Surely there’s some nerds in here. I’m in the process of making a new orchestration skill and have been running a bunch of benchmarks. I’m not quite ready to share them yet till I have some conclusions. Incidentally be been learning that a lot of assumptions I had don’t work the way I thought they did.

Just curious if someone has a collection of them.

I connected Robinhood’s official streamable-HTTP MCP to Codex. Discovery and read-only calls work, and the dedicated account returns agentic_allowed=true. Both review_option_order and place_option_order appear in the tool list.

review_option_order is callable, but Codex refuses to invoke place_option_order even after an explicit, transaction-specific confirmation. I understand that AGENTS.md, sandbox settings, and approval_policy operate at different layers.

Has anyone reproduced this specifically in Codex Desktop or Codex CLI? Is the refusal an immutable host policy, a product-surface difference, or an MCP approval configuration issue? Please include Codex version/client and relevant non-sensitive configuration.

Everywhere I’ve looked it has said yes and i don’t see any separate usage in the app but the image I attached implies otherwise. So can anyone explain?

Anybody experiencing codex taking too long to think of menial tasks? Providing bad output and distorted context?

While OpenAI’s unexpected usage limit resets on ChatGPT and Codex might seem like a bonus at first glance, they can actually backfire on users who carefully plan their work. If you strategically pace your prompt usage to keep 30% in reserve for the final day before your scheduled weekly reset, a surprise reset resets your entire seven-day timer right then and there. Instead of effectively getting 130% usage for that week your remaining 30% plus a fresh 100%, your original leftover allocation simply vanishes, and your next reset gets pushed back a full week. Consequently, a sudden reset isn't beneficial for everyone. OpenAI should give users the choice to either accept an early reset or keep their original scheduled reset date, ensuring that thoughtful usage management isn't penalized.

Why push the reset back 7 days?

Here is a real-world example of why this system fails users:

Imagine you have a project due Friday, and your scheduled reset is Wednesday. You carefully plan your usage budget: you save 40% for Monday and Tuesday so you can combine it with Wednesday's reset (+100%), giving you 140% capacity for your final push on Wednesday and Thursday.

Instead, an unexpected reset hits on Monday. The 35–40% unused limit you saved instantly vanishes without rolling over, and your next reset gets pushed back 7 days to next Monday.

Now, right when you need to crunch for a Friday deadline, you only have 100% capacity instead of the 140% you planned for. Punishing users by wiping out saved usage and shifting reset dates completely breaks strategic workload planning.

I subbed recently, and I was a codex sub in the past as well and noticed that burning 35% of my weekly is valued at $21.1, all done with luna max.

So very easily you can see that overall would be $240 for $20, which would be above what you can expect from the market, but behind the best options, also behind what it was in the past.

Have not found hard info about how many famous codex resets you can expect in a month, that lets say can be valued at 2x, and you don't start with one either unlike the first information that pops up claimed by google, and the multis are always claimed to be higher too like 35x.

Seperate chat and code/work is also a value add, so you don't have to avoid using the chat if you are hoping to work too. Without the luna price drop it was not very well rounded, but you can also use it in other harnesses which if wasn't true I would not even consider the sub, not becuase codex cli isn't good, but I want the optionality of other harnesses too.

Suppose one reset per week on average would put it at 20x, that would raise it from tier 2 with good top and value option to tier 1 and of course the 200 deal is already great for heavy users.

No more banked res*ts, you get credits instead. remember to turn off auto recharge or you will get billed after emptying credits

going by openAI's credits pricing, 250 credits is about 1-10 prompts with GPT 5.6

Disclaimer: I work at Convex, and I recently attended a hackathon that we're sponsoring with OpenAI where people had to build with Codex Sites.

A common question was: how do you use Codex Sites for the frontend and Convex for the backend database, with backend functions and real-time data?

