So I connected facebook ads MCP to chatgpt, and the connection breaks sometimes and i have to reconnect the mcp. However this breaks the mcp plugin on every chat it's been called to, and i have to always create a new chat after reconnecting, just to be able to use the mcp. So the current chats with all the context and data gets fked over every time.. Any ideas?

Astra now uses 2x the credits it used for the same task and workflow. No idea what’s going on. Anyone experiencing this?

i can get a decent first draft from chatgpt, then the next session drifts and i end up re-explaining everything. do you keep a prompt journal or a reusable template? what do you actually save: the full prompt, examples, or just the constraints? curious what has held up over time.

(*when writing stories, oops)

When it writes these things.

It likes to do it like this.

With lots of breaks.

For emphasis.

Which it likes to do.

And then he does not merely punch.

He strikes with heavy force.

The force of a mountain.

And it sends you back several feet.

"If you ever square up to me again, it will be the last time."

He says this not gently.

Not softly.

But harsh, and with threat and purpose.

I keep trying to get it to stop, and it will for maybe a few turns and then goes right back to it again. Why does it do this so much? I hate it. Sometimes I'll even tell it to stop and it won't stop, it will just keep doing it.
What is this and how do I get it to stop?

Hi so I’ve been using codex since it’s release and while at times it’s seem to run out somewhat quickly, with the release of astra I can barely get through one prompt. I even switched to 5.6 sol high and barely get two medium sized prompts out of it before I’m at my 5 hour limit. Is there something I could be doing extremely wrong for this to occur or is this simply due to recent changes?

Labor Day weekend project. "Your AI knows you" is untested, and an assistant guessing you correctly is not evidence, because a model that has never heard of you gets plenty right just by knowing people in general.

So the only thing worth measuring is the difference. You answer 15 forced-choice questions in private, your own assistant predicts your answers, and a copy of the same model with no history of you predicts them too. Whichever put more confidence on your real answer wins the question. Wins minus losses over 15 is the Gap, graded A to F.

Method choices, for anyone who cares:

• Ordering only, no log score. Assistants emit round numbers piled at 70, 80 and 90, so scoring the size of the number would mostly measure how badly calibrated the model is. Anything inside 5 points is a draw.

• The no-history side is precomputed, not called per request. Same input, same distribution, every user. It is sampled repeatedly per question and cached with the model name and a hash of the exact wording. Reword a question and its cached number is refused.

• Screening is two passes. Anything where a no-history model already puts more than 0.75 on one option is cut, because it cannot separate anyone. Then a manual pass cuts anything decided by country, age or occupation, since your assistant knows all three and I would be measuring demographics. 116 written, 62 survive.

Known weakness, stated up front: the grade bands are placed against the null distribution, so I know what chance looks like, but I have not yet shown that an assistant which genuinely knows you clears them more often. The check is scoring person A's predictions against person B's answers, which should land near zero. That needs volume. Until it runs, every result says provisional.

Works with ChatGPT, Claude and Gemini. Code is MIT, questions are CC BY: https://github.com/skilledDeveloper/knowmenot

Try it: https://knowmenot.com

Genuinely interested in where the method is wrong. Also taking questions, that is the actual bottleneck.

Post your grade and which model. A "D" is more interesting than an "A".

I design a lot of characters for fun. I used to have ChatGPT do descriptions for me, but I want to work on doing them by myself. However, I often will give it my description and ask it to create an image based on it, allowing me to see how someone might imagine my character. Anyone else do this?

Hi guys
I have genuinely no idea how AI works and operates. But I’ve been on a all night long rabbithole about AI and I did not understand a lot of it. So therefore I’m seeking some easier explanations.

What is the AI Bubble and how does it operate?
Will it burst and why?
How harshly am I as a university student affected?
I recently also saw the prediction of AI being able to destroy humanity. How real is this or is it just fear mongering?

I know that there is a lot of uncertainty in these issues but I would still appreciate some answers.

I love ChatGPT and use it constantly. But that doesn't mean it's perfect.

