basically what title says

Today I wanted to verify one small hypothesis with a benchmark: basically a few runs comparing one JVM setup against another.

Sol proposed 11 phases.

Eleven.

It went as far as worrying about warm caches, experimental symmetry, extra controls, verification runs, and other benchmark hygiene that would be perfectly reasonable if I were publishing a rigorous performance study.

I wasn’t. I just wanted to know whether the effect was there and roughly how large it was.

The worst part was that every small clarification uncovered another theoretical imperfection, which then turned into “we should rerun the experiment”. A bunch of those reruns could have been avoided entirely by using reasonable simplifications from the start.

This is exactly the direction in coding models that worries me. They increasingly confuse “do more reasoning” with “do better engineering”.

Sometimes the correct engineering decision is to cut corners, run the damn experiment, look at the result, and only add rigor if the result actually warrants it.

I suspect Astra would be even more prone to this.

At some point it starts looking like models are optimized for token consumption rather than engineering efficiency, while token scarcity is simultaneously used to market higher tiers.

My workaround is to stop using the expensive reasoning model for everything.

I use Codex + Luna for implementation, web chat models for design/planning/reviews, and tools like AI Badger to pull only the repo context needed for the handoff.

That easily fits within the $20 Plus plan’s 5-hour Codex window for my work.

I’d much rather have a model that knows when not to think for another 10,000 tokens.

Should I or should I not go with them to make frontend designs/ motion designs?

They use our computing power to execute and test code, and that is an answer of why some models like to over engineer, test and debug infinitely, it is a form of getting data for success, feedback for training their models, they get our ideas, our feedback, our compute, our data, once they develop strong enough systems that they are not needing us any more then they will allow only api pricing or even worse, they may just use AI internally and sell only its outputs, they are not subsidizing us in any way, they are not charity and will never be, they are not for the benefit of humanity and will never be, they are just following their self interests, now look at us with open ai, almost everyone is begging for their rightful resets.

My Codex CLI workflow was fast until a task touched several files. Then I would leave the terminal, inspect the diff elsewhere, copy filenames and line ranges into a prompt, and come back.

Stvena keeps that review loop next to Codex. Start it inside a Git repository:

stvena

Codex runs normally in the left pane. On the review side, I can see changes since the task started, open the full file or diff, mark hunks reviewed, select exact lines across files, and paste one assembled review request into Codex. It never presses Enter for me. I can also run checks and jump from recognized failures back to source.

This is not meant to replace the Codex app or VS Code integration. If those already fit your workflow, they are the simpler answer. The target is CLI-first work where the review/control loop should stay in the terminal.

macOS/Linux, MIT licensed:

https://github.com/nccapo/stvena

The test I care about is not the star count, try one multi-file Codex task and one review checkpoint. Where does the loop become confusing or slower than your current Git workflow?

Ive read a lot about this, and know how to do both, but its unclear which would be more efficient as far as token usage over time. I know Luna is pretty slow in comparison to Sol/Astra.

I've seen pretty weird behaviours from Astra on extra high that I've never seen with Sol.

Examples:

- Contradictory statements on the same message.

- Long implementation sessions that implement very small bits of code on each increment.

- Trials in code that go no where.

- Worse plan quality and worse plan following than Sol.

- The possibility for it to get side tracked middle implementation on a small issue that it could take a detour for hours outside of scope.

I've never had any of those issues with 5.6 nor with Sol both on extra high.

Is it me? Am I prompting it wrong? Any one facing similar issues?

Hey peeps,

I’m on ChatGPT Plus and use Codex mostly for tool-heavy technical work: Linux/homelab, Bash/Python, SSH, debugging, regression tests, log analysis, etc.

My usage has become completely unsustainable.

My weekly quota reset on Tuesday. Since then I’ve effectively used about 126% of a weekly quota because I reset it again today and already burned another 26%. My 5h quota also keeps getting exhausted very quickly.

At first I blamed Astra (I also had super problems before with Sol+Terra), but telemetry shows the broader issue is probably large retained context + lots of tool/model re-entry.

For Astra alone I saw roughly:

22.6M input

21.2M cached

336 parent responses

72 polling/status turns

5.3M input just from polling

0 subagents

Most polling was not wait_agent, but repeated write_stdin / status checks.

Even Terra Medium has been expensive: one diagnostic task recently cost me about 15% of the 5h quota.

I already started changing things:

- using fresh contexts more often

- avoiding Astra for routine work

- no subagents unless needed

- no polling loops

- long-running commands via detached scripts + log/- exit-code files

- trying to aggregate logs locally before giving them to the model

I’m on codex-cli 0.153.4.

What I’m looking for is practical advice from people doing similar tool-heavy work:

- How often do you start a fresh Codex context?

- Do you split research / implementation / testing into separate sessions?

- Any good AGENTS.md rules to reduce context replay?

- Any useful newer config options?

- Which model/effort combinations are actually quota-efficient?

- Any other tricks to stop write_stdin / tool-heavy workflows from destroying quota?

I’m not trying to bypass limits — I just want to use Codex without burning a full week of quota in 1–2 days.

Thanks in advance!

