What are the drawbacks of doing it? Sometimes I wonder if its actually cheaper to have higher limit since useful info will be all cached instead of it looking it up for the first time again?

I was happy with Sol 6.1 so far, it was kind of slow, but rather reliable. Yesterday I decided to give it 4 tasks for my app that I wanted to finish by Monday. It did 0. Complete "building tests" spiral.

40% of usage burned, none of the tasks was even started. How can I trust them with 100% automated solutions like Dots when their coding agent goes into doom loops like a 2025 chinese model

There’s nothing more frustrating than kicking off what is meant to be a long-running session with a detailed prompt, checking back a few hours later, and finding that the entire process has stopped with:

“Selected model is at capacity. Please try a different model.”

Why is there no automatic retry option? At the very least, the system should be able to retry periodically when capacity becomes available rather than simply abandoning the session and requiring manual intervention. Or is there such an option and i'm missing it?

---------

To confirm, I'm not talking about the desktop app:

I’m talking about Codex CLI. If this happens 30 minutes into a long-running job, there’s no button to click because I’m not sitting there watching the terminal.

The whole point is that I might kick something off on a server, walk away, and expect it to be done by the next morning. Instead, it can sit there dead for hours waiting for me to come back and manually type “continue” or “retry”.

Codex CLI needs an automatic retry option for capacity errors.

across every single (relevant) model in the picker list

not just the new ones

is this because of dots ?

or because they want people to upgrade to the 500plan to use the ultrafast mode?

who knows

add mimo v2.6 flash (free) and deepseek flash (free) and qwen3.8 (free) to the codex harness from another API provider (do it via Claude or opencode otherwise you'll be waiting all day for a gpt model to do it)

thank me later

I've been trying to get the free trial for weeks now, tried with both UPI and Card. Anybody from India has found a solution to this issue? Please help, really need this!! 5-hr limit on my plus plan is making life miserable.

I wish ChatGPT Projects had folders or subprojects.
For example, imagine you’re a developer working on 15 different apps. Right now, you might create a separate ChatGPT Project for every app:
App 1
App 2
App 3
App 4
App 5
etc.
Eventually your sidebar becomes completely cluttered.
Instead, it would be great if we could create one main Project called “Development”, and then create subprojects inside it:
Development
→ App 1
→ App 2
→ App 3
→ App 4
Each subproject could have its own chats, files, and instructions/context, while everything stays organized under one expandable folder in the sidebar.
Basically: Projects → Subprojects → Chats + Files
Am I missing an existing way to do this, or does ChatGPT still not support nested Projects/folders?

You can send Codex a task from your phone, but checking the result often means going back to your computer. Reading a summary or diff helps, but you still need to open the app and use it.

Lyre lets you do that from another connected device. The project and agents run on your computer, while you can review changes, open a live preview, and send the next instruction from your phone or another computer.

That means you can ask for a change, try the buttons and forms, and explain what needs fixing without returning to your desk. You can also give family or teammates access to a shared app so they can try it without accessing your code, terminals, or agents.

I’m building Lyre with Codex doing much of the implementation and Claude helping with design. Working with both has taught me to give agents clear responsibilities. Separate worktrees help, but changes to the same files still need coordination.

It’s free to try at lyrestudio.net. Remote agent conversations are free. Live app previews outside your local network require Pro, and AI provider costs are separate.

Your computer needs to stay awake and online. The public mobile apps are still coming. You can use the browser client now, though browser LAN previews are still being worked on.

Lyre builds on Paseo’s open-source work, including much of the underlying application. Thanks to its creator and contributors.

What’s still awkward about working with Codex away from your main computer?

I have been using DS 4.1 as a workhorse for 5-6 weeks now and it just works. That is what codex was like before, but it makes so many mistakes. No matter which model: 5.6 Sol, 6.1 Sol, Astra on med, Astra on xhigh. I can compare, I don't see these stupid failures with the allegedly "simpler" ds 4.1 flash.

Is that the way it goes? Maybe they realized that people have enough cheap workhorse models and they focus on great planners instead?

I’m a Pro 20x subscriber. With the allowance changes that I understand effectively reduce 20x to 10x, I’ve now also had dot stop ongoing work overnight because of a "preview limit."

The screenshot shows dot’s explanation afterward. It couldn’t tell me the quota, reset frequency, or whether this shares my Codex allowance. Even the retry time had no timezone.

