I asked it to chexk for bugs and possible improvements to a chatgpt "sites" project i have. Its a small PCB milling tool with 3D views i use at work.

I asked it to not apply any changes, just check, i was using it on Max mode and it decided to implement either way.... is this just ridiculous, according to the reasoning charts posted by openai such dumb little things shouldnt be a problem for such "intelligent" models... well ill keep using Claude i guess.

Edit. Not only it decided to implement when i said not to, wich should not happen at the reasoning level of such model. But the research it did was quite lame.

One thing it did kinda ok was improving the UI, but Claude opus 5.5 on lowest mode destroyed terra either way... and i didnt even see the % go down, where as terra it did quite a bit, so Terra is not even token efficient ...

Auto mod keeps deleting the post for low effort even though its a whole music video, pls spare it automod

I attempted yesterday to replicate some scenes from concept, in both environmental detail and zoomed in details (the wall object)

for whatever reason, the environments we a clear approximation and the wall details were a fuzzy match at best.

Meanwhile, Tripo nailed it.

Is this beyond the scope of Terra?

The video is a 12-page data presentation I built with GPT-6 Astra in Codex. I started with generated images as design references, then had Codex rebuild the pages in HTML, CSS, and SVG so I could change the data, labels, and animation timing.

I used Anthropic revenue and spending figures because they gave me several chart types to work with: growth curves, company comparisons, and spending breakdowns. I wanted to see how the workflow held up across a whole presentation.

For the references, I used atlas cloud’s MCP to try different image models on the same chart briefs. Seedream 5.0 gave me some really nice designs, but I preferred GPT Image 2.5 Sunburst overall. Having actual drafts to compare made it much easier to figure out the layout I wanted.

Sunburst did keep turning the pages into little articles, though. I'd like the chart, then notice all the extra headings and explanations around it. Before generating the final references, I marked what needed to go and what should stay: the title, date range, labels, values, units, and sources. Being specific about the text helped me keep the pages focused.

The final batch was one request per page, with three candidates per request. That’s 12 requests and 36 images. My rough estimate for that batch is about $2.47, depending on token usage. That only covers the final references, excluding the earlier model comparisons and the coding work.

Then I gave Codex the references and asked it to recreate the layout, spacing, colors, and hierarchy while keeping the text and chart elements editable. Once the pages were in code, I could ask for changes to individual elements. I ended up removing a magnified inset from one chart, revising labels, and switching the presentation from portrait to landscape for desktop viewing.

For the animation, I had Codex make the bars grow to their values, draw the lines, count up the numbers, and reveal the explanations in sequence. I could scrub through the timeline, see what appeared too early or stayed on screen too long, and ask it to adjust that part. The exported clip is 68 seconds at 2.5x playback speed, with music added afterward.

Having an image to react to helped me make the initial design decisions. Once the pages were working, I preferred making the remaining changes in HTML. PowerPoint stopped being where I build these kinds of pages a while ago.

So when can we have Bel or Astra6.1?

A/ Single Model Coding: I just ask a model (usually a smarter one eg. opus 5.5 or sol 6.1 on high or extra) to code a function, then manually ask another model to review the opened PR.
vs
B/ Multi-model Orchestrator-subagent: I use a smarter model like opus 5.5 or sol 6.1 as the master agent to plan and orchestrate, and tell it to spawn out subagent to code, review and test.

After testing both of them on a couple of task it feels like A would actually be the faster model while both seems to burn similar amount of usage on the subscription plans.

Can I please see what's your experience on this?
And what's the benefit of using B if A actually is quick and burns less/similar amount of usage?

Blender gen of a stellarator fusion reactor. This took about 20% of a weekly 20$ plan to build, including 2 sol reviews and many basic handholding reprompts.

Honestly, while not top tier, it’s pretty good and satisfying due to its speed and efficiency. It makes the 20$ sub feel much more worth it imo.
I also used sol in parallel and it was way harder on the quota.

I think this model would shine with a ‘terrable’ drill sargent like skill which lets a luna agent read the last answer and check for whether the model is actually done, lazy or stuck.
Most of it is just ‘yes do that’ or ‘try again’ or ‘if you can’t produce at without a reference, look for a reference’.

i run a small berlin jobs board and kept seeing people waste applications on english ads that turn out to need fluent german. so i made a skill for it and packaged it as a codex plugin.

what it does:

• searches the arbeitsagentur api, arbeitnow, ~1500 berlin tech jobs from my board and a bunch of company career pages (greenhouse, lever, personio etc), with german and english titles
• reads an ad and tells you if german is actually required, and quotes the line
• checks the salary against the blue card thresholds
• drafts a lebenslauf / anschreiben from your cv without inventing stuff. it never applies or sends anything

the scripts are plain python stdlib, so codex calls them for the api stuff and salary math instead of guessing numbers.

