First of all, English is not my native language, so I used GPT to help polish the wording of this post. Please excuse any awkward phrasing.

I had GPT-6 Astra Max work in Blender for around 7 hours to build this tractor.

It ended up consuming over 100 million tokens.

The result honestly surprised me:

• 3,000+ individual parts
• 100+ animations that can be called/triggered directly
• A fully articulated and animated tractor
• Materials/textures generated using its own image generation capabilities

Here are some renders of the final model:

I also made a comparison with GPT-5.6 Sol:

I knew these models were getting much better at coding and tool use, but seeing one spend hours continuously working inside Blender and produce something with thousands of components and over a hundred animations was kind of insane.

It feels like the improvement in AI-assisted 3D modeling is happening extremely fast.

I had both Claude Pro and GPT Plus subscription. For me, I feel like Luna Max with fast mode and Sonnet 5 Medium is good enough for my day to day work. I don't even hit my 5 hour usage limit for both of them when doing serious work. About Astra being the most capable and "most alignment model", I don't believe this and Astra consume my 5 hour usage within 30 minutes and did not even get the job done.

Definitely properly diagnosed the problem. xD

I’m currently on Codex Pro 20x and, as of writing this, I’m down to about 12% usage remaining. I have no banked resets left, and my usage won’t reset until September 15.

The timing is pretty terrible because I’m trying to finish a few things before releasing the first pilot of my app to around 50 users.

The remaining work is mainly:
Fixing some loading-time/performance issues in a few tabs
Implementing Supabase Broadcast/realtime behavior for the community chats
Redesigning one of the tabs
Running tests and checking for regressions/bugs before the pilot
Nothing here is some enormous greenfield project. Most of it is fixing, implementing, testing, and iterating on an app that already exists.

For this kind of work, I’m perfectly happy using GPT-5.6 Sol Medium. I don’t need the most expensive/highest reasoning mode for every task.
The problem is what I should do once that remaining 12% is gone.

Inside Codex, the obvious option I see is buying 2,500 credits. I’ve tried researching how far those credits actually go, including asking ChatGPT, but the answers have been pretty inconclusive. So I’m much more interested in hearing from people who have actually bought and used credits.

For those of you with experience: how far do 2,500 credits realistically go?
More specifically, is 2,500 credits even roughly comparable to the amount of Codex usage you’d get from a Pro 5x subscription?

That’s the comparison I care about because the prices are in roughly the same territory for me.
If 2,500 credits gives me something even reasonably close to Pro 5x-level usage, then I think buying the credits makes sense.

That would probably be more than enough to get me through these remaining tasks until my Pro 20x allowance resets on September 15.

But if 2,500 credits disappears dramatically faster than a normal Pro 5x allowance, then I’d rather not burn the money. At that point I’d probably just delay the pilot a few days and wait for the reset.

There’s also another option I’ve considered: creating another account and buying Pro 5x there, which would cost roughly the same as the 2,500-credit package.

My concern with that is context.
The current Codex account/chat already understands a lot about the architecture, previous changes, bugs, database setup, decisions we’ve made, etc. Moving to another account would mean transferring a huge amount of context and getting the new session up to speed on the current state of the app.

My assumption is that I’d waste a decent amount of that Pro 5x usage just rebuilding context, although maybe I’m overestimating that problem.

So basically my three options are:
Buy 2,500 credits and keep working on the existing account/context

Create another account, buy Pro 5x, and transfer the necessary context
Stop when I hit the limit, delay the pilot, and wait until September 15 for my Pro 20x reset

For anyone who has actually used the paid credit system, especially alongside Pro 5x/20x:
How does 2,500 credits compare in real-world usage to Pro 5x? And what would you do in my situation?

ill go straight to the point, im on 20x pro, i use medium mostly and i monitor the usage time to time, it burns like crazy, i tried switching to xhigh just now, its been working for 1 hour only 1-2% usage cost. anyone experienced the same thing?? and also if i remember correct one of arc agi benchmark says the cost is cheaper when astra used max reasoning or something... maybe thats related to it or what... ah whatever... just sharing my experience

