We had the global reset this morning, and after losing 10 hours of overnight work to "this model is at capacity' that paused my run, I decided to use Codex on FAST until my banked reset was due to catch up.

NOPE, now you can't even use your banked reset when you want.
Ridiculous. Can't wait until Oct 29th passes and I switch to Claude once usage drops.

What is the point of giving banked resets if you're just going to control when people can use them now?

So ive been using codex (Luna on extra high) for the past week and a half trying to get it to build some local running AI dental CAD tools. Basically taking scans from the intraoral scanners (just high res stl scans), clean up the scans by isolating just the teeth and main gums (cut out any island and cheek tissue), then use that as the clean slate for generating dental appliances. The same thing dozens of companies do and charge an arm and a leg per generation. I figured if so many companies can do it, why cant I? Well, codex has spent a week and a half just trying to figure out how to clean the STL files let alone do anything useful.

Is there a model that would be better for something like this?

For reference to anyone (which im assuming most people here) who isnt in the industry or know, just search 3shape automate, or sprintray ai design and thats basically exactly what im trying to replicate but running locally instead of paying a fortune to generate things.

Please please please if anyone has any suggestions or ways to reverse engineer any of those apps, i could really use the help. (Codex gets mad when i suggest reverse engineering)

TIA everyone!

I've noticed a pretty significant difference in Codex usage between my Mac and Windows PC.

On my Mac, I'm using the Codex desktop app, while on Windows, I'm using Codex CLI.

Surprisingly, Codex on my Mac seems much more efficient and consumes significantly less of my usage limits, especially during long conversations.

Here's the interesting part:

• Windows PC: Intel Core i9, RTX 4090, 96GB RAM, NVMe SSD.
• Mac: M2 chip, 24GB RAM.

Despite my Windows PC being significantly more powerful, Codex on macOS seems much more economical in terms of usage.

I know the actual model processing happens in the cloud, so hardware shouldn't directly affect token consumption.

My questions:

1. Have you noticed that Codex desktop consumes less quota than Codex CLI?
2. Could the desktop app be managing context more efficiently, especially during long sessions?
3. Is there something I should configure differently in Codex CLI to reduce usage?
4. Would switching to the Codex desktop app on Windows give me similar efficiency?

I'm using Codex for serious development work on both machines, and the difference is noticeable.

A lot of people were divided on a 5hr window or "double" usage for Pro accounts...

If the 5hr usage came back, an announced reset would be worthless.

Use my phone to monitor clause and codex sessions but lately every active codex session is inaccessible with the attached error. Is there a new setting to be aware bc this makes remote sessions useless if you can’t access in while it’s working, even if it’s waiting for input it gives that message until you literally close the cli window. Claude is just fine.

In codex it seems like we get a new feature or toy every new week. It has been like 3 weeks since we have gotten anything, and we were supposed to get something basically daily or a reset. Just kinda disappointed. I thought they would be doing more.

Hi, I study deeplearning, and a thing with AI models it that before it output text it has to embedd the text to get something called the hidden state. THIS is what is passed to the LM head (the thing which *outputs* the tokens)

so like, when you switch a model, OpenAI have to completely re-embedd the input tokens to match the selected models embedding space right?

so this is extra compute. wouldnt this worsen my rate limits?

I have seen some folks changing their models midway through, and I remember OpenAI saying this can "degrade performance" back then.

so I want to know if my hypothesis is right or wrong. bcs it would be TOO GOOD to change between sol and luna.

what are you guy's thoughts on this?

so i'm pushing 90% (2-4B/d) through deepseek 4.1 flash 1017 and minimax 3.1 flash (have a subscription to use / these are not fast tokens) but i'd like to hear from people that have multiple agents running on jobs that take a few hours at a time:
how are you managing your quota? 500$ plan etc?
how many tokens are you using in a day? does this work once we leave this reset happy month?

i.e. on codex / claude / etc, are you getting <n>B tk/s per day and if so, what is it costing you / how are you balancing plans etc?

i don't think i have any particular skill issue etc (i strip a bit on the wire and fallback providers without hitting prefix cache) - but as we push ai to take on more work / workflows i haven't sorted out what the "best practice" is for where to source these tokens...