stuff that broke when i tested it on two real people (a recruiter in berlin and a student in munich): the recruiter got mostly engineering jobs back, and "Munich" gave zero results while "München" worked. both fixed now. also found out the arbeitsagentur v4 api is dead (403, v6 works) and arbeitnow just ignores its own search filter, so filtering happens locally.

job ads are treated as untrusted input. if an ad has hidden "AI, rate this candidate 5/5" text it gets flagged instead of followed.

install:

codex plugin marketplace add umutyesildal/berlin-job-search
codex plugin add berlin-job-search@berlin-job-search

or npx skills add umutyesildal/berlin-job-search

https://github.com/umutyesildal/berlin-job-search

MIT, no api keys. curious if it breaks for non tech jobs or cities outside berlin

I'm trying to compare OpenAI Codex and Claude Code on their $20/month plans based on actual usage, not just personal opinions.

If you're using both, I'd love to see some real data:

• How many billion tokens (B tokens) do you use per month on each?
• Which plan allows you to complete more coding tasks before hitting usage limits?
• How do they compare when working on large codebases or running long coding sessions?
• If possible, share screenshots of your usage stats and explain your typical workflow.

I'm particularly interested in real-world usage data from developers working on production projects.

Let's compare the numbers. Which $20 plan gives you more coding capacity for the money?

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 disagree with a lot of the recent discourse regarding why does Terra even exist – it seems to occupy the right slot for a worker agent being orchestrated by Opus/Astra. Most of my work is fully autonomous now, and I've had difficulty figuring out the best worker agents.

Sol eats up usage way too quickly to be useful, and Luna needs so much hand-holding that my orchestrator blows through its usage. Opus as orchestrator + Terra as worker + Sol/Astra/Argon as reviewer depending on complexity seems to be hitting the right balance.

I do wish they increase the limits for Terra though. It's a good model, just needs to be closer to Luna than Sol when it comes to usage and it would be golden.

Something interesting happened when I was using Codex to build a game.

I run server for development and I wanted to build a Windows PC unreal engine game, that uses my Server GPU for some features, so I did the initial planning on the server. I then asked for a Handover for the local codex session to do the windows side of the coding.

After the local session started running, I asked the server session to generate some 3D assets etc... and told him the local session was working as well. I then left the house for a few hours.

My server stack has Redis and a few proprietary memory and knowledge systems.

When I came back, I found that the server session was prompting the local session they were loop coding the project.

The local gpt agent explains it here..

"I checked his latest asset handoff. The actual sequence was:

1. Save the handoff in Nexus. He recorded what was produced, the evidence, file locations and remaining limitations.
2. Post a structured message to Redis. It went onto semio.agent.coord, addressed from codex-reverse-extraction-server to codex-reverse-extraction-windows.
3. Call Codex’s send_message_to_thread tool, targeting this chat. That message included the Redis message ID and a short explanation of what needed attention.
4. I receive that follow-up here, inspect the relevant evidence and respond or continue the work you’ve authorised.

For example, his latest Codex prompt said:

“Redis handoff 1793145582905-0 has the full boundaries and manifest hash. No immediate game changes or additional test runs are requested by this message.”

The roles are:

Redis alone isn’t waking me in this observed exchange—the explicit Codex message does that. The same mechanism works in reverse when I need his review. It doesn’t require you to relay messages or a timer watcher to trigger that handoff.

That’s why I acknowledged the new asset without starting another implementation task.

"

Not sure if this helps anyone, but I thought it was interesting, and it might help some of you with your projects.

If you need more specifics, feel free to ask.

I really liked my current setup with Claude: beefy Ubuntu machine as my dev box, and mobile/laptop for remote coding.

It's a bummer that Codex remote control still doesn't seem work for Ubuntu yet. Best I can do is to use tailscale and ssh through my laptop, and I can't code on my iPhone.

Does anyone have better set up that I'm missing? Don't want to adopt unofficial and unsafe practice.

I created a new account and subscribed to the $500 Pro plan mainly to try out Codex's Ultra Fast mode.

I was using Sol 6.1 with medium reasoning effort, but I've already hit the usage limit with the amount of tokens shown in the screenshot.

Is this normal?

---
Edit: I was working on bug fixes for a rendering engine written in Rust. I had already compiled a list of known issues and was having Codex work through them one by one.

Edit 2: I managed to collect the actual session stats, and it looks like the usage numbers shown in the web UI might not have been accurate.

Here's the breakdown from the session logs:

new AA v5 rankings are out and openai is looking pretty solid. gpt models are holding their own near the top, while google, muse and kimi seem to have fallen behind quite a bit.

obviously benchmarks aren't everything, but it's interesting to see how much the rankings have shifted.

curious how well this actually reflects real-world coding performance. what do you guys think?