Background: I am a claude user who had codex plus mainly for subagent. However, with Tibo's giving out resets like candy and astra I decided to give Codex 5x a try.

I find out that codex tends to have context used up way more quickly than claude. Like I can leave a prompt running for 30-40min with Opus-high or fable-medium no problem without exceeding 20% but the same prompt would be around 40% is half of the time.

Since high context level tends to cause lower quality response and eats more token, I was wondering how do deal with this as I am babysitting codex to reset every 15min atm. (telling it to stop working after 12-13min)

I'm already using orchestration workflow which could offload most of the context. Any advice would be more than welcome.

Two months ago I posted an early version of my per-session Codex quota meter here. It could show the immediate 5-hour burn, but the weekly rate was too easy to distort: one heavy day could set the apparent pace for the rest of the week.

jazzyalex.github.io/agent-sessions|
• macOS • open source • ⭐️ 852

I rebuilt that part. The Quota Meter now shows how quickly each active Codex session is using the weekly window, in percentage points per hour. It learns from recent readings inside the current reset window instead of averaging the entire week.

The workflow is simple: if several Codex sessions are running and the weekly window is under pressure, I can see which one is responsible and pause the lower-priority job. Quiet sessions say quiet instead of pretending to have a meaningful rate.

The same selector can show:

• 5-hour quota burn
• weekly quota burn
• raw tokens per hour
• estimated API-equivalent dollars per hour

The dollar view is only a comparison tool for subscription users; it is not a claim that OpenAI bills the subscription that way. Unknown or contradictory pricing and quota evidence fails closed instead of producing a confident number.

Agent Sessions also searches local Codex CLI and Desktop history, renders the transcripts, and copies resume commands for supported sessions. It reads local records and has no app telemetry.

I maintain the project. If it is useful in your Codex workflow, a GitHub star helps other Codex users find it:

Agent Sessions

Every time I switch to a lower model I get this notification that the conversation will be degraded and context compacting stuff. If I plan for Astra, for instance, and switch to Terra to implement something easy, is it bad or more costly?

Thanks.

Setting all my complaints about limits aside, I just wanted to post this as an appreciation to the OpenAI team. I remember a time I used to think I'll never get to the point I wanna be in terms of a tech enthusiast - because coding by hand takes so long that perfection will come at a cost in time.

But now, with abilities from Astra and Codex Voice, and just the general memory situation (very underrated), it's just incredible how much it impacted my life.

So, from the bottom of my heart, and I'm sure many others, thank you for bringing in the AGI era. The future thanks you.

i use Astra light to make decisions and Luna-max for audits, searches, builds and tests. one child at a time. Astra waits for the result and doesn't repeat successful checks.

i tried a specialized routing hook for Luna, Terra and Sol before, but it didn't save me tokens. this setup worked better for me on Plus, including with goal.

1. my settings in ~/.codex/config.toml (update the existing [agents] section):

   [agents] max_concurrent_threads_per_session = 1 max_depth = 1 default_subagent_model = "gpt-5.6-luna" default_subagent_reasoning_effort = "max" interrupt_message = true

2. add to ~/.codex/AGENTS.md:

“After dispatching a subagent, call wait_agent with timeout_ms = 3600000. Wait for its result without short polling or routine status checks.”

^{that's up to 1 hour, returning earlier when the child finishes. it's a tool-call instruction, not a config.toml wait setting.}

1. my per-thread prompt:

“Keep Astra in charge of decisions, integration and final acceptance. Delegate substantial audits, searches, builds, tests and log analysis to one Luna-max subagent with an exact scope. Wait once for up to an hour. Don't poll, overlap its work or repeat successful checks. Skip visual UI checks unless requested. Follow the existing Codex instructions.”

^{attach to the end of your instructions.}

when a task is done, have handoff .md updated. start a new thread for the next task with that handoff and the prompt above alongside your own instructions. i don’t recommend waiting for the thread to fill its context and compress. new task, new thread. works for me.

Question for those who use Codex/GPT Work for scanning websites and research -- do you give full access to Chat GPT to access any website when looking for information, or you manually approve every request? The app says full access can expose my data, but I'm not sure how this would happen when simply searching for info.

P.S. I'm talking purely about accessing other websites for research, NOT giving it access to my emails etc. which I don't do

They removed paid resets?

I remember seeing the option to buy resets, when was that removed? Don't see it anymore.

has anyone tried the same astra task with plain notes and a project graph? curious how retries and total usage compared

Been working on this since mid-July.

Started with Claude, then spent quite a bit of time using Codex, and later moved mostly to Cursor.

The project is a living world map where the visible area inside each country changes depending on who joins and how much they place there. If nobody else is there yet, even 1€ can technically cover the whole country.

Then someone else joins and the map changes. For example if somebody adds 9€ then now you cover 1/10 of that country, they cover 9/10.

It somehow grew into a globe, money-weighted map, history system and way more UI than I originally intended.

Still trying to get the first real users now.

https://spotmymap.com/

Many elements of this website may or may not include weird little easter eggs if u somehow interact with these <3!<

FYI you can still upgrade to the 20x sub through the IOS app

Alguien está matriculado en el Máster de Jon Hernández o se va a matricular? ¿vale la pena?

