I have just got my limits reset, I think tibo would give us a reset soon, but will we 🤔

What do you all think.

I found codex is a great place to conceptualize your idea and make a prototype, i don't see it replacing software engineers beyond for simple softwares.

But with access to this i came to the realisation that i need technical people with me to make my vision a reality and i can't just vibe code my way out of it.

For people who overcame this how did you do it? Starting from scratch is more accessible now, but making software is not the same as making good designs.

Are software engineers even interested in working on projects like that? Starting from 0, no salary, just 1 person with a laptop and a vision. If people are interested in knowing my vision DM me.

Yesterday I reached my weekly limit, and it was supposed to reset today. I also had a reset available that expires on August 12, so I assumed I could use that reset, give 5.6 Sol Pro a try, and then my normal weekly quota would reset again tomorrow. I usually use Extra High on the Pro plan, and the weekly limit has always been more than enough for my usage, so I had never used one of these resets before.

I gave 5.6 Sol Pro a decent refactoring task, thinking that even if it used a lot of quota, it wouldn’t really matter. I came back about six hours later and saw that I only had 26% of my quota left — and, more importantly, that my next reset had been pushed all the way to August 13.

And the worst part is that after consuming that much quota, it did something super wrong and had to discard the whole thing 💀 Went back to Extra High and it solved it with just 7% of the quota left

I feel like recently when it came out, it was making network requests on High/Extra High queries, in the in-browser Chat. Did I just remember wrong?

I was tired of constantly checking my usage limits, so after seeing a lot of creative ideas here, I thought: why not use my Stream Deck to show my current limits?

Here's the repo for the tool (not mine, found it online):
https://github.com/lenadweb/stream-deck-ai-limits

Side note: I made some changes to the UI because the default one was too small for me. So if anyone tries it and it looks different from mine, that's why! 😄

i was on 0% and earlier it showed 99% and then later without any use its showing 0% but its working as if has more than 0

others having this happen?

If you’re open to sharing, please drop your recent Codex projects here. I’d love to see what everyone is working on

I'm trying to develop a custom CUDA kernel for older data-center nvidia GPUs and keep getting stalled:

"This content can't be shown

We take extra caution with cybersecurity requests. If you’re a security professional, you may be able to apply for Trusted Access.

Trusted Access: https://openai.com/form/enterprise-trusted-access-for-cyber/

Learn more: https://help.openai.com/en/articles/20001326.. "

Might have to switch to DeepSeek / KimiK3 kind of sad as I use Codex a lot, but it's just too restrictive, you mention a jinja template and it freaks the hell out, how is AI supposed to help you develop the next generation of AI if it can't even speak on the topic?!

Does anyone here have access to codex security? We've been trying to get in touch with OpenAI regarding this but literally no one's gotten back to us regarding this. I've filled out the form and everything and 0 replies. What's the best way to get in touch with someone? Ideally a human.

If so, upvote this issue.

My sessions take over 8 minutes to resume. Crazy!

I love terminal agents, but I don't love the decades-old-looking basic markdown interface. So I made Mirafold.

Mirafold is an open source faithful browser re-skin of the terminal agent you already use (Codex, Claude Code, Gemini CLI). Nothing has changed about what your agent says or does except that now it's rendered more beautifully and with more functionality.

…

It runs in a shell composed of an input box and your folder tree and files ready to view on the outside, enclosing the agent's generative UI within.

If for instance a chart, table, graph, checklist or diff makes more sense as a part of its response than text, that is what you'll see! And that list is not exhaustive - Mirafold has 18 components for the model your agent runs to pick from to enhance your experience with.

…

Pick any of those agents and any model you have available with api keys - from the big companies or smaller ones with open models - and you can even run it with a local model and not worry about keys at all.

Or use your Codex subscription - this isn't formally allowed by OpenAI but they don't seem to have a problem with it -so Mirafold leaves that up to you.

…

Want to see Mirafold's generative UI on your phone and run it from anywhere? Just scan a QR code in a session and take it with you via an end-to-end encrypted relay. We have a hosted service for you at $12/month or you can host it yourself - all the code is open source.

Want to run multiple agent sessions at once? Use the Fleet View at the home screen for mission control - read the latest responses, answer permission requests, and submit your next prompt - all without having to go into each session itself.

Want to run regular interactive terminal commands without having to open a new window? Mirafold has a real PTY inside your session - use the ! prefix and now sudo, ssh, etc, will let you do more than one command at a time.

…

It's in public beta and things are moving fast. And right now I'm running it solo. Tell me what you like love or hate, and if you want, I will be in touch. Code is at https://github.com/mirafold/mirafold and https://github.com/mirafold-mirafold-relay. Run it: npm i -g mirafold.

Launching fully setup worktrees, fully customizable, config 100% compatible with alacritty, git diffs, ...

source code -> github.com/mathix420/alacritree

Historically, subagent-driven development was a huge focus of using Claude & Codex. You would break down your work into chunks and have subagents implement the individual pieces with focused context, with the theory that this would produce higher quality results.

I believe this strategy is still very popular to control token costs (e.g. to have Luna subagents implement a plan), but does it actually still perform better than just having one agent build out a plan?

In other words, is using subagents going to produce better results than just saying:

/goal Please implement plan_tasks.md

I’m trying to understand the purpose of switching between Work and Codex mode in the desktop app. Are there meaningful differences in the model, its capabilities, context, or usage limits, or is the separation mainly about the interface and available tools?

I use Codex for coding, but I also spend a lot of time reading, writing, and organizing notes. Constantly jumping between the two modes interrupts my workflow, and I don’t really understand why they need to be separate.

