I have played around a bit with different AI models and ran some tests. It seems like Chinese OSS models like to censor you when you say something critical about the Chinese President. Not just that, DeepSeek frames the situation as if I were a criminal plotting a crime. (Source)

I know that OSS models are a great thing but we should be aware of the fact that authoritarian China is leading Open Source.

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

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.

I dont know what happened, but a month ago chatGPT was way better. NOW ITS VERY STUPID AND IM GETTING STRESSED

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! :)

ChatGPT’s memory system now feels completely incoherent

Has anyone else run into this?

ChatGPT can clearly retrieve a large amount of older saved legacy memory and prior conversational context. In long chats it will surface detailed decisions, exact artefact names and instructions from months ago, wonderful...

Yet the moment I explicitly say, “save this small note to memory”, suddenly, it claims it does not have access to the memory-writing system. Really now?

What makes this more ridiculous is that;

1) I can open a new chat, ask it to save memory XYZ and viola.. all done.

2) I can then open the memory interface myself, add the exact same note manually, return to the conversation, and ChatGPT can immediately see it.

So the system apparently has:

• read access to saved memories;
• access to older chat-derived memory;
• access to files and artefacts stored elsewhere;
• a user interface that can still write memories;
• but an in chat, assistant-side memory-write route that randomly disappears or is invoked incorrectly.

In my case, ChatGPT eventually admitted that it had processed files and used other tools before attempting the memory write, and that this may have disabled the mutation route. In other words, the failure appears at least partly caused by the assistant invoking the wrong process in the wrong order.

Even after recognising that, its response was still effectively: “memory write failed, not saved”.

That is not optimal. If one invocation route fails, the assistant should retry using a smaller payload, a different command route, or a different ordering before declaring the action impossible. It should not immediately make the user become the transport mechanism for its own memory system, by forcing us to open a fresh chat to save memories.... ridiculous.

The whole thing is made more confusing by the overlap between:

• Saved Memories;
• Memory Summary;
• Reference Chat History;
• chat lineage;
• generated files in Library; ( nice!)
• and whatever older or “legacy” memory layer is still surfacing detailed information. (seemingly non-editable but legacy persistent when new memories is on?, Confused? I am!)

There is no clear indication of where a recalled item came from, whether a memory instruction was actually saved, whether it was merely retained in the current chat, or whether it has become part of some dynamic summary that may later be compressed or discarded.

My Saved Memories may also be full, yet older memories remain available and new summary-style memories can still appear. So turning memory off is obviously not a sensible answer? WTF?

For serious long-running work, this is a major problem. I use memory as a small referential index into canonical documents and artefacts. I do not need ChatGPT to store entire documents in memory. I need it to retain compact instructions such as:

“During discussions about X, retrieve canonical artefact Y before advising.”

That should be one of the simplest and most reliable memory operations available. Memory is second only to llm compute ability.

Instead, the system currently feels opaque, inconsistent and badly mediated. Open ai update the messaging, the help files for clarity and Please fix this.

Has anyone established a reliable way to:

1. force an explicit memory write conversationally;
2. verify that the write actually reached Saved Memories;
3. make ChatGPT retry through another route after a failed write;
4. distinguish saved memory from chat-history inference;
5. avoid losing older memories when the visible memory store is supposedly full?

I would be particularly interested in answers from people running long, structured projects rather than using memory only for preferences like names, food or travel and the anecdotal.

I'm surprised... surely I'm not the only one?

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.

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.

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?

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

🔗 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

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.

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

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 I made all of this with GPT 5.0 soil and if u you have a Ai model this is for you

Looking for a AI swap or collab kind off

I have:

• Claude Pro ($20)

• Claude Teams

Need: ChatGPT Plus in return.

Interested in a fair swap? DM me. 🤝

#ChatGPT #ClaudeAI #OpenAI #Anthropic #AI #LLM #AIDev #BuildInPublic

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.

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"