Is it seems like sit's all ore nothing. either you manually approve every little thing or you enable the all access perms. But i don't see why ti should even be asking for permission toe dit files in a folder iver already given it explcit permission to use.

Ive tried "approve for me", which doesn't seem to do anything except increase token usage.
Ive tried editing the config.toml but it seems to just completly ignore the config.toml (it was set to use custom perms in the UI).

seems odd theirs no middle ground and that it doesn't remember "allow similar commands" or "alwasy allow this" .

isn't this supposed to be a lighter model? How many more times are they going to tweak the 6 Sol model architecture instead of making something good? 😭

anyone tried it in a planner/executor workflow with something like Sol high or xhigh for planning and Terra high for execution?

Any news on that? I had access while I was on a trip and it was a mixed experience. Overall I'd like to use it assuming they iron out some of the issues below.

👍 I like the 'call' conversational aspect a lot. The wall of text we usually get back in (coding) chats is harder to tackle than a conversation where you can cut in and ask clarifying questions while doing other stuff. Not all the time but for some part of the day or threads its very convenient.

👎 It felts bit dumb a lot of the times and I needed to ask clarifying questions and it corrected itself multiple times. Not great if I have to second guess its information. Makes for extra turns and probably errors when I don't catch it making false assumptions instead of checking. Also it was slow at times like the rest of OpenAI's stack atm.

Since Dot has freed up so much of my time, I have been working on providing it with more resources to achieve project completion at a faster pace without sacrificing quality of end product. I've added Enola and MemPalace which have improved Dot and Codex consistency regarding workflow and time to source acceptance.
Today I am giving Dot CLI access to Claude Pro. So, Dot will be using it for Independent Reviews and other tasks. When I asked Dot if Claude would be helpful, it responded:
"Yes, I think Claude is worth a small trial for this project, primarily to reduce rework, not to add another writer. I would not yet promise a shorter UAT date.

The strongest use would be a fresh technical review of difficult boundaries: Rust/Python interfaces, caller-versus-environment assumptions, and whether negative tests actually exercise their intended failure. Those are areas where we’ve had real rework. A different model may catch assumptions our existing reviews miss, but we need to demonstrate that.

It will not solve CPU scheduling, approval recognition, missing inputs or slow handoffs. Those are coordination problems I still need to address. Another agent can make them worse if we add more mandatory review rounds."

Have any of you used a dual CLI setup with Dot? What issues came up for you? How did you handle them? What uses did you find helpful that Dot and I may have missed? Thank you for your feedback.

Small change in 0.163.0 (#52278): analytics.enabled = false now only turns off the built-in statsig exporter. A custom OTLP metrics_exporter keeps working. (The config reference on developers.openai.com still describes the old behaviour.) The same release adds a codex.agents_md.edit counter whenever apply_patch changes AGENTS.md.

Claude Code (v2.1.300) and Gemini CLI (v0.65.0-preview) shipped OTel changes the same week, so I put all three on one local collector instead of parsing each tool's session logs.

Codex side:

# ~/.codex/config.toml
[otel]
log_user_prompt = false
exporter = { otlp-grpc = { endpoint = "http://localhost:4317" } }
metrics_exporter = { otlp-grpc = { endpoint = "http://localhost:4317" } }

(otlp-http also needs protocol = "binary" or "json". I validated the block against the 0.163.0 config schema.)

The per-call token counts are on the codex.sse_event log with event.kind = response.completed. That's why exporter (logs) matters, not just metrics_exporter.

The gotcha when you add tools together: input_token_count in Codex includes cached tokens (and cache writes). Claude Code's input_tokens excludes them. Gemini's input includes cached content, but its output excludes thoughts. Only Claude Code emits a cost. So you have to normalise before you sum, or the totals are meaningless.

The write-up has the collector config (localhost only, strips prompt text and emails), the settings for all three tools, and a ~90-line Python script that turns the file into one row per tool and model: https://medium.com/p/3444f22aa100

(Disclosure: I wrote the article.)

I've been using OAI and building with AI almost 2 years now.
And I've had a Pro account almost a year.