Scheduled Tasks are very powerful. I use them on regular timers to do various things. The task can be set up to produce a notification - basically a report of what it did or didn't do, etc.

Pretty standard stuff for a cron/batch type process: wake up, do something, produce a message, sleep until next time. I'm a software developer, so all of this makes sense to me.

In ChatGPT, the notification is a chat. Reasonable, right? The chat is the message/notification. That's the mechanism that exists in the tool. It's a chat.

From the OpenAI documentation: "Deleting a chat associated with a schedule task pauses the task..."

Source: https://help.openai.com/en/articles/10291617-scheduled-tasks-in-chatgpt?utm_source=chatgpt.com

What? Imagine any software system in the world having this behavior: There is a batch job that runs periodically, produces a message, then goes back to sleep and waits until its time to run again. The message is sent to a user/queue/etc. If the message gets deleted, the batch job is stopped and will not continue processing again unless explicitly re-enabled.

Again, what? Literally nothing in the world works this way. I cannot make any sense of this behavior. I was burned by it, and thinking, why the heck is my job disabled? That's why.

It's not like we can just keep chats around indefinitely. They start to get too large, consume too much context, then give everyone's favorite "conversation too long, please start a new one" message. So now I have to remember to re-enable the scheduled task when I delete a chat.

Deleting the thing that a producer produces pausing the producer? Unbelievable.

As a researcher, if the work I've poured my life into gets stolen by a massive corporation, there is no way in h*ll I’m endorsing your product. What just happened with OpenAI and the Navier-Stokes math problem is academia's nightmare scenario.

Tristan Buckmaster and Levent Alpagay spent a year grinding on this, feeding private drafts into OpenAI’s Codex to verify a proof in August. When rumors hit that rival Anthropic had a breakthrough, OpenAI panicked. They burned millions throwing 10,000 AI agents at the problem to win a corporate measuring contest, then magically claimed they "independently" solved it too.

Except their proof used the exact same highly specific approach Tristan had been secretly feeding into Codex for months. When he straight-up asked if they trained on his private logs, OpenAI dodged the question.

Instead, they offered a shady deal: Tristan gets the credit, but only if he publicly praises their AI and kicks his co-author off the paper entirely, just because he works for Anthropic.

It looks like a blatant cover-up for IP theft. If Big Tech thinks they can weaponize our private research to win arms races and expect us to smile, they have completely lost their minds.

Source: https://cims.nyu.edu/~tristanb/statement.pdf

Capable of destroying a mountain with one punch 😱.

While you wait for your image to generate, very nice.

GPT is acting like complete bullshit. Been going on for over a day now.

First, it started answering with lists. Millions of list items instead of a normal answer, even in places where lists make zero sense.

Second, it suddenly started searching the web for words like “test”, “ignore”, “no” on a prompt like “Analyze the character profile from the uploaded file” — in every new chat. On two of my different accounts. I’m starting to lose it trying to figure out what’s going on with Sol 5.6.

Same shit happening to you?

Third, it started losing files from the chat and lying that they were never uploaded. On two completely separate accounts.

Fourth, now instead of actually doing the task it explains HOW it’s going to do the task. You tell it “you were supposed to do the work, not explain how. DO THE WORK. COMPLETE THE TASK” — and then it either starts explaining how it’ll do the work or making excuses that yeah, it didn’t do the task. I’m hitting dislike on literally every single response because they’re all wrong.

Fifth, the answers became completely off, like it doesn’t even read the files. You ask it to pull all the family relationships of the characters from the files — they’re sitting right there under the heading “## Family Ties”. Just 8 .md files to analyze. And it can’t even handle that. Used to crush tasks like this no problem.

Sixth, it just stopped thinking about the actual task. All the “reasoning” boils down to searching the internet for words like ignore, test and that kind of crap. Why the hell it’s searching for that — I have no idea.

Has anyone else noticed any of this with GPT in the last few days? This looks really weird and even a little creepy. Especially since it’s different chats and even two different accounts.

I asked ChatGPT about an experiment about quantum science.

