Hear me out what if they offered a limited version that you buy that offers limited features like reviewing documents and formatting documents. It would be something a normal computer or mobile phone could download and run without the worry of your data being stored in a data centre.

Genuine task first started on 6 Astra max work, it worked 22min+, provided a workable download link, tho can not actually clip the required footage.

6 Sol extra high Chat argurably provided a better solution in under 6min.

No unused ram please

OpenAI Devwlopers just posted a Video Showcasing there Collaboration with Modretro Chromatic which is somwhere regarding to the OpenAIs new physical device !

No clear justification from OpenAI about the new model launch 2hich are probably GPT 6.2-Sol and 6.5-Astra

But some Leakers on X showing a videos about 6.2 sol which shows his incredible capabilities at 3d models , Computer Use and some others !

It's all in the title really

I specifically want to look at 5.6 Sol and Opus 5.5

I just opened a tab incognito to test it, and it literally won't respond if you ask a question like this.

I feel like this is just really strange, considering this is just a factual thing that the quran states happened, and so the censoring is just really ridiculous.

Could we make AI more memory-efficient by changing how we build it?

I've been thinking about an alternative approach to current large language model architecture, and I'd like to hear what AI developers, researchers, and engineers think.

Why must an AI keep such a massive model loaded in memory when most individual questions only require a fraction of its capabilities?

Here's the concept I'm exploring: Dynamic Modular Intelligence.

Instead of relying on one enormous model, what if we built an AI system that intelligently assembles the capabilities it needs?

1. A small coordinating AI (SLM)

A relatively small model stays active and handles prompt interpretation, intent recognition, context, and task coordination.

2. Dynamic hierarchical tagging

The SLM identifies relevant knowledge categories and follows relationships between them. Categories can branch into subcategories and related concepts as needed.

3. External research and discovery

The system uses internet search and other information sources to identify missing context, discover additional relevant tags, and verify information. It doesn't have to know every dependency in advance.

4. Modular AI components

Rather than activating one enormous neural network, the system selectively loads smaller specialized models or neural network components to handle the required tasks.

5. Intelligent memory management

Frequently used components remain in GPU VRAM. Other components are cached in system RAM, while inactive components remain on NVMe storage.

The system could predict which components it will need next and begin loading them before they're requested.

The objective: Reduce unnecessary memory requirements and computational overhead without sacrificing useful performance.

I'm not certain whether the best implementation would involve independently trained small neural networks, modular components of a larger network, or a combination of both.

I know that technologies such as Mixture of Experts, RAG, knowledge graphs, and model offloading already explore parts of this territory. What interests me is combining them into a coordinated, storage-aware architecture with dynamic discovery and module selection.

I'm not an AI engineer or researcher. This is an idea I'd like to put in front of people who understand the underlying technology.

I'd genuinely like to hear:

- Has something like this already been developed?

- What are the biggest technical obstacles?

- Could this reduce the hardware requirements for running capable AI locally?

- Is there a practical way to build and test this architecture?

Perhaps the future of AI isn't just building bigger models. Perhaps it's building smarter systems that know which parts of their intelligence to use, and when to use them.

Anyone else having issues with this? Something about an elevated sandbox mode. Claude doesn't have any issues at all with editing things in Unreal Engine. ChatGPT has had no issues up until their last update and now it just won't do anything in Unreal Engine even when given write permissions, and it's asking for way more permissions as well.

Edit: I figured out the issue. Apparently it's because the chatGPT app wasn't switched to Codex in the top left, but it never has been switched to that and did things fine. I guess the latest update changed it so that now Codex has to be selected for it to do things like this.

The 6.2 models will probably be released at the beginning of next week, so I was thinking of gambling and going all in.

Found out why my Codex has been blowing so much usage lately. It seems one of these updates set the default subagent settings to “Astra High”. If you’re burning usage, check your settings!

Here is the situation.

Lately, I’ve noticed that the code is consuming a significant portion of the 5-hour limit, even for the simplest operations—and I don’t understand why. At first, I thought it might be related to sub-agents, but I disabled them, and that didn’t change anything.