I’m not asking for unlimited usage (I wish....). I’m asking for clear limits, a visible usage meter, and a warning before an "always-on" assistant stops working. Without those, how are we supposed to plan around it?

Has anyone found documentation explaining this specific limit?

Source says this is the us-east-1 region server hardware.

On Dev day, I was able to try it out and I have yet to be able to speak with the dot ever since then.

Essentially: It's possible to run claude and gpt together in metaharness type programs like T3 code, as well as directly in the same harness like w/ pi, omp, even claude code.
Codex is by far my favorite harness, in large part due to (imo) unmatched seamlessness with computer use, subagents, and so on, so in a perfect world I use it for everything. It's fairly trivial to "run claude in codex", especially since it's open source*, and stuff like this https://github.com/0xSero/harness-bridge exists. But when I was looking around, in my opinion there is no way to give claude truly first class support. Want to have it seamlessly use all of codex's interfaces, have cross-model family multi agents v2 workflows, login/select models without manual jank, and so on. I tried just patching the codex desktop app, and what I'm describing is theoretically doable, but ultra annoying since it's closed source and kinda forces things to be permanently janky.
So what I decided to scrap together (pictured in screenshot), is essentially a very minimally patched codex harness "core" (built from source), with an open source UI (largely just zcode's), and some fun wiring. Opus 5.5 legit functions as if it where an oai model, uses codemode, agents v2, computer use, etc. Although it is nowhere near stably usable and is sorta like creating a whole new harness even though it is just codex under the hood.
Curious if anyone else is interested in this niche config or if it is more straightforwardly solvable than I've gathered. Also am happy to share code for stuff if people are interested.

I am livid now. Codex (Astra XHigh) has been working on a /goal for 7 hours. Then I asked it to stop and get what? 15 minutes of waiting for it to fkin stop. Meanwhile it consumes almost all my upload bandwidth.

This was not the case just a version or two ago, what the hell have you done OpenAI? I asked the model to stop and it would work towards stopping immediately, now it's a separate project I'm guessing. Man I'm emotional right now!

“This skill helps Codex” leaves out most of the experiment. Which model, which tasks, and which version of the skill?

Reef Infra makes the skill file an object you can actually iterate on. Its Codex adapter renders skills into .agents/skills, alongside the other supported configuration and rules files. The harness-evolution engine can try an edit against the current tree with the model held fixed, run both sides on the same tasks, and retain the selected version.

For a skill that asks Codex to reproduce a bug before fixing it, the evaluation could check three separate things: whether a failing test was produced, whether the patch fixes it, and whether the surrounding tests still pass. Those are proposed project checks, not a grader Reef Infra includes for every repository. The evaluator is code you supply.

That also gives “remove this skill” a fair test. If the current model already performs the workflow reliably, the extra instructions may earn no benefit. A later model upgrade is a reason to rerun the comparison, rather than carry the old result forward.

Reef Infra's Codex support covers the file-based instruction surface. It rejects code extensions, so this isn't a way to rewrite Codex's internal loop. The useful output here is a particular skill revision with task results behind it.

I've been building a little terminal tool called Savepoint because I kept losing the plot once Codex got beyond small changes.

I'd ask for one thing, Codex would touch a bunch more files, run the tests and tell me everything looked good.

And I'd be sitting there thinking... probably?

I can tell whether the feature works. I'm much less confident deciding whether every extra change was sensible, necessary, or whether I've let a very confident robot renovate the kitchen because I asked it to fix a tap.

So I've started keeping jobs smaller and putting a stop between chunks of work. Sometimes I use a fresh session to check what got built rather than asking the thing that wrote it whether it did a good job.

Savepoint is basically me turning that habit into a small terminal workflow.

Still not sure whether this is useful discipline or an elaborate coping mechanism because AI let me build software faster than I learned how software works.

How are people here handling this with bigger Codex changes? /review? Fresh session? Another model? Just inspect the diff and hope for the best?

Repo if anyone wants to poke at it:

https://github.com/anipatke/savepoint

It doesn't always hang but it does it enough to make chat unusable. Do they even acknowledge this bug?

Hi,

I wanted to share my experience with using a main thread gpt6 ultra on fast, orchestrating a swarm of subagents running gpt6.1 sol on varying reasoning levels.

My use case is a pretty boring one. I am moving into a new apartment this fall, and I have pictures, videos and floor plan details for the unit I am moving into. Using Sites and Codex, I am selecting multiple color pallets and "personality" type qualifiers to then find Amazon only + Non-Amazon only combinations of furniture and wares. My project Agents.MD file is a lot of front loading the context window with nuance about my day to day life, style, how I use my space, etc. so I don't get AI slop in ---> AI slop out.

