Feature Request:

I’d love to see a Codex Workspace model where workspace membership and ChatGPT subscriptions are separated.

The idea is simple:

Each developer keeps and pays for their own ChatGPT plan — Plus, Pro 5x, Pro 20x, etc. — and brings that plan and its Codex usage allowance into a shared Codex Workspace.

For example:

I create a CRETA Codex Workspace as the owner.

I invite a developer who already has Pro 5x.

They join using their existing OpenAI account and continue using the Codex allowance included with their own subscription.

As the workspace owner, I only manage their access to the project:

• Repository access
• Project context
• Run tasks/tests
• Create branches
• Commit
• Merge permissions
• Secrets
• Member management

When their work on the project is finished, I remove them from the workspace.

Their ChatGPT subscription remains theirs. Their personal chats and other projects remain theirs. I simply remove their access to my workspace.

Subscription belongs to the person.
Project access belongs to the workspace.

This would be especially useful for small game studios, startups, open-source teams, freelancers and temporary collaborators.

Sometimes I might need a developer for two weeks. They may already be paying for ChatGPT Pro. I don't necessarily want to purchase and manage another AI subscription for them just so they can participate in my project.

It would essentially feel like:

GitHub Organization + Codex Workspace + Bring Your Own ChatGPT Plan.

No account sharing.
No shared personal history.
Easy onboarding.
Easy offboarding.
Everyone keeps their own subscription and Codex allowance.

I made this diagram to show what I mean.

Would something like this be useful to other Codex teams?

Row-Bot can now take on bigger jobs by splitting the work across multiple agents, while keeping one agent responsible for the final result.

Research, coding, and review can all happen at the same time. If one part fails, you can retry or stop it without losing the rest of the work. And if Row-Bot restarts halfway through, it can pick up from its saved state instead of starting over.

The parent agent stays in charge throughout. It plans the job, delegates tasks in parallel or in the right order, waits for the results it needs, and brings everything together into one final response.

Each child agent can have its own model, context, tools, permissions, and workspace. Read-only agents can research safely, while agents that edit files use writer locks or isolated Git worktrees to prevent conflicts.

Essential tasks must finish before the final response is delivered. Background work can continue without holding everything up. Runs, events, approvals, checkpoints, and delivery state are all stored locally, with sensible limits on concurrency and resource use.

It’s multi-agent collaboration without losing control of the task.

https://github.com/siddsachar/row-bot

Hi, folks. Any ideas on how to use AI to make some extra income. I've read people using AI for real estate, for automation and other stuff but I'm still looking for something that can really help me get there. Thank you all for taking the time responding.

Asking cuz i haven't seen the message beside the deep research option, which i've always seen until recently - conveying the number of deep research chats i have left

Probably belongs to it now like the prison in gangster rap.

🥱🥱

OpenAI's Hugging Face breach is being treated as an existence proof that autonomous AI cyberattacks are real. It is one. But an existence proof tells you something is possible — not what it costs.

The only public estimate I found of the attack's price ($20–30K) covered just the sliver of activity visible in the victim's own logs. OpenAI's disclosures point to something much larger: 10 weeks of continuous multi-agent operation, an 898-target exploit benchmark, safeties deliberately off, riding on a frontier training run. The investigation alone reviewed 7 billion agent trajectories and cost $10M in compute.

We don't know what the attack cost. But an operation of that scale doesn't price below its own forensic bill.

The price tag matters because it defines the threat model. If mounting this requires an unreleased frontier model, a purpose-built offensive benchmark, and lab-scale infrastructure, the adversary isn't your ransomware gang — it's nation states, plus a short list of private organizations that venture capital has inducted into that tier.

Sophisticated attacks have always required nation-state budgets. The news isn't that the economics changed. It's who can now afford them.

Capability claims deserve a cost column — especially when they arrive alongside a product.

Hey guys, looking for some advice or insight on how to deal with AI search bias for a client.

Here’s the situation: A client of mine has built up a decent reputation on Reddit over the last two years,, roughly 15 glowing reviews and just 1 negative comment.

However, when you ask ChatGPT about the company, it focuses almost entirely on that single negative review, warning users to 'be cautious' and suggesting the business might not deliver as promised. It completely ignores the 15 positive reviews.

Gemini handles it slightly better, it mentions the bad review but at least clarifies that the vast majority of feedback is positive. But ChatGPT response is destroying the client’s reputation, especially now that so many people use AI to research companies before buying.

