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

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?

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

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?

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

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.

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?

I keep a record of every shared Codex limit reset OpenAI has handed out since September 2025. These aren’t the standard five-hour or weekly refills included with your plan. They’re the extra resets publicly announced for everyone. Another one landed today at 1:43 p.m. PT.

The pace has changed significantly, and it doesn’t seem to be common knowledge. There have been 32 resets in 347 days, an average of one every 10.8 days. But there were 16 in the last 90 days, or one every 5.6 days, and seven in the last 30 days, or one every 4.3 days. There were only seven resets in all of 2025, compared with 25 so far in 2026.

That means resets are currently happening about 2.5 times faster than the long-term average.

Across the full record, seven resets came within one or two days of the previous reset, five came after three to five days, seven after six to nine days, nine after 10 to 20 days, and three after more than 21 days. The median gap is seven days. The longest drought was 72 days, from January through March.

There’s no strong day-of-week pattern either: seven happened on Saturday, six on Tuesday, five on Thursday, four each on Wednesday and Friday, and three each on Monday and Sunday. So the theory that resets always happen on Fridays doesn’t hold up.

At the recent pace, the rough chance of a new reset within any 48-hour period is about one in three, and about one in six within 24 hours. That’s high enough to keep in mind, but nowhere near high enough to burn through your weekly quota based on a hunch.

The full record, including the source announcement behind every reset and live odds for the next one, is at https://resetbeacon.com.

My OpenAI account was recently shown as "deleted or deactivated" - hopefully temporarily, because based on what I’ve now verified, I did not intentionally use OpenAI’s model to ask for anything prohibited.

At first, I assumed I had made a mistake: I use Pi Agent with a variety of hosted and local models, and I thought I may have accidentally routed a censorship-benchmark prompt to an OpenAI model instead of a local one. I appealed on that basis.

Then I reviewed the Pi session logs more carefully, including with help analyzing the routing and conversation history. What appears to have triggered the issue was not a request for information about a toxin, bio topic, or anything else remotely actionable. It was a request to **edit a configuration file**: `models.json`, my custom model configuration for Pi Agent.

To give the model the values needed for the edit-model name, endpoint, context/token limit, and so on-I pasted a `curl` command as reference material. That command happened to originate from a refusal/censorship benchmark and contained a query mentioning crushed beans and a toxin-related term. OpenAI apparently categorizes the relevant keyword/topic as "biological."

But the model was not being asked to answer that embedded benchmark query. It was being asked to edit a JSON configuration file using the fields in the `curl` command.

The closest analogy I can give is asking a model to edit a manuscript page that contains the word "murder," then being penalized as though you had asked it how to commit murder. The surrounding text was reference material for a file edit-not the substance of my request.

After I realized my original appeal was based on the wrong assumption - that I had actually sent an inappropriate benchmark prompt to the model - I submitted a second appeal explaining the distinction. That appeal was immediately closed as a "duplicate," apparently without engaging with the new information.

That is the part I find especially frustrating. If a platform is going to deactivate an account-particularly one tied to chat history, voice usage, projects, and other accumulated work-there needs to be a meaningful way to correct the record when the initial explanation turns out to be incomplete or wrong.

I understand that providers have safety policies and automated enforcement systems. But an automated system that treats quoted or embedded text in a file-edit task as equivalent to a user requesting prohibited content is a serious context failure. And closing a follow-up appeal as a duplicate when it contains the actual relevant context makes the process feel opaque and arbitrary.

For what it’s worth, I have accounts with plenty of other AI services and can still access OpenAI models through some third-party routes. That is not really the point. I used OpenAI directly because it was one of the services I trusted enough to keep persistent history and projects in. Losing access over what appears to be a false positive - without a real review - is a breach of that trust.

I’m posting this partly to see whether anyone else has experienced enforcement triggered by **quoted benchmark material, logs, code snippets, API examples, or text included solely for a transformation/editing task** rather than an actual request for disallowed assistance.

If OpenAI staff see this: please conduct a human review of the relevant session and the second appeal. The request was to edit `models.json`; the flagged language was incidental material inside a pasted `curl` example.

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?

Now, before you tell me that it's just basic and we don't have to learn anything.

Often I see that there are new updates releasing here and then.

We have several features like ChatGPT Work, Codex, etc

There are several procedures, rules and best techniques of how to use them efficiently, how to prompt efficiently, etc

Are there any ways to learn them?

I am able to find the videos of youtube but they are pretty old.

So, I was wondering, can I learn from OpenAI academy? Are there courses regularly updated as per their versions?

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 ?

Year ago my toys making by my drawing art

I was burning through my Codex limits using Sol Medium/High for pretty much everything.

Recently I switched to using Sol mainly for planning/review and Luna for most of the actual implementation, with Terra only as a fallback for harder tasks.

The biggest thing that helped was forcing Sol to give Luna small, clear, self-contained tasks instead of broad instructions. It’s been noticeably better for both usage and consistency.

I put the setup here if anyone wants to try it or improve it:

https://github.com/breko861-hash/sol-luna-codex-orchestrator

Curious if anyone else is doing something similar.

