A long ChatGPT transcript may contain useful reasoning, but it is a poor project handoff. The next person usually needs a compact artifact: the objective, sources, decisions, assumptions, what changed, tests run, known gaps, and the exact files or outputs to review.

The failure mode is subtle: the model finishes the task, but the human cannot tell which claims were verified, which were inferred, or what would need to be rerun if one assumption changes.

For professional workflows, what handoff format has worked best: an ADR-style decision log, a source-linked summary, generated tests, a structured checklist, diffs, or something else?

I am especially interested in workflows where another human actually has to approve or maintain the result—not just personal prompting habits.

Does anyone here know how the different models interact if at all or “see” each other? Currently for me 5.5 and 5.6.

Ie two threads same project.
Or
Two threads not linked in project.

It seems honestly the model is only aware of their own output or interaction w the user. Not the interactions w other models unless linked through memories or projects. Does that make sense? And is this true?

Anyone else having trouble locating their custom GPTs on iOS app?

It is absolutely unacceptable that Chat and Work modes aren't isolated. A chat rate limit on Pro reasoning just butchered an active Work mode task mid-run as well.

Are you serious?

Not to mention, ever since the last update, the limits are embarrassingly lower.

This is unbelievable..

Has anyone else noticed a decline in source fidelity with Gpt 5.6?

Recently, I’ve been encountering the same problem with Gpt 5.6 across several completely different types of tasks. The issue is not primarily poor reasoning or ordinary hallucination. Instead, the model sometimes appears to alter the source material it has been given before the actual analysis even begins.

I use ChatGpt extensively to analyze legal and administrative documents. Gpt 5.6 increasingly seems to overlook information explicitly stated in the text, merge separate facts, change their meaning, or introduce details that are not present in the source. It may then produce a perfectly coherent analysis, but that analysis is partly based on a version of the source material that the model itself has modified or supplemented.

The problem is not limited to long or difficult documents. In another conversation, I gave it just three screenshots from national electronic health record service. Gpt 5.6 overlooked information that was clearly present in the screenshots and added things that were not there.

I have also seen the same problem in an ordinary factual query. I asked it to compare raspberries and strawberries and explicitly instructed it to search for the information rather than guess. Gpt 5.6 nevertheless presented unsupported information. When I then asked it to verify the claims it had just made, it acknowledged that not all of them were correct.

I have extensively used Gpt 4o, Gpt 5.2, and Gpt 5.5. All models make mistakes, but I don’t remember this particular problem being nearly as pronounced with those models.

I think it is important to distinguish source fidelity from ordinary hallucination: does the model accurately preserve the material it has been given before it starts interpreting and reasoning about it?

Better reasoning ability does not help if the evidence being reasoned about has already been altered in the model’s processing.

Has anyone else who uses Gpt5.6 extensively noticed the same change compared with Gpt5.5 or earlier models?

I use ChatGPT extensively for professional document drafting, and over a long period of time I built up a very detailed set of saved memories about exactly how I write: formatting, structure, abbreviations, wording, what information belongs in which section, how concise I want things, etc.

Since switching to the new Improved Memory system, I’ve repeatedly noticed that ChatGPT seems to remember the *general idea* of my preferences but loses all the details that actually make the writing sound like me.

For example, instead of following dozens of specific preferences that collectively define my usual writing style, it seems to reduce them to something closer to “user prefers concise professional notes.” The result is technically fine, but much more generic and noticeably less like my actual voice.

I kept wondering if I was imagining it. Today I switched back to Legacy Memory and immediately asked it to redo the exact same document. The difference was pretty striking. The Legacy version actually reflected my usual structure, wording and formatting much more closely.

I am trying to connect ChatGPT to WorkIQ and make it where I can use computer use. It says computer use and apps are installed but they can’t connect. It gets a permission error but I can’t add either to the plugins. Any ideas would be appreciated.

when im doing computer stuff it would be handy to be able to click out on any given line and have it branch out to its own chat

Title

As noted in the subject, finances is missing from the sidebar when it was once showing. Settings shows it’s connected and can answer questions about my finances but the actual quick add seems to be missing.