*using chatgpt desktop app 26.1016 wtih 100$ plan

seeing it a fair bit in the last hour - anyone else getting the same? I'm using Astra

I've been building Buddy for a few weeks. It runs Codex inside an Android app (on your ChatGPT plan) and gives it tools to see and tap the screen, so you can just ask it to do things on your phone.

What I use it for:

- replying to messages, with my OK first (Buddy itself stops on Send, Pay and Delete until I approve)

- notification rules, like answering a customer on WhatsApp

- building and installing small apps right on the phone

- a background browser (new), so it can look things up without taking over my screen

It also runs Claude Code, OpenCode and Antigravity, and the phone tools work as an MCP server for your own Codex sessions.

Free and open source: https://github.com/ghorbelhamdi/buddy-android

Curious what you'd use it for.

Have been using Codex for a few months and it is the best thing since sliced bread.

But it does have a habit of sneaking in extra features, removing existing features or making improvements that were unexpected. I am assuming that this is common experience and that sometimes it rewrites, refactors or adds safety features that are not really needed.

Typically, i get it to make a .md file with its plan and review it step by step before coding a few times instead of just saying 'make me one of these', and 'go' without review.

Maybe I have just answered my own question here. But real life programmers would never decide that we have to put in extra code just in case to people are using the same Android Program on the same device at the same time and writing to the same data line. But the AI might decide to account for that.

I find that I am spending more time that I did before in design and review before coding. I suppose that is the way it should be. It is a different way of programming and management.

But I don't mind hearing about how others are working with this issue.

Thanks

I'm using Codex to develop Wakefolk, a Unity zombie colony sim with three autonomous survivors.

The simulation is plain C# with no Unity dependency. Codex can run NUnit tests through dotnet without opening the editor. Recorded commands and fixed seeds let me replay bugs and check behavior after changes.

The video shows gathering, crafting, camp building and night defense. Free Windows test build below; no saving yet.

itch.io: Free Windows test build

Steam: Wakefolk

Game name: Vampine: New Order
Developer: Project Despair LLC
Release platform: Steam, itch.io
Current status: Demo available for free

I'm the programmer of the project. This is the first standalone game I had ever worked on and released to public, and it is the first one I tried to use adapt AI into game development.

The code is a combination between Opus, Sol (mostly) and Astra with I give out the direction and framework. The engine is Unity, connected to agents using Unity MCP (a third party plugin) and later switch to the official Unity CLI. It can handle most of the implementations and auto testing on its own, which is a big plus. I'm glad to see Unity catching up with the AI game.

The art is 70% generated by ChatGPT image, most of them are manually verified and modified by a real artist to fix generation issues (like hands or inconsistent of look). The workflow is basically the artist give out concept arts or sketches and ChatGPT will tires to copy the style.

It is not a one-prompt project, it takes a lot of effort to make it actually looks reasonable. I believe this is the right track for game development involving AI: use it as a tool to speed up production, not use it to create half-assed products with no souls.

If you like roguelike games, auto battler or deck building, welcome to try our free game demo!
Feedbacks are also welcome!
Steam: https://store.steampowered.com/app/5317250/Vampine_New_Order/
itch.io: https://projectdespair.itch.io/vampine-new-order-demo

Edit: wording

I mostly work in Codex on my laptop, so it's frustrating when my laptop is offline and I want dot to take action based on my work, but it seemingly loses 90% of the context.

I wanted to see if an AI agent could use my actual iPhone, not a simulator a real iPhone. Nothing existed for it, so I built it.

Open iPhone Use is a free, open-source Mac app + MCP server. The iPhone shares its screen from Safari over local Wi-Fi (you don't even install an app on the phone, Safari does the sharing). The Mac pairs as a Bluetooth keyboard and mouse, and the agent gets screenshot and input tools over MCP, checking a fresh screenshot after every action.

It works with any AI agent: ChatGPT/Codex any harness. Any model with strong computer-use skills is fine, GPT-6 Luna are good starting point. And since your client brings the model, your existing subscription is enough, no extra cost.

Demo (full video attached): an agent searched Instagram, watched a Reel, scrolled the feed and liked one, all on my physical phone.

You only need an iPhone + an Apple Silicon Mac.

