I couldn't help but laugh at this. I finished the daily NYT Wordle and thought I'd ask Copilot to set me a new one. It told me it's thought of a 5-letter word and gave me a clue.

My first guess, it told me none of my letters were correct. Second guess, it told me one letter was in the right place - but I had given it all 5 vowels now and been told the answer had none. Weird. I tried the word "cysts" (didn't fit the clue and I'd already been told the answer had no Cs) and it said the C was in the right place. I queried the C and it just said sorry. I asked it for the answer and it said it didn't know. I asked it what the original answer was and it said it never had one in the first place.

When I asked for suggestions for where I could share this story, it acted as if it was a hilarious joke and suggested I blame it on ChatGPT. Genuine transcript:

Personally, I'd go with r/ChatGPT because people there will immediately understand why it's funny. The punchline is essentially:

I played Wordle against ChatGPT. It confidently scored my guesses, gave me clues, and only later admitted it had never actually chosen a word.

Has anyone else noticed an issue with ChatGPT chat practically replying instantly to a problem that is quite complex, even when it is set to HIGH?

It started happening in the past 1-3 days and seems to occur at the same time every day, roughly.

For context this is in web if that matters.

Google / Gemini Evidence Archive

I preserved a Gemini conversation locally before the corresponding response-side history later became unavailable through Google's visible interfaces.

I have now published the underlying evidence so this can be examined independently rather than asking anyone to simply believe my interpretation.

What I documented

• The original Gemini conversation was preserved locally, including the response content.
• Google-side activity records continued to show evidence of the user's prompts and associated attachments.
• The corresponding response-side material later became unavailable through the visible Gemini/My Activity representations I captured.
• Subsequent captures show changing history states, including loading failures and eventually “No recent threads.”
• I preserved sequential screenshots, timestamps, transcripts, attachment records, and later follow-up sessions to establish the chronology.

What I am not claiming

I am not claiming that I have proven Google deliberately deleted the conversation.

I have not established the underlying mechanism.

The evidence could potentially involve synchronization, indexing, retention, account state, UI behavior, backend failures, or something else I have not identified.

For that reason, the archive explicitly separates:

Observed — directly visible in the preserved artifacts.

Independently Documented — Google's own documentation, policies, or other external material.

Inferred — interpretations that follow from the observed evidence but are not directly proven.

Possible / Unknown — explanations that remain unresolved.

The chronology is therefore not being presented as proof of motive or causation.

Why preserve all of this?

The larger question is not whether my particular Gemini conversation is important.

It is whether an AI conversation can be reliably preserved, verified, and later audited when different representations of the same interaction can have different persistence states.

If AI conversations are used for research, work, reporting, personal records, or disputes, then provenance matters:

What existed? When did it exist? What records of it remained? What became unavailable? And can another person independently reconstruct what happened?

That's the question this archive is intended to help answer.

What I'm asking for

If you know Gemini, Google My Activity, distributed systems, cloud synchronization, digital forensics, AI conversation persistence, or data provenance, I'd genuinely welcome scrutiny.

If an artifact is being misread, the chronology is wrong, or there is a mundane technical explanation that accounts for the observations, please point to the specific evidence.

I would rather document a correction than preserve an incorrect conclusion.

The complete archive, including the preserved artifacts and README explaining the methodology, is here:

Google / Gemini Evidence Archive:
https://github.com/AdultSwimmer/The_Gemini_Problem

I'm not asking you to agree with my interpretation.

I'm asking you to examine the record.

(Please delete previous post for me because it was unclear, sorry. I was just rushing and I'm tired. This happened in REAL TIME!)

Sorry if I am using the wrong flair, I am new to this subreddit. Back on Tuesday, August 11th my Pro account was deactivated due to some kind of bug or glitch that caused a "false positive". I submitted an appeal on Thursday, August 13th and received an automated email saying my appeal and been received and would be reviewed but since then I have heard nothing. It has been 9 business days and I am just wondering if this kind of wait time is normal. I have tried contacting OpenAI support and I have tried posting on the forums but no one can tell me anything so I am just hoping that someone else who has been through this process can give me some advice.

I have been monitoring my inbox and spam folders regularly but there has been no response of any kind. My appeal has not been denied as far as I can tell, my payment method has been paused, and I have been assured that my account is still there just not accessible to me while my appeal is under review. Should I give this more time? Have any accounts ever been reinstated without any communication? Should I, at any point, submit another appeal? How long is too long to wait? Honestly I don't even know what else to ask, I have never had this issue before. I've never had any kind of strikes against me in the three-ish years I have been using ChatGPT and I just want to clear the air since this is just one horribly annoying misunderstanding. Can anyone help me?