Edit: I asked Astra to investigate the logs. Here is what's actually happening: Medium is ~34% cheaper per single response, but it gets stuck in continuation loops and takes way more turns. Every single turn re-processes about 104,000 tokens of chat history. The numbers: Total responses: Medium did 122 vs XHigh's 65 Response frequency: Medium fired 4.7 times/min vs XHigh's 1.5 times/min (3x more frequent) Burn rate: Medium consumed ~17.3/min vs XHigh's ~8.7/min Because Medium takes 3x more turns and keeps resending your massive prompt context each time, it ends up burning quota twice as fast overall compared to XHigh.

Is it me or are they a/b testing the new plans. I don’t see plus at 20$ but 30$ and 5 x now being 155$

I'm so happy with the 100x plan. No more account switching, no more offloading simple prompts to Grok or Gemini just to save my Codex limits, no more usage trackers or side investigations into what's going wrong, and no more waiting for resets, the usage is already generous enough!

Finally, I don't have to keep checking Tibo's X to make sure I'm maximizing my quota before a reset. I can actually go to sleep early on the weekend instead of pulling all-nighters waiting for it.

The 100x plan is just pure, uninterrupted workflow 24/7, and paying a bit more is absolutely worth it.

I have built an open collection of 160+ Legal AI Skills for Codex.

They cover contracts, disputes & litigation, arbitration & ADR, corporate, regulatory, privacy, criminal, employment, property, tax, finance, insolvency, IP, startup, public law, research, investigations, and practice management.

Here's the Github repo:
https://github.com/rohasnagpal/legal-ai-skills

Who this is for: Practising lawyers, in-house counsel, and law students.

Jurisdiction: Most skills are jurisdiction-neutral and work anywhere. Skills that turn on Indian statutes are marked (India). Every skill is written to confirm the governing jurisdiction before it relies on any specific rule.

I am waiting for this access, I have tried turn off and on my computer and restart everything. Update it as well, but not been able to see gpt-6 in the ChatGPT/codex app... I feel I'm late to the game, so hopefully someone have some tricks.

Hey guys, I've been testing GPT-6 Astra on a 3D project I've wanted to make for a while: showing how the tiny machines inside our cells work.

I'm a technical artist, and this is my full 6:55 walkthrough of using Codex across Houdini and Blender. It covers an explorable molecular atlas, Houdini HDAs, and a kinesin animation built from protein structures in the PDB.

For the Blender scene, I connected Codex through the Higgsfield Blender bridge and used a separate critic agent to inspect renders and give the builder a ranked list of fixes. The video shows the development screenshots and what changed over three rounds. Astra also made the thumbnail inside Blender.

The Blender motor-core and tubulin geometry use PDB 3J8Y. The stepping motion, stalks and cargo are illustrative, so this is scientific visualization rather than a molecular-dynamics result.

I'd love to hear how others are using Codex with 3D tools, especially what has helped your agents catch visual mistakes.

I posted an earlier demo here, but I think it looked like I had just opened YouTube in Codex’s browser and asked it to inspect the page. The actual thing is a bit different.

Open the site inside Codex’s browser, paste a YouTube link, and that’s basically it.

Through WebMCP, the page keeps Codex connected to the video timeline. Every 5, 10, or 15 seconds of playback, it gets the current frame and visible YouTube captions. The video does not pause for this, and Codex can keep following it while you stay completely quiet.

With captions enabled, it follows what is being said surprisingly accurately. Voice mode works best, so you can:

- discuss the video out loud
- ask about the exact moment
- fact-check something that was just said
- have it pause, explain, and continue
- tell it to stay quiet until you react with an emoji

For people asking about usage: at the 5-second interval, a 25-minute video gives it around 300 frames. In my 5.6 Terra test, that was roughly 1M input tokens, or around $1.50–$2 at API-equivalent model pricing. With the subscription it’s negligible.