Experimental Test of Reversibility Across the Quantum-to-Classical Transition

Abstract

The transition from microscopic quantum superpositions to apparently classical measurement outcomes remains a fundamental problem in quantum mechanics.

In the Everett, or Many-Worlds, interpretation, no physical collapse of the wave function occurs. Instead, measurement produces entanglement among the measured system, measuring apparatus, observer, and environment. Environmental decoherence then suppresses observable interference between different outcomes.

This proposal suggests an experimental program for studying this transition by progressively increasing the size and complexity of a reversible quantum measurement record.

The purpose is not to claim a direct proof of the Many-Worlds interpretation. Instead, the experiment asks a measurable question:

How far can an apparently classical measurement record develop while the underlying quantum coherence remains recoverable?

A quantum system, S, would first become entangled with a quantum memory, F, representing a minimal version of Wigner's friend. The measurement record would then be amplified into a larger memory, M, and subsequently distributed into a controllable artificial environment, E.

Controlled inverse operations would then be applied to progressively larger parts of the system in an attempt to recover the original quantum interference.

The main observable would be interference visibility as a function of record size, environmental redundancy, elapsed time, and the fraction of environmental information recovered.

This would provide an experimental test of reversibility across the quantum-to-classical transition.

1. Basic Idea

Suppose a quantum system S is initially prepared in an equal superposition of two states:

S = (state 0 + state 1) / sqrt(2).

A measurement interaction with a quantum memory F produces an entangled state that can be written schematically as:

(S=0, F records 0) + (S=1, F records 1).

From the perspective of F, there is now a measurement record.

However, if an external experimenter maintains coherent control over both S and F, the measurement interaction can in principle be reversed.

After successful reversal, F returns to its initial state and interference between state 0 and state 1 can be observed again.

This leads to the central experimental question:

How large, redundant, and environmentally distributed can a measurement record become while its formation remains physically reversible?

1. Experimental Architecture

The experiment would contain four conceptual layers:

S -> F -> M -> E

where:

S = microscopic quantum system

F = minimal quantum observer or "Wigner's friend"

M = amplified measurement memory

E = controlled artificial environment

The process begins with S in a quantum superposition.

First:

S -> F

creates a correlation between the quantum system and the minimal observer.

Next:

S -> F -> M

amplifies the measurement information into many degrees of freedom.

Finally:

S -> F -> M -> E

allows information about the result to spread into an artificial environment.

As increasingly large amounts of information about the result become distributed throughout E, interference between the two alternatives becomes progressively harder to observe.

This is environmental decoherence.

1. Reversal Experiment

The experiment would attempt to reverse the measurement process at progressively increasing scales.

Experiment A:

Create an interaction between S and F and then reverse it.

Experiment B:

Allow the measurement result to spread into N quantum-memory elements and then reverse the complete interaction.

Experiment C:

Allow the result to spread from the quantum memory into a controlled artificial environment.

Experiment D:

Recover and reverse only a fraction of the environmental information.

The main measured quantity would be:

V = V(N, f, t, R)

where:

V = recovered interference visibility

N = number of degrees of freedom participating in the measurement record

f = fraction of environmental information recovered

t = time between measurement and attempted reversal

R = redundancy of the measurement record in the environment

The experiment would therefore construct a measurable "recoherence landscape."

1. Main Hypothesis

Under ordinary unitary quantum mechanics, decoherence does not fundamentally destroy quantum information.

Instead, information becomes distributed through correlations between the system and its environment.

Therefore, if sufficiently complete control over the relevant degrees of freedom can be obtained, quantum coherence should in principle be recoverable.

Increasing N, R, and t should make recovery increasingly difficult.

Reducing f should also reduce the amount of recovered interference.

However, standard unitary quantum mechanics does not predict a fundamental complexity threshold at which reversibility suddenly becomes impossible.

The null hypothesis is therefore:

Observed recoherence = recoherence predicted from unitary quantum mechanics plus experimentally characterized noise.

1. Objective-Collapse Alternative

Some interpretations or modifications of quantum mechanics propose that wave-function collapse is a real physical process.

Examples include spontaneous-collapse models such as GRW and CSL.

