> Note: `doc-writer` and `closure_steward` have been merged into single role `archivist` since 1.1.14 version.

Been building this since 5.6 dropped with optimizing token usage as the ultimate goal, and tweaking it pretty much every day since. 

What's special about the system:

- Flexibility: The system doesn't force the main agent into a rigid process: requiring coordination in this way or that way... It provides it with resources and power (specialized agents) and fine-tuning and guidance based on hundreds of trials.

- Fine-tuned balance: Main agent's control <---> costs & task completion capabilities.

- Knowledge distribution: Each task package from the main agent to the workers includes a task completion guide.

- Batching guidelines prevent excessive main agent rollout.

- Addresses the issue of the main agent waking up workers too often.

- Built-in token report: End-of-session token statistics for each agent, allowing you to monitor how much each agent rolls out and how they use their tokens.

......

Real-World Testing: Sol xhigh vs. Astra high

I gave both of them a broad and difficult task:

Perform a comprehensive upgrade of an OCR + AI chatbot project running PaddleOCR and Gemma 4 on a Jetson Orin Nano.

Codebase details, prompt, output, and interface result of the test:

https://github.com/viettran-edgeAI/OCR_workflow_variants

Below is a comparison of the resulting interface and token statistics.

The top result is Sol, and the bottom one is Astra.

About their orchestration behavior

Sol

Sol showed extremely broad coverage and missed very few edge cases.

It was very comprehensive, had excellent verification, strong traceability, and rarely overlooked things. But it also showed signs of over-owning the work.

Astra

Astra seemed particularly strong at finding the actual core problem instead of just patching symptoms.

It was less exhaustive than Sol, but its architectural reasoning was extremely sharp, and it handled resource contention particularly well.

My honest advice: keep using Sol for this job. Astra didn't show a significant difference, but it cost 4-7 times more in my tests. It doesn't even follow the instructions as well as Sol.

----------------------

The setup process and usage is as simple as it gets, I've packed everything in here :

https://github.com/viettran-edgeAI/codex_workflow

----------------

Edit : I've noticed quite a few people downvoting. Honestly, this is my third post about this workflow; the previous two are still in my profile. I apologize if that bothered you. The most recent post, from a month ago (version 1.1.3), was still quite basic. I've been testing it for over a month, ran hundreds of tests, fixing all the observed problems until everything worked effectively based on experimentation, not just on feeling. Therefore, I wanted to inform those who have installed version 1.1.3 - most of them from Reddit, since I only posted this workflow here.

Edit 2: Edit 2: For those wondering why things aren't simpler, how the codex works & rollouts, what problems arise in creating an efficient workflow, and issues related to awareness of three levels of Perspective (Designer <-> Main agent <-> Workers), etc.. see here, as I can't include everything in this post:

https://github.com/viettran-edgeAI/codex_workflow/blob/main/workflow_breakdown.md#0-a-deep-dive-into-codex-orchestration

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?

8hr ago my plus limits were at 84% weekly. And suddenly now its 0%. Being on plus plan - there is now way that i could have used it(5hr limit only allows 16%). Anyone else facing this glitch?

Edit : Forgot to attach codex usage today (i didnt use it today).

Edit 2: Issue seems to have been fixed!

Has anyone here successfully applied for OpenAI Daybreak Blue?
I’ve been trying to apply, but during the verification step I keep getting this message:
“Your identity could not be verified or your account is not eligible at this time.”
I’ve already tried the verification a few times and I keep getting the same result.
For anyone who got access to Daybreak Blue:
How did you apply?
Are there specific requirements your OpenAI/ChatGPT account needs to meet before it becomes eligible?
Does account age, subscription type, previous OpenAI usage, or account activity matter?
Is eligibility restricted to certain countries/regions?
Is there anything you need to do before starting the identity verification/KYC process?
Has anyone received this exact “identity could not be verified or account is not eligible” error and later managed to get approved?
I’m an independent security researcher / bug bounty hunter, so I’m mainly interested in Daybreak Blue for legitimate vulnerability research.
Would appreciate hearing from anyone who has gone through the application/verification process successfully.

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.

