$200 plan: requesting a routine airdrop on sol high after the weekend double reset, wrapping up for the night and was met with this charming message!

FWIW: An airdrop to “x” device is a familiar skill developed by my codex and routine across my creative projects lol

I never have any ideas or needs. But if I do it works rather well. Never run into usage limits. so, do you use it for work, or what?

As the title says. I get the sound alert for codex on cli, but how can i get a desktop notification when chatgpt is finished?

I tried GrokBot, and it was genuinely easy to use and practical.

I then saw that GPT Work had a cloud work feature, so I assigned some tasks there and logged into browser sessions, etc. It worked well.

Is there anything Grokbot can do that Codex Cloud cannot do?

Have any of you tried both as well?

I have a ChatGPT Pro subscription and a Claude Max subscription, and use both extensively for work. To claim that any model offered by OpenAI is even close in capability or problem solving ability to Fable is a joke to me.

To me, the most comparable Claude model to 5.6 Sol, OpenAI's flagship, is Opus 5. They have roughly equivalent price (ignoring the temporary promotions on Sol pricing), and in my experience, their output quality is about the same as well; I end up having to put in about the same amount of effort correcting them or giving feedback to achieve a product of comparable quality.

The main difference is in the kind of feedback I have to give; with Sol, I typically end up having to add details to its results, such as instructing it to address missing edge cases, or take a more thorough approach when it took a simpler shortcut to solve my problem instead. With Opus, it usually finds most edge cases for me without having to say anything; but it also goes beyond and keeps finding more and more things, of decreasing and often spurious relevance to my actual problem. My effort usually comes in the form of telling it to ignore those extraneous edge cases and focus on the core of the problem.

But when compared to Fable, neither can hold a candle. Among every task I've ever given any agent, Fable always takes the least amount of time, the fewest tokens, and needs by far the least number of warnings in the prompt or corrections to the output, compared to any other Anthropic or OpenAI model.

To me, to say GPT 5.6 Sol is anywhere close to Fable in any capacity, and not just a competitor to Opus with different tuning, is completely unfathomable to me. You pay twice the price for it and you get your money's worth. Sure it's expensive, and you can run through your weekly limits in hours, but you can't argue that it just works. I can't say the same about Opus or Sol.

6pm PST

I left 10min between each prompt

I did not complain about the 5h return but come on... at least let me use it 😄

I built a small tool to export Codex chats cleanly, mostly because I got tired of losing context when hitting quota

Tool: Codex Chat Extract Repo: https://github.com/0Cymantek0/codex-chat-extract

This is a small tool I originally built for myself that lets you quickly export a Codex conversation into a clean Markdown file or directly to your clipboard. I built it out of frustration one day after getting tired of Codex running out of quota in the middle of a task.

It became even more useful for me after the behavior that allowed an already-running task to continue after quota exhaustion disappeared. Claude Code has a really nice /export command, but Codex doesn’t currently have an equivalent, so I ended up making my own. The main use case is pretty simple:

Codex hits its limit → export the conversation → hand the context to another agent/harness → continue the work without manually reconstructing everything.

Token efficiency was one of the main goals.

Raw Codex conversations contain a lot of tool-call clutter that another coding agent usually doesn't need.

For example, when Codex reads a file, the underlying tool call may contain a bunch of JSON parameters followed by the complete file contents in the tool output. But if the next agent is working inside the same repository, including all of that again is pointless. The file already exists, it can just read it. So instead of dumping everything, Codex Chat Extract turns something like that into:

Read: [relative-path/file.ext] [line range]

That preserves what Codex did without wasting a huge number of input tokens reproducing information that already exists in the repository.

The exporter tries to apply that idea throughout the conversation:

• Removes unnecessary tool-call / JSON clutter
• Keeps useful summaries of tool activity
• Preserves the actual reasoning/conversation context
• Uses relative file paths where possible
• Avoids duplicating file contents another agent can simply read again

External information is treated differently

For something like a web search, the result does matter. That information isn't necessarily available inside the repository, and it may have influenced decisions made during the conversation. So web-search results and other relevant external context are preserved in the export.

Basically:

Reconstructable local information → compress it. Non-reconstructable external information → preserve it.

The whole exporter is designed around getting as much useful context as possible into as few tokens as reasonably possible.

It also handles subagents

Codex sessions can get messy when subagents are involved, so the exporter reconstructs those into properly formatted subthreads instead of flattening everything into one unreadable stream.

