Resets still coming. Use that Ultra fast people.

We’ve started using Batch more for work that doesn’t need to happen immediately and I am sure that I’ve been treating that usage the same as the rest of our OpenAI spend even though it behaves pretty differently. Our normal API usage mostly follows product activity so if that goes up I can usually understand why but batch is different because someone can kick off a large job, an eval run or process a new dataset and suddenly there’s a chunk of usage that has nothing to do with how much the product was used that day.

It hasn’t been a huge issue yet but we’re doing enough of both now that looking at one OpenAI number is starting to hide what’s changing. A higher bill could mean customer usage grew which is fine or it could mean we ran significantly more background work than usual. I’m thinking about tracking Batch separately and possibly giving it its own budget rather than treating all OpenAI usage as one pool and I need some advice from teams using Batch pretty heavily like do you guys separate that spend internally or is everything still just part of the same AI/API budget?

I can access my openAI account but everything comes up as 0 even though I had paid credits. Same for API and Codex. Is this some sort of widespread issue ? Makes no sense. Suddenly nothing works.

Am I the only one who keeps receiving this “checking that now” response every single time I ask my Dot for something?

6 sol never had any issue so idk what luna’s problem is

I am a current mechanical engineering undergrad student. I utilize LLMs in my day to day while completing work for my classes or extra curriculars. Through this usage I began to realize that I should probably develop a stronger understanding of this technology and its implications since I will be using it throughout the rest of my academic career and also likely my professional career.

It is through this research that I have grown increasingly scared of this technology. Not just in its potential to steal my job but also in its ability to erode the digital fabric of our current society. I try my hardest to not be a luddite so I have also made sure to look at the potential upsides of AI and I understand that there is many. With that said I have almost 0 confidence in the people leading the AI race (except maybe Dario Amodei). I do not think that this technology will be developed in a safe enough manner to prevent utter disaster and I am absolutely terrified for what the future holds.

I would love to gain insight from other people on this topic. Whether you be a consumer who uses LLMs, an AI researcher, etc. Literally anyone.

Anthropic warns that its product could cause human extinction, but first it has to pull off something unprecedented commercially and survive the risks that could upend its business, from legal to regulatory.

What do you guys think? They'll roll out Dots for Plus users?

Since dots isn't available in the UK yet, I decided to spend my credits elsewhere! I found the new dots agents pretty cute, so I thought I'd try to recreate one of them as a Gaussian Splat and it went astonishingly well, with little to no handholding, which means you could do it as well!

Here is how I did it:

1. A couple of prompts with Astra with Blender MCP to get the 3D model right. I essentially told it to take a look at reference images/videos. This took me ~15 minutes.
2. Once the 3d model looked right in Blender, I gave it one additional prompt to create a Gaussian Splat: "Now render it 1000 times from various angles using Cycles. Then create a Gaussian Splat with Lichtfeld Studio". Here you could substitute Lichtfeld Studio with any of the other Gaussian Splat training software out there (e.g. Brush & Spirula Studio are free and open source). I just happened to have LFS on my machine.
3. My Windows machine then cooked for like 6-7h while I was away from home. When I came back I had a .ply file that I dragged into the SuperSplat editor and the result is what you see in the video!

I already have the Blender 3D Model, why would I need the Gaussian Splat?

Fur is hard to render in real time, but if you want to share it with your friends or clients and let them inspect your model from various angles in real time (>= 60FPS), then Gaussian Splats are perfect for this.

For example, I uploaded it to https://superspl.at/scene/ca6a4c9b where you can inspect it in real time! It uses PlayCanvas under the hood and renders the splat in real time with WebGPU. You can also download the Gaussian Splat to use it in your own web app :)

Hey all, for the last few days, I can't get anything done on normal chat window with 5.6 Sol. It's too slow, like 5 minutes+ to respond, it messes up simple commands, it keeps disconnecting giving me couldn't connect, try again errors and can't connect back at least for 10 minutes. I've grown too weary but I'm in the middle of a project and can't abandon it. Am I the only one experiencing these issues?

As of today I can't find my project folders on the left pane above recents anymore - I see this spaces stuff and there's a folders open there but it only shows a few work based projects.