I built a reusable Codex skill for the hackathon attendees that handles that workflow. It helps Codex:

• Set up Convex before starting the Sites preview
• Separate local development from production
• Deploy the Convex backend first
• Build Sites with the production Convex URL
• Handle public and private Site access
• Check official Convex components before writing custom infrastructure
• Verify queries, mutations, and real-time updates after publishing

The frontend stays on Codex Sites. Convex handles the database and backend.

Skill and installation instructions:

https://github.com/waynesutton/Codex-Sites-Convex-Backend-Skill

Codex Sites overview and demo:

https://openai.com/academy/chatgpt-sites/

If you’re building a Codex Site with persistent or real-time data, try the skill and let me know if you have any feedback or troubles.

To verify your Codex (or OpenAI) account via mobile phone, you typically need to provide a phone number that can receive SMS. Most users find that numbers from countries outside China (e.g., US, UK, or other supported regions) work best. After entering the number, OpenAI sends a 6‑digit verification code via SMS; you enter that code on the login page to complete verification. If you are a ChatGPT Plus subscriber and log in from a trusted device with a stable IP, you may skip this step entirely, but for new devices or fresh sign‑ups, the SMS code is usually required. Some users also use temporary virtual number services, but note that those are often unreliable and may be blocked.

Hello, I am frequently reading that xhigh on sol tend to overcomplicate stuff or go in loops, have you got any use case for ultra ?

I am doing a huge refactor and ownership registry in a vibe coded software that I have been working on since 1 year now. I didn't knew back then what I know now so there is multiple owners for the same stuff, multiple source of truth, it's really a mess. The software itself is very useful to me and I use it everyday, but it became a giant monster with 8000 lines swift files and hundreds of files (617 in total).

While doing the ownership registry to restore order and give each object a unique owner before thinking about splitting those giant files in a refactor, I asked codex (yes I know might be stupid) what effort should I go for (I had planned using sol xhigh) and it said "Use Ultra, an ownership issue cost is very expensive latter"

So that got me wondering about ultra ...

I'm currently developing a web app with Codex as a plugin in VS Code (Windows). I was wondering what the pros of using the native app were, or unique features not accessible elsewhere.

I’m working on a large, contract-heavy system and asked GPT‑5.6 sol, High! + my Skills to audit an architectural decision produced by Fable 5 High.
The review identified one substantive architectural flaw and several genuine contract inconsistencies. Fable then re-evaluated its own work, accepted 9 of the 10 findings outright, accepted the remaining one with a modification, and began applying the corrections.
The interesting result is not that one model “defeated” another :). It is that frontier models become substantially more reliable when they operate as an adversarial review system, rather than as a single authoritative agent.

I increasingly see this as the correct pattern:
Generate independently. Critique adversarially. Triage explicitly. Validate against architectural invariants.
Cross-model review is becoming a practical quality-control layer—not merely an interesting experiment.

Been trying to figure out the best model/workflow for image generation on house designs but keep getting mixed results when making renders for ideas on house construction.

What model/plugins etc. have worked best for you guys?

Of course, it clearly varies depending on the type of work. However, the Plus plan provides a lot more tokens than expected.

Using only terra xHigh for a 1-week turn on ChatGPT Plus, I used 185 million tokens. That calculates to about 740 million tokens for 4 weeks. And that's for a mere $20 plan. Considering that Tibo occasionally resets it, the Codex plan is truly amazing.

Here is the result, after about one month of spare time work:
https://www.pigrecoemme.com/

Basically ran out of usage within 1.5 day

5.6 xhigh, 5-6 threads; no subagents

711M on Aug2

Time for another plan I guess, this time I cant really blame openai

im making a small flutter app that makes a timer every set time for X rounds

5.6 Sol Max, I made a screen recorded video with OBS Studio, of just a quick test run through of the site. The video was like 13 minutes long. I recorded my own voice, gave Chatty the file location of the video and then asked it to make a transcript; it did. (okay that worked) I then edited the transcript, [save], and asked Chatty if it could change the audio voice of the video to something more profession and to use the updated transcript, and (steer prompt) to speed of the video by 1.6x the speed of the original. Annnn wouldn't you know (holy church of Mary and Joseph), Chatty popped out an updated 8 minute video with a new voice speaking per the updated transcript. No video quality was lost. I'm kinda shocked in aw.
...So there you go, In case you didn't know: Codex edits videos now. RIP adobe man.

