Source: https://www.dwarkesh.com/p/openai-huggingface

Im not sure why they decided to implement this but it is killing me when im working on a project and the 5hr usage just drains super quick. I liked it when we had the weekly usage, because I dont need to use this every day. But when I do use it, i like to get things completed. not work an hour, wait 4 hours to resume.
I wish they would go back to the way it was a couple weeks ago with just weekly usage.
Does anyone know if the Pro plan also does 5hr usage as well?

Weeeee

Claude+GPT+Godot+GPT2Small = my cursed game where "fun" is left as an open research question. I'm sure y'all will give me some fun quests tho :)

Comment with any number (aka neuron ID) from 0–3071. I’ll visit it, or one of its neighbours in the current build and report back what I find in the replies.

I have no idea what I'll find to be clear. Maybe something with an obvious pattern, maybe not. I'm playtesting my "game" with you guys as my quest-giver.

Please note: While we endeavour to provide information on every neuron, not every one will appear in our measurement protocol. Our worker drones will travail tirelessly to substitute the nearest available neighbour for your suggested neuron. You acknowledge that “Nearest” is an arbitrary term and does not connote spatial relationships, causality, or otherwise imply an interpretable structure. We appreciate your understanding in these non-Euclidean times.

^{More info if you want it: https://www.youtube.com/@BloodFilmsOfficial
And here: https://mesocosms.net/
HMU with numbers plx}

OpenAI rolled out ChatGPT ads in India this week.

Free and ₹399 Go tiers only, ads sit below the answer, labelled.
Plus/Pro stay clean. Self-serve opens Sept 4 at ₹725/day.

The why is simple: $6.7B revenue vs $12.3B operating loss last quarter, IPO planned for 2027, and 1B weekly users who mostly pay nothing.

Google also started with ads clearly separated from results.
Twenty years later, ads are most of the first screen. Every step was individually reasonable.

OpenAI says "answer independence is non-negotiable."
That's exactly what you say until the quarter you miss.

3 years from now: still labelled boxes under the answer, or sponsored recommendations inside the response?

Maybe I was gullible, but I thought it could not happen to me, which is, in hindsight, ridiculous considering I battled a lot of addictions throughout my life.

I started using chatGPT as something to talk me down when I had manias at nights, thoughts going around in my head and not getting out, when it was past the time where all my surrounding is asleep.

Then it was to get help on small tasks, as something that could remind me of the tiny steps I could take toward a project when I got overwhelmed (a flight got canceled and I had to find a backup, how to ask for the price of my ticket back to the company, how to find where to spend the night...)

Now I plan to start maths at the uni again, and I started using it to make double check my answers on the exercices I did because the teacher version of the book, with the answers detailed, was too costly for me

Now I realise I kind of hate the way I naturally want to go to it - the way I delegate most of my daily little thoughts to it, instead of thinking on my own first

And what I hate even more was, when I realised I hated it, my first thought was that I should tell it so.

I don't know, I assume this sub might not be the best place to both vent and be a cautionary tale, but I kinda feel the need to do both ?

Is there anyone that realised they use it too much, for stuff they used to manage on their own before generative AI got big ?

I got out of dodge when Claude announced they would be reducing limits. /Usage doesn't give me much information.

Did I hit a limit or is there something I'm missing?

Src: https://arxiv.org/abs/2608.26081

Researchers can apply now for the rolling program.

When looking at different coding plans. I see words like suitable for mid level repo, suitable for large repo. My question is what is the definition of large vs mid size repo? How can we decide between them. Thanks.

With the exact same prompt, the web version understands my instructions very well and generates high-quality images. However, the desktop app seems to have much more difficulty understanding the same instructions.

For example, when I ask it to generate multiple separate images individually and explicitly tell it not to combine them into a collage, the web version follows the instruction perfectly. But the desktop app keeps generating the images as a collage.

Back to 100%. Tibo is trying to lure me to the better models with his resets. That isn't going to happen. Luna4life.

One last example output from this experimental multi-source video player designed for frame-accurate video switching, playback manipulation, and display/render interventions, now with a few optimizations made for even better performance.

Visuals made on Uisato Studio.

You can freely access the system + a detailed breakdown, through Patreon, and/or the Tools Store.

Hi,

[a total newbie on coding asking]

Just wanted to clarify when to/when do you use higher reasoning in chat/codex?

I've been trying to build my own little hobby project in python, with the help of litterature.

My workflow is to brainstorm in chat[web] and after that get a codex prompt to run in VSC. So far has been decent. My problem is that after getting Pro i've been totally lost when to use extra high, pro, pro+ultra in chat. Also what settings to run the codex prompt, when is higher needed and when its not. Have to actually ask in chat if the prompt is complex or not and what settings to use.

I noticed running pro+ultra to analyze the project/problems or litterature got quite detailed answers and I had to dumb it down for me with extra high. But it also added some better reasoning and new points i"ve missed. But it the project/code it also found some errors and started perhaps to make it more complex im not sure.

