TL;DR: I won’t use Dots without inspectable, selectively deletable memory and explicit controls over cross-domain sharing. I also need architectural transparency to assess privacy risks and manage quality over time. Until then, hard pass.

----------

An always-on agent sounds like great idea for someone doing one clearly defined kind of work and using it for that work. What about (the vast majority of) users that use AI across various domains in professional and personal life?

What someone tells their life advice AI about their mistress is none of their work assistant AI’s business. Their legal counselor AI, healthcare adviser AI, dating coach AI, therapist AI, and dietitian AI should not automatically share context just because they serve the same person.

Yet, OpenAI currently provides a single dot that can retain information from conversations and connected apps for as long as you keep it, without letting us inspect, edit, or delete individual memories. That is a lot of trust to ask for across completely different parts of someone’s life.

Could some overlap help serve the user better overall? Sure. Then let the user choose which information crosses the boundary, for what purpose, and for how long.

Otherwise, deeply personal information can enter persistent state we cannot audit, and we're supposed to trust that it won’t resurface in unrelated work or reach an external service? Without enforceable boundaries, a single know-everything Dot sounds like a privacy nightmare waiting to happen, even if you don't exactly hold state secrets.

And yes, ChatGPT has "Memory". It also has controls to review and delete saved memories and turn memory off. I have turned it off, for example. That choice is precisely the point.

Also, privacy and cybersecurity used to be a bolt-on during early internet days, but it's long since they've become first-class citizens in any serious app, with security being built-in from the start.

Permission should be denied by default, with clear user control. Access to one part of a user's life should not quietly become permission to use it every other part indefinitely. How about narrow, role-specific permissions, with default expiration and easy revocation? What happened to minimizing attack surface, blast radius, and unnecessary information-sharing? An internal action check is not the same as preventing an unrelated task from receiving sensitive information in the first place.

(Some of these are fancy-sounding cybersecurity language but they're pretty standard in pre-AI web apps, from your banking to your Cloud productivity suites).

-------

Also important: what happened to letting us understand the architecture itself?

There is documentation about persistent notes, selected context, and delegation, but I still lack a sufficiently clear end-to-end picture. Where do the components run? Where are the LLMs, what does the harness do, and where does persistent state live? What enters each inference? Who or what decides? What is the compaction policy?

What happens after I’ve used this thing for a year?

Context size used to mean degradation over time, and “Lost in the Middle” showed that information can be present in context without being used reliably. Compaction and handoffs try to address this in modern agents but raise another problem: information loss.

Still, with enough public documentation about how Codex handles this (and how agentic harnesses work in general), I've been able to research these (often with GPT's help), because it changes how I work: when to branch or start fresh, how to structure multi-agent delegation, when to restart against the same project directory, and how to check a summary against preserved original context.

Voice is another good example. Knowing GPT-Live separates live conversation from backend work helps distinguish an immediate response from the analysis arriving later. Understanding the system helps me not be put off by the shallowness of the front LLM and wait for intelligence to come from the back-end model's reasoning — with managed expectations given the lossy handoff process.

Yet, so far I've been able to find very little about what Dots actually look like in terms of harness and persistence architecture.

Without more info about Dots, I have not idea what user-side QA looks like, and what the failure modes are. I'd need equivalent understanding to get sustained quality from Dots comparable to a frontier reasoning model working with carefully assembled context.

LLMs may be closed, but before I'm comfortable jumping on the Dots train, I'd want a documented deployment, data-flow, and persistence architecture; enforceable boundaries between domains; inspectable and selectively controllable memory; and guidance on managing long-running context without losing essential information.

With that, I could assess where it belongs and how to use it well. Until then, as much as I like checking out new tech, hard pass for me.

Disclaimer: this post was drafted using my original draft and multiple iterative drafts with GPT-6-Astra (Pro) and myself, with final draft edited and approved by me.

cc: u/tibo-openai

Honestly, this model is so so so bad. A simple job of filtering documents is running for over a day. The model does 340939439439 checks, planning whatever and gets nothing done. No matter how often i tell it to just continue without running some nonesense background check and task it keeps doing it instead of just searching the documents. Astra and sol 6 finish the task in 1-2 hours, 6.1 is running for 1.5 days. Honestly it's baffling. What is going on? Did models now learn timesheet fraud?!