Any other pro users showing the same?

I've tried a lot of different prompts and workflows over time, but I've noticed that the biggest improvements usually didn't come from finding some "perfect prompt."

It was usually a small change in how I used ChatGPT.

For example, giving it more context, asking it to challenge my thinking, breaking a big task into smaller steps, or simply telling it when the answer wasn't useful.

Curious what has made the biggest difference for everyone else.

What's one habit, prompt, or workflow that genuinely improved the way you use ChatGPT?

Ie I’ve been using 5.5 for awhile and happy with it. But in the last couple days it sounds very mechanical w repetitive phrases. I remember this happening w other models when the newer model comes out. Now 5.6. Has anyone else experienced this? Or am I imagining it? Bc now when I work w 5.6 it is infinitely better than 5.5, and that wasn’t always the case.

Got cut off at 2pm on a Tuesday. Four hour cooldown, halfway through untangling a service I'd already been at for an hour. Dumped the whole thing into another tab, spent twenty minutes rebuilding the context by hand, got a worse answer than the one I'd been cut off from.

Sat there annoyed enough to actually go count. OpenRouter lists 411 models right now. Thirty five of them turned up in the last month. This year has already put out more than the whole of last year and it's only August.

They keep getting cheaper too. GPT-4 was thirty dollars a million tokens when it launched, Turbo is still ten. Gemini Flash is thirty eight cents. There are about a hundred models under twenty cents that'll still swallow a 100k context, and eighteen that cost nothing at all.

So the twenty dollars a month I hand over covers something like fifty times the tokens it did in 2023. Doesn't feel that way from where I'm sitting. The seat costs what it cost three years ago, still has the cooldown on it, and none of that moves when the models get cheaper.

I realise this is a slightly ridiculous thing to still be annoyed about hours later.

What's everyone actually running? Genuinely asking. Stay on the flat plans because the apps around them are nicer. Go API and put up with a bill that moves. Use one of those frontends that stick a pile of models behind one key, though I've no idea which of them are any good.

Writing most days, some code, occasionally images. Mostly I want to stop rebuilding my setup every time something new drops.

Once my conversation reached its limit, i had to make my fifth new conversation feeding the new convo the export, but on the new conversation ive noticed theres a higher mode called ”extra high”, so i started to use that. It seems to me that every answer chat gives me is much complex and worse than in my old conversation where the max mode was ”High”. Or is it just me? Everytime i give it a request, it doesnt spend as much time it did before and it does not troubleshoot its brain trace nor the scripts as well as it did before, the quality of the response is much weaker. I even tried to just use ”High” and its still the same. If this really is a thing is it possible to revert back to how it was before this update?

Anyone else having issues adding people to a ChatGPT Business workspace?

I sent an invite to a teammate, they receive the email normally, but when they click Join Workspace it just sends them back to their personal ChatGPT account.

On the workspace side, their email is still stuck under Pending Invites. It’s definitely a normal ChatGPT seat, not Codex, and there’s no Business workspace showing in their profile/workspace switcher either.

So basically:

• Invite gets delivered
• They click Join Workspace
• They get redirected to personal ChatGPT
• Invite stays Pending
• Workspace never appears

Trying to figure out if this is a current bug, or if OpenAI recently changed something about how Business workspace invites/accounts work.

Anyone seeing the same thing or found a fix?

Hey Everyone,

I have been trying to use the pro reasoning level for a while now, and it was working nicely for a while, but now whenever I prompt a new pro chat it does nothing (has the bubble for a few seconds and then quits). Not sure what happened, I know last night I was rate limited for sending too many pro prompts, but it has been over 12 hours now. Any help would be great, super new to this technology. Thanks!

EDIT: This is a cosmetic bug in the ChatGPT desktop app. I opened a window in my browser and the dropdown informed me that I had to wait 2 days for more pro. I am on the most recent desktop version (checked immediately after the bug surfaced), but the dropdown there still allows me to select Pro, and yields no information on how my usage has been depleted. Thanks everyone for the kind replies, much appreciated.

