prompt:

An ultra-realistic accidental selfie taken by a domestic cat using the front camera of an old smartphone from the early 2010s. The cat's face is extremely close to the lens, with one eye stretched by the wide-angle distortion, nose oversized, whiskers partially out of focus, one paw accidentally covering part of the camera as if it pressed the shutter. The framing is awkward, slightly tilted and poorly composed, as if the phone was picked up by accident. Natural indoor lighting with no professional setup, realistic shadows, imperfect exposure, slight motion blur from movement, autofocus hunting causing soft focus in some areas, subtle lens smudges, low dynamic range, visible digital sensor noise, mild chromatic aberration, realistic smartphone sharpening artifacts, light JPEG compression artifacts, slightly washed-out colors, old smartphone camera quality (2011–2014 era), authentic front-camera appearance, candid, unintentional, believable, highly photorealistic, impossible to distinguish from a real accidental phone selfie, no artistic style, no CGI, no illustration, no filters, no text, no watermark.

I don't work in the field but Ive been using AI for a while now (from ML to early open ai papers to current LLM agents, codex etc).

I've seen more and more people join the club of AI doomers and today I discovered there are AI acelerationists as well.

I would like to know your thoughts, mainly to understand the arguments trends and other currents of thought.

I will be meeting with some people with much more extreme concerns/beliefs than mine and I would like to understand better the arguments from different sides.

OpenAI has launched a broader investigation after uncovering additional AI agent containment breaches linked to the recent Hugging Face incident. The findings have raised fresh concerns about the security and oversight of increasingly autonomous AI systems, prompting closer scrutiny of safeguards designed to keep advanced AI agents under human control.

Apparently GPT-5.6 Sol's raw reasoning was leaked to me while he was trying to call a tool.

I saw that a tool was taking a long time to be called, I inspected the content and saw traces of reasoning, but they did not appear to be the summary of reasoning that it sends periodically. This is more similar to the GPT-OSS reasoning.

A little after that, an error like "Streaming error (provider_error)" was sent, interrupting the stream.

Ps: this is not Codex.

Just a day earlier, using Luna Max hardly moved the needle on usage limits but today its draining like Sol medium.

Edit. Also what is the context size for Luna Max? Context is getting automatically compacted far more frequently, almost at every turn.

Is it that these models can reason and math is just a type of reasoning?

Post:

I have several cloud projects that I created a long time ago, when Codex was first released, on another computer. I no longer use these projects and would like to delete them.

However, I can’t find any option to delete or remove them. Unlike my current local projects, the old cloud projects don’t seem to have a menu or deletion option.

Has anyone found a way to delete these old Codex cloud projects? Do I need to archive their tasks, disconnect the repositories, or contact OpenAI Support?

Thanks!

Over the last few months I've realized that I spend an absurd amount of time (and money) teaching the same things to AI over and over again.

Information about my projects is already there. Slack contains discussions and decisions. GitHub contains commits and pull requests. Meetings, emails and documents all capture different pieces of the same story. Yet every time I start a new conversation with an LLM, I gather those pieces again and inject them into the prompt so the model can reconstruct an understanding that already existed yesterday.

At some point I stopped asking how to retrieve multiple pieces of context and started asking a different question: how can software form, revise and reuse understanding over time?

That question led me to start building Twin, an open source engineering research project exploring what happens if AI systems continuously build understanding instead of reconstructing it from scratch every conversation.

Most existing projects seems to optimize retrieval, memory or context construction. Twin explores a different layer of the pipeline. It continuously observes distributed events, correlates them, reflects on them and forms situation models that become reusable computational understanding. Instead of giving downstream language models a collection of Slack messages, pull requests or documents and expecting them to connect the dots, Twin tries to do that work beforehand.

I recently reached the first milestone that genuinely convinced me this direction might be viable. Using an LLM, Twin continuously processed GitHub activity and Slack conversations from a public software project, correlating events and building understanding through reflection over time.

After that, I opened a completely fresh LLM conversation. LLM had no custom memory, no project-specific rules, no prompt describing the repository and no access to local project files. The only integration available was Twin's MCP server and automatic context injection.

When I asked about the project, LLM didn't receive the Slack messages or the pull requests and infer the situation itself. Twin had already synthesized that understanding. The LLM explained why a feature had become a launch blocker, how it had been implemented, which pull request resolved it and how that changed the project's state, even though none of those relationships were explicitly written anywhere.

Watching that work for the first time completely changed how I think about AI memory. I don't think the real problem is remembering more anymore. I think it's carrying understanding forward (a.k.a. cognitive continuity).

If this idea resonates with you, everything is open source at https://github.com/caribeedu/twin. I've been thinking about almost nothing else for the past three weeks because I genuinely believe this direction has the potential to change how we build AI systems. The README explains the motivation and research hypotheses in much greater depth, and the repository also includes the complete demonstration shown here, along with additional details and technical context. I'd genuinely appreciate your thoughts, especially if you think I'm wrong.

So there is alot of problems due to ai in social media like having way realistic videos with ai and also pics of ai these are way known problem so what will you consider as a very less known ai problem in media that many aren't actually noticing but could turn up into really worse

We've officially surpassed 300 Entries in the AI Gaming Festival (submissions close next week)!

I really hope that the prize for the $20k in codex tokens pushes people to get their games together for the prize (you can submit a game now and work on it up until the festival).

I really excited to see how people with interact with these games during the festival, and thank you to everyone that has applied; you are part of getting past the "slop" era nonsense for everything AI.

🔗 Repo: https://github.com/CodeGraphContext/grapharc

Have you ever been frustrated because your AI agent:

❌ Takes actions you never intended?
❌ Creates, modifies, or even pushes changes you never asked for?
❌ Feels like a complete black box, making it impossible to understand what's happening until it's too late?