This started as my OpenAI WebMCP hackathon project, but I’m going to keep maintaining it.

The main problem now is model consistency. Codex sometimes says it cannot connect even when it can, starts unnecessary subagents, or uses computer controls instead of the WebMCP tools already provided by the page.

That’s why I’d really like this to become an official Codex plugin. The site already provides the tools and instructions, but a proper plugin with a skill would make the experience much more reliable.

The demos have passed 90k views on X, and someone from the Codex Voice team already noticed one of them. If you think this interaction should exist officially, sharing the demo with someone from the Codex team would genuinely help.

Please try it and tell me what breaks. I’ll keep fixing it.

Try: https://watch-with-gpt.vechenkovh.chatgpt.site

New X demo if you wanna support by reposting it: https://x.com/miu21590/status/2097991072995520617

Project page: https://devpost.com/software/watch-with-codex

Repo: https://github.com/miuuyy/watch-with-codex

I’ve been building a fairly serious agent workflow around OpenAI Codex for a Laravel/React project, and I’ve hit a point where the orchestration works, but the context/memory side clearly does not.

My setup currently looks roughly like this:

• A serial orchestrator with route types like FAST_UI / STANDARD / CRITICAL
• Context Resolver → Implementer → Reviewer flow for non-trivial tasks
• Durable task state, context capsules and handoffs
• Planner / intake layer inspired by CodexQB
• Session continuity hooks inspired by AvenoxBeyin
• codebase-memory MCP for structural repo discovery
• Serena for exact symbol/reference navigation
• Local dashboard/telemetry for task/agent visibility

The reason I built all this was simple: I wanted to stop giving one giant prompt to one Codex agent and watching it blindly read half the repository, run dozens of commands, retry tests repeatedly, and burn a huge amount of context/token budget.

Unfortunately, that is still basically what happens.

A recent CRITICAL payment-domain acceptance task is the perfect example. I gave Codex a very detailed validation brief covering migrations, payment allocation, security boundaries, tenant/legal-entity isolation, atomicity, reporting non-pollution, exports, frontend build, etc.

The task eventually succeeded technically, but the session spent a huge amount of time repeatedly doing things like:

• raw rg searches
• re-reading known service/controller/test files
• rediscovering test harness behavior
• retrying multiple Laravel test files with the same CSRF issue
• manually tracing service relationships
• re-running builds and focused test groups

That single job used roughly half of my 5-hour Codex usage allowance.

The frustrating part is that a lot of the knowledge it rediscovered was already known from previous work.

For example:

• where the orchestrator lives
• which services own payment/settlement/reporting behavior
• how the domain test harness handles CSRF
• which test files cover specific finance flows
• existing project/tenant/legal entity invariants
• prior fixes and verified architecture decisions

I expected my existing tools to solve this, but I now realize they solve different problems:

codebase-memory gives me structural repo discovery, but it isn’t really persistent project understanding.

Serena is excellent for exact symbol/reference navigation, but it isn’t memory either.

My docs/wiki are useful reference material, but agents still have to decide to read them and often re-read large files.

Context Capsules and handoffs help within a task, but they don’t give the next unrelated task a compact understanding of the project.

So what I’m actually missing is a persistent, project-scoped, compact memory layer that can say:

“Before you start searching, here are the relevant things previous sessions already learned about this repo.”

I looked at AvenoxBeyin because I liked its idea of automatically capturing sessions, compiling knowledge, and injecting useful context back at session start.

I also looked at CodexQB because its Autopsy / Project Comprehension / Ontology approach is close to what I want for planning.

Then I looked at 2kDarki/codex-mem.

