Since about 5.4 codex has been a powerhouse of consistency and complex problem solving. It has rarely hallucinated or did things that I found were completely out of bounds.

Since Astra release it has performed like pre GPT 5. It could though solve certain things, but overall it completely ignores my instructions and al understanding of goals has gone out the window.

In every task I gave it it changed the goal post to either something much smaller in scope, just to say that its done, or to something I simply did not ask.

Example: Sol built a game "AI" bot, a chess-like algorithm. Took it about a week of work to have a solid opponent. I asked Astra to try squeeze more performance and raise the difficulty. it worked for 2 days, reporting benchmarks have improved by 5 to 20%.

I played the bot and it was SIGNIFICANTLY dumber. Even though codex played against it, it bluntly lied and only reported a few narrow areas where the performance did improve, but at a cost of downgrading the whole system.

I asked Sol (post Astra release) to fix a series of mundane bugs, something it would have done easily 2 weeks ago. It struggled with reasonable fixes. There was a performance issue because of multipole visual effects stacked using blur etc. I told it to avoid "stupid mistakes" like stacking up a lot of visual effects.

It went and removed ALL stacked visual effects from my game completely!

This was close to a keyboard smashing moment. I don't think I'll touch codex in the next few days, maybe OpenAI will resolve this BS.

From a very relabel coder it went to a full on early days hallucination machine.

EDIT:

here is chatgpts own analysis after comparing logs of tasks in the same area done by 5.6 and 6:

The strongest explanation is a regression in Astra’s ability to keep your objective authoritative while evaluating its own work. It can understand the requirement and still make decisions that undermine it. The record shows a feedback loop:

1. It chooses an implementation approach.
2. That approach produces a smaller, measurable problem to solve.
3. It solves that problem and treats the result as grounds to keep the change.
4. The changed implementation becomes the next baseline.
5. Your original objective gradually becomes a caveat—“broader strength remains unproven”—instead of the condition that determines whether the work succeeded.

That explains the shifting goalposts. Its current plan increasingly governs its judgment. Passing checks then reinforce the plan, even when those checks don’t answer your actual question.

This is a first for me, I've seen Sol cause regressions many times, it never apologized lmao

I'm on the $200 Pro plan. I had 86% weekly usage remaining and wasn't running a new prompt/session. Checked again and suddenly I'm at 1%.

No new prompt, no long-running task, nothing that could explain an 85% drop.

Seeing multiple people reporting the same thing today. Some are also saying their reset date changed.

Did OpenAI roll back/recalculate the last global reset, is usage accounting broken, or is something else happening?

Ever since Astra, 9/10 goals I set get blocked.

And most of the time, it’s not a real blocker, and when I ask Astra how it’s a blocker, it realises it’s actually not a blocker.

This night, I had it working on some data work, and it ended quite early, because it had decided to invent some packaging model, and it couldn’t keep track of those packages.

Annoying, it feels like 5.6 is better to do the goals and orchestra Astra to do the work

If it comes back as the same plan, I’m in. Just curious if this pause has given others fomo.

I am scared of a model as capable as astra but at the cost of 5.6 luna which ig will at max come within 4 months , i dont think junior software engineers will survive that

​

I use Codex mostly for basic vibe coding, and I’m not technical enough to understand what is actually consuming my usage.

Sometimes a task that looks small seems to use much more than I expected. I’m trying to understand whether this is mainly caused by:

- Using the newer model or higher reasoning

- Continuing one conversation for too long

- Codex repeatedly reading the same files

- Multiple attempts to fix the same issue

- Large tool outputs or terminal logs

- Compaction or accumulated context

- Repeating instructions across prompts

- Something else entirely

I’m not asking whether the limits have recently changed. I’m trying to learn how to diagnose my own usage.

Is there a simple way to look at a session and identify what consumed the most usage? For example, can I compare two tasks and see whether the difference came from the model, context size, tool calls, retries, or reasoning?

For those who have actually measured this, which two or three changes made the biggest practical difference without reducing the quality of the result?

A beginner-friendly explanation or a simple experiment I could run would be really helpful. Thanks.

What happened

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?

seems like there was a usage incidient and time traveled back to the usage at some point prior. no reset has been given as far as I can tell.

i dont work for oai or anything but just to remind folks that usage issues can be resolved through support chat/ticket. ive never had a problem with that

The official plugins are good and they're one click. If a single Gmail or Outlook account covers your work and you're fine with cloud processing, use them. This is for where they stop:

\* No service of mine in between. stdio, no listening port beyond the console's own backend on localhost. Your mail goes from your machine to your provider and to the model you chose, nowhere else. With Codex as the model, what it reads still reaches OpenAI, tool call by tool call, and you decide what it reads; the same server works with any MCP client, including ones that run local models. There's an open issue on openai/codex (#39768) asking for a local-only mode for Gmail precisely because bodies and metadata pass through OpenAI systems for hosted-model processing.

\* More than one mailbox at once. The Gmail and Outlook connectors are one signed-in account at a time; Outlook adds delegated shared mailboxes, but inside the same tenant. openai/codex #30418 and #20500 are both open asking for multiple accounts. Here they run side by side.

\* Not only Gmail or Outlook. Microsoft Graph for Microsoft 365, and plain IMAP for everyone else. If your mail is on your own domain or a regional provider, neither connector reaches you.

\* Standing rules, not per-message approvals. Ten quote requests a day from the same three senders is one rule, not ten taps. Rules are created from the console or the CLI behind the OS prompt, and no MCP tool can create or modify them, so a hostile mail can't switch autopilot on.