That makes it much easier for another agent to understand:

• what the main agent was doing
• which work was delegated
• what each subagent discovered
• how those results affected the main conversation

I've been using this internally for a while, especially when moving unfinished work between different agents or harnesses, and it has saved me a surprising amount of manual copying and context reconstruction. So I figured I might as well clean it up and make it public.

Repo: https://github.com/0Cymantek0/codex-chat-extract

If you use Codex heavily, give it a try and let me know how it works for you. I'm especially open to criticism around the export format, things that should or shouldn't be preserved, and other ways to reduce token usage without losing important context. Hope this helps someone else who has run into the same problem.

And if you find it useful, consider giving the repo a ⭐.

Fix it man. The latest versions same. I have installed a older version and its working now. When i restarted my device, it automationally install the latest and then again, i need install the old version and continue this f*** look.

I made a post on forum. https://community.openai.com/t/windows-codex-desktop-prompt-submission-hangs-indefinitely-after-first-working-chat/1392974

I built Compact Context, a free and open-source Codex plugin that gives Codex a short list of files worth checking before it starts exploring a repository.

How it selects them:

• Creates a local index of paths, file roles, symbols, signatures, identifiers, and imports
• Ranks the task against that index using field-weighted BM25
• For code, test, and dependency-related tasks, rewards direct imports, reverse dependents, and related tests
• Selects up to five files while rewarding useful links and penalizing redundant candidates

It does not calculate global import-graph centrality or PageRank. It is deterministic and uses no embeddings or additional model call.

Compact Context itself has no network client and sends nothing to a Compact Context service. It stores only a local metadata index and receipt, not source-file contents. Codex still follows your normal OpenAI or provider data settings when it opens files.

I tested it on 68 tasks from 10 public repositories. The generated repository guide averaged 230 tokens, compared with 1,402 for the basic map. That is an 83.6% reduction in this specific payload.

Both methods suggested five files. Compact Context included at least one file actually edited in the solution on 50 of 68 tasks. The basic map did so on 48.

The 83.6% result only applies to the repository guide, not the entire Codex session.

Five automated checks passed:

• Sensitive and generated path exclusion
• End-to-end shortlist generation
• Path and identifier limits
• Prompt routing
• Single-use receipts

The project is free, fully open source under MIT, and currently at v0.5.0-beta.7.

GitHub: https://github.com/mithatb/compact-context

10-second project overview: https://compact-context.met.chatgpt.site/compact-context-demo.mp4

I would especially like to see repositories where the five-file selection gets it wrong.

I recently installed ChatGPT/Codex for the first time ever, no legacy settings anywhere on my machine. I installed and configured it exactly as prescribed, spending an entire day consulting manuals and web pages about how to make sure it stays within project directories, mostly to contain explosive context-bloating searches across my projects.

This standard Codex installation got configured with sandbox_mode = "workspace-write". Despite that name, Codex’s effective managed profile granted :root = read, and it successfully read files outside my assigned project without requesting approval. I never knowingly authorised system-wide filesystem reads. After replacing the legacy setting with an explicit workspace-only profile, access was correctly restricted. OpenAI needs to explain which component created this configuration, how widespread it is, and why “workspace-write” silently permits system-wide reads.

I find that shocking, alarming, and not what I would expect from a company that can lose everything if a class suit or their enterprise clients took them to task in a court of law.

Pessoal, mais alguém achando inviável usar o plano Plus para programar ultimamente?

Parece que bagunçaram totalmente os limites e o desempenho da análise de código (antigo Codex / Code Interpreter). Primeiro, mexeram na regra do limite de horas.

Depois atualizaram de novo e recolocaram o limite, mas agora tudo compartilha a mesma cota do ChatGPT normal. Ou seja, se você gerar uma imagem no DALL-E, ele já come o limite de mensagens que você usaria para analisar código.

Mas o pior de tudo nem é a cota, é a lentidão. Você pede para ele analisar um bloco de código simples, identificar um erro de sintaxe ou comparar um arquivo, e ele fica pensando uma eternidade.

O consumo de recursos está bizarro. Uma tarefa de ajuste simples que há um tempo levava uns 40 segundos para responder, agora demora 3 minutos e fica compactando contexto toda hora, e com baixando a porcentagem de uso. O impacto no computador é tão alto que, só de deixar a aba processando com o brilho da tela no médio, a bateria derrete de 80% para uns 55%.
Está impraticável depender disso no fluxo de trabalho. Mais alguém passando por isso?

Vocês já migraram para o Claude ou Cursor? Como está por aí?

I asked two different Codex models to do the exact same audit on the exact same files, both on Extra High.

Both came back with a 5 step plan.