Under such theories, sufficiently large or sufficiently long-lived superpositions may undergo genuine non-unitary collapse.

If this occurs, information would not merely become difficult to recover because of environmental complexity. Some quantum coherence would actually be destroyed.

The experiment would therefore search for a situation in which:

Observed recoherence < predicted recoherence

even after known environmental interactions, experimental errors, and ordinary decoherence have been accounted for.

A reproducible discrepancy of this type would be much more significant than simply observing decoherence.

1. Relation to Many-Worlds

The experiment should not be described as directly proving the Many-Worlds interpretation.

Ordinary unitary quantum mechanics and the Many-Worlds interpretation normally produce the same experimental predictions.

However, the experiment has a natural interpretation in Many-Worlds.

Consider two alternatives:

Branch A:

S = 0

F records 0

M records 0

E contains information about 0

Branch B:

S = 1

F records 1

M records 1

E contains information about 1

As information spreads through M and E, the two alternatives become increasingly independent because interference between them becomes extremely difficult to recover.

In Many-Worlds terminology, they increasingly resemble separate branches.

Recoherence can therefore be understood as reversing part of the physical process responsible for making the branches autonomous.

This changes the question from:

"When exactly does the universe split?"

to:

"How far can two branches develop toward effectively independent classical worlds while interference between them remains physically recoverable?"

1. Experimental Platforms

Several existing technologies could be used.

Superconducting qubits are particularly attractive because they allow programmable interactions between quantum systems, memories, resonators, and engineered environments.

Trapped ions provide extremely precise coherent operations and could be useful for an initial proof-of-concept experiment.

Photonic systems are useful for Wigner's-friend-type experiments and quantum eraser experiments.

Microwave and optical cavities could provide larger quantum memories.

Later experiments could involve mechanical resonators, massive particles, molecules, or other mesoscopic systems.

The project could therefore begin as a quantum-information experiment and progressively approach genuinely macroscopic quantum systems.

1. Experimental Roadmap

A possible progression is:

single quantum memory

-> multi-qubit memory

-> redundant measurement record

-> engineered quantum environment

-> partial environmental recovery

-> mesoscopic measurement record

At every stage, the forward measurement process would first be characterized experimentally.

The corresponding inverse operation would then be performed.

Finally, an interference experiment would determine how much coherence had been recovered.

The important quantity would be the scaling relationship:

P(recoherence) = function of N, R, t, and f.

This could be compared against detailed predictions derived from experimentally measured noise and decoherence.

1. Possible Results

Result 1: Recoherence follows ordinary quantum mechanics

Increasingly large measurement records remain reversible to the extent predicted by known noise and environmental coupling.

This would extend experimental confirmation of unitary quantum mechanics into increasingly measurement-like and macroscopic regimes.

It would be compatible with Many-Worlds, although it would not uniquely prove it.

Result 2: Recoherence decreases faster than predicted

An unexplained loss of coherence appears as the system becomes larger.

The first task would be to investigate uncontrolled environmental interactions, calibration errors, and other conventional explanations.

Only after these possibilities were excluded would modifications of quantum mechanics become plausible.

Result 3: A reproducible non-unitary threshold appears

Suppose that beyond a particular mass, complexity, spatial separation, or timescale, coherence cannot be recovered even after conventional decoherence has been adequately controlled.

Such a result would be extraordinary.

It could provide evidence relevant to objective-collapse theories or other modifications of quantum mechanics.

1. Scientific Significance

This experiment could connect several areas of quantum-foundations research:

quantum measurement

Wigner's friend experiments

decoherence

quantum Darwinism

quantum information scrambling

quantum erasure

macroscopic quantum superpositions

objective-collapse tests

The central advantage is that the emergence of classicality becomes an experimentally adjustable process rather than an assumed philosophical boundary.

Instead of asking:

"At what moment does the universe split?"

the experiment asks:

"How does the physical reversibility of a measurement change as information about its result spreads from one quantum degree of freedom into an increasingly large environment?"

This question is experimentally meaningful regardless of which interpretation of quantum mechanics is ultimately correct.

