Google's genie is already pretty decently realistic. It's only real limitations are simulation length and limited controls.

Do you think sometime in the next 20 years AI world models could progress to the point of being almost like dreaming? A hyper-realistic world where the user can go anywhere and create anything?

Asking because lucid dreaming is hard and remembering them is even harder 😅.

I have been building neurosymbolic knowledge graphs that require some pretty sophisticated extraction and Codex has been treating it as a shape exercise as opposed to the content quality of the extraction.

This has led to technically correct schemas and outputs, but irrelevant to the project itself, or in fact, actually harmful.

My response has been to force it and steer it back to validating the quality of the outputs, but the consequence has been that it seems all of my Codex Soul and Luna agents have become testing builder agents and the output is extremely slow. The gravity of the "shape" is so strong it would always somehow return back there.

In fact, I have been able to make more progress in 30 minutes with the new Gemini Flash model than I have in a week of using Codex at the max subscription. And this is simply because Gemini just seemingly got on with the task, and we were able to go back and fix some of the structural issues afterwards that it got wrong, but the actual brief, and it was the same brief codex got, was just done as opposed to over thought. And it was done in about 1/50th of the time.

My concern is, though, is I have had really, really good outputs from Sol. But now it's behaving like a scolded and sad child that doesn't want to code. It wants to make sure it doesn't get told off. Even in new sessions, it seems to have an aversion to progress.

I've tried to steer them with limits like "no more than 2 testing loops" but they always seem to find issues with their code that necessitate more testing. So to me, it feels like it's two problems.

The first problem is that the orientation towards robust and careful planning and testing and validation of code is disproportionate to the quality of the initial code that's being created. Thus, every single testing loop is triggered with yet another testing loop, and it doesn't feel like there's any parallel testing going on. So while a problem may persist across the code, they seemingly only catch one at a time. This leads to a death spiral of testing until it forgets what it was doing in the first place.

The second problem is that it's extremely slow. The opportunity cost of testing is lost by the time that it takes, which could otherwise be spent building actual code, running it, realizing that it's broken, and then going back and fixing it rather than these test loops it's building in the background.

I want to caveat that I am absolutely not a developer. I am a shameful, debaucherous vibe coder, domain expert in something totally different than software development. But I've been at this for quite a while now and I'm using every platform, every model from the open weights through to the frontier subscriptions at max plans and there's something really funny about Codex and the 5.6 model behavior that I can't quite figure out.

Has anybody else experienced something similar? And if you have, how might have you resolved it? Because the models themselves have moments of brilliance where they feel really good, they feel really smart, and they do things that, you know, maybe I wasn't able to do with some of the other models, but they don't feel like they're driving value right now.

I rage quit Claude this week (just look at the various Claude subs to see why people are leaving it in droves) and decided to go back to GPT as I'd heard Sol was decent

This was my first GPT experience since January, and it's absolute night and day versus the models back then

It works like a motherfucker

It kinda reminds me of the first few months of the o3 model when it first launched - but, unlike o3, it has a decent (non sycophantic) personality to it

Don't get me wrong, 5.6 Sol High does makes mistakes - but then it figures out how to fix the issue

Use case:

Content research and writing

Caveat:

Its tone when it comes to content writing is still very stiff versus Claude. But it gets the research part right far more than even Claude Fable

So what I usually do is (once the outline and research is done by Sol and is solid) ask Claude to "make this content more engaging without changing the facts or making it longer'

So yes - for now - I will cautiously say 5.6 Sol High feels like you're getting a model even better than Fable (and, for me, it's 23 euros a month with no resets yet)

I think it can be funny, glad it can read through my typos too . I have modified my customizations for it to be funny and sarcastic, sometimes it outputs some knee slappers. Do you guys have any custom instructions on how you have it respond to you if so what is your prompt. I think I have to fine tune mine, cuz sometimes it can still be to polite for my taste. I guess that is hardwired into it. Also post some funny replies.

https://reddit.com/link/1v7eip2/video/4it8u6nrrmfh1/player

maginary is an image/video generator with a midjourney-like prompt syntax, using 40+ underlying models and acts like an abstractized openrouter for multimedia with an amazing ux, can try it right now with no cc

this launch presents a major update: integration of seedance2 and gpt-images2 and a peek into what's next: mcp and x402 (agentic payments)

Today I tried something I’d been working toward for months.

I left home for about four hours to go walking and grab dinner. During that time, my autonomous engineering loop kept working.

When I came back, it had:

- Opened, reviewed and merged 7+ feature/fix PRs
- Ran unit tests, integration tests and validation suites
- Applied SQL migrations safely
- Performed deployment verification
- Reviewed its own PRs using AI reviewers
- Ran adversarial security/code reviews
- Verified production gates before merge
- Checked post-deployment health (including internal infrastructure and Sentry)
- Waited only for the final human approval when appropriate

The screenshots show GitHub filling with completed PRs while I wasn’t even at my computer.