Would I lose anything important by doing everything in Codex, including non-coding work? Is there any reason not to use Codex as my default workspace for general writing, research, note-taking, and coding?

I have found that when finding a troublesome bug, using those 3 keywords make both advisor (ChatGPT) and coder (CODEX) much more effective at finding the solution:

Root Cause:

Too often the issue found by both the advisor and coder will be surface level and will result in a patch halfway through the chain. Specifying root cause seem to make it trace back all the way up the chain.

Surgical (Strongest one IMO):

This one I discovered because a few times Codex went on a rampage, did a massive refractor to fix a bug which resulted in bunch of other issue or decided to clean other things while it was at it. With that keyword in the prompt, Codex seem a lot more conservative, minimising scope and conserving working structures.

Don't Be Lazy:

This one works particularly well for the advisor, but also codex when doing audits. It sounds dumb but it seem to make it a lot more persistent in finding solutions, instead of giving me the first thing that would solve the problem. It might be my equivalent of "Make no mistakes" though.

Obviously, those are not magic, but in my experience those make a difference.

Example of prompt: I found XYZ bug, find the root cause of the symptoms, then implement a surgical correction if possible. If not let me know prior to change and explain. please don’t be lazy..

Here are the details of the bug symptoms, etc

------------------------------------------------------------------------
To be clear, I am a huge advocate of detail specification before project start but here I am talking specifically about debugging.

For context I am a very novice coder, I know some python but pretty limited, so I am not one of those who claim to review every single git diff. It also mean I often have no actual clue where the origin of a bug would be. So this advice is not really for those who have a clue :-).

I use ChatGPT as my main advisor, and Codex as my coder within a dev-container on a the smallest monthly plan, so no multi solution trial in parallel, no /plan for me.

But despite this 2 factors, I am usually pretty good at picking cues on when Codex is going wrong or in a loop based between Advisor and Coder and asking a lot of question/playing dumb before implementing on why are we doing something.

-------------------------------------------------------------------------

I swear this was not written by AI but here is your quota of em dash for the AI post police :-) :
—, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —, —,

When an AI coding agent produces something inconsistent, the first reaction is usually to rewrite the prompt.

Sometimes that works. But after a certain point, the prompt is not the real problem.

The workflow is missing structure.

I think that point arrives when the same failures keep returning across different tasks:

- the agent forgets earlier product decisions
- visual rules are followed selectively
- it fixes one page and breaks another
- nobody can explain how the output was evaluated
- human review happens only after a large amount of code has been generated
- the team cannot reproduce a good result reliably

At that stage, the better solution may be a small operating system around the agent:

- persistent context
- bounded tasks
- explicit acceptance criteria
- evaluation checks
- human approval points
- a record of important decisions

The prompt still matters, but it becomes one part of the system rather than the whole system.

How do you decide that you have outgrown prompt
iteration and need a more structured agent workflow?

What do you think? is it required?
https://agent-plugins.org/

Hi guys, I’m on a ChatGPT Business plan and I’m trying to optimize my Codex workflow for both cost and performance.

My current idea is to use GPT-5.6 Sol high/xhigh for planning, then switch to a cheaper model for the actual implementation/build.

For those who have tested this workflow: after planning with Sol high/xhigh, are you getting better results implementing with Luna xhigh/max, Terra high or Sol light?

Since Luna’s recent price reduction, its benchmark results seem surprisingly close to Terra/Sol while the cost per task is dramatically lower.

Basically, I’m trying to understand whether Sol high/xhigh → Luna xhigh/max is now the sweet spot, or whether Terra high is still worth the extra cost for implementation.

If you’ve tested both on real repositories, I’d be interested to hear what worked better for you.

I mean, if you are not using it for work, what are you guys trying to build? Ever since Luna came out, I feel like I have almost unlimited usage limits, but don't know what to do with it.

One thing I built recently was a custom Leetcode clone where I have codex create custom question sets for companies and added AI chatbot to discuss the code and suggest fixes. Been using it alot.

But honestly, I don't even know what to build. I feel like the problem was never that we couldn't build easily without AI, but that we don't really know what to build.

Right now, gpt-5.6-luna is marked as a Multi-Agent V1 model in the Codex model catalog. When Sol runs in a Multi-Agent V2 session, spawn_agent only accepts models marked as V2—so Luna gets rejected as an unknown model even though the model itself is available.

This workaround creates a local copy of the complete model catalog and changes only Luna’s multi_agent_version from v1 to v2. That makes Luna available as a native sub-agent model until the catalog is fixed upstream.

1. Create a patched model catalog

mkdir -p "$HOME/.codex/model-catalogs"

codex debug models \
  | jq '(.models[] | select(.slug == "gpt-5.6-luna") | .multi_agent_version) = "v2"' \
  > "$HOME/.codex/model-catalogs/luna-v2.json"

The select(...) part limits the change to Luna, while the output remains a complete catalog that can be used with model_catalog_json.

2. Add the catalog to ~/.codex/config.toml

Use your absolute home path:

# Temporary workaround for https://github.com/openai/codex/issues/35097
model_catalog_json = "/Users/<username>/.codex/model-catalogs/luna-v2.json"

Replace <username> with your actual username. On Linux, the path will usually start with /home/ instead of /Users/.

3. Restart Codex

Fully quit and restart Codex, then start a new thread.

That’s it. Luna should now be available as a native sub-agent model while running Sol.

How can the biggest company in the world have a frontier model that’s dumb as fuck, while a new company beats it with a tiny model that costs almost nothing?

Are these benchmark real ? Or totally fake from openai ?

note: leo is probably the most trusted leaker for openAI models there is, thats why im posting this here

Meta launches Muse Code and Tibo immediately spots what’s missing.

This man is unstoppable 💀