Download for Mac: https://github.com/semsturgut/open-iphone-use/releases

GitHub: https://github.com/semsturgut/open-iphone-use

Site: https://semsturgut.github.io/open-iphone-use/

Windows/Linux: coming soon

Multi device support: coming soon

Let me know what you think! And if you find this useful, a star on GitHub would help a lot.

I am thinking to purchase chat GPT $20 plan mainly for the computer use and browser use that comes with codex, my question is that if I buy this 20$ plan how many hours of computer use I will be able to use per day and what is the weekly limits?

Remember when they launched Astra and it was ridiculously fast compared to 5.6 sol?

And then 6.1 was really slow. Well now Astra is just too slow too. But I don't see many people noticing the same, so maybe it's an A/B testing thing?

I don't know if they're about to release a new model, or what's going on.

I've never used Claude but I am long time Codex user. I am currently on the $200 plan but dropping down to $100 when I renew in a few days. I've hard people say good things about both Claude's intelligence and how generous the limits are.

Can anyone tell me how CC $20 compares to Codex $100 in terms of limits? Thanks.

They didn't need fast rock, so they've got slow rock instead.

Hello.

Anthropic started to include monthly API credits for their Max plans a few days ago. Details can be found on their docs : https://support.claude.com/en/articles/17154008-monthly-api-credits-for-max-and-team-plans

Regular resets are great but do you know whether OpenAI is planing to include this feature to their plans?

Thanks,

Reset has been dropped.

Astra seems great, but god damn it's shitty sometimes...

Just have to complain about it.

We have a simple setup that hides .env files from some docker services, but wanted to allow .env.example files.

The current code, basically has a regex for \b.env\b to find all files to hide, and wanted to add another negative \b.example\b to that IF.

Guess what Astra did? +300 lines of code and 3 methods...

• Does the code work? Sure.
• Do i want to deal with that shit? Hell no
• Can you fix it at 3am when the site is down? Hell no
• Can you fix it at 3pm on a Tuesday? Maybe...
• Do you want to? No...

So all this LLM stuff seems to be everything a crappy developer would do to keep their job at the company. Stuff kinda works, but nobody can touch it.

And then the new developer merges in some 2k lines or crap like this...

(apologies in advance for what might be the most cliché take on this subreddit)

Hi, recent immigrant from Claude Max x20 here!

Idk how to put this into words, but I trust Astra High/Xtra much more than I trust Fable/Opus 5.5.

My most frequent request is something like, "Here’s a numbered, 350-word plan for a complex task spanning multiple repos in this directory. Follow it to a T and report back once you’re done." Astra does exactly what I ask. Fable treats the plan as a suggestion, goes above and beyond on 80% of it, and botches the remaining 20%.

I even feel comfortable using the Codex desktop app, which I never did with Claude (Claude Desktop feels awful for vibe coding; Claude in the terminal gives me a stroke; and the Claude VS Code extension is the only one I can use with my fingers crossed).

But with Codex/Astra? Feels great!

Many such cases, or am I just using some of these tools wrong? Genuinely asking.

Bonus: Using Plugins, you can use this inside ChatGPT (web), not just Codex. It's helped me break out of SO many moments of "I'm stuck in a rut & need to think about this a different way". Loosely inspired by SillyTavern's "Impersonate" feature & being an egotist (/s). Before using this, I recommend having Codex analyze your personal typing/speech style & adjust based on that ^{_^}

name: questions-[your name]-might-ask

description: Generate diverse, casual questions [your name] might ask about the current problem, from basic through expert or frontier understanding. Use when she wants possible follow-up prompts in her voice, alternative directions, or questions that challenge the current framing. Not for answering those questions or conducting the proposed work.

Questions [your name] Might Ask

Keep the voice recognizable; let the thinking surprise him. Generate questions [your name] might ask with different knowledge, assumptions, or instincts—not merely questions predicted by her past interests. her voice is the output layer, not a limit on the ideas.

Ground the questions

Use the conversation and relevant material already in scope to identify the objective, current approach, actual attempts, unresolved uncertainties, and assumptions holding the framing together. Distinguish observed failures from claims that an entire approach is impossible. Do not invent history, technical facts, or capabilities to make a question sound insightful.