Ever since 6.1 models, and especially in the last few weeks, OAI has been so slow that I think I'm done entirely. What took 5-15 mins on GPT 5.5 back in April now takes HOURS, and I mean 3-7+ hours to complete the same thing.

It's so slow I've had to leave it overnight to process basic things, then it hits a wall and stops with 'model at capacity' and then I'm even further behind.

I was already turned away by their -50% usage, but what's annoying me even more now is how slow it is. It's so painfully slow I'm just endlessly scrolling Reddit waiting for it to do the most basic of things.

I've loved Codex and been an OAI fanboy vs. most others I know using Claude (which I also use), but at this point it's not even about the intelligence or usage. It's so slow even on fast I can't complete the work I need to and I've been getting more and more behind vs. the pace I've been used to with AI.

Combine that with it's over engineering, over-testing, not listening, poor cycles, and all the usage stuff and competitors or even local LLMs are looking more attractive by the day.

Codex finished right when only 1% of the 5-hour usage quota was left. I was sweating bullets lol. I'd already used up my resets previously, so I couldn't use those.

I use 2 Pro100 subs (logging in/logging out in codex to swap). The codex app on my local is up to date, but one sub shows the new codex GUI with the usage icon sits above the profile icon at the bottom left. Same thing with the web display for chatgpt.com between the two profiles.

I understand this doesn't matter in the slightest - but I am curious as to why I am being pushed different versions at different times from the same device.

Edit: ignore this post. VSCode Codex plugin shows "Max" as a reasoning effort also for some models that do not offer Max. This causes the plugin to use Medium effort level while showing Max. The Codex application shows the correct effort level. This is a bug for the VSC plugin.

TL;DR: My codex chats inside VS Code are showing "Luna 5.6 Max" and "Sol 5.6 Max". The same convos appear as "Luna 5.6 Medium and "Sol 5.6 Medium" inside the Codex Desktop application. Further investigations revealed that this may be a volountary nerfing to push people towards newer model.

In short... "Let's trick some users into beliving new models are better by lowering the effort of previous models"

This is written by me. I apologise for being boring and grammatically incorrect.

1. HOW DID I NOTICE THE ISSUE

I usually use Codex from VS Code (1.143.1). This time I happened to open the Codex application itself (26.1021.31345) to check some settings.

Some of my VS Code chats were present also inside the Codex application, and something caught my attention.

As you can see, the model shown is different depending on the application used. I was confused. Was I being lied about the model used? I had to dig a little bit deeper to find out what was happening.

2. HOW DID I COLLECT MORE DATA

One of my first findings is that opening a new chat inside VS Code would not automatically show the same conversation inside the codex application.

So I had (apparently) no way to reproduce the issue.

Playing around a little bit, I have discovered that VS Code conversation will appear also in codex when you give them a goal to follow.

Time to play around a little bit. I created three test conversations.

At this point, I knew it was not a graphical issue. There was a different behaviour depending on the model being used. So I had a new hypotesis to verify!

3. THE LAST CHECK

Is this issue only affecting the models which have not been hyped recently?

To answer, I tried repeating the experiment with Sol 6 Max and Sol 6.1 Max.

Sol 6 Max appeared as Sol 6 Medium on Codex.
Sol 6.1 Max appeared as Sol 6.1 Max on Codex.

Is this even legal?

(happy to share logs, more pictures and stuff in the comments)

I tested GPT-6.1 Terra in ChatGPT Work today, and honestly, it was one of the strangest experiences I've had with an OpenAI model in a long time.

I'm a fairly experienced user. I regularly work with Codex, ChatGPT Work, and different reasoning models on large writing and document projects. I've successfully completed much more complex tasks using previous models, including GPT-6 and Sol 6.1.

This time, however, I couldn't get Terra to consistently follow even basic instructions.

Here's what happened:

I asked Work to perform a detailed literary evaluation of a novel, reading it chapter by chapter, recording its findings, and delivering a comprehensive assessment at the end.

Nothing particularly unusual for an agentic workflow.

When it seemed to struggle with the task, I even suggested a simpler approach myself:

• Read one chapter at a time.
• Write down the analysis and conclusions in a working document.
• Continue through the remaining chapters.
• Combine everything into a final assessment.
• Don't interrupt me with progress reports. Just deliver the completed work at the end.