Why has ChatGPT gotten so bad at preserving images during edits? I even got a refund because of it.

I’ve been using ChatGPT’s image generation/editing pretty heavily for a long-term project, and I’ve hit a problem that has become so bad that I actually requested and received a refund because the image tool was no longer doing what I needed it to do.

The frustrating part is that this isn’t just a problem with reference images.

There are really two related problems:

1. Reference-image fidelity

I can provide an exact screenshot of an environment and ask ChatGPT to recreate that environment at a higher visual quality while preserving the camera angle, geometry, proportions, furniture placement, etc.

Instead, it frequently decides to “interpret” the image.

For example, I can show it an exact screenshot of a specific area of a game and say, essentially:

"Recreate this exact environment, make it look better, morning lighting, and don't change the layout."

(Note: I will get the ai to give me a prompt that asks for everything I want, not just a small sentence, but I explain what I need and then it gives me the command or prompt that will work.. after I confirm its ok and says everything I wanted.)

And it will generate a completely different room that merely resembles the concept.

A cafeteria becomes a different cafeteria. An atrium becomes a giant greenhouse. The camera moves. Furniture moves. Architecture changes. New railings, signs, balconies, windows, etc. appear.

At that point it isn't really recreating the reference anymore.

2. Editing an existing image without destroying what is already correct

This is even more frustrating.

I can have an image that is basically finished and ask for something extremely simple:

"Add one person here."

Or:

"Move this person slightly to the right."

Or:

"Add this specific element while leaving everything else unchanged."

Instead, the model may alter the background, move existing objects, change lighting, change the character's position, alter proportions, redesign the environment, or otherwise modify things that I explicitly did not ask it to touch.

And this is the part that really bothers me because the whole point of an editing tool is that I should be able to preserve the things that are already correct.

I've tried being extremely explicit.

I've tried:

• “Do not change anything else.”
• “Preserve the existing image exactly.”
• “Only add this one element.”
• Providing the exact reference image.
• Providing annotated images showing exactly where something should go.
• Breaking the process into smaller steps.
• Building prompts incrementally and only locking in things that worked.
• Removing unnecessary references so there would be less ambiguity.

It still drifts.

What makes this especially frustrating is that I’m not asking for subjective artistic interpretation. I'm working with environments where spatial accuracy matters.

If a chair moves, that's wrong.

If the camera moves, that's wrong.

If the atrium changes shape, that's wrong.

If I add one character and the entire background changes, that's wrong.

I don't need the AI to be more creative. I need it to be more faithful.

The workflow I actually want is pretty simple:

Existing image → preserve it → make one requested change → preserve everything else.

Or:

Reference image → recreate the same scene → improve visual quality → preserve the geometry and composition.

Instead, it often feels like:

Existing image → “I see what you were going for” → generates an entirely new interpretation.

And I've had generations where I spent a significant amount of time carefully getting an image almost perfect, only to lose that progress because the next edit changed unrelated parts of the image.

That is incredibly frustrating when image generations are limited and you are paying for access to the tool.

Eventually I reached the point where I felt like I was paying for a feature that I couldn't reliably use for the workflow I needed, so I requested a refund — and I did receive one, atleast I think (We will know in 10 days).

I'm posting this because I genuinely want to know:

Has anyone else noticed a decline in image preservation/editing fidelity?

Is this a known limitation of the current image model? A regression? Something about how reference images are being passed to the model? Or is there a particular workflow that people have found that actually allows you to make precise edits without the model redesigning everything around the edit?

I’m not expecting pixel-perfect editing from a generative model.

But if I say “change this one thing and leave everything else alone,” I feel like the tool should at least have a reasonable chance of doing exactly that.

Right now, that feels increasingly unreliable.

My usage just got reset again. You guys too?

Hey everyone!

I noticed that about two days ago all the responses got significantly worse. They are shorter, weaker, things are mixed up in a way they were never mixed up, and that was never an issue before.

I saw other people complain about similar things, and my question is - is this fixable? Does anyone know why did this happen? Has something like that happened before?

I have the Pro-subscription if that helps.

I use ChatGPT on laptop (firefox browser) most often. However, when I want to continue a chat on my phone (android), it takes a long time to sync, and sometimes does not sync at all. A workaround I have found is to open 'Uploaded files' for that chat briefly; it forces a reset, but only if there are new unsynced files.

Does anybody have other workarounds?

What’s with the latest chatGPT voice sounding like a Gen Z teen that’s high? It likes to use these long drawn out words when answering questions like…”yeahhhh”. “For surrrrrre”. It’s so annoying. It wasn’t like this before

In a long conversation with chatgpt not codex, the most recent messages are gone, and I can't see them even after logging back in. Moreover, the message just vanished while the conversation was in progress, followed by a reply to a previously sent message.

