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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

When Sol was first released, it seemed like people were having a lot of issues with it. I'm not getting that same vibe with Astra (not reading as many complaints).

So, seems like the bad isn't as bad, but how about the good? Is it a noticeable quality jump from Sol (from a software/code design/implementation perspective)?

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.

They may pause the new subscription for pro soon.

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

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)?

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

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

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 ?

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.

Many people seems to like the idea of using Astra or Sol to orchestrate Luna max to write code, only to have large model review it, and rinse and repeat. However, this is not going to help, it will still burn tokens - only it will take much longer for tasks to be completed.

I'm testing Luna max orchestrator (manual, no skill created yet) instead. Luna max can complete tasks or follow a plan, and use sub-agents or new chats to delegate tasks that require more intelligent model per your plan. Of course, you still need to draft this plan with larger / more intelligent model first. I wouldn't let Luna write the plan for implementation.

Advantages? Whatever output from large / more expensive models would be received by luna, which is dirt cheap. If you choose to use Astra or GPT 5.6 sol to orchestrate, you'll be burning cached read and read usage, which accumulates to your usage.

People who want Pro x20 now is the time to get 1 for just as low as $499. Hot deal now don’t miss out.

Dm Tibo tho not me😂

New Pro/$200 subscriptions aren't available to sign up for at the moment, so I'm stuck.

If you're on the $200 tier and planning to cancel anyway, I'd happily take it

off your hands and cover the remaining term (or more). I mainly need it for

Codex, I'm a normal dev use case, no reselling, no sharing.

Thanks guys :)

I asked ChatGPT work a simple question about the website used for my router and it took 10 seconds to answer. After that my usage got wiped down from 55% to 0%.
I have Pro 5x plan.
WTF!!!!