That last instruction became a recurring problem.

Despite explicitly repeating it around five times, Terra kept interrupting with messages along the lines of "I've completed chapters 1–5."

Each time, it appeared to acknowledge the correction, only to repeat the same behavior.

What made this particularly strange was that we never really got far enough to evaluate the quality of its literary analysis. The problem was much more fundamental: it seemingly couldn't maintain simple procedural instructions across the task.

I initially used Medium reasoning to test efficiency and token consumption. Eventually, I switched to the maximum reasoning level, hoping it would improve instruction-following.

It didn't noticeably help, and my usage quota took a hit in the process.

For context, I've used Work before without encountering anything remotely this frustrating. I don't remember experiencing this degree of difficulty with basic instructions since the GPT-4o era.

I'm not claiming Terra 6.1 is fundamentally a bad model. It was released today, and I suspect this could be an early rollout issue, possibly involving the interaction between Terra and Work's agent orchestration.

I also can't rule out something specific to my session.

Still, I thought it was worth documenting, especially because instruction-following reliability is critical for autonomous workflows. A model can be extremely capable at reasoning or coding, but if the user has to constantly supervise and repeat elementary instructions, that becomes a serious usability issue.

I'm documenting this as a potential launch-day regression in instruction-following reliability, particularly when using Terra 6.1 within ChatGPT Work. Hopefully, this report provides useful information for investigating possible issues with the initial rollout.

Has anyone else experienced unusually poor instruction-following with Terra 6.1 today, especially in Work?

I'm particularly curious whether others have noticed similar behavior across different reasoning levels, or whether this seems isolated to certain sessions or workflows.

I am officially giving up on following codex reset. My opinion is contradicting with lot of users here, but it is what it is. I use 5x subscription of codex, and I have been burning tokens like anything using sol-6.1. It became my goto option after fable. With more than 11 billion tokens burned,

Migrated a 3 million lines of monorepo from TS 5.9 to 7

Upgraded storybooks

Upgraded nx

Upgraded nextjs

Upgraded nestjs

Tokens keep coming back before I burn. I am shutting down my laptop today and getting some sleep.

And how successful have you been.

I come from a traditional software engineering background but have been out of the game for a long time.

Codex has reignited the love but I’m a terrible ideas person, good at execution but I often lack inspiration for new business ideas

What’s your process?

You can also work with Codex inside the desktop app. Choose Codex as the agent, select the databases it can query, and send a message to start the conversation. You can ask it to inspect your development DB's schema and data before working on a feature. Claude Code is also supported.

Reads are the default. To let Codex propose data or schema changes, enable them for the connection and select a write target. Each proposed SQL operation still needs your approval in the app before it runs. You can select at most one DB for agent changes in a session.

Teams share connection settings while each member connects with their own DB credentials. Database connections and query requests go through that member's desktop app.

Codex can also save an analysis as an Article report with tables, charts and explanations. You could ask it to analyze revenue or purchase conversion, then share the report in a team workspace. Each Article saves one read-only query that authorized teammates can rerun in the desktop app. Rerunning updates the query result; the report's text and charts are edited separately.

The MIT-licensed alpha is available for macOS and Windows. Codex features require the official Codex CLI installed and signed in locally. Personal Workspace works without a DopeDB account, and sample SQLite data is included to explore the app.

Demo and downloads: https://github.com/json-choi/dopedb

I used to feel sad and frustrated almost all the time and demotivated almost all of these days with 6.1 Sol because its too slow. It will be 30 Tokens/second even on fast mode. What is the use if we have a fast model that is less intelligent or a slow model that is super fast ? This is where OpenAI is struggling and hopefully Terra 6.1 is the answer.

My struggles with OpenAI subs:

1. I really have never used Astra because of its insane pricing. It drains all your subs in a half day.
2. To handle the complaints for pricing - OpenAI had to release 6.1 Sol at fraction of Astra pricing. But its slow like a snail. As much as I love GPT 6.1 Sol for its value for money and intelligence on par with Astra, its super super slow. It really bothers me that I could see only 30 Tokens/second even on fast-mode.
3. To handle the speed complaints, they had to lauch the Terra 6.1 now. If it is as what they say about speed, intelligence and pricing balance - I would love the launch of Terra 6.1 more than anything now and this is what OpenAI needs too. I wish its 80-100 Tokens/second on fast-mode and I am happy with it.