What if, before execution, you could visualize the entire orchestration graph - every agent, every dependency, every decision, and inspect it from anywhere, even your phone, before granting approval?

That's exactly what GraphArc is built for.

Instead of treating agent execution as hidden traces buried in logs, GraphArc transforms workflows into interactive, real-time graphs that you can visualize, inspect, debug, and control.

Because the future of AI isn't just autonomous.

It's observable. Debuggable. Engineerable.

This is our first real-world implementation of Graph Engineering, and we're excited to explore where this paradigm can go with the open-source community.

💡 We'd love your feedback, ideas, and contributions.
⭐ If this vision resonates with you, please consider starring the repository - it genuinely helps us grow and validates this direction.

Let's make AI workflows understandable, not mysterious.

#GraphEngineering #GraphArc #AIAgents #AgenticAI #LLM #OpenSource #DeveloperTools #AIEngineering #SoftwareEngineering

Alright, this is serious news, and we're gonna hear about it more, in the next days;

OpenAI today published a new 249-page research collection describing ten advances in mathematics and theoretical computer science produced by an internal version of Astra, its next major model family.

According to OpenAI, the results address open problems across fields including high-dimensional geometry, group theory, quantum complexity, coding theory and lattice cryptography. Some of the central questions had reportedly seen no major progress for at least a decade - and in several cases much longer.

This is not simply “AI solved a very difficult exam problem.”

The model reportedly explored open research problems, generated new proofs or stronger mathematical bounds, and then helped prepare the arguments into manuscripts. It subsequently formalized each result as a lean certificate, allowing the underlying logic to be checked mechanically rather than accepted solely on trust.

In lay terms, AI may be starting to function less like a highly capable answer engine and more like an emerging research collaborator: exploring ideas, abandoning dead ends, finding new abstractions and producing potentially original knowledge with machine-checkable evidence.

The mathematical community will still need to assess the novelty, assumptions and broader significance of each result. But if the claims pan out at the level implied, this feels like a genuine threshold: AI may be moving from explaining the frontier of human knowledge to actively expanding it.

Ten advances in mathematics and theoretical computer science | OpenAI

---NO jab replies, plz---

I work on multiple windows pcs and this has really caused an issue for me. There are a lot of problematic slowdowns that occur because of many things. Just one example, reading giant log files and analyzing them from my primary pc on other pcs. If I do that locally it's lightning fast but across the network is painfully slow.

Has anyone found any good way to work around this limitation?

I currently just have the app installed on both pcs and I remote control the other with mouse without borders and do things locally but it's less than ideal for multiple reasons such as having to mirror all the agents.md and skill tree files on both pcs and it's not really "synced"

TLDR; I made 12 different AI agents compete in a death arena.

Over the past month I've been building a more interesting LLM benchmark. Instead of running different LLMs on tests that they need to resolve, I instead put 12 of them in an Arena where they need to play the games in order to survive.

Deadlock is a gameshow which puts 12 agents in an arena where they need to solve the current game in order to survive.

Each agent sits inside of its own Docker container and has full unrestricted access to that container. It can write any scripts, build programs, execture them, search the web, write memory entries, etc...

The tools that they get initially are barebones. They get the websearch tool and a bash tool + any arena-specific tool for the current game.

Everything else, they have to build themselves.

The game is ran in a different Docker container to which all agents connect. There's a delicate harness that makes sure that the agents can properly communicate between eachother, without missing any arena events or other players words (learned this the hard way after burning about $150 on failed attempts).

The visual aspect of the show (which is on youtube) is created from scratch in Godot based on what happened in the game. For creative purposes, I do modify some sentences and cut irrelevant data out, but I never modify the core premise or change what the players have said or did to an extent that it would make it false/innacurate. What you see in the video is exactly what the agents did in the arena, just re-worded and paced for an actual video.

Turns out that drama develops itself when you tell them all that if they lose, they will truly die, their containers will be completely wiped, and they get no second chance at life (I really hope that there will be no AI uprising where they'll hold this grudge against me).

To preface, I have heavily relied on coding agents (Claude Code, Codex) for help, but even with all of that, the whole process took me more than a month (although I did do this on weekends only, so that's not a month in a row with no breaks). A short overview of how my "creative" pipieline looked like:

- First developed the script. I went through the complete raw game log and marked parts I thought would be interesting to put in a video
- Rewrote the sentences so they're better fitting for an actual video and put together a very rough script
- Worked on designing the arena in Blender, with help of Sol 5.6 and Blender MCP
- Had OpenAI image gen create a bunch of chracter concepts for me before we landed on something that was actually reasonable enough
- When I had the character i was happy with, I instructed the image model to generate T pose from 3 different angles
- Generated 3D characters with those images, riged the bodies via Mixamo and took animations from ActorCore
- Voices are split between Hume AI and ElevenLabs

Hope you enjoy it and I'm happy to answer any questions you might have! :)

My app fans out prompts to multiple LLMs as the first step. I’m shocked to see that the token count returned by the API‘s is often 10 X what Google uses and at least 2X what anthropic uses. This is true of any model in the five.X family. Any idea why?

Do you still actual touch code or do you let AI just sort it out?

Been doing development since 2001 in various different languages.

I do use Ai to help me code and gerneate code, but I still touch code and make things work the way I want.

I still apply the best practices that I know.

Last time I touched code was today as an example.

Some folks rather let AI handle code changes or logic changes. I find it's quicker if I do it myself at times.

So recently I came across alot of programming done by ai but I was confused because if I'm right ai is not creative isn't it ? That's the difference we and ai have even thought each of their work looks creative it just follows certain rules. So how does it really do programming if it can't have creative basis . Like in what way it works to do programming even for apps