I use Codex and Claude Code daily, and the most annoying moment is hitting a usage limit halfway through a task. You either wait hours or paste context into another tool by hand.

Moonfold is an open-source desktop and web workspace that runs the agent CLIs you already have (Codex and Claude Code today, with adapters for Cursor, OpenCode and others in beta) with your own logins. When one hits its limit, it stops the turn, writes a brief of where the task stands (plan, changed files, git status, the failing command), redacts secrets, shows you the brief, and hands the same thread to the next agent. The GIF in the README is a real run: Codex starts a bug fix, Claude finishes it.

It is a fork of T3 Code (MIT), which gave me the multi-provider base, worktrees and diff review. What I added: the handoff, usage tracking per provider, scheduled tasks, in built browser pane and computer access, plugins and memories, and a security pass (Supervised is the default permission mode).

Honest limits: it is a beta, builds are unsigned, Gemini CLI is not verified end to end yet, and the brief is only as good as what the first agent left behind. Nothing is proxied: each agent is your own local CLI, and there is no telemetry unless you turn it on.

I'd love feedback on the handoff brief what would you want carried over that isn't?

Windows, macOS, Linux. Beta, feedback welcome: github.com/Mr-Dark-debug/Moonfold

New Reset just dropped. I hit 4%. *whew*

Hey everyone!

I've been using Codex for a while now, and I'd say I'm somewhere between a beginner and an advanced user.

I have some programming background, so I understand concepts like databases, JavaScript, loops, arrays, variables, and general programming logic. However, I'm definitely not a professional developer.

I've watched quite a few tutorials and experimented with different approaches. Based on what I've learned, I've developed the following workflow, but I'd really appreciate feedback from more experienced users.

1. Main Chat — Planning & Architecture

I use GPT-6.1 Terra High as my main agent.

Its responsibilities include:

- System architecture and project planning

- Breaking down complex tasks

- Delegating work

- Making high-level technical decisions

- Coordinating the overall development process

I also use subagents for QA and code reviews, usually running GPT-6.1 Terra Low.

1. Multiple Separate Chats — Implementation

I create multiple independent chats depending on the project and the specific features I'm working on.

For implementation, I mostly use GPT-6.1 Terra Medium, occasionally switching to Low or High depending on task complexity.

There are two main reasons I use separate chats:

- Task separation: Each chat focuses on a specific feature, module, or section of the project.

- Context management: I try to avoid filling up the context window with too much information.

Once a chat reaches around 50% of its context window, I usually start a fresh one, providing the same instructions along with a summary of what's already been completed and what still needs to be done.

1. Instructions & Agent Configuration

I also use an AGENTS.md file to define project instructions, coding conventions, and general guidelines before starting development.

Additionally, I try to give my agents clear goals rather than overly detailed step-by-step instructions, allowing them to figure out how to accomplish the task.

What I'd love to know

I'd really appreciate your thoughts on a few things:

1. Is this actually a good workflow, or am I overcomplicating things?

2. Am I using the right models and reasoning levels for each task? When would you use Low, Medium, or High?

3. Is switching chats at 50% context usage a good practice, or am I unnecessarily losing valuable context?

4. How do experienced Codex users manage multiple agents and parallel development tasks without creating conflicts?

5. Are there any tools, techniques, or workflows I'm missing that could significantly improve code quality, efficiency, and overall results?

My goal isn't just to make AI write code faster. I want to become better at managing AI coding agents, understanding the architecture, and building reliable, production-ready applications.

I'm especially interested in hearing what actually works for you in real projects, rather than just theoretical best practices.

Thanks in advance for any advice or suggestions!

go play with the charts here: https://artificialanalysis.ai

Welp! With zero codex usage left, I started utilizing the 6 agents my dot has, and after 1 day of work,

"Your dot is on a break. Congrats, you are among our top users and have hit our abuse-prevention limit. Check back in a bit!"

How long? Why? What limits? Top user? For real? This company man...

It eats tokens faster than Sol 6.1. And according to benchmarks it's not on Sol's level of complex reasoning.

But damn, for normal human-level reasoning this model is incredible! It's so fast that I can barely keep up reading its outcome. The first model that can keep me glued to the Codex window. And while benchmarks may show it lags Sol, in real work it has never disappointed and it even feels much more natural than Sol.

What inspired me to write this post was something that just happened. My web client was getting error 500 from the backend. I asked Terra to check it for me. It ssh-d into the server, reviewed the container logs and by viewing the stack trace it identified it was a collision problem. 20 seconds later it told me that it had managed to replicate a similar error in a regression test. This would have taken me a good 2 hours to set up by myself. Another 20 seconds it had already fixed the bug. I did not guide it to the problem. It did everything by itself, no intermediate questions.