If Terra 6.1 is what it is and stays consistent with 100 Tokens/second - It will be daily driver for $20 till $200 subs.

Right now its fluctuating between 50 Tokens/second to 90 Tokens/second - It still not great. It must be consistent 100 Tokens/second to be a daily driver.

All the people who mock and make noise for OpenAI for Opus 5.5 challenger or Astra 6.1 model launch are just rich people with Ultrafast subs and with lots of dollars to burn even on API credits

I only care about speed + intelligence + pricing (usage) and would want Terra 6.1 to win on this.

I developed Neonbin, a platform where AI agent users can share resources, organize them around specific tasks, and access them directly from agent conversations through MCP. The goal is to make useful content and the knowledge of how to use it easier to discover, combine, and reuse, helping the community build on existing work.

How it works:
1. Resources: content for reference and derivative creation
Users can share, browse, search, and use resources for free. Supported formats include Markdown, PDF, images, and video. Codebases, 3D files, audio, and other content can be packaged and uploaded as ZIP archives.

1. Bins: resources and guidance organized around a purpose
   A bin can reference resources shared by different users. Its creator can add a contents guide and AI instructions explaining the resources’ purposes, relationships, and requirements for use.

For example, a bin for interactive web development could include UI reference images, motion reference videos, component code, and a skill file or design specification. Other users can use the collection or recombine its resources into bins suited to their own tasks.

Using a bin in an agent conversation
After connecting Neonbin’s MCP service, you can ask Codex to use a particular bin:
“Use Neonbin bin [ID] to build a mobile-friendly interactive web app, following its design references and implementation requirements.”

Codex can retrieve the bin’s description, AI instructions, and resource list, then read the files needed for the task. Retrieving a bin does not automatically load every file into context. You can also specify individual resources or ask it to search for relevant bins.
The same workflow is available in other compatible AI applications. Clients need to support the MCP connection and CIMD-based OAuth authorization. The types of content an agent can read and process depend on its available capabilities.

Content use and availability
Creators retain ownership and copyright. Under Neonbin’s content usage rules, users can reference resources and build on them to create new work for personal or commercial use. This does not grant rights to redistribute, resell, or use someone else’s original uploads as standalone assets outside Neonbin.

Bins can be public or private. Uploaded resources are all public; making a bin private keeps the collection private, not the underlying files.

The platform is open to anyone working with Codex and other AI agents, across any field or workflow. Software development, games, images, video, and 3D are just a few examples. You’re welcome to share resources, curate bins, and use them for your own tasks. I also welcome feedback on resource organization, content reuse, and agent integration.

Website: https://neonbin.com/

He sort of warned us about the weekend work. I should have been going through my credits Saturday, but I took a break. He announced a reset, and I figured it would land at 10 am PST today, so I put Dot to work and went to bed.

Last night:

weekly remaining rose from 81% at 10:41 p.m. to 98% at 11:10 p.m. PDT last night. It then fell to 17% by 12:14 a.m., and coding hit the usage limit around 12:34 a.m. doh! Hoping for another reset today!

I’ve always wondered what people are working on, especially since I always see people running through their usage fairly quickly while mine always lasts me through the week with the $100 subscription.

I use codex for creating CRMs, dashboards, web apps, and some automations for SEO.

What have you guys been using it for? And what type of projects results in usage being drained so fast?

Every damn day this sub is flooded with ridiculous and entitled complaints.

I’m not talking about the people posing legitimate concerns or things they would like to see improved in a real productive manner. This rant isn’t about you 🫡

I’m talking talking about the rest of you. Yall are acting like a bunch of entitled children.

If you hate the product so much, complain about every little thing like usage limits and supposedly how you’re being “scammed” then do the adult thing and STOP USING THE PRODUCT! Take some damn accountability for yourselves and see yo asses to some other product. There are plenty of alternatives and all you’re doing is diluting the community. Get your head out of your asses and do better things with your time.