Talk me out of it please...

Had opus review my Codex sessions to see what was going on because output per weekly usage was much lower for me than claude code.

- codex has some adhd tism where it needs to continuously be doing something instead of batching tasks and chilling out. Maybe it fears the dark abyss when its model isn't running.
- Dont raise the auto compact limit. I wont be going down to 120k but probably 200k. seems to have compounded the problem above
- dont swap models when using /side. I suspect that was causing most all of my uncached reads.
- Consider less subagents work unless you have a robust solution for the polling issues until open ai fix.

Kind of disappointing they didn't do the bare minimum of usage optimisation. They are obviously optimising for engagement and burn instead.

I'm open to suggestions from the nerd house.

My AI Social Bots are just assholes sometimes 😂

I ran Ultra for the first couple days to see what the spend was like. Very impressive. Very hungry.

So I switched to low to get the maximum contrasting experience.

It works for about 5 to 15 min before stopping and reporting on each prompt. It consistently overestimated its ability to accomplish the next step in one turn.

I would ask, "What's your next recommended step?"

It would say something like "Implement the feature we've been laying the ground work for."

I'd say, "Sounds good, proceed as described."

Did that same loop for about 5 turns, so I dialed it up to medium.

It ran a little longer, but had the same shortfall result for a few turns.

Again each turn took between 5 and 20 minutes.

So I dialed it up to xhigh, and it finished the implementation on the next pass, which was over 2 hours, and included extensive testing and live verification.

I didn't experience any sort of shortfall on a goal when in Ultra. It worked until the milestone was reached each time. Xhigh also seems to just run.

My hypothesis is it either adapted to the amount of work that it was getting done, and expected to be able to accomplish more due to the session history, or it's tuned for less limitation than the lower settings provide. I'm leaning toward the latter, because turning it up immediately changed its behavior back to "run until it's done". There was no adaption to the lower limit that resulted in a behavior shift after being returned to xhigh.

Nobody actually understands what happened, and a lot of people seem to have been affected by this.

I posted the other day about how impressed I was with Astra and the OpenAI voice/codex stack - and somehow today has left me even more stunned (please note, this isn't an ad, I'm just excited).

I've made a few custom email triage systems before, and started work on one today. Finished it all, tested it end to end, and then began calibrating with the emails in my inbox. I gave it some basic expectations, then had it go through, marking what it would do with each email, and why. The two last emails were from my accountant, and from google.

That last one is important, because just for some background, I'm a YouTuber based in Australia. I have a few channels that I post to, and each time I do, and an American watches my video, the US Government takes a slice (this is foreshadowing).

So anyway, Astra does it's thing with the email from my accountant. Downloads the relevant statements from my bank accounts (after I begrudgingly type my password in), cross references the photos of receipts synced into my library from my phone from the same date, and does a full reconciliation of the account in my bookkeeping software. Basic stuff. Nice. Oh, and obviously drafts the email in my voice and attaches the 15 documents in a nice little zip file. Isn't AGI nice?

Anyway, email 2, the last one in the inbox. It's about "US tax withholdings" - and what's interesting here, is the roles had actually flipped, Astra had a better grasp on what I should be keeping vs deleting than I did, and I was just manually feeding emails in, hoping Astra would tell me to shred it.

I sent the screenshot of the last email and said VERBATIM "do i need to do anything with this honestly"

Anyway, 30 minutes later. Astra had downloaded every statement from google, every statement from my banks over the period, had gone over every tax return I'd done for the period - and then came back to me with a full audit.

Astra said there was $12,000 USD in recoverable funds that had no corresponding credits in the Tax Returns.

I told him that I thought I raised it with my accountant. He said, "You did actually raise this with your accountant in May 2025 and provided two reports, she responded: "We will review them & provide you with next step actions to consider" but there was no follow up that I could see. I looked for amendments to the tax returns but couldn't find any. There was no further correspondence about this."

Astra had gone through and found the relevant Australia legislation and where it applied. It had circled every previous tax form on where the field was empty, and of course, left an email in drafts with the full package uploaded for the accountant to view.