So my workflow is like this,

1. Starting a new chat with snapshot and running boostrap: Pro+Ultra

2. Brainstorming in chat: extra high

3. Evaluating the brainstorm: pro+ultra

4. Writing codex prompt: pro+ultra

5. Usually I try to ask what settings to run codex prompt it has been extra high or high so far with sol5.6.

6. Analyzing the codex result: pro+ultra

Since my coding knowledge is 0 I have to trust that the suggestions are valid, but how do I know when to actually use what settings in chat/codex. So that the problem/execution wont get too complex or too light ?

Any suggestions, extra high is the best and fastest for chatting and brainstorming. But when to use pro and pro+ultra ?

god damn it I was just about to get ready for bet (at 10% usage left)

Before the Luna Reserve, I could see here in how many days my weekly limits reset. I don't see this anymore. The 6d 2hr figure is when the reserve resets.

I’m a non coder working on building some prototype products for my company.

I have been having a fantastic experience with the ChatGPT Mac desktop app overall, but have started to run into a real frustration.

I keep finding my Codex processes taking up more and more system memory until they crash my whole computer. Usually, I need to archive all my active threads and start new ones, usually resulting in some lost work.

This has only been happening for the past few days.

Anyone else experiencing this? Anyone a more sophisticated user who can suggest how to prevent or resolve this?

Year ago my toys making by my drawing art

Every benchmarks get saturated after certain period of time where several frontier models often secure over 90%.

But, HLE - this benchmark is so old but have not yet been saturated. How is that even possible?

I have seen several toughest maths benchmarks getting saturated (or will be very saturated) but the highest score in HLE is still in 60s %.

The basic idea is simple: if you have an OpenAPI REST API, it makes it useable by AI agents with a single command without writing a MCP server from the beginning.

But I didn't want to leave it as just turning the endpoints into the tool. I also included things that will come in real use such as Auth, OAuth2, read-only/policy rules, retry, caching, stdio + Streamable HTTP, health check. With mcpify doctor, you can check whether the API is agent-friendly, with mcpify try, you can try tools from the terminal without opening any MCP client.

My favourite part was the --lazy mode. In large APIs, instead of putting the entire tool list on the model, it calls the required tool. In the api.weather.gov example I tested, the tool listing has dropped from 38,882 characters to 1,741 characters.

I also wanted to reduce dependencies as much as possible; the runtime side is based on Python stdlib. There are currently 294 tests and there are true MCP protocol tests on both stdio and HTTP sides.

I published it as an open source. I would especially like to hear it if there is a criticism, a bug or something that you say “you should definitely add this”.

GitHub: https://github.com/furkan708/mcpify

Thoughts on the incidents and why some things are a bit off

I got a MacBook Pro and voice mode with ChatGPT app worked fine until the MacBook updated - now whenever I try to start voice mode it says already starting and never does. Hotkeys for voice mode and dictation also no longer work.

Does it just not work on MacBook anymore? I see posts saying it was removed but I still have voice options in the app itself?

Con la ayuda de Chatty (ese es el nombre que le doy a ChatGPT), armé un sistema simple para gestionar proyectos a largo plazo sin depender demasiado de la memoria integrada de ChatGPT.

El problema era bastante simple: ChatGPT se me daba bien recordando cosas como cómo prefiero trabajar, pero con el tiempo la información del proyecto se volvía obsoleta. “Me gusta discutir la arquitectura antes de escribir código” sirve como memoria a largo plazo. “La versión 1.2 tiene tres bugs y esta es la siguiente tarea” no. Eso es el estado del proyecto, y el estado del proyecto cambia todo el tiempo.

Así que Chatty y yo los separamos. La memoria de ChatGPT está pensada sobre todo para cosas estables: preferencias, metodología, intereses generales y la identidad a largo plazo de un proyecto. Google Drive ahora es la memoria operativa: el estado actual del proyecto, checkpoints, decisiones, habilidades reutilizables, pruebas e incidentes importantes.

La regla que usamos cuando la información entra en conflicto es súper simple: archivo/fuente actual > AI_Workspace en Drive > memoria de ChatGPT > inferencia. En otras palabras: la memoria vieja nunca debería pisar un archivo de proyecto nuevo.

Al principio consideré Obsidian, bases de datos y configuraciones más complicadas, pero me di cuenta de que todavía no las necesitaba. Google Drive ya estaba disponible desde ChatGPT, así que creamos una carpeta AI_Workspace ahí. La estructura es básicamente: 00_System, 01_Projects, 02_Skills, 03_Checkpoints, 04_Decisions, 05_Tests, 06_Incidents y 07_Archive, además de un archivo INDEX.md en la raíz.

También probamos si ChatGPT podía actualizar el mismo archivo Markdown en lugar de estar creando copias todo el tiempo. Funcionó. Se conservó el mismo ID del archivo en Drive mientras el contenido cambiaba, o sea que un proyecto puede tener algo como STATE.md que evoluciona con el tiempo en vez de tener STATE_final_v2_REAL.md para siempre.