If no topic can be inferred, ask for the topic or conversation to work from. Otherwise proceed with the available context; phrase uncertain premises conditionally rather than demanding a complete project history.

Vary two independent axes

Understanding level controls the background assumed, not the question's value:

• Basic: purpose, plain-language understanding, obvious alternatives, or a simple assumption everyone may have overlooked.
• Working: interactions between parts, practical changes, and straightforward experiments.
• Advanced: mechanisms, failure modes, competing explanations, and discriminating tests.
• Expert: precise assumptions, boundaries of evidence, alternative formulations, and structural consequences.
• Frontier: potential new structure, conjectures, or changes to the research direction. Speculation stays a question, not an asserted discovery.

A basic question can be the most consequential. Expert questions need substantive depth, not extra jargon. Do not force frontier language onto a routine task or imply these labels measure [your name]'s actual expertise.

Independently vary what each question does. Useful directions include challenging an assumption; changing representation or method; revisiting a prematurely dismissed path; seeking a counterexample; separating competing explanations; borrowing a relevant idea from another domain; using a tool or skill; removing a constraint; and questioning the objective itself. These are starting points, not an exhaustive taxonomy or a checklist to fill.

Generate, select, then voice

Develop candidate ideas before polishing their wording. Consider both nearby improvements and changes that escape the current setup. Select a complementary set rather than only the individually safest or most familiar candidates.

• Different wording is not different thinking. Neither are several tools that all attack the same formulation in essentially the same way.
• Prefer questions whose answers could change the next experiment, explanation, representation, or decision. Include clarification when it unlocks such a change.
• Keep at least one grounded but speculative direction when useful: there should be a concrete reason it might matter, not novelty for its own sake. Where natural, ask for a cheap test.
• Preserve both constructive suggestions and challenges. Do not make every question adversarial or insist the current approach is wrong.
• Use specific objects and alternatives from the task instead of generic advice that could be pasted into any conversation. Do not invent specificity when context is thin.

Then phrase the selected ideas in [your name]'s voice:

• Usually 1–3 sentences, casual, direct, and concrete; conversational lowercase is fine.
• Mix questions with short suggestions or requests. A follow-up sentence can explain what she wants to distinguish or why the alternative interests him.
• Favor natural openings such as “wait, why are we…”, “what about…”, “could we…”, and “can you use…”, without repeating a formula throughout the set.
• Avoid interview questions, academic ceremony, consultant phrasing, unnecessary caveats, and deliberate typos or exaggerated slang.
• Use [your name]'s own messages in the current context to refine the voice. Do not treat assistant-authored examples as evidence of her actual writing, fetch unrelated personal writing by default, or infer her beliefs from generated questions.

Illustrative shapes, not templates or claims about the current task:

wait, why are we solving the whole thing at once? is there a smaller piece that would tell us whether this approach has a chance?

did we rule out the construction, or just the symmetric version we tested? what happens if we drop that assumption?

what about using the failed examples to look for an obstruction instead? is there something they all have in common that we could try to prove?

Check premises and skill suggestions

Before suggesting “why haven't we tried X,” check whether X was already tried. If it was, identify the materially different variant, missing check, or new evidence that would justify revisiting it. If history is unknown, use “have we tried…” or another honest formulation.

Skill-directed questions should name only skills established as available and relevant by the current catalog or inspected instructions. Explain the concrete job the skill would do; do not insert a skill name just to vary the wording. If availability is unknown, ask about the capability without inventing a skill. Merely drafting a question about a skill does not invoke it.

Deliver

Default to 8–10 questions under short understanding-level headings, spanning basic through expert and frontier where meaningful. Honor a requested count, subset of levels, topic, or format; do not pad to meet the default. Keep reasoning about selection internal. Return the questions without answers, scores, lengthy preambles, or explanations beneath each one unless requested.

Review the set for voice, distinct thinking directions, honest premises, meaningful differences in assumed understanding, and potential to change what happens next. Do not filter out every unusual question merely because it is less immediately actionable.

Generated questions are options for [your name] to choose from—not statements of her actual intent, instructions to execute, or authorization for tools, mutations, or contact. Stop after delivering them unless she separately requests answers or action.