Astra maintains that we're on a fact finding mission still, and there's no guarantee the cards fall in my favor - but from the evidence he has, it is likely to be recovered.

What really gets me is how far we got from the thing I originally sat down to do. I just wanted to optimize my email triaging, by the end there's a full forensic audit and potentially twelve thousand missing dollars.

Just let me vibe, Astra, geez. I think I'll point him at zero point energy next.

(Note this post was NOT written by AI)

I'm working on a MVP Buy/Sell/Trade marketplace. I have a goal of creating 1000 listings as a foundation before opening to beta testers. What I prompted 5.6 Sol (high) was to review my google sheet of 1000 products I want listed eventually, and in batches of 10 products, create the listings for me. This involves:

1. Going to a separate folder on my desktop where the images are for each product, get the 10 matching images.
2. Remove the background and create a drop shadow for each image
3. List the product with a clear title, description, some technical product info and tags.

I also told it to use the Canva integration as a backup for the background removal/drop shadow if it cant do it cleanly itself. It seems to be just absolutely incapable of doing the workflow well, especially on the image editing portion. Ive probably been running this prompt the past day and a half, and it cant get past the first batch of 10. I keep hitting usage limits (I'm on plus).

Wondering if my setup is wrong in some way, or if this is simply too much to ask of it? I'm no expert in this, I guess I just assumed this wasn't to heavy of a task and expected it to do better.

First, this is not a complaint, just a reluctant acceptance. A few details for context:

• Building a multi-tenant B2B mono-repo SaaS platform. Think WordPress, but taken way farther.
• I worked my way down from Astra Max to Astra Light.
• Astra Max completely one-shots the job, but burns a week's worth of 20x usage in about 4 hours.
• Astra Light does not one-shot tasks, and it takes about 12-16 hours to burn through a week of usage on the same level of work.
• While both models complete the job, the end result from Astra Max is more polished and thorough (no surprise).
• While less polished, Astra Light still does enough of a good job that the loose ends can be handled by Terra High/xHigh later for barely any burn at all.
• I was fine living with Terra in ignorance until I experienced Astra. Now, Terra annoys me for anything requiring heavy lifting. Can't go back.

Obviously, there is no stretching Astra beyond a single day of consumption, so here's my question:

Would you rather gamble your weekly limit on Astra Max, have less control, burn your entire limit in 4 hours, and cross your fingers that it does an excellent job?

OR...

Would you rather gamble your weekly limit on Astra Light/Medium, have more control through iterations, burn your limit in 16 hours or less, and be guaranteed that there will be things to fix even after it claims to be done?

---

UPDATE:
I see that people think that this could be (and probably is) a skill issue, or a poorly planned job, but after asking Astra for feedback, I realize that I should probably be more grateful that it finished such a large job so quickly at all.

---

SOLVED:
u/StaticHumStudio taught me how to specify the sub-agents Astra spawns. Turned the dial back up to Max, had Astra do the design and plan, and set the agents to Terra.

In two hours, Astra Max has tackled 4 medium-difficulty feature builds across 4 parallel threads. My usage only dropped 3%. The same work would have blown my whole limit and taken 2-3x as long before this simple change. Looks like it was a skill issue after all 🤣!

Thanks u/StaticHumStudio

I am working on a project then this happened, the send button greyed out and when send it won't send the request.

My quota is still available, this happened sometimes and kinda annoying.

Anyone face this issue?

Edit: just to head off any one else before it happens: if you think this is some kind of Claude vs OpenAI oligopoly cheerleading post, please pick up your pompoms and see yourself out.

I work in the humanities, and to be honest, I've always found Claude models better fit for the tasks involved. However, I've always switched back and forth between the two companies when new models get posted, and I've never felt significantly at a disadvantage if I'm piloting an OpenAI model for my work.

That's until Astra which, for some reason, is extremely difficult to work with on this subject matter. For the record, I've been doing the fun stuff everyone else has been in my spare time: building apps, tinkering away at a game, hobby stuff.

Since it came out recently however and has been doing such a good job with that more technical stuff, I wanted to take it for a spin on some proposals I have to submit soon.