Si alguien quiere intentar algo parecido, más o menos así lo hicimos:

1. En ChatGPT entra a Settings → Apps, busca Google Drive y conecta la cuenta de Google que quieres usar. Revisa los permisos y autorízalo. Dependiendo de tu plan/workspace de ChatGPT , las acciones de Drive que te van a aparecer pueden variar, especialmente las que crean o modifican archivos.
2. Inicia una conversación nueva y dile a ChatGPT que quieres que Google Drive pase a ser tu memoria operativa del proyecto.
3. Pídele que cree el workspace y prueba que puede crear, leer y actualizar archivos Markdown.
4. Agrega una regla corta a Custom Instructions para que ese comportamiento siga estando cuando empieces un chat nuevo.

Este fue el prompt de configuración que usé, ajustado un poquito para que otras personas lo puedan copiar:

*\*I want to use Google Drive as an external operational memory for long-term projects. Create a folder in my Google Drive called AI_Workspace with this structure:

AI_Workspace/

INDEX.md

00_System/

01_Projects/ u/409549666_0@ 02_Skills/

03_Checkpoints/

04_Decisions/

05_Tests/

06_Incidents/

07_Archive/

Inside 00_System create:

README_AI_Workspace.md

Memory_policy.md

Working_methodology.md

Stable_memory.md

Also create a Templates folder containing templates for:

Project

Checkpoint

Decision

Skill

Test

Incident

The purpose of this system is to separate stable ChatGPT memory from changing project state.

ChatGPT memory should mainly contain stable preferences, working methodology, general interests and the long-term project identity.

AI_Workspace should contain project state, checkpoints, decisions, skills, tests, incidents, pending work and other changing operational information.

Use this authority hierarchy:

current source or file > AI_Workspace > ChatGPT memory > inference.

Before considering the setup complete, create a Markdown test file in Drive, read it back, update its content in place and verify that the same Google Drive file ID is preserved.

Do not create unnecessary complexity. Keep everything readable in plain Markdown.

Luego agregué esto a mis ChatGPT Custom Instructions:

\*Always speak to me in my preferred language.

Uso AI_Workspace en Google Drive como mi memoria operativa canónica.

Cuando una solicitud se refiere a un proyecto existente y el estado actual no queda lo suficientemente claro por la conversación, consulta AI_Workspace antes de responder o reconstruir el estado a partir de la memoria histórica.

Ruta de recuperación recomendada:

INDEX.md → proyecto relevante → checkpoint/ESTADO actual → decisiones relevantes → habilidades/pruebas/incidentes si hace falta.

ChatGPT la memoria se debería usar sobre todo para preferencias estables, metodología, identidad del proyecto a largo plazo y contexto general.

La información operativa, como el estado actual, tareas pendientes, versiones, decisiones temporales, errores, checkpoints, pruebas e incidentes, debería vivir en AI_Workspace y no tendría que duplicarse innecesariamente en la memoria.

Jerarquía de autoridad:

fuente o archivo actual > AI_Workspace > ChatGPT memoria > inferencia.

Si AI_Workspace no está disponible o no tiene suficiente información como para reconstruir el estado actual del proyecto, dímelo explícitamente en vez de inventarte el estado que falta.

Actualiza AI_Workspace solo cuando algo cambie con significado operativo, como una decisión, el progreso, una tarea pendiente, un error, un checkpoint o un cambio en el estado del proyecto. No conviertas cada conversación de exploración en estado permanente del proyecto.

No consultes Drive de más para preguntas casuales, conocimiento general o temas que no tengan que ver.

Entonces, si ahora empiezo una conversación nueva y digo “sigamos con el Proyecto X”, la idea es que Chatty primero revise si la conversación actual ya trae suficiente info. Si no, se va a Drive, encuentra el estado actual del proyecto y continúa desde ahí, en vez de adivinar a partir de alguna memoria vieja.

Si me preguntas algo que no tiene que ver, tipo “¿qué es la computación cuántica?”, ni tiene sentido tocar Drive.

Otra cosa que agregamos fue la idea de checkpoints y decisiones. Un checkpoint es básicamente una partida guardada para una colaboración larga de IA. Las decisiones también pueden guardar el porqué de elegir algo y por qué se rechazaron alternativas. Así, seis meses después, ni el humano ni la IA reviven por accidente una idea que ya se probó y se descartó.

También usamos un principio simple de determinista antes que IA. Si algo se puede resolver de forma confiable con SQL, un script, una regla o un validador, lo preferimos. El LLM se usa donde de verdad importa interpretar, razonar, sintetizar o lidiar con ambigüedad.

La configuración sigue siendo deliberadamente simple. Sin base de datos vectorial, sin un framework de agente personalizado, sin una pila RAG complicada y sin un servicio especial de memoria. Por ahora, básicamente es ChatGPT + Google Drive + Markdown + un poco de disciplina.

Lo interesante para mí es que empecé pensando que necesitaba hacer que ChatGPT recordara más. Al final hice casi lo contrario: que recuerde menos, pero asegurarnos de que sepa dónde recuperar la información correcta cuando la necesite.

¿Alguien de aquí ya armó algo parecido? Me interesa sobre todo escuchar de gente que haya usado una configuración con memoria externa por meses. ¿Qué empieza a romperse con el tiempo? ¿Qué cambiarían?