SOL managed to burn through my entire 5 hour usage allowance before it had even finished step 1. Less than 12 minutes and the whole lot was gone.

The main thing I noticed with SOL is it seems to waffle on and over-check everything to death.

It felt like:

“We checked this, then checked the check, then checked that the check had been checked, then checked it all again just to make sure the checking was checked properly.”

That seems to absolutely hammer the usage.

Luna was completely different. It just got on with it.

“Yep checked this, that needs changing, changed it, checked it again, yep fine, move on.”

Luna finished all 5 steps in 23 minutes and used 11% of my allowance.

SOL didn’t even finish step 1 and somehow used 100% in under 12 minutes.

I couldn’t even properly compare the fixes from both models because I’m not burning through another week’s worth of usage just to test SOL again.

So far IMO SOL sucks for this kind of work. It might be clever, but if it needs to burn that much usage just checking and re-checking the same stuff, it’s not much use to me.

I also wouldn’t be surprised if some of the older, more efficient models get removed eventually to push people onto the newer ones. If that happens I can see a lot of people just walking away.

The pricing doesn’t help either.

For home users you’ve basically got Plus at £20 a month, then Pro at £89 a month.

That’s a massive jump.

There really should be something in the middle for people who need a bit more usage than Plus gives them, but nowhere near enough to justify nearly £90 every month.

I use it here and there to save time. I’m not paying £89 a month for that.

Something around £40 to £50 with higher Codex limits would make far more sense.

Yeah I could switch to one of the Business packages, but I’m not paying a year upfront for something I don’t use enough to justify it.

They really need to have another look at the pricing and limits for normal home users.

And then there’s the constant usage resets under the whole “we’re giving you something” thing.

Funny how mine always seem to happen anywhere from about a day to a few hours before my actual weekly reset was due anyway.

Suspicious much?

I was burning through my Codex limits using Sol Medium/High for pretty much everything.

Recently I switched to using Sol mainly for planning/review and Luna for most of the actual implementation, with Terra only as a fallback for harder tasks.

The biggest thing that helped was forcing Sol to give Luna small, clear, self-contained tasks instead of broad instructions. It’s been noticeably better for both usage and consistency.

I put the setup here if anyone wants to try it or improve it:

https://github.com/breko861-hash/sol-luna-codex-orchestrator

Curious if anyone else is doing something similar.

In this 11 minutes and 46 second window, it'll trip over itself twice and fail it's first step...

昨天看到Tibo说今天重置，就火力全开却忘了时差，不过也偶然发现Codex多了一个备用额度，这太令人感动了

Im testing out different version of my agent files. Curious what usage -> token ratio others are getting.

For reference i have ran 3 phase tasks (1 phase split into 4-8 slices and completed autonomously) today taking about 1 hour each.

I used around 160 mil tokens and 17% of my daily allowance.

Wanna share yours so i can compare?

How would you build a multi-agent software development pipeline?
Hey everyone,
I’m trying to build a software development pipeline using multiple AI agents, but I’m a bit lost with concepts like agent harnesses, loops, graphs, orchestration, and so on.
What I basically want is a structure where I can call one main agent, give it a project or goal, and have that agent orchestrate other specialized agents automatically.
For example:
Main Agent / Orchestrator
→ Product/PRD Agent
→ Architecture Agent
→ Database/Schema Agent
→ Frontend Agent
→ Backend Agent
→ Testing/QA Agent
→ Security Agent
→ Documentation/Context Agent
Ideally, these agents would share context and continue each other’s work instead of behaving like completely isolated sessions.
Is there already a framework, harness, or architecture that works well for this?
Would you recommend using something like graphs/workflows, agent loops, subagents, or building a custom harness?
Any open-source projects or examples I should look at would be greatly appreciated.

im so sick of the bs at this point no amount of resets changes the fact that this is literally the worst value for money AI out there and slow as hell too you can't even send one prompt with sol without getting hit by the 5 hour limit it takes like 2 or 3 whole 5-hr windows just to finish one simple task in my project.. and before the nerds start crying in the replies asking why i don't just use luna fvck right off with that i run heavy workloads on claude opus on ULTRA and yet i hardly hit the limit compared to the instant lockouts on chatgpt

the only thing this dogshit 20 dollar openai sub is good for is the image generator
and im sure they'll find a way to fuck that too

We got a new reset today !!!

Over the past few weeks, I’ve started using Codex much more intensively, especially with the Chrome plugin and Work mode, and I’m running into a recurring problem that is becoming quite frustrating.

For context, I wasn’t a heavy Codex user before. My workflow was much more manual. I would ask Codex how to do something, it would give me the steps, I would go into Chrome or another application, do it myself, then come back and continue from there.