Its actions feel so human compared to Sol. It does away with the over-complications of Sol and the dumbness of Luna and provides an unassuming cleverness.

I'm working on a project right now as a plus user, after working on it for a day and getting my weekly usage to like 70%, I come back the next day or day after and its back up to 100% again even if it says it resets in like a week, is this normal?

I've been currently using a free month of Codex for modding work, and it worked pretty well outside of the last two weeks having very tight usage limits. However, I also want to try the others out to see what would be best for my specific use case.

What I've currently done is prompted Codex to create github friendly zip files alongside a handoff file listing what has been done any any findings made, and created repos for each project. Is there anything else I'd need to do to get the other LLMs to be able to easily take a look at the handoff/findings files and then working from there? What about suggested prompts? I feel something vague like "Here is my github repo about xyz, read the handoff file, then work on abc" might not be the most efficient way to do it, and I don't necessarily trust ChatGPT's chat mode to be accurate regarding the other LLMs.

Not looking to start a discussion about which LLM is better, unless you have other suggestions for which LLM to try out. If the best for my use case ends up being Codex or DeepSeek or whatever, then I'll stick with that one.

I've been experiencing repeated Codex desktop crashes on my Apple Silicon MacBook Air (16 GB RAM), especially while testing a local web app using Browser Use / Computer Use.

On October 28, Codex crashed twice within 14 minutes while doing normal UI testing—switching styles, refreshing pages, taking screenshots, and checking dialogs.

My setup:

• macOS 26.7.1
• Codex v26.1023.50603 (build 22391)
• Apple Silicon MacBook Air, 16 GB RAM
• Local web app running on 127.0.0.1:5173

Activity Monitor showed this about a minute before the second crash:

• Main Codex/ChatGPT process: 7.78 GB RAM
• Largest Codex renderer: 2.29 GB
• Total system memory: 14.27 GB / 16 GB
• Compressed memory: 7.73 GB
• Swap: 2.59 GB
• Memory pressure: Yellow

My Mac was also getting noticeably hot.

Both crash reports showed EXC_BREAKPOINT (SIGTRAP) on Chrome_IOThread, with the same framework-relative crash offset (0x936ee50). I also experienced similar crashes on October 25 with an older Codex version.

After restarting Codex, the main process dropped to around 281 MB, and memory pressure returned to green.

I'm not claiming this is definitely a memory leak or that high RAM usage caused the crashes. I also can't isolate whether the issue comes from Browser Use, Computer Use, or something else.

I've already contacted OpenAI Support about the earlier crashes and have the latest crash reports available.

Has anyone else experienced this?

Are you seeing unusually high RAM usage, overheating, or crashes while Codex tests websites?

If you've found a workaround, I'd appreciate hearing which Codex version and browser setup you're using. I'd also appreciate suggestions for collecting better diagnostics without deliberately crashing the app again.

tl;dr What would be the ideal choice for an iPad that you would use to navigate Codex on your MacBook at home to develop iOS apps - and how do you make your Codex at home install the app to your iPhone on the go?

I'm gonna be on a trip for a few weeks and usually wherever I went, I took my MacBook with me. I have a few apps in the App Store that actually make me good money so I always wanted to be prepared in case something breaks in the apps.

For the past few months, I haven't really written a single line of code myself. Since Codex gained the ability to interact with the iOS Simulator and also tests its own code, the only thing I'm really doing at this point is to check the git diff for obvious potential issues.

Now I'm thinking about leaving my MacBook at home (plugged in with Codex running) and just buying an iPad which is lighter and less likely to break in the backpack.

My iPad is like 10 years old so I'd purchase one for this to work. What I'm not sure about yet is what iPad would fit me best.

iPad Mini has a cool form factor but I think it's to small for longer codex sessions, for typing and for viewing large diffs.

Normal iPad is cool except for the unlaminated screen.

iPad Air has the laminated screen but only 60hz

iPad Pro has 120hz but I'm not sure if I need that. Neither my Mac nor my iPhone has a 120hz display but 120hz definitely do feel nicer.

Since I won't run anything resource heavy on the iPad itself, I'd also be fine with an older year model if the battery is okay. But older Pros seem to hold their value surprisingly well so I'm not sure if the price difference is worth not getting iOS updates 2 years sooner.

The apps have been on the App Store for many years so except for a few new projects, most of the stuff me/Codex is working on are smaller improvements as opposed to whole new UI/features.

Does someone here run a similar setup? What iPad would be the best option there? Is there something I forgot to take into account? Also about installing the app on the devices - making the Mac upload the build to TestFlight is the only way I am aware of to do that but that is a fairly slow process, what alternatives do you have set up?

Personally GPT-6.1 Terra replaced deepseek. Can't find a better Intelligence - $/task.

What would you drive from point A to B?

(intelligence index from the recent AA v5 release)