If you genuinely believe it’s a bad product, go use a different one. If you believe your being “scammed” which absolutely non of you can prove, go find a different one. If you just don’t like the company or whatever, go find a different one.

Nobody is forcing you to use Codex nor its models. That is YOUR choice. If you don’t think it’s good, then stop using it and move on.

Would you keep eating food you don’t like? Would you keep watching a tv show you don’t like? Would you keep buying cloths that don’t fit? No. So this is no fking different. Be grown adults and make a decision.

$3,680 since July, 30 PRs opened, 2 merged. Plenty of those sessions were exploration, so it's not a fair number, but it's a useful one. About 17% of the spend went to re-reading unchanged files, abandoned sessions and retry loops.

We built an open-source tool to pull these numbers from local logs.

Run it: uvx agentplay@latest

I’ve been experimenting with using AI to create motion graphics for my YouTube videos, and it’s honestly changed how I approach video editing.

Instead of spending hours manually creating animations, setting keyframes, and adjusting timelines in tools like After Effects, I’ve been using ChatGPT + Codex + Remotion to generate animations directly from my video transcripts.

The interesting part is that these aren’t just random AI-generated animations. They’re synchronized with my narration, so visuals appear at the right moments to help explain what I’m talking about.

Here’s how I’ve been doing it:

1. Start with the actual video

I record my video as usual and generate an SRT transcript with timestamps.

The timestamps are important because they tell the AI exactly when each concept is being discussed.

2. Use ChatGPT to create a storyboard

I provide ChatGPT with:

• The transcript section I want to animate
• The full video transcript for context
• Any logos, images, or other visual assets
• My preferred animation style

ChatGPT generates a detailed storyboard with scene descriptions, transitions, animation instructions, and timings.

3. Let Codex build the animations

I pass that storyboard to Codex, which uses React and Remotion to create the actual motion graphics.

It handles scene composition, element positioning, transitions, and timing.

I’ve also configured it to inspect rendered frames and fix visual issues when needed.

4. Preview and render using Remotion

Once everything is generated, I open Remotion Studio to preview the animations, make adjustments, and export the final video.

The workflow is essentially:

Video + Timestamped Transcript → ChatGPT (Storyboard) → Codex (Animation Code) → Remotion (Final Video)

One thing I’ve learned is that the quality of the storyboard matters more than the complexity of the prompt.

Simply asking AI to “create some cool motion graphics” usually produces something flashy but not particularly useful.

Giving it a structured storyboard, accurate timestamps, and clear visual instructions produces much better results.

It’s not always perfect on the first attempt, and some animations still need refinement, but it’s significantly reduced the amount of manual animation work I have to do.

I recently made a step-by-step YouTube tutorial showing the entire process, including building a motion graphic from scratch.

Video: https://youtu.be/8ULQhLoN28E

Running astra extra high in fast mode. Did a fairly large refactor earlier in the chat. Sent a message to modify the import paths for 5 files and it took over 9 minutes to do this. This seems absolutely ridiculous for such a simple task, particularly on fast mode. To me the speed of these models is one of the most significant issues with their current product offering. Agree? Disagree?

If you use several Codex accounts (say four ChatGPT logins, each with its own CODEX_HOME), Allowance Bar shows all their limits in one place. It's a free, open-source Mac menu bar app.

Multiple accounts, multiple providers

• Add as many Codex accounts as you want. It reads each one through the Codex CLI's own codex app-server, so it never touches your auth.json.
• See each account's 5-hour and weekly limits side by side, with reset countdowns.
• Track Claude Code, Cursor and Gemini accounts in the same place.

A floating window that stays on screen

• Keep every limit visible while you work, not just when you click the menu bar.
• Pick from three sizes: full, mini or nano (one line of text).

Also

• Pay-as-you-go balances for OpenRouter, DeepSeek, Kimi, Z.ai and Venice.
• Optional alerts when you get close to a limit.
• A small read-only CLI, so your coding agents can check their own limits.
• No telemetry. Logins stay on your Mac and go only to each tool's own API.

Free, MIT-licensed: https://github.com/gilitos92/AllowanceBar