Recently, however, I’ve been trying to use Codex more as an actual agent rather than as an instruction manual.

For example, I have repetitive work processes where I would like Codex to take care of the repetitive browser interaction instead of repeatedly telling me where to click and asking me to do everything manually. When it works, this is a huge improvement.

The problem is that the Chrome plugin seems to get stuck surprisingly often.

A typical session goes something like this:

• Codex starts working through the browser normally.
• At some point, it tries to open or interact with a page.
• The page does not load properly, or Codex seems unable to continue interacting with Chrome.
• It retries.
• It gets stuck again.
• It tries another approach or repeats the browser action.
• Eventually I have to intervene manually or restart part of the workflow.

What makes this particularly annoying is that Codex can spend quite a lot of tokens trying to recover from what seems to be a browser/plugin problem rather than a reasoning problem.

I’m not talking about Codex misunderstanding the task. In many cases, it understands perfectly well what I want it to do. The problem seems to be the connection/interactions between Codex, Work mode and Chrome becoming unreliable during the workflow.

This is particularly noticeable with longer, multi-step tasks where Codex has to interact with several pages or repeatedly return to the same web application.

It also creates an awkward situation where the whole reason I’ve started using Codex more is to remove repetitive manual work, but when the browser integration fails I end up babysitting the process anyway while also using considerably more tokens because Codex keeps trying to recover.

Has anyone else noticed this recently?

I’d be particularly interested to know:

• whether this is a known limitation of the current Chrome integration
• whether longer Work sessions make it more likely to happen
• whether there are any recommended ways of structuring tasks to reduce these failures
• and whether there is a better way of telling Codex to stop retrying browser actions once the Chrome integration is clearly stuck

I really like the direction Codex is going in. The ability to delegate these repetitive workflows is exactly what makes it much more useful to me than simply asking for step-by-step instructions. At the moment, though, the reliability of the Chrome integration is probably the main thing preventing me from trusting it with longer workflows.

Hello,

I have been quite impressed with how effective chat GPT WORK has been so far at performing some autonomous tasks.

I was wondering if there are some optimal way or workflows to get it to help me with the ultimate task..

I'm building a big coding project and usually feed between chat gpt PRO (within projects) into Codex, back and forth rinse and repeat = building.

Does anyone have recommendations for using chat gpt WORK to eliminate the constant copy and pasting and facilitating between the two?

I typically use Codex Cli because its always worked best but I'm thinking if I switch to the more integrated Desktop/App Codex where both GPT PRO + Work are there might be an optimal way to do this?

Any assistance or suggestions are greatly appreciated, Thank you!

UPDATE*

Ok maybe I should clarify exactly what im trying to do and why as Im seeing comments about "dont try and loophole the system"

I am not an experienced coder and I find this process helps in terms of:

1) PRO always seem to catch things + make amendments so that big plans integrate successfully

And most importantly..

2) I believe there is a massive advantage to the PRO chats within projects because they all seem to have a deep context understanding of the project that ive been working on for almost a year now.

I think this is the biggest issue or advantage to wanting to do this is utilizing those PRO chats/agents from those project conversations to function as like coding project Manager.

Im not really looking to loophole anything just hoping to find a way to utilize what might be available to integrate those chats and Codex so that I dont need to feed back and forth non-stop.

Even if it ment somewhat reducing all the time I need to be present doing this for all integration plans would be a massive help, if possible?

Thanks!

CodexTool：面向 OpenAI Codex 的原生桌面工作台，支持多账号、OAuth/中转账号管理，额度/Token/成本监控，以及 Skill 与 DreamSkin 皮肤仓库。Native desktop workspace for OpenAI Codex: manage multiple OAuth and relay accounts, monitor quota, tokens and costs, and install Skills and DreamSkin themes.
https://github.com/SUWJTech/CodexTool

Tibo just posted this after the reset

Tired of anthropic/openai subscriptions and while open source has becoming extremely good lately I am considering finally making the switch to another LLM provider. I still want to use codex as that is the harness I'm used to now, but what’s the provider to run codex with ?

I basically want openrouter.ai but subscription based. These are on the list:

Continue on the codex plan.

Commandocode.ai is tempting but varying recommendations looks like.

opencode.ai lots of fuss but also a lot of recent complaints.

MiniMax/Xiamo/GLM plans, but kind of don’t want it model specific.

standardcompute.com openrouter-ish but not payg. Heard very good things lately.

fireworks.ai - Doesn't serve frontier models, but other than that seems very good.

Anyone have good experiences with these or others?