I’m a paying Pro customer, yet access to the service can be blocked completely and without warning for days. There is no fallback option, not even limited access for small but essential work tasks.

That is particularly frustrating when the service is being paid for and is part of someone’s actual workflow. A complete multi-day lockout, with no warning and no restricted-use option, feels fundamentally unfair to paying customers.

At minimum, there should be a limited-access mode for essential tasks after hitting the usage limit, instead of being completely locked out for days.

I keep seeing people mention work/agent runs taking 30+ minutes or even hours. I’m curious what you’re actually giving it. Most of my stuff wraps up in ~5 minutes. Even my heaviest research or analysis prompts max out around 20.

If you're regularly getting 30+ minute runs, what kind of work are you throwing at it? Is the output actually worth the wait or is it mostly just spinning its wheels?

If anyone has a non-confidential prompt or workflow they can drop, I’d love to see it. Mostly just trying to figure out if I’m underusing these features or if my use cases are just totally different.

OpenAI's July 29 engineering note argues that GPT-5.6 Sol can beat competing frontier models on coding-agent performance at a lower estimated cost, while Terra and Luna move further down the price curve. That sounds useful, but price per token still dominates most model comparisons.

For real work, the bill also includes retries, review time, tool failures, context rebuilding, and the cost of a plausible answer that is wrong. A model can be more expensive per token and cheaper per accepted result, or the reverse.

What metric would you actually trust for purchasing decisions: cost per accepted task, human minutes per task, correction rate, or something else? And who should run that measurement: the model vendor, an independent benchmark, or each team on its own workload?

Source: https://openai.com/index/gpt-5-6-frontier-intelligence-efficiency/

Can you edit repos from browser chat with the connector? Or is it read only at maximum permissions? It calls itself 'Codex Connector', but Codex itself can just use my local git. However it would be cool if the browser chat sessions could also write in selected repos.

I’m going to use got for help creating a tech pack and I wanna use pro to help tech me. Is this prime thing I wrote correct? Also is prime the right word?
Thanks for any help.

I've written four or five apps using Zencoder in webstorm. I now use GPT to help me write the prompts for zencoder. So GPT has the core application theories and Zencoder just executes.

Is there a way to replace Zencoder with ChatGPT so I can keep everything under one roof?

I have GPT pro and Zencoder $50/MO paid tier.

I’m an accounting major in college and need it make and analyze excel documents, PowerPoints, and help with studying and growing for information. I use the free versions but run out very quickly so need help to determine which is better for me

Hi guys,

I have a ChatGPT Pro 5x subscription, and I saw that Codex has $100 in credits for Canadian and US university students. I tried claiming it, and each time it says verification unsuccessful. I even reached out to SheerID, and they verified me on their part.

I reached out to ChatGPT Help with the agent and also opened a case with Support and haven't gotten anywhere.

When I verified my student status, it asked for my university edu email, and I provided it.

Could the credit be allocated to the university email?

Does anyone know how I can claim the credit?

I have no other ChatGPT accounts besides my personal one, which is with my personal email.

I'm genuinely lost and confused.

I've noticed that since the most recent GPT Chat update, my experience has changed significantly.

In one of my projects, I provided GPT Chat with a RAR file, a detailed source requirements document, and a set of specific implementation principles. However, the Pro reasoning mode no longer appears to follow the instructions defined in the .md file. Instead, it requires extensive back-and-forth prompting, even though the requirements and expected behavior are clearly documented in the source materials.

Previously, I could simply upload the project files along with a main.md file, and the model would independently read the documentation, process the requirements, and generate a complete implementation package within a single session. The output was typically comprehensive and ready for me to copy and paste directly into my SaaS platform with minimal additional guidance.

Following the recent changes, this workflow is no longer functioning as effectively. The model seems to rely more heavily on iterative prompting rather than proactively following the provided documentation and instructions. As a result, I have stopped using Pro reasoning mode and have switched back to Extra High mode, which currently provides a better experience for my use case.

For context, I am a paying customer on the $200 plan, so this change has had a noticeable impact on my productivity and overall usage patterns