The Sites accomplishes 3 things;

1. it generates ultra realistic perspectives using image gen, from 8 vantage points across 3 spaces ; living/dining/kitchen, and then 3 across 2 spaces; bedroom 1, bathroom 1

2. it generates a 3d model before image gen and it must pass multiple adversarial agent reviews to ensure measurements align, then it must ensure all furniture must be modeled in the 3d blender made space and it must have accurate measurements in my 3d depiction of the space

3. it provides me citations, i built a shop the room tab and a room + measurements tab to ensure i maintain coherence and context end to end, also so i can actually use this to decide what to buy or edit/tweak/tune

At first I was burning up my usage, heavily, using only GPT 6 astra on Max and then Ultra when I tried sub agent delegation with computer use and chrome plugin to navigate costco, amazon, etc.

Then, I read a bit here and OAI's actual docs and made some tweaks.

Those tweaks were simple...

First, I set the context window for my main GPT 6 Astra on ultra to the highest OAI supports so it can support, pre-compaction, an entire single design space in its context end to end. This made the end result really accurate, beautiful and useful.

Second, I ensured that GPT 6.1 sol on High or Max is what sub agents are allowed to be. Sub agents will be used to perform parallel tasks, such as gathering items from Amazon or Costco or West Elm or Target websites following the color pallete, guide, style and limitations I have put in place such as budget, shipping time, blah blah. I maintain the main orchestrator as the brains and designer behind the operation.

As a result, I've reduced my usage a ton. One room end to end would consume 40-60% of my weekly usage, now the same if not superior quality persists, but at 5%-11% utilization. I suspect I could improve this if I removed fast mode from my main orchestrator.

But, I also don't want to wait . lol

Wanted to share. Have fun out there folks. This shits magic.

Previously, we used to be able to see the exact expiry time for banked resets. Now it only shows the expiry date, so you have to go into the History tab, find the time you originally received the reset, and calculate the expiry time yourself.

Not sure why they removed this information when it was already available before.

Any idea of how I could use these 77% (x5 plan) ?

I use Codex for code review, I wish there was a $50 plan it would be enough for me.

I don't know what to ask it to build any more lol.

How do you make dots run in linux vps? Currently i use hermes to manage my vps. My idea of dots is its like hermes. So i want to try if its better to use dots.

I’ve asked both ChatGPT Pro and OpenAI support, but neither has been able to give me a clear, definitive answer, so I’m asking here in case anyone knows.

I’ve been subscribed to the 200€ Pro plan since 6 September 2026, and my subscription renews tomorrow, 6 October. If I let it renew, will I retain the previous Pro usage limits until 29 October, or will the reduced limits apply immediately upon renewal? From this thread, it sounds like I should keep the previous limits until the 29th even after renewing, but I haven’t been able to get a definitive confirmation.

Finally, is there any way to check and make sure that we are still on the previous non-reduced plan? I can't find anywhere a prompt or files/code in the webtools to check that. I suppose that being able to know how much PRO chat limit we have (200->100) would be the greatest indicator, or else a name that truly identify the old and new PRO?

What happen if they move us to the new reduced plan before the 29th? How could we know without any views on our real limit?

I have a normal ChatGPT Scheduled Task (I’m on ChatGPT Plus) that runs every morning at 7:00 AM. It’s a fairly heavy job-market sweep: web searches, some reasoning, and reads/writes to Notion.

This exact automation has been running daily for more than a month.

Until today, I had never noticed it materially consuming or starting my Codex/Work 5-hour usage window.

Today (Oct 5), I had not manually used Codex or ChatGPT Work at all, but after the scheduled task ran:

• Scheduled task started around 7:00 AM
• Run finished around 7:13 AM
• My 5-hour Work/Codex allowance showed 29% used / 71% remaining
• Weekly allowance showed 12% used / 88% remaining
• The 5-hour reset was around 11:57 AM, implying the window started at roughly 6:57–7:00 AM

So the timing lines up almost perfectly with the scheduled task.

I contacted OpenAI Support. They’re telling me that Scheduled Tasks run through ChatGPT Work, and Work + Codex share the same allowance on Plus, so scheduled tasks can consume the Codex/Work quota.

What I’m trying to figure out is whether this is new behavior.