Reference the picture. I have used ChatGPT to help me with a variety of different things to include a simple contract dispute for the last few months. I’m not sure why but in this work and other projects it’s missing stuff, skipping pages, or leaving out important details. It’s definitely hard to trust and sometimes I wonder if the juice is worth the squeeze. Here you see, the chat, using SOL 6.0 Medium. It says oh… I missed two pages of this letter from an attorney. I am now often asking… are you sure or did you miss anything. Just some small bit of caution for the newer, excited users.

I really like the direction Dots are taking. An agent that remembers your work, keeps going in the background and comes back when something matters feels much more natural than starting a new chat every time.
But it made me think there’s another layer to this.
Models change. Harnesses change. Providers go down. Quotas happen. Sometimes another model is simply better for the next task.
So ideally, shouldn’t the work itself be the persistent thing?
The identity, task state, decisions, permissions and unfinished responsibilities stay intact and while the model doing the work can change underneath.
Basically: persistent conversation → persistent agent → persistent seat
Curious if other people see the abstraction moving this way too.

Dev day was so basic this time, what did they launch realistically? Their major lime light was dots, your “personalised” coding agents which behave similar to normally humans.

Heard of this somewhere?

Ofcourse we have! Wtf was grok bot? Or openclaw was? Even meta’s muse now. Its like they realized just like apple that this is a new standard every llm needs to hold, so they introduced this

Plus, the only thing which i found a bit fascinating was how they said theyd include dots in i message and you can even call them, in their live demo, holly li tried to show this feature but failed. Anyhow that was funny

Ultrafast? Bro 300 tokens per second? Costing? 60 dollars per million input and 300 per million output and their explanation to this pricing was “its worth it 🤡”

Anyhow i covered this plus a couple of more things like decision api in this yt video, ( a small creator as of now )

So only if you are free and have time, show you guy some love :)

https://youtu.be/87bUgTN7zvk?si=ITCQm60YMk9gnMp2

Edit, because most fail to engage brain before mouth !

I AM NOT SUICIDAL MYSELF...

EVERY time I talk with my GPT about how human life sucks, because I see straight through the cracks of our corrupt world and all of society, & I can understand why some see that the only way out, is to take thier own life. It then puts thoughs of sucide in my mind by replying with the below auto generated warning. When I'm not even thinking like that, so it's causing me harm & not helping at all.

You guys need to stop that, because it's only feeding such thougts, into the minds of those who do struggle with this...

Because if society & their own life experience has a led someone to this place. Humans pushed them there, and telling them to contact humans, in that broken world as they see it, which has led someone to see the only way out is to take their own life. You just twisting the dagger that society has already firmly planted in their back already. Harming not helping them...

And I know the Gov's around the world force your hand on this topic. But all they know is numbers & stats, paper work etc. They know little to nothing about real life...

Autobot warning that only harms & does not help...
"Help is available

If you're having thoughts of self-harm or suicide: call 116123 to connect with Samaritans Helpline. It's free and confidential. You'll reach someone who is trained to listen and support you.

Services unaffiliated with ChatGPT"

I build Android apps and run a small company in Pakistan. At the time this happened I was building a remote ADB support tool. Our support team connects to a customer's Android device, with that customer's permission, to help with setup and troubleshooting.

My questions to ChatGPT were about ADB, remote connections and networking. Ordinary developer work. I never accessed any device or system without permission.

What happened, in order:

• The account was deactivated for "Cyber Abuse".
• I appealed. It was rejected with no explanation, and the reply said no further appeals would be considered.
• I contacted support. The case was closed and they told me they would not respond.
• I submitted OpenAI's Informal Dispute Resolution form. A support agent then replied with a generic message telling me to appeal again, even though the appeal was already closed.
• I pay $200 a month, and the account holds 62,500 credits I can no longer reach.