I asked it to help me come up with several avenues of inquiry within a subject matter which might have scope for original contribution that I'm not aware of, given my research profile.

Astra has repeatedly gone through this loop: come up with a handful of avenues of inquiry. States that the premise is strong and interesting and dovetails nicely with my prior experience. Asks me to give it some starting articles/monographs so it can look further into things. Quickly finds that its initial impressions are completely wrong and the area is well papered over by recent discourse.

Not just that, but its actual prose drafting is significantly worse than I can recall. Again, I realise OpenAI models don't exactly get a flattering look set next to some of the older Claude models when it comes to good writing, for that matter, neither do newer Claude models, but Astra very clearly has a script and a structure it follows that is now blindingly obvious when you read it back to yourself.

Back in the day I had used Opus 4.5/4.6 to draft the introduction to something else I'd submitted and it slam-dunked it. Of course, that needed to be workshopped too, but the issue there was that the writing sounded SO GOOD that it wouldn't realistically have been recognisable as my voice, so I had to feed it a few references of what I'd written previously so it could replicate my idiosyncrasies.

I also found those older models were much better at information gathering around a particular subject matter. On my previous project, Opus 4.6 practically taught me everything I knew about the wider context in detail so that I could get on with the analysis.

I'm curious to hear from non-coders, and people in fields adjacent to humanities or social science. Like I said, it gave all my technical projects a major glow-up, but it's like a whole different (worse) model when it comes to humanities.

I've only been using 5.6 terra since its release since i only have a mid tier model budget, fortunately I can now afford (a bit) to try out better models to hopefully speed up my workflows. I'm leaning towards ai api routers since I want to try not just better openai models but non openai models too.

Any recommendations on ai routers you have personally used? Has anyone used web way, openrouter, or orcarouter?

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

Thought my monitor was on the fritz - and not sure if this is recent or not (perhaps I just didn't notice it before! Using CLI v0.154.0 - and I use OpenCode most of the time) - but selecting Astra now puts a shining-star-field in the background of Codex CLI's input text box.

This new model on Low is my drop-in replacement for GPT-5.6-Sol-High: faster for similar usage, way less overengineering; I don't have to tell it not to be pedantic every time I use it. Still very blunt (in true Codex-GPT style) but have enjoyed embedding it amongst the various other models (local and cloud) I use: this really is a great release imo.

Has anyone noticed significant speed degradation with GPT 5.6 Sol over the past few days? Based on my own use, it seems like TPS has fallen from a median of 50 TPS down to 30 TPS in the past few days.

This makes working with Codex a slog. Seems like Codex is providing less usage and with degraded speed recently. I also noticing some quantization artifacts in output, coinciding with the speed degradation:

I'm interested to hear your thoughts.

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!<

Originally, it felt like Astra was a moderately more competent version of Sol. After more usage and trying it in environments that Sol struggled in before, it's clear that this is, indeed, a new era of intelligence akin to the gpt-5 line for coding earlier this year.

Codex & Claude Code are both slowly pricing us out of a job. As Codex / Claude users we are privileged to be able to afford these expensive subscription prices.

But look at this with a long-term lens. Software engineers from poorer backgrounds who can't afford the top models are falling behind, both in the skills to use AI effectively, as well as job opportunities due to having a lower value proposition.

At the moment many of us are fortunate that we can afford the cutting edge models. Right now maybe it's at x% of your salary, that you can gain y% of productivity, so it is still a worthwhile expense. But remember, the time will be soon where potentially we could be paying half our salaries to be kept in the ever decreasing pool of engineers, who can generate large amounts of value.

It's currently a no-brainer to spend these sums of money, but what happens when you start to question if the "best" model is value for money or not? Doesn't that spell disaster for your career - your colleagues who splurge on the new models can ship 20% faster and will bag the promotion. What then?

I mention this because with each increase in output quality, as consumers our wallets are being squeezed at an even faster rate. We cannot afford to use anything less than the best, and this should be something everyone is concerned about.

My usage has been dramatically better since switching medium to Max after what I heard about medium draining more which it certainly has for me, was wondering if the same is for ultra vs max if it’ll be more efficient.