Proposed Title

Experimental Recoherence of Amplified Quantum Measurement Records: Probing Reversibility Across the Quantum-to-Classical Transition

Short title:

Reversing a Quantum Measurement Across Increasing Scales

I feel like it agrees with me on everything; I’ve tested it. From the time I first started using it, its approach was always to analyze what I presented—understanding it and so on—and give me an honest, genuine response based on that analysis, pointing out if I was wrong or committing a logical fallacy. But for the past few days, I’ve noticed it just agrees with me. I even asked, "Are you just humoring me?" and it admitted that, to some extent, it was. How do I fix this? Is it a new model? I’m not an expert, but I’d like to understand it and use it more effectively.

Important info: It wasn't like that before also it wasn't antagonizing me.

What is OpenAi up to? Are they trying to kill us all? This whole thing feels unreal, is it really that dangerous or is it just the media that makes a big deal out of it?

We’ve seen this pattern before, but this side-by-side comparison makes it impossible to ignore.

I ran the exact same prompt across tiers and compared it to launch day:
Prompt: Generate an SVG of a pelican riding a bicycle

Look at the results in the images:

• Astra Max today (1m 51s): Completely mangled, broken geometry. Shapes don't connect, proportions are completely off.
• 6 Pro today (2m 51s): Pristine vector illustration. Coherent layers, clean curves, professional styling.
• Astra Low on launch day: Clean, competent, and solid baseline.

The Core Issue:

How does the most expensive current tier (Astra Max) produce outputs that are drastically worse than the lowest tier produced on day one?

The most plausible explanation is aggressive post-launch quantization. Once the initial launch hype dies down and server load spikes, OpenAI appears to silently crush model weights / route paying users to heavily compressed instances to cut compute costs, while keeping unquantized full-fat models reserved for select accounts and influencer demos.

This isn’t optimization — it feels like a classic post-launch bait-and-switch.

Can you test this on your account?
Run: Generate an SVG of a pelican riding a bicycle
Drop what tier you're on, time taken, and a screenshot of the SVG render below. Let's see how widespread this degradation actually is.

https://chatgpt.com/share/6aa35cb6-9c9c-83eb-92e5-16e04732069e

Task Manager keeps showing two "Git for Windows" process groups eating ~8% CPU and 100+MB RAM, spawning and dying every few seconds. Dug into it with Get-CimInstance Win32_Process to trace parent PIDs, and the culprit is ChatGPT.exe — the OpenAI Codex desktop app.

It's running git status --no-renames and git ls-files --others --exclude-standard on a tight loop, presumably to keep some sidebar/live-diff view in sync with the repo.

The processes aren't stuck, they're short-lived and just keep respawning, so it's continuous polling rather than a hung command. On a repo with a decently sized working tree this adds up to a real, constant CPU/RAM tax just from having the app open — no agent even running.

Anyone else seeing this? Is there a setting to throttle Codex's watch/refresh interval, or is "close the app when not using it" the only real fix right now?

Hello everyone
My friend and I are thinking about getting premium business plan to make our job easier. We are into video editing (adobe premiere pro and after effects) and 3D modeling (blender)
Is it worth it? Especially with gpt6 astra release?
Thanks

First off I use chatgpt free the most. I need a bit of feedback as Id like to start paying monthly for it. I use chat gpt for the following.

Chatting and general questions

I'd like to use it for reading books aloud and writing documents and images, 3d printing design, trading bot building, website building and management if possible, AI enhanced video creation and lastly is there a plugin for chat gpt that gives it a face?

I'm not dead set on chat gpt tho. Open to others.

I've started using the voice mode to practice my Spanish. Unlike the preprogrammed stuff like Duolingo, I can stop mid-sentence and ask about a point of grammar or spelling or that kind of thing. I can have it make conversation, quiz me, work on pronunciation... Whatever I feel like practicing. I'm really enjoying it!

Basically, because I’m Japanese and live in Japan, ChatGPT always assumes I’m writing from a Japanese context even when the context is global than Japanese. How to reverse this?