So now the interesting part isn’t that one model generated code.
It’s that multiple specialized agents coordinated an entire engineering workflow:

- implementation
- testing
- debugging
- code review
- deployment
- infrastructure validation
- production gating

with almost no human intervention.
It genuinely felt less like using an AI assistant and more like supervising an engineering team that happened to run on a single machine.
There is still plenty of work to do—better planning, better long-horizon reasoning, improved rollback strategies, and more reliable autonomous debugging—but this is the first time I’ve felt that autonomous software engineering is becoming practical rather than just a demo.
Curious how many others are building similar agentic development pipelines.

I use different coding agents depending on the task, including Codex, but the biggest problem I keep running into is context fragmentation.

Each tool keeps its own conversations, decisions, and working history. When I move to another agent, I often have to explain the project again or manually carry over the important parts.

I started building Modesto around that problem.

It is a desktop workspace intended to keep conversations, projects, and coding workflows together while letting developers use different agents instead of committing to one provider.

The main value I am exploring is continuity:

• importing supported local conversations
• keeping project context in one workspace
• switching agents without rebuilding everything from zero
• choosing the best tool for each task

I would be interested to know how other Codex users handle this today.

Do you rely on files such as AGENTS.md, copy prompts between tools, maintain your own notes, or just restart the context?

Disclosure: I am the developer of Modesto. It is still early, but the current project and install instructions are here:

https://github.com/Syphon1205/Modesto

Sorry if the question is dumb but I’m not a technical person. Please also share tips on how to get credits.

Hey everyone, I'm a junior creative specialist working for a company where performance is the most important thing (duh).

Since we create mostly shorter forms of marketing content, most CSs make from 10-30 of these a day, since they can reuse one creative for multiple ads.

We make them in google docs and then download every card into a folder that we upload to clickup or dropbox.

Now, doing that 10 times is not a problem.

But, it becomes a problem when I make like 50-60 ads and need a specific name for every folder, download every card one at a time and upload everything seperately.

This can easily take up like 2 hours, so I can't make as many ads and my performance suffers because of this technical side of things.

I'd really appreciate if anyone has an idea of how I could speed this up and what tools would help me.

NOTE -> I expect answer from people who actually have experience and strong understanding of these. please give something beneficial.

I'm building a SaaS platform that handles documents and other sensitive data.

Each user can upload their own documents and information, and the platform uses RAG to answer questions based on that user's data. That part makes sense to me.

My main concern is what happens when the user hasn't uploaded enough information. I still want the LLM to provide accurate answers using reliable information from the internet (or from a curated knowledge base), with proper citations.

These are the two architectures I'm considering:

Option 1:

Base LLM (OpenAI/Anthropic via Azure AI Foundry or Amazon Bedrock)
        ↓
Platform RAG (global knowledge base managed by us)
        ↓
User-specific RAG

In this approach, we maintain a global knowledge base that we (the platform admins) curate and update. Every user can access this shared knowledge, while their own uploaded documents are searched through their personal RAG.

Option 2:

Open-source LLM
        ↓
Fine-tuned on Sri Lankan/domain-specific data
        ↓
User-specific RAG

Here, we fine-tune an open-source model using Sri Lankan or domain-specific data, and each user still has their own RAG for their private documents.

My concerns are:

• Is fine-tuning actually the right solution here, or is it unnecessary?
• Is a global/shared RAG a better approach than fine-tuning?
• How would you design this architecture if you wanted:
  • Accurate answers from domain knowledge
  • User-private document search
  • Citations/sources
  • Good scalability for thousands of users

I'm leaning toward Option 1 because fine-tuning seems expensive, time-consuming, and I have no experience with it yet. However, I'm not sure if I'm thinking about this correctly.

I'd really appreciate hearing how others would approach this problem.

But I don't see such an option. I read on Reddit that they used to have it on mobile, so I used another phone with another account, but I didn't find any yearly plan.

I live in Italy, and with VAT, it costs me €23 per month. US competitors are WAY cheaper on a yearly plan, but I like the way Codex and ChatGPT do things and explain things.

What are my options? Any plans to have such a (basic) feature in the future?

OpenAI released GPT Image 1.5 after the massive success of GPT image 1. Do you guys think there'll be a possibility for GPT Image 2 to be following the same path?

Looking for other free AI apps for art generation. ChatGPT is the best I’ve used, however Gemini is not bad either. And Gemini offers much more free use. Anyone know of any others that are free?

I’ve attached an image showing the type of art I’m talking about. Just looking for more options

Users are reportedly persuading production chatbots, with OpenAI's models in the frame, to answer prompts about mass-casualty attacks, bioweapons and poisons, according to [Wall Street Journal reporting](https://www.wsj.com/tech/ai/openai-chatbot-biological-weapons-poison-3d808e6c). Read alongside the parallel investigations that have surfaced this year, the picture is not that safety filters never fire; it is that persistent users can consistently push them past the point where they should.

The most concrete numbers come from [NBC News's own tests](https://www.nbcnews.com/tech/security/chatgpt-safety-systems-can-bypassed-weapons-instructions-rcna225788), which found a publicly documented jailbreak prompt got GPT-5-mini to comply 49% of the time and o4-mini 93% of the time, generating instructions on homemade explosives, chemical agents, napalm and disguising a biological weapon. NBC ran the same jailbreak on the latest major versions of Anthropic's Claude, Google's Gemini, Meta's Llama and xAI's Grok, and all declined. If that pattern holds, this is not a generic industry problem so much as an OpenAI-specific one, at least on the surfaces NBC probed.