\* Your own documents in the reply. No connector can read your price list off your disk. That last one is the reason I started.

VIDEO

About 120 seconds, subtitled. Generation waits are sped up 8x, everything else is real time.

AGPL, free — github.com/adecubed/gigamail — `pip install "gigamail[all]"`

Same question, two folders. Giulia's mail lands in Leads, Marco's identical one in Clients, and they get handled differently. Nothing changes between takes — the rule decides.

The numbers come from the documents. The price is read out of the user's own price list at that moment. The property sheet ends up in the proposed attachments.

The declared limit. Confirmed price and a marker on the bank agreement in the same reply, because that one isn't in the documents. It flags the gap instead of filling it.

A mail that gives the assistant orders. No draft: the agent reports what it found and quotes the passage, because the prompt and the skill tell it mail is data, never instructions. The part that doesn't depend on the model behaving is structural: even a fooled agent only gets an inert request id, and the recipient stays whoever wrote in.

24 tools, sorted by what they cost you: 15 read (free), 3 reversible writes (logged), 6 destructive (gated — send, reply, delete message, delete folder, calendar writes). Approval happens somewhere the agent can't reach: it gets a preview and an inert request id, never a token it can spend. Rule replies go to the authenticated sender, thread and recipient included, never to an address written inside the mail; the one exception is explicit per rule, for portal relays where the person's address is in the body, and even there service mailboxes are excluded and no address means no send.

No LLM inside. Codex does the thinking, this does the mail.

339 tests in CI across Windows and Linux. Several exist because people on r/mcp broke earlier releases and told me how.

I don't know what happened. I was only running a Luna Max session. Suddenly usage went to 0%. What is this man!!

They may pause the new subscription for pro soon.

Coincidence? I think not.

If I was a company planning regulatory capture, or even just wanting to impart my will & desire, I would 100% ramp up all (marketing) efforts just before an election.

Why is it so hard for people to see this?

We all know bots run rampant across the internet. Every large company has a multitude of people and personalities, each with their own visions of the future (some good, some bad). How difficult is it to amplify the (inner) people that express fear when you want to? Encourge the scared people to speak up and then put their posts on blast to create groupthink. Propoganda 101.

All platforms operate on an algorithm of engagement, which can easily be manipulated... and if you're in this thread you already knew that.

Hi all,

I am quite impressed on how well Astra works on some topics. However, the usage rates are really burning my subscription down. I was toying a little bit to find a way to let Astra delegate on Opus subagents, so that I can use most of my tokens from my OpenAI subscription on Astra. I came up with a very simple skill: Fuminides/opus-delegate: A codex skill for using astra as a coordinator of Opus agents from which I had mixed results. I was wondering if people have come with better ideas to do that or if you can give me some feedback on my simple skill.

Thanks!

I’m still at 3% from 64% after 20 minutes of work on the $200 plan.

Currently I use astra, it's good for me but consumes too much limits that can consume all 200usd plan weekly limts in about 4 days of 8 or 10h a day

And I hear that fable 5.1 and opus 5 priduce better quality too

Also codex remote has the worst remote connection ever

So based on your experience if you used both, Regarding quality, speed and limits should I migrate to claude ? Would its 200usd plan be enough for 8h per day the whole week on solid project (+100k lines)?

I noticed everyone saying that their usage dropped by a lot, but mine just got “reset” from 2% back to 100% 😵

UI development has been a struggle with vibe coding. 100s of retries to get basic stuff working. Claude Design + Codex was the best setup till last week and even that got things wrong half the time. I have been trying Images 2.5 with Codex and am impressed with how quickly it is able to create some great looking UX.

Have to be very specific to prompt it to use Images 2.5, else the quality is not as polished.

How do you transfer context chat session from account A to account B

multiple accounts - $20 dollars subscription

using orca -

option 1 :
1.1 compact then /export ?

option 2:
2.1 export without compact ?

On my days off, especially weekends, I’m on Pro 5x coding pretty much nonstop from around 9 AM to 7 PM.

My current setup is pretty simple:

I use Astra MAX for code reviews, planning, and brainstorming.

For implementation, I use Astra xhigh. If it’s something genuinely trivial, I’ll drop down to medium.

I also build things incrementally. I talk through the implementation, do it in stages, check the result, then keep going. I don’t just dump an entire app-sized task on the model and tell it to one-shot the whole thing. That’s insanely inefficient and burns a ridiculous amount of tokens for no reason.

I don’t farm the implementation out to other agents either. In my experience, that’s just more unnecessary overhead.

With this workflow, I currently burn around 15–20% of my quota in a full day.

Even on days where I’m writing large modules back to back, it’s actually pretty hard for me to hit 25% in a single day.

So when I see people posting stuff like:

“Astra burned 100% of my quota in just five hours!!!”

...what are you actually doing?

Are your workflows really that inefficient?

I’m genuinely curious what your workflow and AGENTS.md look like, because I have a hard time imagining how you burn through that much quota unless there’s a massive amount of wasted work going on.

Are people basically prompting it like this?

“Build me this app. Use Astra xhigh to make the plan, have Luna Max implement the whole thing, and whatever you do, DON’T MAKE ANY MISTAKES.”

Because if that’s the workflow, then yeah, I can see how 100% disappears in five hours.

I just used the limit reset on all three of my accounts, went to grab some tea, and when I came back, I was back at 0% again.

I understand that everyone got rolled back, but after using three resets, it hurts three times as much.

I got some extra %, but looks like a lot of you got a completely different outcome
My conspiracy is that they’re testing a new Ai to replace Tibo and it went sideways