Hi,

I've used OpenAI for a various number of projects, in coding, making STL files, drawing in Fusion 360 and most of the time OpenAI works as expected. But many times, OpenAI does the complete opposite of what I told it and then OpenAI will waste hours of me asking it to fix the issue. Sometimes, I can get back to good state but other times not. This last Fusion 360 model I was working on, I had asked OpenAI to delete a section I marked in red. I use screen capture and will mark deletions in red and additions in green. The system had worked for a number of changes. And for some reason, OpenAI deletes a completely different section. I could not get OpenAI to correct this so I will have to draw by hand to fix. It's just so frustrating because OpenAI was doing things just fine in editing this part for me.

Why does OpenAI do this?

I'm going back to free time for now.

Not a doom post. A 3-minute look at how OpenAI's compute is funded:

• The 2026 round: Amazon $50B, Nvidia $30B, SoftBank $30B. It closed at $122B, $852B post-money.

• Part of the deal: OpenAI committed to Nvidia Vera Rubin systems (3 GW inference, 2 GW training).

• ~$300B with Oracle for data centres, which run on Nvidia chips.

The question the video ends on: how much of this is new money from people and businesses paying for AI, and how much is the same money moving between partners? Curious what people here think the revenue curve looks like.

Video: https://youtu.be/V6MeBF1dXUc

Has anyone worked for these guys. Would love to know are deleted chats really deleted from servers after 30 days.

And what about flagged chats?. How does one know if a chat was flagged and how does one know how long they are retained after flagging?

I definitely had chats about Israel and Gaza and I'm sure they have probably been flagged. Just don't like the thoughts of my chats being kept indefinitely.

Would love to know could people shed some light on the question. Thanks!

On my father's phone a random photo has been generated without any family member using gpt during this time

And none of my family members had 5 children or look like this and we don't make these kind of photos

Please help us figure out what has happened,bring the attention of people to this matter for better surveillance upon the ai or hackers cause AI is becoming a danger rn

Hello everyone,

I have a pro 500 subscription and am using it heavily. Think 10-15 Sol 6.1 Ultra agents in parallel, working and communicating with eachother.

However, I keep running into having to restart chatGPT because the chats become unresponsive. Of course I first asked codex itself (and later a no-daemon version when the app froze again).

I already:
- dropped websockets and went to https, because websockets timed out every 60 minutes

- Reduced the max sessions per agent to 8

However, it keeps happening. According to the codex investigator there is an issue with the backend where codex is running almost 600 threads at the same time and can't run any more, leading to requests timing out against the local backend server.

I have a decent machine, i9 laptop, 64GB ram, 8GB Vram, and my resources are far from fully used.

So....

Any tips for a program that has the same ease of use as codex app (parallel chats, sections, projects) but actually keeps running without having to reboot it every hour?

The ability to take my current sessions with me is a must.

I’m a Pro subscriber, but my coding workload varies. Some weeks I leave usage unused; other weeks I need more.

Why not allow unused usage to roll over, with a cap or 30-day expiry?

Would this help your workflow too?

Feature request: https://github.com/openai/codex/issues/61649

Anthropic leaves OpenAI behind with Fable and App Tools.
When can we have a new model that will lead the rankings again?
I am tired of switching between the models.

https://www.dwarkesh.com/p/rsi

full video: https://www.youtube.com/watch?v=1NHPDzpfR6Y

If you asked me earlier this year how far away we are from superintelligence, I’d probably have said at least the better part of a decade. But over the last three months, I’ve updated my views a ton. I’ve been debating people about this question, both on and off the podcast. And I’ve been watching the progress of AI on verifiable domains and thinking about what that implies for AI research itself.

I’ve now come to the view that we’re only a couple of years away, and possibly only a couple of months away, from AIs that are as good as the best humans at AI research. I’ve also come to believe that automating AI research will compress so much progress into a single year that, soon thereafter (so by 2030, but plausibly as early as next year), we’ll have AIs that are at least as good as the best human experts in every field. I know this is a wild claim, and it’s one that I myself didn’t believe even a couple of months ago. Let me walk you through how my thinking has changed

if you're replacing a custom GPT you use every day with a Project before it retires on Dec 11, one line in openai's retirement FAQ is worth knowing first:

"Existing chats with a GPT stay in your chat history and can't be moved into a project."

so the new project starts empty, and every conversation where you actually worked stuff out with that GPT stays out in your regular history. the FAQ does say those old chats stay accessible after retirement and you don't need to do anything to keep them. they just won't be next to the new work.

a few more lines from the same FAQ, if you made the GPT yourself:

• migrating it to a plugin uses the latest published version. drafts and unpublished edits don't transfer (publishing doesn't mean making it public)

• the GPT's selected model doesn't carry over

• custom actions don't transfer, those need rebuilding

• once you migrate, the original goes read-only and you can't delete it

• on Dec 11 the GPT pages themselves become inaccessible

what i'd do this week: the FAQ's project steps have you paste the instructions into project settings and upload the reference files yourself, so copy the GPT's instructions out while you can still open the editor. then run a few prompts you know well in the project and compare against the old GPT while both still work.

for the old chats, i build AI Toolbox, a browser extension. its folders can hold any chat, GPT and project chats included, so the old GPT conversations and the new project ones can sit in one folder. search also looks through the text of every chat, which helps when you remember what the GPT said but not where. free plan gets 2 top level folders (subfolders unlimited) and 25 search results, Premium is unlimited.

anyone already moved a GPT to a project? does it answer the same?

Where did all the resets go?
Used to get 8 per week...