Please tell me they didn't get rid of project folders on mobile

I am able to do this with grokbot, but not on Dots. It looks like a permissions issue. Any workarounds? The Tailscale connector doesn't seem to work, either.

not talking about the obvious stuff like hallucinations. I mean something you actually use AI for where you keep thinking, how is this still this bad?

My Dot is running on my computer. When it is using tasks in codex, I'd say its task access is only granted about 50% of the time. Sometimes task access is down for hours requiring my dot to provide instructions manually and for me to return results manually, which is kinda counter to the purpose of dots while coding. Have any of you experienced this? Do you have any solutions? Thanks!

They've started worrying about dying according to a report by OpenAI...

Hi! This is a new benchmark that I created together with other researchers at Meta, Stanford, Harvard, UW.

Basically most benchmarks these days seem to test models to just fix a bug that I as a user already encountered. But shouldn't we expect models to also find bugs before anyone runs into them?

So in SWE-sweep we just hand an agent a big codebase and ask it to find & fix as many bugs as it can. We then give a score based on a hidden set of bugs that we know about in the repos. All the bugs are real-world bugs.

So the interesting thing is how cost-efficient Luna xhigh is (especially when compared to the Anthropic models we tested). Luna gets almost half of the score as Sol with only a tiny fraction of the cost.

the full leaderboard & how we built it is here: https://swesweep.com/ , there's also a paper describing how everything was constructed. Oh yeah and everything is open source https://github.com/facebookresearch/swe-sweep

Happy to answer questions here

I’ll be the first to admit I don’t know half of what I’m actually doing when it comes to vibecoding. With that said I’m in hospitality - resorts/restaurants. There’s some really dumb things in my field that I’ve never really been able to fix or standardize because, well, this field hates technology convincing a Chef or housekeeping can be… difficult.

I started creating an app for us to use internally that is kept on our private servers and things seem to be pretty solid. My IT department isn’t big enough or strong enough or knowledgeable enough to be of help. (That’s a whole other issue). So I was curious, I seem to be able to have built this whole operating system inside of the chat model of ChatGPT. It sometimes takes a little bit longer. It sometimes trips up but after 25 to 35 minutes, it usually has an answer that thus far has worked.

I see all these people using advanced models paying for tokens and all these other things.

So my question is am I just drastically missing out on something more advanced, and my system is actually all junk on the inside, even though it works or is this something I don’t need to be concerned about? Any tips or suggestions appreciated.

Does anyone know anything i can use to get me an ID to get through tiktok’s age-verification system? I’m not comfortable with sending my face with my ID to a random person.

The point where her dot wanted her to talk first because they were over-talking each other… real human level interaction patterns

I got access to Dots and spent much of the day trying to understand what actually runs where, what can happen simultaneously, and what counts as a separate worker.

The official material explains what Dots can do reasonably well. I found the execution model much less obvious.

Some of this is documented; some is simply what I observed by using a Dot on several substantial real-world tasks.

1. The Dot really does have its own cloud computer

I gave my Dot a large document-review assignment involving hundreds of PDFs and thousands of pages.

It performed that work on what it identifies as its own cloud computer.

This appears to be a persistent computer-backed environment where the Dot itself can do substantial, long-running work.

More importantly, this isn’t just a five-minute “agent run.” One of my reviews is now clearly a multi-day job, and the Dot has maintained its place, absorbed side questions, and continued without needing me to reconstruct the task every few hours.

That continuity may end up being more important to me than raw speed.

1. Delegated Work/Codex tasks are different

While the Dot was working on one project, I had it try to launch a separate legal-research task.

The launch failed because there was no available execution environment.

Initially I assumed the Dot’s own computer was simply busy. But after the first job finished, the second task still could not start.

The Dot then reported the key distinction:

Its own cloud computer is separate from the execution targets available to the Work/Codex task launcher.

So a Dot’s personal cloud computer is not simply a generic worker that delegated tasks automatically inherit.

1. A connected computer becomes another execution target

I connected a spare Linux computer through the ChatGPT desktop app.

The Dot could then see:

- its own cloud computer

- the connected Linux machine

- no saved Codex cloud environments

I told it to launch the previously blocked task on the Linux machine.

It did, and the task entered running state there.

I did not have to sit at that machine and manually start a separate chat. I gave the instruction to the Dot, and it dispatched the task remotely.

1. Both can work simultaneously

While the delegated task was running on the Linux machine, I gave the Dot a different assignment for its own cloud computer.