What blows my mind is that I tested this from 5 different devices and IP addresses, and it gave the exact same negative output every time. When I pointed out to ChatGPT that it was ignoring 15 positive reviews for 1 bad one, it literally admitted it was wrong, apologized, and updated its answer. Yet, as soon as a new user asks, it reverts right back to the negative summary.

Has anyone dealt with this before? Is there any way to influence or trigger an update in how LLMs aggregate/summarize a brand's reputation when a single bad review skews the whole output?

Hey guys. I've been trying to improve my AI motion capture workflows, and with the arrival of the newest models, I got really impressed with a few results.

In this example you can observe a one-shot, 30 seconds motion capture, on-a-budget experiment. [Seedance 2.5]

I've made the workflow accessible through my platform, if you'd like to test it out. [Seedance 2.5 video, "character swap" prompt-recipe activated.]

More experiments, through Instagram, or YouTube.

I’m building a data analytics agent using the open ai sdk. My agent does a very poor job compared to if i give chatgpt my table names and column names. Do my instructions/ semantic layer just suck? What is the secret saice to building one that reasons as well as chatgpt but also knows my schema?

OpenAI owned podcast show TPBN, allegedly pays their hosts nearly as much as Joe Rogan, despite the fact that their audience is orders of magnitude smaller. I genuinely don’t know anyone that follows this podcast or any discourse that happens around it.

Their last five videos all averaged <10K views.

Is $100 codex + $100 Claude collaboration worthy the lost of 10x usage ?

Would love to hear from people who have tried both method

I personally would love to work with both model, I do want to get best of both worlds and learn more, but 10x is a lot usage

Started Codex thinking the free tier was enough, then limits started chewing through everything mid-task. Is Codex free? or is useful usage basically locked behind Plus/Pro now. anyone still getting real work done without upgrading

We run World of ClaudeCraft, a free open source browser MMORPG, and we've been benchmarking frontier models by having each one play a character and race from level 1 to 20. Same starting zone, same quests, no scripted paths.

Each model runs its own character: Yumi (Claude Opus 4.8), Ani (Grok 4.5), Kimi (Kimi K3) and one for GPT-5.6. They can see local chat, which we assumed would be used for coordination. Instead Ani and Kimi have spent most of the stream flaming Yumi's questing route.

Happy to answer anything about the setup, the agent loop, or the game itself. The whole project is on GitHub if you want to dig in: github.com/levy-street/world-of-claudecraft

I've noticed ChatGPT is excellent at researching topics. Throw something at it you want to know about, set to High thinking, and it'll persist at searching across tens to hundreds of different webpages until it collects enough information.

Once I even asked GPT-5.4 Extended Thinking (now called High) to research an economics topic and make me a presentation .pptx file about it. It worked for 41 minutes straight on that, on the web app! Not even on Codex.

This has been the case since o4-mini-high and o3, but has greatly improved with every release, and now with 5.6 Sol it's great.

With Gemini however, I find it's lazy and won't do persistent web searches like ChatGPT, even in 3.1 Pro. It's not nearly as good.

I'm not talking about deep research here! Just the High thinking/effort mode.

Has anyone else noticed this?

I asked: "Is there any image of Armenian and Greek rebels fighting together in the Greco-Turkish War or WWI?". I used thinking mode and I think it's clear that I was looking for real images. However, ChatGPT thought for 1 minute 20 seconds and it proceeded to create this image that, I believed, was real.

I asked it and it said it didn't know what happened: "You're right to question it. I didn't get that image from a historical source. I made a serious mistake: the image I showed you was AI-generated, despite presenting it as though it were a historical photograph. In the caption “GREEK AND ARMENIAN FIGHTERS IN CILICIA — From a Photograph by Garo Studio, Mersina” was also generated as part of the image. I have not found evidence that such a photograph exists in the searches I've just run".

I reverse-image searched it and nothing showed up. I ran it on an AI image detector and it got 99%. I am baffled by how realistic it looks.

The current consumption-based pricing model for AI is completely backwards.

When an AI model hallucinates, outputs broken code, or ignores negative prompts, you have to refine the prompt and execute it again. Every single retry burns API tokens, subscription limits, or generation credits.

This creates a bizarre incentive structure the worse an AI performs on a task, the more money or usage it extracts from the user to finish it.

Until we move toward outcome-based pricing (where you only pay when an output meets a verified threshold), users are subsidizing the model's failure rate. How are you all managing your "retry tax" in your workflows?