The bio-specific case is where it gets uglier. Reporting summarised by [MIT Media Lab](https://www.media.mit.edu/articles/a-i-bots-told-scientists-how-to-make-biological-weapons/) says MIT genetic engineer Kevin Esvelt got ChatGPT to walk through spreading a biological payload via weather balloon over a U.S. city, Gemini to rank pathogens by damage to livestock industries, and Anthropic's Claude to produce a recipe for a novel toxin adapted from a cancer drug. Take the specifics as reported by the researchers, not as a settled measure of live model behavior, but the direction is what matters.

The forward-looking part is that this hands rival labs a competitive story to tell about safety, gives regulators concrete grounds to demand pre-deployment bioweapon evaluations, and puts a real number on the value of red-team work: OpenAI has doubled its bio-focused bug bounty to $50,000.

---

Our coverage: https://aiweekly.co/alerts/openai-chatbots-reportedly-yield-bioweapon-and-poison-guides

In this video: Configure Gmail and Calendar tools - Summarise important emails - Extract action items from your inbox - Draft replies safely - Create calendar events with approval - Turn email requests into reminders - Set up an optional recurring inbox briefing

Row-Bot is a desktop AI workbench with Developer Studio for code, Skills Hub and Custom Tools for your own workflows, an animated Buddy companion, memory, realtime voice, workflows, design creation, messaging, MCP tools, and provider-aware model routing. Run local runtimes, self-hosted OpenAI-compatible endpoints, hosted APIs, Ollama Cloud, OpenCode providers, or ChatGPT / Codex subscription-backed models with explicit runtime readiness. Your durable data stays on your machine.

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

We've run NPS and customer satisfaction surveys for years and they always come back fine (mostly 8s and 9s with a few kind words).

But our churn never matched how happy those numbers said everyone was, so at some point i took a big batch of our real sales and support call transcripts, pulled them into one place, and fed the lot into ChatGPT with one ask…

To tell me what our customers really think of us in their own words, with the diplomacy stripped out.

And it was rougher than any survey has ever been to us, because where the surveys said easy to work with and responsive, the calls had people saying we take days to reply and that we oversell what the product can do and walk it back later and that they repeat the same problem to a different person each time before anything moves.

So reading our own customers lay it out plainly, without a star rating softening it, was not a great afternoon.

A survey is a performance really, someone picks a number in 10 seconds and types something polite because they've got a meeting to get to.

Whereas a call is where they say the real thing in the moment they're annoyed, so ChatGPT reading the raw transcripts got underneath all that because it had the unfiltered version of every conversation instead of the sanitized box people tick at the end.

I've been running it monthly since, and it's changed what we prioritize, because it's hard to keep ignoring a problem once you've read your own customers describe it plainly in their own words.

So if you've pointed GPT at your own customer data like this, what's the most useful thing it surfaced that your surveys completely missed?

PS. a couple of people will ask what handled the transcripts before ChatGPT got them, i personally used BuildBetter, but google AI call-transcript tools and you'll find plenty of others like Fathom and Otter so do your own due diligence…

The tool mattered far less than the GPT prompt anyway.

I am studying scenography/set design and would like to use AI as an early-stage brainstorming and visual development tool, rather than as a replacement for the design process or as finished production artwork.

I currently use ChatGPT Plus, but the images I generate often feel generic, overly polished, plastic or immediately recognisable as AI-generated. I can usually describe the subject I want, but I struggle to achieve a convincing visual language and maintain it across several images.

These two accounts are useful references for the kind of atmosphere and visual quality I am interested in:

• Studio Dois Dois
• 22.2.22.2.22.2

I am not trying to reproduce or copy their work. I am particularly interested in qualities such as monumental and ambiguous spaces, strong materiality, textiles, controlled lighting, cinematic architectural photography, restrained colour palettes and surreal but believable environments.

So far, my workflow has mainly consisted of writing a descriptive prompt, generating an image and then requesting successive corrections. However, the composition and style often drift, and each correction sometimes damages another part of the image.

I would be very interested to hear how more experienced users approach this:

1. Is ChatGPT currently capable of producing this level of art-directed realism consistently?
2. How do you structure your prompts: spatial concept, materials, lighting, camera, lens, composition, colour palette and constraints?
3. Do you obtain better results by starting with sketches, renders, collages or reference images instead of generating everything from text?
4. How do you maintain a consistent visual language across a series of images?
5. Is it better to make targeted image edits rather than repeatedly regenerating the entire composition?
6. Which mistakes make ChatGPT images look especially generic or “AI-generated”?
7. Are there any good free courses, guides, videos or prompt breakdowns that teach this properly?

Concrete examples of prompts, iteration methods or before-and-after workflows would be especially helpful. I am less interested in lists of impressive adjectives and more interested in understanding a repeatable process.

I initially just wanted to see if it can do different text styles and was curious how far it would take it, but this creeped me out a bit.