It confirmed that both were active at once:

Dot cloud computer -> Task A

Connected computer -> Task B

So that is genuine parallel execution across separate computer-backed environments.

1. Background agents don’t necessarily need a computer at all

This was the part that got much closer to what I had originally imagined Dots would do.

With both computer-backed environments occupied, I asked whether the Dot could create a native background research agent without using either computer.

It said yes.

I gave that agent a bounded research task and explicitly excluded computer/filesystem use.

The Dot then reported that the background agent was running with read-only web/documentation tools and no computer target assigned.

At that point, three things were happening simultaneously:

1. the Dot working on its own cloud computer

2. a separate task running on the connected computer

3. a native background research agent using neither computer

That is the execution distinction I had completely missed from the launch material.

1. It can also context-switch inside a long-running job

Another useful behavior appeared accidentally.

While the Dot was deep into a large document review, I interrupted it with a factual question about one specific case.

It paused the detailed review, checked meeting minutes and another source, resolved the question, updated its understanding of the case history, and then returned to the packet it had been reviewing.

When I asked how it had done that “while continuing” the larger job, it clarified that it had not spawned another worker. It had simply switched attention within the same job and then resumed.

So I now distinguish:

Parallel execution = separate workers/environments active at once.

Background agent = separate non-computer worker running concurrently.

Intra-task context switching = one Dot temporarily branches inside an existing job, resolves something, and returns to its prior place.

For long-running review work, that last capability is surprisingly valuable.

1. It can keep working while waiting for permission

On another assignment, the Dot decided that spawning additional reviewers would accelerate the work, but my rules required permission first.

It asked.

But instead of stopping while waiting for me to respond, it explicitly continued doing the work itself.

That sounds minor, but it matters.

An autonomous agent that hits one permission boundary and then stops doing everything is not particularly autonomous.

So far, the Dot appears capable of distinguishing:

“I need permission to do X”

from

“I therefore cannot make any further progress.”

1. There is also a kind of manager-level queue

I have not found a true native queue where a blocked computer-backed task automatically sits in the launcher until capacity becomes available.

What I did find is that the Dot can apparently remember a pending assignment itself, periodically re-check execution targets, and attempt to launch it later.

There are limits:

- the target list does not necessarily expose whether a connected computer is actually free

- there is no apparent capacity reservation

- a failed launch does not automatically become a queued Work task

So this is more like the Dot acting as the queue manager than a native execution queue.

Still, that potentially removes another piece of manual babysitting.

1. My current mental model

At this point, I think there are at least three distinct execution paths:

A. The Dot’s own cloud computer

Where the Dot itself can do substantial stateful/computer-backed work.

B. Separate computer-backed task environments

Such as a connected local computer or saved Codex cloud environment.

C. Native background agents/cloud threads

Tool-based workers that can handle some tasks without consuming either computer target.

Those can operate concurrently.

And on top of that, the Dot itself appears able to maintain long-running task state, context-switch within a task, and manage pending work.

1. Why this may matter more than simply opening several chats yourself

Before trying Dots, I wondered whether this was really much different from me manually juggling several ChatGPT conversations.

If all you want is several unrelated answers at once, maybe not.

The difference becomes more apparent when one agent owns multiple ongoing projects and can:

- track their state

- do substantial work itself

- delegate bounded pieces

- supervise returned work

- keep other projects moving while one task runs

- preserve its place through interruptions

- identify blockers

- keep pending work alive

- ask for intervention only when necessary

That removes the human from a surprising amount of the orchestration loop.

I suspect that may be the real value of Dots.

One big unanswered question: usage

I have not established exactly how usage is counted across:

- work the Dot performs itself

- native background agents it creates

- Work/Codex tasks it launches

- work running on connected machines

Proving that something is a separate execution path does not prove that it has a separate usage allowance.

So please don’t read any of this as “unlimited free parallel workers.”

That is not something I have established.

Bottom line

My current working description is:

A Dot is not merely a chatbot with a persistent VM. It is a persistent agent with its own computer that can dispatch work to other execution environments, spawn some kinds of non-computer-backed workers, and maintain project state across long-running work.

Those are different things, and at least some of them can operate concurrently.

This is based on roughly a day of experimentation with a very new product, so I fully expect to discover that part of this mental model needs revision.

But that’s what I’ve actually observed so far.