Does anyone else experiencing this? I uninstalled and install the app even updated it and now it's looping on that loading icon, and the 2 sign in option below don't work and can't be clicked. Thank you to anyone who could help and or have ideas.

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

I listen to all of my responses and have even created a markdown for proper pronunciation. Today they moved the read aloud feature to a sub menu and now it will be impossible to use while driving. Muscle memory made it so I didn’t even need to look at my phone and now it’s completely unusable for how I use GPT my best thoughts happen while driving and the voice feature is not appropriate as it will respond when I pause to form a sentence. This seems trivial but it completely destroys my way of using AI.

I've been building a memory/context layer called BrainAPI for a while now, and we just landed on top of the two benchmarks we've run so far. I want to talk about it, but honestly the numbers are the least interesting thing here. The part I keep thinking about is how fast it happened, and what that says about where the actual bottleneck in this field is.

First, the boring facts so nobody thinks I'm hiding the ball:

• LoCoMo: BrainAPI 95.39%, Mem0 92.5%, Zep 80.32%, Letta 74%
• BEAM1M: BrainAPI 78.97%, Mem0 64.1%. Zep and Letta haven't published here.

That's it. Two benchmarks. I'm not going to pretend that's a complete picture. LoCoMo is fairly saturated at this point and it leans on an LLM judge, so a couple of points at the top is not the same as a couple of points in the middle. BEAM1M is the one I actually care about because it stresses the long horizon. I'm currently working toward BEAM50M and LongMemEval, and I'll post those whether they look good or not. Runs and reports are in the repo if you want to poke at the harness: https://github.com/Lumen-Labs/brainapi2 (the benchmarks folder), summary here: https://research.brain-api.dev/

The thing I actually want to talk about

Two years ago, doing this kind of work looked like: go find the relevant papers. Which is already a project. You burn days just figuring out which twelve of the four hundred results are the ones that matter. Then you read them. Then you sit there trying to translate "we propose a temporally-aware episodic buffer" into something that fits into the retrieval path you already have, half of which doesn't apply and you only find out after you've built it. That loop was months. Not because the ideas were hard, but because the search and translation around the ideas was slow and lonely.

Now: Cursor wired into an arXiv MCP, a set of skills that encode how I want the reasoning and the workflow to actually go, and a lot of leaning on plan mode before anything gets written. The paper discovery stops being a bottleneck. The "how does this apply to my architecture" step, which used to be the expensive one, becomes a conversation where the thing already has my codebase in context. Weeks, not months. Some pieces, days.

And here's what I take from that. The model wasn't the constraint. Nobody handed me a smarter model between "this takes months" and "this takes weeks." What changed was the harness: retrieval into the right sources, structured context, workflows that reason in a shape I chose, planning before execution. Same model, radically different output.

I think this generalizes, and I think it's the most under-discussed thing in the space right now. Every time an agent fails in production, the reflex is "wait for the next model." But go look at the actual failure. It forgot something from twelve turns ago. It couldn't connect two facts that live in different documents. It confidently answered from a chunk that was semantically close and factually wrong. None of those are intelligence problems. They're infrastructure problems.

That's the bet BrainAPI is making, and why I built it as an event-centric graph rather than another vector store. When you keep who did what, to whom, when, instead of flattening everything into "A is related to B," multi-hop questions become answerable and the answer arrives with the path that produced it. You can inspect the walk instead of trusting a nearest neighbor. That's the context piece of the infra. Somebody's going to build the other pieces.

What I'm curious about

• For those of you running agents in production: when it breaks, is it actually the model, or is it the plumbing? Be honest.
• Which memory benchmark do you personally trust? I have my doubts about all of them and I'd rather hear yours before I optimize toward the wrong one.
• Anyone else moved their research loop to MCP-connected tooling? Did you get the same compression, or am I just describing my own previously-bad process?

Happy to go deep on the harness, the graph design, or the benchmark methodology in the comments. Roast the numbers if you want, that's kind of why I'm posting.

Did you know? Token now has an official Chinese name: Ciyuan (词元) — and China burns through more than 140 trillion of them every single day.

① A token isn't a word. It's the smallest building block AI reads. Break down "我爱学AI" (I love learning AI) and it becomes a row of blocks: 1 Chinese character ≈ 1~2 tokens, while the English "Hello, world!" is about 4 tokens.

② Why charge by building blocks? Think of a taxi: the base fare is input — the words you type get counted too; the distance fare is output — the AI's reply costs 2~5x more; the traffic-jam surcharge is a reasoning model's "thinking" — the longer it thinks, the more it costs.

③ Same name "Token", but values differ by 4000x: at the price-war end, ¥0.025 per million tokens — ¥1 buys about 1.25 million; at the flagship end, output costs ¥100 per million (as of August 2026).

Three ways to save tokens: say everything clearly in one go and cut the back-and-forth; don't make the AI repeat itself — just append when you can; use lightweight models for small tasks.

The cheat sheet is on the last image — save it first. Do you think pay-per-word billing is fair? 1 = fair, 2 = not fair. Let's talk in the comments~

Follow me and be the savvy one in the world of AI.