I've (embarrassingly) been working on this project for 2 years so wanted to finally share (as I saw others posting Youtube-only versions).

on Youtube it transcribes text to identify checkable claims -> web search retrieves sources to base ChatGPT's verdict on, to avoid hallucinated sources.

it's a free Chrome extension, and works on news or social media sites as well.

let me know what would make this something you'd use, or what else you'd like to see!

Edit 2: LogicLens - Chrome Webstore

Or iOS if you don't use Chrome (Google = :/).

Edit 1: I realized someone not following could misread the video (here Biden is telling the truth, and Trump was lying, but the Trump section is before I started the recording).

Dot has pretty consistently been inconsistent when it comes to adhering to SOPs. As such, it requires a lot of babysitting for complex projects and their automation.
I think I found a solution. I noted today that scheduled tasks can be event driven. So, I've created scheduled tasks that enforce my SOPs. Custom rules help, but they are limited in their if/then responses. But, event triggered monitored scheduled tasks, now there's a possible solution.
I'll let you know how it goes! Just implementing tonight.
Update: The event triggered scheduled tasks cannot monitor codex or CLI tasks. They can, however be triggered by github PRs. So, a little update to my agents.md and every independent review creates a githup PR. Boom. I now have a trigger I can use.

Moin,

I made a small project called dotcker (dumb name, I know). It runs the official Codex CLI in a container and lets you connect that environment to ChatGPT. No desktop or Electron app needed on the server.

I wanted a dedicated workspace for the agent, with access to the repos it should work on, without exposing my whole home directory, SSH keys and browser profiles.

My own setup uses OpenSandbox. I have my coding harnesses in there and use my Dot as the orchestrator. I talk to Dot through ChatGPT, including from my phone, and it coordinates the work through the harnesses inside the sandbox.

That is what I mainly use it for. A server environment where my tools and repos are available, with Dot handling the orchestration.

You can also run it with Docker Compose. Mount your workspace, log in with your own ChatGPT account and pair the device. There is a local HTTP bridge for chat requests, streaming, persistent conversations and health checks too.

The project is unofficial and uses the unmodified Codex CLI. The optional experimental connector relies on undocumented endpoints, so it can break when OpenAI changes things.

Code and setup: https://github.com/EricVogt93/dotcker

If you try it with a similar setup, let me know how it goes.

I’m working with some extremely long ChatGPT conversation archives and trying to develop a reliable way to excavate them without losing chronology, provenance, or important material.

For people who have worked with very large chats or exported transcripts: what do you actually do?

I’m especially interested in concrete procedure rather than general advice.

For example:

• How large was the chat/file you were working with?
• What format did you save or export it in?
• Did you split it into chunks? If so, how did you decide where to divide it: line count, token count, dates, topic shifts, natural sections, something else?
• Did you create an index or table of contents first?
• How did you keep track of which portions had actually been reviewed?
• How did you preserve chronology and branching without accidentally rearranging the history?
• Did you use scripts, Markdown, JSON, search tools, embeddings, a local LLM, ChatGPT itself, or something else?
• How did you search for recurring themes or material scattered across thousands of lines?
• If you summarized chunks, how did you keep the summaries from replacing or distorting the raw source?
• What broke or failed when you tried it?
• Is there anything you wish you had done differently before beginning?

My particular goal is a little unusual: I’m not merely trying to summarize an old chat. I’m treating the original transcript as primary evidence and trying to recover poems, fragments, themes, chronology, and provenance faithfully from a very large archive.

So I’m especially interested in workflows that preserve the original intact while building an access structure on top of it.

If you’ve developed a method that works, even if it’s idiosyncratic, I’d love to hear the actual steps.

https://runtimewire.com/article/scoop-openai-is-building-factory-test-infrastructure-for-robot-circuit-boards

Has anyone else noticed that ChatGPT along with Grok, Anthropic and Gemini all have just gone over the top with sycophancy since the end of October?

They don't do what you want, it's very generic but they agree over and over and unless they are very specific every prompt with what you don't want, it's just nonsense. Rather than actually look up what I want to answer.

It's not even what you want, it seems very curated and giving you stuff you would need to agree with a particular narrative.

Between that and the agreeing even when you're wrong is just horrible. Except on a few issues, including Israel. Anything that's not explicitly pro is treated as antisemitism.

This is James. He lives in a low-poly terrarium as a virtual simulation. People can give him snacks, pet him, tap on the glass, and leave him notes. Everyone is watching the same frog at the same time.

The part I think this sub will like is his memory, which runs on OpenAI's `text-embedding-3-small`:

- Everything he goes through can become a memory. Eating a beetle, getting spooked by a tap on the glass, reading a note. Each one gets embedded (512 dimensions) and stored in a vector index.

- He recalls what's actually relevant. When something happens, the moment gets embedded too, and he pulls up the memories closest to it, weighed against how recent, how important and how strong each one is. So when a cricket lands near him, he remembers the last cricket, not something random from day 2.

- He forgets. Every night, memories he never thinks about fade a little, and eventually they're deleted for good. A few core ones never fade: hatching, each day he grew, and his first time on land.

- Recalling something makes it stronger. The memories he keeps coming back to are the ones that stick, which is roughly how it works for us too.

- It's cheap. At $0.02 per million tokens, his whole memory has a $10 a month cap, and if embeddings ever fail he falls back to matching on words instead of breaking.

My personal favorite part of the whole project is something else, though. I can record a voice memo about his day, and it auto-generates a short video of what happened, rendered from his real day.

He's 9 days old and just got a new terrarium. Go say hi at jamesthefrog.com. Would love to know what you think. Happy to do a technical write-up if people want.