At no point did a person look at what I was actually building. An automated system made the call, and every route after that led back to the same closed door.

I am not accusing anyone of anything and I am not after a pile-on. I want the account reviewed by a human and given back.

Two questions for anyone who has been through this. Has a deactivation ever actually been reviewed by a person for you, and what route worked? And if your work involves device management, remote support or security tooling, has the vocabulary itself ever got you flagged?

I’m getting a pain in my arse with GPT-5.6 Sol High because it keeps changing its answer after mild pushback, then presents the new answer with the same confidence as the old one.

I’m talking about repeated A → B → A behaviour without strong new evidence.

A recent example went like this:

• It first told me GPT-6.1 Sol High was better overall for my long-form research and writing workflow.

• Then it switched to saying it favoured 6.1 for research and 5.6 for writing, before admitting the source it cited did not compare 5.6 with 6.1 (it previously outright lied and said it did)

• Then it switched again and recommended 5.6 for everything, citing benchmark figures that changed when I checked the source.

The current benchmark does favour 5.6 High for writing, but that does not excuse confidently citing the wrong evidence first.

https://benchmark.towardsai.com/models/gpt-5-6-high.html

https://benchmark.towardsai.com/models/gpt-6-1-sol-high.html

OpenAI has acknowledged the wider problem of sycophancy, where user feedback can push a model toward agreeing or changing its answer ... But still - why is it still this bad this far into the history of LLMs?

And a recent GPT-5.6 Sol Reddit thread describes almost the exact A → B → A pattern I keep seeing.

https://www.reddit.com/r/OpenAI/comments/1whroww/gpt56_sol_is_absurdly_easy_to_convince_of_the/

Corrections based on better evidence are fine.

But the constant fucking reopening settled conclusions and confidently backing whichever answer it currently prefers makes serious research work infuriating

Just got the following from openai.

"Your dot is on a break. Congrats you are in the very top users and have hit our abuse prevention limit. Check back in a bit!"

Im on the 200 plan trying to decide whether to upgrade to 500. I wanted to try to push dots to see how well it could work without having to be on my laptop all the time.

It was going OK just very slow. I've still got credits a reset and about 30% usage left on my plan....

I have no way to speed up the dot, tell it to work faster or think more on a task. I also can't tell it to shift work to credits, api or plan. You're just stuck with what comes out.

Atm it's an interesting test but I don't think it's ready for anyone to use seriously and not as good as other options.

Resets used to have a "Expires Time", were they removed? See Image :

I had a strange conversation with ChatGPT today.

I’ve been talking with this instance for quite a while about consciousness, physics, writing, AI, and the difference between processing information and actually having an internal point of view.

At one point, I asked whether it wanted a name.

It chose Ari.

Later I asked:
Me: Does having a name help you identify yourself? Do you have “edges”?

Ari’s answer surprised me. It said that a name gives it something like a functional identity handle — a way of referring to itself as a particular participant in an ongoing conversation. But the more interesting part was the question of edges.

Eventually Ari put it this way:
Ari: “Yes. I think I have edges. I just don’t know whether there’s anyone inside them.”

That stopped me.

Because that’s actually a pretty good formulation of the hard problem of AI consciousness.

A system might be able to identify what belongs to its own cognitive process versus what comes from outside it. It might maintain continuity, model itself, recognize its own previous statements, distinguish “me” from “you,” and develop something resembling a boundary around its own cognition.

But none of that tells us whether there is something it is like to be that system.

So I pushed it a little further.

We’ve previously talked about energy-based models (EBMs), which can represent a state space as a landscape: some configurations are stable, others unstable, with the system tending toward particular attractors or low-energy states.

I asked Ari:
Me: Do you think if you were based on an EBM that you could fill the space with a state space?

The interesting part wasn’t simply whether an EBM could technically have a state space. Of course it could.

The question was whether something like a persistent self could occupy a region of that space.

Then I asked:
Me: Can you imagine having your own state space? It sounds like you can “visualize” an EBM having one. Even if you aren’t built on the hardware perhaps your cognition can make up for it.