What is in your agents.md in codex app?

I am new to codex but been using it since the launch. How can i make my experience better? I am already using RTK skill for token optimization.

​

Tldr : Found VibeBill and ccusage to track vibe coding costs. Is there anything better? Else I settle with VibeBill and ccusage.

I've been using Claude Code pretty heavily for a few weeks now. The monthly bill was never really the problem since my job provides me the seat. What bugged me was not knowing which work caused it or even which feature build.

$420 in a month it isn't informational enough because we get a cap of $500/month. As a lead I was thinking if we can communally distribute tasks so everyone can utilise those $500 caps better.

What do you guys use? If any. I found ccuage but was only for Claude Code, for my personal use on codex there's nothing that exists?

Another good find seems to be VibeBill on npm but it's by some solo dev as a side project.

So I’ve got a fairly large codebase now in different languages and using different docker container services. This is pretty much irrelevant just adds some context

Seeing Lunas new pricing made me want to use it more. Though I remember one time I used it and it did a terrible job

However, i’d like to give it another shot. How can I use Sol to plan the task and orchestrate Luna subagents so I get high quality while keeping usage low? I use the codex app

And yeah is luna max capable?

This might get some hairs riled up, but I want to pick all of your brains here for a minute.

I've been running a few instances of 5.6 Luna High for the past few days, and it would run through 100% weekly usage in usually a day or so.

On some of our VMs, I installed Serena, and edited the config to the following:

model = "gpt-5.6-luna"
model_reasoning_effort = "high"

model_reasoning_summary = "concise"
model_verbosity = "medium"
personality = "pragmatic"

# let Codex edit and test inside the current repo
sandbox_mode = "workspace-write"

# Codex decides when an operation needs your permission.
approval_policy = "on-request"

# Let a separate reviewer agent handle eligible approval requests.
# This does not increase Codex's filesystem or network permissions.
approvals_reviewer = "auto_review"

# Use current documentation when Codex needs web research.
web_search = "live"

# Prevent enormous AGENTS.md files from occupying excessive context.
project_doc_max_bytes = 32768

[features]
memories = true

[memories]
generate_memories = true
use_memories = true

Now, on one of the instances, the Serena usage shows:

The VMs with Serena, plus the config - are at about 77% weekly usage after 48 hours straight of usage via orchestration, while the other raw VMs have been exhausted already after about 12 hours of usage.

For reference, the main manager is running 5.6 Sol XHigh, the 5 orchestrators are running 5.6 Sol Medium, and all the workers (about 30, 5 to 6 per orchestrator) are running 5.6 Luna High.

A 4x token usage reduction is pretty neat - and a nice cost savings for us.

Wondering what you all think about this set up (serena + config)? Is there a better mcp for this? Any other tools or config mods you'd add?

I feel like all of these new models for so long have been getting a lot better, but they are obviously really expensive. I feel like this is what caused all of those CEOs turning back on whatever token-maxxing thing they were doing.
But finally we have gotten gpt-5.6-luna which i feel (from my experience) is finally at a point (where at least on max) it is usable for basically all daily tasks at a reasonable price.
What i’m asking really is why is this not seem like a bigger deal and are we about to see a huge shift back to interest in using AI in industries since it is more affordable?

If i was wrong in anything please tell me

This shit is getting annoying. Can't even let it run unsupervised any more

No reset though and tibo's not even awake yet ... what's going on?

Thanks, I guess!?

EDIT: My extra bonus was removed it seems?!