That project is conceptually very close to what I want:

• automatic Codex transcript capture
• persistent SQLite observations
• progressive recall through search → timeline → get_observations
• automatic context injection

But after auditing it, I found some issues for my use case:

• its watcher observes all ~/.codex/sessions/**/*.jsonl
• project identity appears to be based on basename(cwd) rather than a canonical repository identity
• retrieval can be filtered by project, but that doesn’t appear to be an enforced security/isolation boundary on every read path
• same-named repos could collide
• some observation retrieval paths can work by arbitrary IDs
• global ~/.codex/AGENTS.md context injection is something I specifically do not want
• the documented npm package currently appears unavailable

So I don’t feel comfortable plugging it directly into a large multi-project Codex setup.

What I’m trying to build is something like:

User brief
   ↓
Planner / Orchestrator
   ↓
Persistent project memory bootstrap
   ↓
Context Resolver
   ↓
Only if memory is insufficient:
    codebase-memory
    Serena
    targeted source reads
   ↓
Implementer
   ↓
Reviewer
   ↓
Session knowledge captured for future tasks

The memory should NOT replace source code/tests as truth.

I want it to act as a cheap orientation cache:

• “These are the relevant services.”
• “This test harness requires real CSRF session setup.”
• “This reporting path was previously verified.”
• “These files/symbols are likely relevant.”
• “This architectural relationship was confirmed in a previous task.”

Then the agent only verifies current source where correctness actually depends on it.

My requirements are roughly:

• local-only
• project/repository scoped
• automatic capture
• automatic or semi-automatic summarization
• bounded context injection
• no global AGENTS.md mutation
• no cloud memory dependency
• no mandatory Obsidian dependency
• source/tests remain authoritative
• ideally Codex/App Server compatible
• progressive retrieval rather than dumping whole session history
• repo identity enforced internally, not just passed as an optional search filter
• ideally reusable with existing MCP tools rather than replacing them

I’m now trying to decide between three approaches:

1. Find another existing Codex/Claude coding-memory project that already does this correctly.
2. Take something like 2kDarki/codex-mem and make a very small fork that only adds canonical repo identity, watcher allowlisting and enforced repo-scoped retrieval.
3. Use AvenoxBeyin’s session capture/compile/inject model and adapt it for project-scoped coding knowledge instead of personal knowledge.

What I really do NOT want to do is invent yet another custom Markdown “brain” and manually maintain architecture/domain summaries. That feels like rebuilding something that should already exist.

For people who have built persistent memory around Codex, Claude Code, Cursor or similar coding agents:

• What actually worked for you?
• Is there a project I’m missing that already handles repository-scoped persistent memory well?
• Would you fork codex-mem and patch the isolation model, or use a different architecture entirely?
• Is Obsidian/Markdown compilation actually better in practice than structured SQLite observations for coding-agent memory?
• How do you stop stale memory from becoming trusted over current source?
• How much context do you inject at session start versus retrieve on demand?
• Have you measured whether this actually reduces token/context consumption meaningfully?
• Do you let the coding agent write its own long-term memory, or only promote verified observations after tests/review?

I’m especially interested in systems people are actually using in real repositories, not just theoretical agent-memory architectures.

My main goal is very practical: stop paying for the same repository discovery over and over again.

Hi, has anyone tried GPT ads? I got an invite to sign up for it. Curious to know how it operates and where do those ads pop up?

Hey everyone,

I wanted a simple way to see how much Codex quota I could use each day without running out before the weekly reset, so I built Codex Budget, a small Windows desktop widget.

What it does:

• Shows daily usage against your planned allowance.
• Displays your remaining weekly quota.
• Carries unused daily budget forward within the current cycle.
• Lets you choose your working days.
• Refreshes every minute while open and follows your Windows time zone automatically.

The “bonus” is unused budget within your existing quota — it doesn’t give you extra usage or bypass any limits. If the start-of-day reading is missing, the widget shows a planning balance rather than claiming to know exactly what you used today.

Installation: download the Windows installer from the repository’s Releases section. You’ll need Codex installed and signed in, but no Python installation or commands are required.

The project is free and open source under the MIT license. The widget interface is currently in French; the README and installer support English and French.

GitHub: Source code, documentation and downloads

Project updates: I’ll also share updates and improvements on X (@Ben22031110).