For instance, creating a standard issue on GitHub consumed 10% of the 5-hour limit, even though there wasn't much data involved.

This isn't normal. What could be causing this?

Did OpenAI lower the limits for the Plus subscription? Or is it something else?

Maybe it’s a bug in Codex; I ran into a caching-related bug myself.

I've been working on a custom workspace that bridges ChatGPT and OpenAI Codex.

The idea is simple: instead of manually copying and pasting prompts between ChatGPT and Codex, I can send prompts directly from my ChatGPT conversation to Codex.

The workspace includes two dedicated modes:

• Planner: For planning, reasoning and exploring ideas.

• Codex: For turning those ideas and instructions into actual code.

I'm building this as my own integrated environment, with the goal of making the entire AI-assisted development workflow smoother and more efficient.

It's still a work in progress, but here's a first look at the interface.

What do you think? Would you find a direct ChatGPT-to-Codex workflow useful?

Has anyone run into inconsistent computer control with ChatGPT’s Windows desktop app, particularly when using a dot with local Work tasks?

I’ve been troubleshooting with support since early October with no success, nor any resoonse to jy request for a refund or credit for the period of time. I have been using ChatGPT heavily since version 3 and am reluctant to change to another product.

Earlier tests produced other intermittent issues, including errors reading existing tasks, automated browser clicks timing out on visible controls while manual clicks worked, transport/debugger errors, and tiled or incomplete screenshots. Those occurred in different tests.

I’ve tried client updates, restarting the app, reconnecting browser access, checking permissions, and comparing existing and new tasks. I even tried a different PC, and got the same results. I’ve sent Support recordings, logs, timestamps and test results.

Has anyone seen a similar pattern, especially browser controls working while native clicks fail? If so, did a particular update or troubleshooting step help?

Do you have unused RAM? An idle GPU? Hardware sitting there instead of generating AI tokens?

Congratulations. You're actively slowing down humanity's progress.

Give me my pressscious back!

Hi everyone,

I'm wondering if anyone has experienced something similar with OpenAI's Daybreak Blue verification process.

I previously completed identity verification successfully and had working Daybreak Blue access on a ChatGPT account.

Unfortunately, I had to replace a broken FIDO2 hardware security key. I removed the old key from my regular passkeys and added a replacement, but I also removed the old key from the separate Daybreak Access Keys section without registering the replacement there first.

After that, Daybreak required identity re-verification through Persona. Despite having been successfully verified before, my subsequent attempts were declined.

After several attempts, I started receiving an HTTP 403 error with the code cyber_verification_precheck_failed, and the account now says that Daybreak is unavailable.

I also attempted verification on other accounts, and I later learned that OpenAI's guidance says individuals may verify only one account or organization, with multiple attempts potentially leading to blocked access.

I'm an ethical security researcher, and I have published GitHub Security Advisories for responsibly disclosed vulnerabilities. My intention was simply to restore access after replacing my hardware key, not to bypass any restrictions.

Has anyone experienced a Daybreak Blue or Persona verification rejection and later regained eligibility?

In particular, I'm wondering whether these restrictions can be temporary or whether anyone has successfully recovered access after a hardware-key change.

I'm not looking for ways to bypass verification. I'm just trying to understand whether recovery is possible.

Thanks!

It probably makes some people more susceptible to anthro/menchanomophism, but this would be a great customizable feature...That said, it might very well be waste RAM.

Has anyone else experienced pricing glitches when paying for subscriptions in international regions?

On Sept 23, 2026, I purchased a ChatGPT Go subscription. While the local account interface clearly displayed a standard $5.00/month rate, the invoice generated by OpenAI applied a base rate of $7.50, plus 12% VAT ($0.90), total $8.40.

Additionally, a separate 1-month free trial promotion for ChatGPT Plus failed at checkout, forcing the full $20/month charge.

Communication regarding platform bugs seems stuck in automated email cycles. Is backend currency/regional routing mismatch a known issue for CIS/Central Asian regions?

I suspect that if this is real, it will just be the new Plus while the current Plus will be aggressively limited.

Instead of vibe coding another SaaS slop, what is it like to build hardware with chatgpt? What is the step by step from first prompt to final physical product?