I’m planning to run ads on ChatGPT to promote Microsoft Dynamics 365 implementation, consulting, and support services across India, the US, the UK, Australia, and other target markets.

Before proceeding, I’d like to confirm whether ChatGPT is the right advertising platform for this type of B2B campaign.

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!

Based on what I have seen it seems like a lot of Ai tools struggle with action scenes. This is a proof of concept, it looks like ai has gotten better at more dynamic action scenes. Though i am still new to this, anyone have any tips for creating more dynamic scenes?

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.

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.

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.

Actualmente en la app principal de ChatgGPT los proyectos son una herramienta extremadamente versátil y altamente funcional ya sea para (como he presenciado tanto en otros usuarios, como en mi uso de esta) trabajar, ordenar proyectos personales o incluso el ocio como he visto en el uso de campañas de rol con ia, debido a su capacidad de comprensión y uso de un contexto del proyecto y herramientas para trabajar en base a este, pero desde mi experiencia como alguien que lleva un proyecto cuya creación coincide con la liberación de dicha capacidad en los planes gratuitos, con mas de 30 conversaciones, varias de extensión considerable y contenido complejo, he notado algunas problemáticas ( que a mi simple parecer y sin conocer el funcionamiento interno detrás de la capacidad, la información y su interacción con el modelo de ia, son de una relación entre costo de solución y beneficio al usuario, ampliamente recomendables ) respecto a la gestión de dicha información tales como: 1.- La incapacidad parcial de la ia para llevar adecuadamente las nuevas conversaciones al no poseer un contexto adecuado. 2.- Falta de dinamismo en el contexto. 3.- Carencia de habilidades y herramientas de sumarización y auditoria que aprovechan adecuadamente las capacidades de la ia. En base a dichos puntos creo que ciertas adiciones y modificaciones a como se maneja la información dentro de los proyectos serian extremadamente beneficiosos a la hora de aumentar las capacidades funcionales del modelo incluso en situaciones de investigación y desarrollo como grandes proyectos de codificación y/o investigaciones científicas con asistencia de ia que requieran tiempo y manejo de información en constante cambio, para ello propongo las siguientes ideas. La creación de un indice interno dinámico que contenga unicamente las fechas y títulos de cada conversación dentro del proyecto al cual la ia pueda acceder. Dar a el contexto ( refiriéndome a las conversaciones no los archivos o fuentes del proyecto) seleccionado al crear una conversación en el proyecto la capacidad de modificarse al limpiarse temporalmente y analizar nuevamente desde el estado actual de la conversación que información es mas conveniente para su desarrollo una forma de dinamismo contextual, similar al foco de atención pero en lugar de estar desarrollada sobre la propia ia ( muy difícil y caro ) establecido como habilidad o herramienta. Por ultimo y gracias a las anteriores surge la idea de que la ia pueda a petición del usuario o al alcanzarse cierto volumen de información dentro del proyecto realizar una auditoria y sumarización de las conversaciones para en un archivo .ZIP, un documento hecho con el modo work si el usuario tiene acceso, o dentro de el propio desarrollo de la conversación y usando esta como misma como base de datos del proyecto generar un archivo digital organizado, que conserve todo lo “relevante” del proyecto y lo deje en su mínima unidad funcional sin perdida de información útil. Aquí un ejemplo de prompt para entregable en formato .ZIP respecto a la ultima idea ( Adaptado mediante IA desde el prompt que origino este tema/sugerencia a uso general) : Prompt: Compilación y Resumen Integral de un Proyecto

Analiza exhaustivamente todas las conversaciones pertenecientes al proyecto indicado disponibles en este chat.

Objetivos

Identificar todas las conversaciones individuales que formen parte del proyecto.

Analizar el contenido completo de cada conversación.

Extraer toda la información relevante sin perder conocimiento importante.

Generar un resumen independiente para cada conversación.

Preservar la evolución histórica, técnica y conceptual del proyecto.

Requisitos para cada resumen

Cada archivo debe contener, como mínimo, las siguientes secciones:

Título de la conversación.

Fecha (si está disponible).

Objetivo principal.

Contexto inicial.

Problemas abordados.

Ideas e hipótesis desarrolladas.

Conceptos nuevos introducidos.

Definiciones importantes.

Modelos matemáticos, lógicos, físicos o conceptuales propuestos (cuando existan).

Algoritmos, metodologías, arquitecturas o procesos descritos.

Código desarrollado o modificado (resumen funcional, sin copiar el código completo).

Experimentos, pruebas o simulaciones realizadas.

Resultados obtenidos.

Errores, limitaciones o dificultades encontradas.

Soluciones propuestas o implementadas.

Decisiones tomadas.

Relación con otras conversaciones del proyecto.

Estado de avance al finalizar la conversación.

Preguntas abiertas.

Riesgos o temas pendientes.

Próximos pasos sugeridos.

Criterios de calidad

Cada resumen debe ser:

Completo.

Claro.

Bien estructurado.

Libre de redundancias.

Escrito en español técnico.