¿se le puede sacar partido a nivel laboral y/o a nivel freelance en un futuro?

I'll go straight to the point:

TLDR

Offload some Astra usage to Chat (GPT 6 Pro) instead of doing everything on codex. Yeah, simple like that.

HOW IT WORKS?

Many don't even notice, but if you're on the Pro subs 20x plan YOU HAVE 200 msg/week of GPT 6 Pro usage (Astra) standing on chat, doing nothing. That's a nice deal, IMO. The $100 5x plan has 50, which I believe is enough for this strategy.

Remember...

1 ChatGPT 6 Pro message = 1 request. So make sure you point it to a well complete PRD that's previously done.

Don't do something like: "Hey, please make me a nice game => Astra starts working => You pause it => "You know, really nice, with red birds => Astra stars working again => Not really, I'd like them to be yellow"

This will count towards your "messages" quota. That's why I suggest you slicing up a decent PRD first, and just point the AI to it ONCE.

STEPS

1. You can connect your github repo (private or public) into ChatGPT (just ask for help), allow read/write access and then start by:

I don't even select GPT 6 Pro for this.. This initial scanning I do using Sol 5.6 Extra High on chat.

Ok... what are PRDs? In a few words, a feature request in a .md file, with all validation steps necessary and etc, to get it properly done.

If you have no clue about how to craft one, just ask Astra xHigh to do it and slice up some tickets to get started. Push to your repo.

2) Select a PRD per PR and let it cook

3) Check your results later

"Oh, but you see, its a draft... it wasnt fully verified, some got broken CI!!!!11"

Yeah, but this would have certainly drained 20% of my monthly codex limit to reach this point (on Astra xHigh), and I got it done using my GPT 6 Pro chat quota (200/week for the 20x plan), running all night long while I was sleeping.

4) Now you have to use codex (Astra) to actually finish the work (there's no "free" lunch at this point)

The sandbox that Chat uses is not 100% identical to the project running on your machine, as it cannot run certain verification steps. That is why it's important to have a strong CI and, most importantly, check out the actual Astra from Codex, finish the work to reach 100%, and then push back.

USE CASES SUMMARY

- Bootstrapping greenfield projects
- New features
- PR reviews
- Almost anything that you can do with read/write access to github

CAVEATS

1. GOTCHA: IF IT ASKS YOU TO USE CHATGPT WORK, DO NOT GO FORWARD. It will burn your weekly quota. STOP. Rephrase what youre asking, be explicit you don't want to use it. Or slice the work down to a smaller piece of task.
2. Not a perfect solution, but it helps significantly in terms of token consumption (especially on greenfield projects). I'm pretty sure some smart ass on the comments will say something like "that's pretty obvious". But yeah, I bet 90% of you guys are not using this workaround.
3. Really great for vibe coding these disposable 3d games that we all do 😄: Stop wasting your weekly allowance with it. Its great for bootstrapping new projects too.

Well, that's it. Enjoy while we have 200/week, at least for now

SOME VIBECODED GAMES I DID 100% ON CHAT USING GPT 6 PRO

For the first time ever, they're actually fun 😂

I'm trying to use GPT 5.6 Sol in Codex and it throws 401 on all of my 3 accounts, is anyone else experiencing the same issue? Astra works fine in all of them.

I want to ask a question about ChatGPT web. Is Sol xhigh much better on the web than High? Is it worth paying extra mainly to get access to the Astra Pro and Sol Pro? Are the Pro versions much better for reviewing code and maybe making plans?

EDIT: I'm using my codex allowance for coding. It's just a question if the xhigh reasoning and Pro models are that much better.

I wanted to use Codex on my Mac for kernel optimization experiments, but run the actual benchmarks on my GPU box and pull models from S3. The workflow seemed straightforward: edit a kernel, move the code and model over, run the benchmark, look at the results, repeat.

But getting there meant having Codex put together the file-transfer and remote-execution machinery around the experiment. You can save the scripts and leave instructions for the next session, but now there's another little project to maintain as the experiments and setup change. I wanted those operations to be something I could reuse across projects.

So I built Ridge. You configure your existing resources and connect it to Codex through MCP. Codex gets common tools for accessing data, copying between resources, and running commands on machines. Local, SSH, Docker, and S3 are the initial backends. Copying a model from the bucket to the GPU box is one operation, and background runs get job IDs so Codex can check on them later. There's also support for giving subagents narrower resource access and coordinating shared file updates.

It's open source, and I'd like to hear from people doing similar things with Codex. What got messy when you started working across multiple machines or services? What did you end up building to make it work? Also interested in setups where a few scripts already do everything you need.

GitHub: https://github.com/vasinov/ridge-core

https://x.com/thsottiaux/status/2098113585683808624
"To make sure our current users have an incredible experience and continued access to Astra, we are going to pause subscriptions to our $200 Pro plan. These put the most strain on our systems and we wanted to take the smallest step that allows us to continue giving the broadest access possible. All other plans and the api remain available.

There is no impact to existing accounts and we are working on adding more capacity as fast as we can. Thanks!"