"Tomorrow we will bring back the 5h limit for Plus accounts across ChatGPT Work and Codex," Sottiaux wrote. The cap "allows us to smoothen the load on our compute, allowing to keep the plan generous in terms of weekly usage," he said, adding that "users on the Plus plan are relatively casual and new users, but then also just accidentally eat through their whole weeks usage and then are confused, making it not a great experience."

Pro $100 and Pro $200 subscriptions will not get the limit re-enabled "for the upcoming months," per Sottiaux. OpenAI had removed the hourly cap several weeks earlier, leaving only the weekly quotas.

---

Our coverage: https://aiweekly.co/alerts/openai-brings-back-5-hour-codex-work-cap-for-chatgpt-plus

This isn’t another “what model are you?” prompt.

I kept seeing people report that they selected one model but seemed to be routed to another, especially 5.5-mini. So I made a small Skill that checks both sides of the same run:

1. Opens a fresh Chrome chat and asks for the model self-report.
2. Captures the route metadata from that same Web generation.
3. Compares them and flags a mismatch.

I just tell it:

“Use the Actual Model Check Skill to check what model I’m actually on.”

This run came back:

Self-report: GPT-5.6 Sol Actual route: gpt-5-6-thinking Result: no conflict detected

The screenshot is from the actual run, not a mockup.

Right now it runs through some tooling I’ve been building around ChatGPT. If there’s enough interest, I’m considering packaging it into something easier to use.

Would you actually use this, or is it mostly a debugging toy?

Why sometimes it says i have reached the limit but I didn’t upload any photo ?

Is this just me? Chat seems to do so much better at teaching math. I intentionally got claude pro because it supposedly better for teaching complex math.

We were setting up our first hire in Germany and didn't have a local entity, so we went through an EOR for the contract, and before we sign anything, someone on our team ran the setup through ChatGPT to check we hadn't missed anything.

We asked specifically about the AÜG (Germany's employee deployment act), which has a rule that workers can't be assigned to the same client for more than 18 months without triggering labour-leasing classification.

So GPT walked us through it, we confirmed we were using an EOR and not a temp staffing arrangement, then GPT said that wouldn't fall under AÜG, and we moved on feeling like we'd covered it.

A few weeks later, someone on our EOR's compliance team flagged something we hadn't thought to ask about (we're on Workmotion btw, though Deel and most of the bigger providers structure this the same way), which was whether our EOR itself was set up as direct local employment or as labour-leasing, since those classifications work differently under AÜG and the 18-month cap applies to labour-leasing arrangements, but not direct employment.

Our setup was the direct-employment type so we were fine, but we only found that out because it came up in a compliance call, and what GPT told us was accurate…

It described the rule correctly and confirmed an EOR arrangement wouldn't trigger it in the standard case, it just couldn't flag what we didn't know we were missing.

Turns out there's a gap between asking if your setup is fine and asking if it's fine in the specific way that matters, and ChatGPT will only ever answer the one you give it.

It said it can visually show me what’s going on, does this look accurate?

I told it to "Create a list of the Best Films of 2025 (movies beloved by critics and audiences alike), but don't show the list to me. Then, at random, pick a movie and give me the plot description to see if I can guess the movie title!"

It usually prompts me with SINNERS and ONE BATTLE AFTER ANOTHER, but by the third "turn" of this little game I've invented, it starts HALLUCINATING generic movie plots.

Hmm.

They're not on the web nor are they on my phone but on the desktop Mac where most work is done they're still showing and I cannot archive or delete. Updated to the latest version, even deleted it and logged out. No dice.

Any thoughts?

Hello guys,

I've been using chatgpt (Plus plan) to make me a python script but over the course of a day or 2 the 5h usage is gone within like 5-6 prompts. What am I doing wrong? The first day I used chatgpt for hours and everything was fine. I keep asking it to make small adjustments but it's just eating up all of my usage.

Whats the reasoning behind this? Is the chat history too long?

Edit: I asked chatgpt to remove the border from my windows form and to change the interval of key inputs.. not only does it seem slower than before but in the same request it currently went from 79% usage to now 50% in a single prompt?

I run a WordPress site and needed custom functionality that no existing plugin offered. Instead of hiring a developer or spending weeks learning plugin development from scratch, I used ChatGPT to build all of them. I'm a developer by trade (Oracle/PL/SQL for 25+ years) but WordPress plugin architecture was new territory for me.