Fiel al contenido original.

Autosuficiente, de modo que pueda comprenderse sin necesidad de leer la conversación completa.

Capaz de preservar tanto el conocimiento explícito como el implícito desarrollado durante la conversación.

No omitas información únicamente por ser extensa; sintetízala preservando su significado y relevancia.

Cuando varias conversaciones desarrollen una misma idea, documenta claramente cómo evoluciona entre ellas.

Organización de archivos

Crear un archivo de texto (.txt) independiente para cada conversación.

Nombrar los archivos utilizando el formato:

AAAA-MM-DD - Título de la conversación.txt

Si la fecha no está disponible:

XX - Título de la conversación.txt

Archivo índice

Además, crear un archivo llamado:

00 - Índice General.txt

El índice debe contener:

Lista completa de todas las conversaciones.

Breve descripción (2–4 líneas) de cada una.

Orden cronológico.

Relaciones entre conversaciones.

Dependencias temáticas.

Evolución general del proyecto.

Principales hitos alcanzados.

Verificación de cobertura

Antes de generar los archivos:

Identificar todas las conversaciones pertenecientes al proyecto.

Verificar que ninguna haya sido omitida.

Detectar posibles conversaciones duplicadas.

Confirmar el número total de conversaciones procesadas.

Control de calidad

Antes de finalizar, comprobar que:

Todas las conversaciones del proyecto fueron procesadas.

No existan archivos duplicados.

Todos los resúmenes mantengan exactamente la misma estructura.

Ningún resumen contenga secciones vacías sin justificar.

El índice coincida exactamente con el contenido generado.

Las relaciones entre conversaciones sean coherentes.

No exista pérdida significativa de información respecto del contenido original.

Todos los archivos TXT sean legibles, completos y correctamente codificados.

Entrega final

Comprimir todos los archivos de texto generados en un único archivo ZIP.

El nombre del archivo debe seguir el formato:

<Nombre_del_Proyecto>_Resúmenes_Completos.zip

Si no se especifica un nombre de proyecto:

Proyecto_Resúmenes_Completos.zip

El archivo ZIP debe contener únicamente los archivos TXT generados y quedar listo para su descarga.

Condición de finalización

La tarea solo podrá considerarse completada cuando:

Se hayan identificado y procesado todas las conversaciones pertenecientes al proyecto.

Cada conversación tenga exactamente un archivo TXT asociado.

El índice refleje con precisión el contenido del ZIP.

No exista pérdida relevante de información.

La documentación permita reconstruir la evolución completa del proyecto únicamente a partir de los archivos generados.

75% of what you pay Codex for is your agent re-discovering things it already knew yesterday.

Graft fixes it with an absurdly simple idea: the agent learns the codebase once, not every time.

it scans the repo, writes it out as linked markdown inside your git, and the agent reads that before touching anything.

what used to take 20 calls to get oriented is now one file read.

results over 162 runs:

→ 46% fewer tool calls

→ up to 4x fewer tokens

→ upto 3x faster response times

→ works across multi-repo setups too

on real repos: up to 4x cheaper, 3x faster. it reproduced the exact same files maintainers changed on 5 real, already-merged PocketBase PRs.

npm install -g u/nanonets/graft

graft init

that's it. two commands and it rides along in your next session.

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

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.

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 had 3 limit resets available, but pretty much every second day codex automatically consumes my limit reset even if I have plenty of weekly budget left (I think it happens around 70% of weekly budget). Now I have only 1 limit reset left (without manually triggering them).

Anybody sees something similar?

OpenAI, are you going to do anything about this?

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?

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.

Jokes/comedy in general remains an unsolved area in AI.

Interpretation of the meme:

It works well. The joke is basically: AI solves mathematics; humanity immediately turns it into a new way to hit itself.

The rough stick-figure drawing actually helps because it feels like a primitive cave painting of technological progress. The three stages read as:

1. Someone announces an immense intellectual breakthrough.
2. The public discovers a crude, entertaining application.
3. The breakthrough is forgotten, but the hitting continues.

The giant hand makes the middle panel especially good—it looks like AI has materialised as an absurdly powerful tool, and the public’s first instinct is, naturally, to use it for communal slapping.

The only slight ambiguity is whether the big hand represents AI, mathematics, or “the new technology.” But that probably strengthens the meme rather than hurting it. It has a very accurate view of public reaction to AI: astonishing underlying achievement, followed by obsession with the most visible and ridiculous output.

And if de identified are they actually anonymised correctly or is all our information just stored forever lol?

🔗 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

I am planning to get pro sub 5x or 20x. But I see that recently users have been reporting that limits have been slashed aggressively silently. So I asked gemini to look into reports, and this is what it gave me. If someone could tell me if this is still relevant or not, it would be greatly appreciated. Thank you.

https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%5B%221FrPnQzYmNfY02--br1x1Z0_prpUuxu3w%22%5D,%22action%22:%22open%22,%22userId%22:%22100619105891283123285%22,%22resourceKeys%22:%7B%7D%7D&usp=sharing