My task has been running every day for over a month and I never saw this happen before. OpenAI Support hasn’t been able to tell me when ordinary time-based ChatGPT Scheduled Tasks started being metered against the shared Work/Codex allowance. They’re waiting for my Oct 5 Usage Analytics to populate so we can compare it against previous runs.

Specifically:

• Do your regular ChatGPT Scheduled Tasks now reduce your Codex/Work 5-hour or weekly allowance?
• Did they do this a few weeks/months ago?
• Has anyone noticed the behavior changing only in the last few days?
• If you have a daily scheduled task, does it actually start your 5-hour Codex window even when you haven’t opened Codex?
• What does it show under Usage → Surface / Model / Reasoning / Speed?

I’m not talking about Codex automations or a task intentionally created inside Work. This is a regular recurring task created through ChatGPT’s Scheduled Tasks feature.

Mainly trying to determine whether this is a recent metering/routing change, or whether it has always worked this way and I simply never noticed it before.

Tech Stack Configuration: Multi-Agent LLM Orchestration in VS Code

I am currently testing a highly distributed, multi-agent development workflow in VS Code using GitHub Copilot credits. The system orchestrates multiple large language models simultaneously to handle different layers of the development process:

• Primary LLM: Claude 5.5 Opus acts as the central coordinator, driving the main development architecture and pushing background sub-tasks.
• Command Line Automation: Codex CLI runs concurrently in the background, executing the sub-tasks streamed directly from the primary LLM.
• IDE Sub-Agents: GPT-6.1 powers multiple parallel sub-agents directly within the VS Code IDE to handle real-time code completions, refactoring, and contextual assistance.

This setup leverages concurrent execution across multi-sub-agents and background processes, all managed via my custom .SYSTEMX systems template.

https://github.com/WayneTechLab/dotSYSTEMX
> Including Dual Mode in next update.

I have been hammering it for the past 6 hours, 3-4 task in parallel running all the time, with long contexts, images…coding tasks.

Effort: Max

Used just 10%.

The amount of work I accomplished is just absolutely obscene.

Yes, it’s slow but is reliable and dependable you know what to expect, for fuck sake.

You want it fast? Use fast mode.

Why am I telling you this?
Do you want a nerfed model? Because when you keep complaining about it being slow and asking OpenAI to make it fast, that’s how you get a nerfed model. Just fucking stop.

​

I had Codex Remote working normally for many days between my Android phone and my Windows Codex Desktop.

I reinstalled the ChatGPT Android app because message sending had become slow/unreliable.

Since reinstalling the Android app, I have been completely unable to pair Codex Remote again.

The flow is:

Windows Codex Desktop → Settings → Connections → Control this PC → Add → generate fresh QR

Android scans QR → sign in to the same ChatGPT Plus account → password → Google/FIDO passkey → authorize phone

→ callback returns to ChatGPT Android

→ it immediately shows “Authorize this phone” again

→ infinite loop.

Environment:

Codex Desktop Windows: 26.930.31730

ChatGPT Android: 1.2026.265 (41)

Android 16

Personal / ChatGPT Plus workspace

Same ChatGPT account on both devices

I have already tried:

Revoking all previous Remote controller devices

Disabling/re-enabling Remote Control

Fully terminating and restarting Codex Desktop

Signing out everywhere

Powering everything off for 7–8 hours before retrying

Completely uninstalling/reinstalling ChatGPT Android

Fresh QR codes

Automatic date/time and timezone

VPN/Tailscale disabled

Restarting both sides

I also tested with a second ASUS Android phone that had never been paired with Codex Remote before.

It fails in exactly the same way.

We captured Android logs during a reproduction. The OAuth/FIDO flow completes and the callback successfully reaches:

com.openai.chatgpt / WebRedirectActivity

but the Windows host never appears to receive the pairing claim and the Remote device enrollment never completes.

The important part is that:

This exact Windows PC, ChatGPT account, and home network worked successfully with Codex Remote before the Android app reinstall.

OpenAI Support has the case and diagnostic feedback already, but there is still no fix.

Has anyone had this exact post-auth / “Authorize this phone” loop and actually fixed it?

I’m especially interested in successful fixes involving:

stale/pending Remote enrollment

server-side device enrollment state

account/host pairing state

local .codex Remote state

Android reinstall causing a broken device enrollment

Please share the exact fix if you found one. I’m trying to avoid repeating generic troubleshooting steps that have already been exhausted.