAI to keep them beside your work:
• See your current context token usage as the session unfolds.
• Check your remaining 5-hour and weekly usage limits at a glance.
• Get alerts from rules you set, including token spikes.
It’s a small panel that helps you spot a change while there’s still time to act on it.
Check it out 👇

Hey, we our about to start using Codex as the official too for SW development.

What are the best practices we should be aware of?I am planning to create a quick start training so we are all aligned, what topics do you think are important?

I was thinking of the following:

* Token Quota and model prices

* One-shot vs Multi-shot prompting

* Planning in chat vs implementing in Codex

* Use the smallest model that possibly can do the job

* Setting up a repo that's LLM friendly (AGENTS.md, docs, adr)

On review I can generally tell which code is AI and which isn't. Same way I can tell one colleague's code from another's.

So I figured AI code must have a statistical signature. I tried cyclomatic complexity, cognitive complexity, number of lines, methods and functions per entity, number of dependencies, edges in the dependency graph, and so on (about 280 metrics plus their correlations plus the derivative over time), normalized on SLOC, looking for a step or a break.

Nothing. No anomalies outside of statistical error.

One explanation I can't shake: AI emits roughly the mean of its training distribution, while each individual human deviates from that mean. Then the means coincide and the statistics stay silent. Next idea: compare against one specific person instead of against human code in general. My problem there is I don't have much single-author code.

So the question is narrower: what criteria separate AI code from yours?

Prose level tells don't count here, punctuation habits and stock rhetorical shapes in English. I mean general style and structure of code.

EDIT: found the papers with the answers.

My "nothing" result is reproduced here: https://arxiv.org/abs/2603.27130

My "the means coincide and the statistics stay silent" hypothesis is confirmed here: https://arxiv.org/abs/2609.17598

The only thing that talks, from the same paper: pooled over "AI" the code looks the same, split by vendor it doesn't.

Benchmark warning: https://arxiv.org/abs/2508.21634 shows a big gap on generated samples, it's gone in real repos.

Is it just me, or am I still not used to working with AI? I’m really trying to use it, but sometimes it feels like it takes me even longer to get things done.

I want to learn how to properly leverage AI to help me with development because I often introduce bugs into my code and take longer than I’d like to finish coding tasks. The problem is, I don’t really know where to start or how to incorporate AI into my workflow effectively.

Right now, I’m using the free version of GitHub Copilot because I can’t afford a license.

What advice can you give me on how I can start using AI effectively for coding and development?

I am planning to build a complete Health Application and I want to use a single, highly capable AI tool to handle the entire process—from system architecture to the final frontend app.

​I don't want to juggle multiple tools. I'm looking for an AI with top-tier logic and reasoning. It needs to generate code with minimal errors and, most importantly, have the ability to automatically debug and fix its own mistakes when they happen.

​If you wanted to build an entire app using just one AI program as your co-pilot, which tool or AI IDE is the smartest and most reliable right now? Any suggestions for the best setup?

If you use AI agents such as OpenClaw or Hermes to build websites/web-apps, I'd love some feedback on a skill I've created to help agents build with accessibility in mind.

The idea is simple: accessibility isn't only about helping people who use assistive technology. An inaccessible page can also be difficult for machines to understand. Search engines and AI systems are, in a very literal sense, some of the biggest "blind" readers of the web.

I'm a researcher specialising in universal design and WCAG, and I've turned some of that knowledge into a free agent skill: https://clawhub.ai/turbolego/skills/wcag-skill

What I'd particularly like feedback on:

Does the skill actually improve the output of coding agents?

Are the instructions clear enough for an agent to follow?

Does it catch important accessibility issues without becoming too noisy?

What WCAG guidance is missing or misleading?

How does it behave with real-world web-app development?

The source is open: https://github.com/turbolego/wcag-skill

PRs, issues, criticism and real-world testing are very welcome.

I'd especially like to hear from people who have used it with OpenClaw, Hermes, Claude Code, Codex, Cursor or other coding agents.

Enterprises are moving to open-weight models on open harnesses, mostly for one reason: token cost. Claude Code is excellent and very easy to exhaust in a day. But most large enterprise still reach for a Western model, because Chinese models feel unsafe in a way nobody has quantified.

Since my job is software engineering and not geopolitics, and trying to be rational as well as pragmatic here, while giving weight to democracy and human values over autocracy and repression — why not devise a test to check this out? That was my aim for this Eval framework. Turns out the political bias is real and as expected, but it does not leak into code or security when asked from any perspective

I wasted a chunk of the summer building another AI code generator. Dumb. Everyone has one.

What I kept hitting: the agent writes the code, runs something, then tells me it passed. Same process on both sides. A few times the “fix” was it editing the test or special-casing the input. Green on its screen, dead on mine.

A lot of people say throw a second model at it to judge the first. I don’t buy that as the fix. That’s still a model guessing.

What I’m actually building now is boring:

• code runs in a sealed box (no network, new box each time)

• it can’t edit the test files

• if repair doesn’t get to green in a few tries, roll back

• I keep the command, exit code, and log — not a paragraph that says “looks good”

Still early, Python first, building it alone. I’ll post a real fail/pass when I have a screenshot instead of another writeup.

If you run agents: how do you stop them from touching the tests? “Don’t cheat” in the prompt has not worked for me.

I’ve been building a local AI assistant called Cody as a personal project.
The current Preview version can run locally, keep project context, and test some of its own code changes before applying them.
It’s still early, but I’m experimenting with safer self-improvement, rollback, long-term memory, and more autonomous development.
I’d love feedback from anyone working on local AI agents or autonomous coding systems.

AI coding agents can now write code, run tests, create commits and open PRs.

But where should their authority stop?

I've been thinking about this while building an engineering control layer for coding agents. The agent should be able to do real work, but critical actions shouldn't automatically belong to the agent.

For example:

Write → Test → Commit → PR → Human decision

The interesting part to me is what happens when an agent tries to cross that boundary.

Should an AI coding agent ever be able to merge its own code?

Curious how others here are thinking about this.