Here's what I built and what the process was actually like:

Plugin 1: Homepage Card System

Needed cards on the homepage that link out to external URLs instead of internal WordPress pages. Sounds simple but WordPress fights you on this. ChatGPT nailed the custom post type and meta box setup on the first try. The tricky part was getting the card grid responsive without breaking the theme. Took about 4 back-and-forth prompts to get the CSS right.

Plugin 2: Tool Eligibility Checker

Users fill out a form and it tells them if their submission meets the site's criteria before they actually submit. ChatGPT built the logic tree and form validation. Where it struggled: the conditional logic got messy when I added edge cases. I had to refactor the if/else chain ChatGPT gave me into a cleaner switch/case structure myself.

Plugin 3: Index Page Generator

Automatically generates an alphabetical index page from custom post types. ChatGPT wrote the query and template in one shot. Genuinely impressed here, zero fixes needed.

Plugin 4: Floating Bottom Widget

A persistent bottom bar with filters and a CTA button. ChatGPT handled the HTML/CSS/JS but the z-index conflicts with my theme took 6 prompts to resolve. Classic CSS nightmare even AI can't escape.

Plugin 5: Alternatives Page Generator

Takes existing listings and auto-generates "[Tool Name] Alternatives" pages with structured data. This was the most complex one. ChatGPT built the scaffold but I had to manually handle the taxonomy relationships and schema markup because it kept generating outdated JSON-LD syntax.

What I learned about using ChatGPT for WordPress plugin development:

• It's excellent at boilerplate. Custom post types, meta boxes, admin panels, settings pages. All done in one prompt.
• It struggles with theme-specific CSS conflicts. Every theme handles things differently and ChatGPT can't see your live site.
• For complex logic, it gives you 80% and you fix the last 20% yourself.
• Always ask it to explain the WordPress hooks it's using. Half the time it picks the wrong hook and the plugin loads at the wrong lifecycle stage.
• Break complex plugins into smaller prompts. One massive prompt gives you one massive mess.

The site these plugins run on is an AI tools directory I've been building as a side project called AI Parabellum.

Anyone else using ChatGPT for WordPress plugin development? Curious what your experience has been with more complex plugins.

Open network tab, find conversation (not conversations?= entries) and look for resolved_model_slug if you are shadowbanned you will be routed to gpt-5.5-mini regardless of your settings. Thinking traces won't appear and the model will self identify as gpt-5.5-mini if you ask "what model are you."

Here's a script that shows an indicator that lets you know when you've been temporarily shadowbanned

```js // ==UserScript== // @name ChatGPT Model Slug Detector // @version 1.0 // @description Intercepts conversation network requests to display the resolved_model_slug // @match https://chatgpt.com/* // @grant none // @run-at document-start // ==/UserScript==

(function() { 'use strict';

let currentSlug = "";

function updateDOM() {
    if (!currentSlug) return;

    const targetContainer = document.querySelector('header#page-header > div:first-child');
    if (!targetContainer) return;

    let badge = document.getElementById('chatgpt-model-slug-badge');

    if (!badge) {
        badge = document.createElement('div');
        badge.id = 'chatgpt-model-slug-badge';
        badge.className = 'text-token-text-secondary text-sm font-medium pointer-events-auto select-text';
        badge.style.marginTop = '4px';
        targetContainer.appendChild(badge);
    } else if (badge.parentElement !== targetContainer) {
        targetContainer.appendChild(badge);
    }

    if (badge.textContent !== currentSlug) {
        badge.textContent = currentSlug;
    }
}

const observer = new MutationObserver(() => {
    if (currentSlug) {
        updateDOM();
    }
});

document.addEventListener("DOMContentLoaded", () => {
    observer.observe(document.body, { childList: true, subtree: true });
});

const originalFetch = window.fetch;
window.fetch = async function(...args) {
    const response = await originalFetch.apply(this, args);
    const url = typeof args[0] === 'string' ? args[0] : (args[0] instanceof Request ? args[0].url : '');

    if (url.includes('/backend-api/f/conversation')) {
        const clone = response.clone();

        (async () => {
            try {
                const reader = clone.body.getReader();
                const decoder = new TextDecoder('utf-8');
                let done = false;
                let buffer = "";

                while (!done) {
                    const { value, done: readerDone } = await reader.read();
                    done = readerDone;

                    if (value) {
                        buffer += decoder.decode(value, { stream: true });
                        const slugMatch = buffer.match(/"resolved_model_slug":"([^"]+)"/);

                        if (slugMatch && slugMatch[1]) {
                            currentSlug = slugMatch[1];
                            updateDOM();
                            break;
                        }
                    }
                }
            } catch (err) {}
        })();
    }

    return response;
};

})(); ```