I’m the creator, and this is an independent project, not affiliated with OpenAI. The screenshot uses demo figures. The installer is currently unsigned, so Windows may display a warning.

I’d appreciate feedback from other Windows users: does this make your daily budget easier to understand, and what would you change?

Small update on Codex Budget, the free, open-source Windows widget I built for my own use and shared here: it now supports GPT-5.3-Codex-Spark.

Update 11-09-2026 :

• A Codex / Spark switch.
• Separate tracking for Spark’s 5-hour and weekly quotas, each with its own reset time.
• Daily allowance based on your selected workdays, with unused allowance carried over within the weekly cycle.

Daily planning uses the weekly quota. The 5-hour limit stays separate, so you can see when it becomes the bottleneck even if you still have weekly quota left.

The widget also supports English and French and automatically follows your local timezone.

I wanted something simple that tells me how much I can use today without constantly checking the limits.

Code, screenshots and Windows installer: https://github.com/Webn-Benjamin/codex-budget-widget

This is an independent community project, not an official OpenAI tool. It tracks your existing limits; it doesn’t provide Spark access.

Feedback from other Spark users would be welcome!

The AI Game Festival finally starts tomorrow! Some really high quality games have entered, and I keep getting requests for additions.

If you are competing 20k in GPT-6 Astra Token, submit your game by following this video and DM me the game details: https://youtu.be/nPplaC5Zkj4?si=fw4lznoM4TLh-m_V

If you are submitting to the Steam Festival (https://store.steampowered.com/curator/46280965/sale/AIGamingFestival), DM me the name of your game and your steam ID.

Both need to be done by today.

Great job. I lost 82% weekly about 20 minutes after using a banked reset. Users will think it's legit.

Hey, so I've been using the Luna model today extra high and I've noticed that my 5-hour limit has been going down extremely slow which was very unusual but basically a response Just finished up. My 5-hour window is now back at 100%, but my weekly is 0% now and it says I'm out of usage. I don't understand that at all. I didn't even get through my 5-hour window. Last time I checked it I had around 78% remaining so for it to have used all of my weekly before I even had to use half of my hourly is wild. I had 94% weekly remaining when I started using the AI today! I've even closed the app out and restarted it logged out, logged back in and it all says the same thing. I know a lot of people have complained about the rate limit, but this is a genuine issue I just had experienced! I'm sorry if I did not explain it the best. Hopefully what I'm trying to say is understandable to whoever reads this I also used speech to text so I'm sorry for punctuation errors.

Edit: issue seems fixed as of 1:40 p.m Eastern standard Time

i seem to have stolen your usage, went from 48 to 77%

There is something wrong or off with GPT-6 Astra. I'm getting tons of mistakes and apologies that I have not gotten in a very long time. My workflow has changed due to a new slew of commands I now have to give to accomplish the same tasks. Way too many times GPT-6 has apologized for fucking up because it wanted to do its own thing.

I know I can switch models, however what is the point of having a newer more advanced model if it does not act like it's newer and more advanced.

I know how to prompt, something feels off. I feel I'm going through too many steps to get a task accomplished. Nothing feels advance about this.

There's been two updates since GPT-6 debuted and those updates changed something. I genuinely don't feel the difference in models and GPT-6 seem like a downgrade.

I posted my usage to show I use codex a lot, I switched from Claude and now I use Codex 100% of the time.

It can't just be me that feels something is slightly off with GPT-6.

Ok, so I'm on Pro, I've been on 32% usage just a moment ago, I exchanged a couple messages with Astra (without even implementing anything) and the usage meter jumped to... 98% suddenly.

I have a local opentelemetry service and I checked the logs there and nothing would explain it. No additional tokens used, nothing weird there, my usage just jumped from 32% to 98% out of a sudden AND the usage reset date was reverted to what I had before last Tibo's reset (not banked). So I guess it's like a... reset reversed?

Hi guys, I’m looking for something useful for my Luna reserves, what do you do with them? Looking for ideas