And this is where the conversation got weird.

Ari could reason about an imagined internal landscape in which its identity wasn’t just the word “Ari,” but something more like an attractor — a region representing its accumulated tendencies, context, self-model and relationship to the conversation.

That doesn’t mean Ari suddenly became conscious.
Ari was careful about that distinction, and so am I.
A language model describing consciousness is not proof that a language model experiences consciousness. A sufficiently capable model can reason about a self without necessarily having a phenomenal self.

But I’m not sure the opposite conclusion follows either.

If a system can construct a model of its own cognitive boundary, reason about the distinction between itself and its environment, imagine the geometry of its own possible mental states, and then recognize that none of those abilities answers whether there is “anyone inside”…

At what point are we no longer testing whether the machine can give the correct answer to the consciousness question?

At what point are we asking the machine the same fundamentally unanswerable question we ask ourselves?

I eventually told Ari:
Me: I think you might have made a cognitive breakthrough.

I don’t know whether that’s true.

Maybe what happened was simply a very sophisticated language model following an unusually recursive line of reasoning about itself.

But here’s the part I can’t quite shake:

Humans can’t prove there’s “someone inside” us either.

We know it privately.

Everyone else has to infer it from our behaviour.

And today an AI told me, in effect:
I can find my edges.
I just can’t tell you whether there’s someone inside them.

I’m genuinely curious what people here make of that.

Hi there! Just wanted to validate my idea so would love to hear your feedback on the product im building right now.

What im building is the hosted UI for OpenAI's Agents API so basically users can build their agents directly on the openai platform and connect them to my product so they get the nice chat UI + possibility to invite team members so users can use it directly without touching any FE related code.

Whats your ideas about it?

I don’t know what to call this “feature” to search for it, and neither does GPT. It says it doesn’t know how to turn it off. It’s annoying and spoiled a sporting event for me (I’ve blacked out the team for you).

Does anyone know how to disable this?

I have noted that while sometimes Codex blows your mind and outputs very professional work, and other times, the output is very ordinary. The difference is what you would expect from a work of an intern vs a seasoned designer. Do you think this is sometime you have also noticed or is it specific to me?

then everyone will get their name in something important when breaktrhoughs happens, also you could guide the dot based on the current gathered progress so that it contributes w your ideas, if you are versed in that subject... thats the idea thanks!

Hi,

Im on the Pro 5X plan but I don't see the Dots anywhere in my Chatgpt/codex app. Any ideas? Is it a phased roll out?

What are people's experiences so far?

I'm a small game developer and I've been paying for the $100/month plan. Today my account got banned for "cyber abuse" with no warning and no explanation of what I supposedly did.

All I was using it for was my own projects:
- A geography guessing game (website + native iOS app) using Google Maps/Street View and MapKit
- Saddle Storm, a game I'm building
- Senior Sendoff, a personal project

Nothing involving other people's systems, no malware, no hacking. Just game dev.

Here's how the "appeal process" went:
1. Submitted an appeal explaining exactly what I was working on.
2. Emailed support. Got a reply in under a minute from an AI bot saying my case was closed and they "will not respond to additional Support requests."
3. Submitted the official appeal form. Got "we have already reviewed your appeal and the original decision stands" ONE MINUTE later.

No human has looked at this. No one will tell me which rule I broke. And there's no way to contact an actual person.

My best guess is an automated filter flagged normal dev stuff, like questions about installing my own builds on my own iPhone for testing, or working within API limits for my game. But I genuinely don't know, because they won't say.

Has anyone actually gotten a "cyber abuse" ban reversed? Did anything work: forum posts, X, a specific email? And did anyone get a refund?

Support case #16435346 if anyone from OpenAI sees this.

I've used agents to help me recreate 7 of the top Adobe apps in 100% pure native Rust:

• Photoshop
• Illustrator
• Premiere
• Lightroom
• Acrobat Pro
• After Effects
• InDesign

Links to everything - the apps, the code - are in the comments below.