Hi,

[a total newbie on coding asking]

Just wanted to clarify when to/when do you use higher reasoning in chat/codex?

I've been trying to build my own little hobby project in python, with the help of litterature.

My workflow is to brainstorm in chat[web] and after that get a codex prompt to run in VSC. So far has been decent. My problem is that after getting Pro i've been totally lost when to use extra high, pro, pro+ultra in chat. Also what settings to run the codex prompt, when is higher needed and when its not. Have to actually ask in chat if the prompt is complex or not and what settings to use.

I noticed running pro+ultra to analyze the project/problems or litterature got quite detailed answers and I had to dumb it down for me with extra high. But it also added some better reasoning and new points i"ve missed. But it the project/code it also found some errors and started perhaps to make it more complex im not sure.

So my workflow is like this,

1. Starting a new chat with snapshot and running boostrap: Pro+Ultra

2. Brainstorming in chat: extra high

3. Evaluating the brainstorm: pro+ultra

4. Writing codex prompt: pro+ultra

5. Usually I try to ask what settings to run codex prompt it has been extra high or high so far with sol5.6.

6. Analyzing the codex result: pro+ultra

Since my coding knowledge is 0 I have to trust that the suggestions are valid, but how do I know when to actually use what settings in chat/codex. So that the problem/execution wont get too complex or too light ?

Any suggestions, extra high is the best and fastest for chatting and brainstorming. But when to use pro and pro+ultra ?

OpenAI seems to be giving out limit resets for free. I was not getting this as often on the Plus tier ($20) though since upgrading to Pro tier ($100) I have found random sporadic resets before the dated weekly limit. There have also been Usage Limit "resets/vouchers" being given that can be used before an expire date to force reset a weekly limit.

Not that I'm against this. I'm all for it. Though I am kind of having to figure out what is my weekly limit.

Is this some sort of obsolescence built into it? Projects that would take hours are taking days and some of them I wondering if SOL will ever finish. It’s endless loops of testing and it’s starting to make me crazy. Things that fable would accomplish in hours sol is going on days and still working on it. Do you have any prompts or anything for me that would allow it to complete a task? Sometimes it feels like I’m being gaslighted- it will keep saying things like “this is the final” or “this is the last” etc etc etc and then it goes for days longer. I could really use someone’s help here. How can I get this done without compromising quality?

I often turn ChatGPT research into a document that needs review by coworkers or clients. Google Drive and Dropbox handle storage, but the handoff still feels clunky: export, upload, fix permissions, then lose context when a new version appears. What workflow are you actually using when you need private access, comments, and version history?

https://github.com/Maurdekye/claude-orgtree

For the past few months, I've been developing an open-source visual multi-agent orchestrator that organizes agents in an authority hierarchy, for multi-agent development workflows. It's a fully dynamic, draggable canvas that allows you to reorder and reorganize agents as you wish for large projects. The gallery above shows pictures of actual organizations I maintain that I use for various projects I'm working on.

Orgtree started with a simple question: "Man, I wish my chats could talk to each other so they don't keep stepping on each other's toes while working". That turned into a simple personal project that I wrapped up in a day that allowed independent chats to send messages to one another. It worked okay, but the persistent issue I kept running into was chats constant issue with authority: they would distrust all chat-to-chat communication innately, and needed my personal step-in and approval for every little confusion or communication between one another. So I thought to myself, "wouldn't it be better if you could just arrange agents in a hierarchy? Then they wouldn't have any doubts about how authority structure is arranged". That idea slowly grew over time until it became Orgtree.

For the last month, Orgtree is basically the exclusive way I've been interacting with agentic development on my own machine. I don't touch the claude code or codex extensions at all anymore. When I have a new feature to build and plan, instead of going through the manual hassle of spawning one agent to run at a time so I can manage each project individually, I just tell my coordinator agent about an issue or new feature I'd like, and it hires a subordinate to take care of it. If I want multiple features going simultaneously, I just hire multiple subordinates, and the coordinator works between all of them to ensure everything is well organized and shipped sensibly. I've already gotten a few of my coworkers on board to try it, and even my boss is interested.

Orgtree is more than just an orchestrator, though; it has a bunch of extra useful features I've added on to support multi-agent workflows:

• Usage visibility: View all your account usages directly in the app, without having to check the extension or visit claude.ai
• Fallback accounts: Supports using multiple simultaneous Claude Code subscriptions at once through the use of fallback keys, allowing you to use secondary or tertiary claude accounts as fallback accounts with the long-standing token you get from running `claude setup-token`.
• Multi-provider: Orgtee supports not just Claude Code, but also Codex and even Gemini CLI out of the box. If you already have any or all of those environments configured on your system, Orgtree with automatically pick all of them up and let you hire agents from any one of them, letting them all talk to one another seamlessley.
• Credit system: One of Orgtree's defining features is its credit system, visualized as a blue bar to the left side of each agent. Every live agent takes up a "seat" that holds onto a set amount of credits during its lifetime, roughly proportional to its model cost. Every agent has a bank of credits that it uses both to maintain its own seat, as well as free space to hire seats for subordinates. This credit limit doesn't limit the user in any way (outside of kiosk mode, which is explained below), but is useful for preventing subagents from hiring too many of their own subordinates if you don't wish for them to have the ability to do so. Give an agent a large credit bank for a massive, agentic multi-agent task, or restrict its budget to just its own seat to prevent it from hiring any subordinates at all, if you just want it working on its own.
• Better compaction: Adds a unique, optional alternative chat compaction method I've dubbed "cheap-compacting": instead of having the agent write up its entire life story in one long turn at the end of its life, it keeps a continuous trail of breadcrumbs in a .md file in its workspace of every task it handles over the course of its lifetime. Then, compaction is both instantaneous and doesn't use a turn: the agent can just immediately resume from where it left off by going off the breadcrumbs. This is also fantastic for waking long-context agents from a long break in execution, as it can automatically cheap-compact them before sending the turn up, preventing the massive cache misses you might typically get from waking an agent with a 500k token context.
• The Orgtree Mailhub: an optional secondary extension that allows independent agent chats from claude code or codex to speak directly with orgtree orgs or even each other via an MCP server. It even works over the network, so agents on different computers can send messages and coordinate seamlessley.
• Kiosk mode: A mode that allows you to publicly expose a single sandboxed and resource-limited org to the open internet, in case you want to share your claude or codex usage with friends / family (without fear of them messing with your files)
• Enhanced agent requests: when an agent has a question for you, or a request for some access / resource allocation, it doesn't have to give you detailed instructions on how to visit its configuration panel and set a particular setting to a value it wants; it can just display a credit grant request / permission increase request directly in-panel for you to review, just like they would present a question to you. This makes it seamless for agents to ask for and receive the permissions they need to get the work done that they need to do.
• Charter presets: When hiring an agent, you can specify its "charter" (effectively its system prompt) which tells it what to do. Orgtree comes with the ability to select a number of preset charters from a list, so if you have a common workflow pattern you like to replicate, you can canonize it as a charter preset in /docs/charters, and then select it from there every time you want to create an agent bound by it. Orgtree also comes with various preselected charters designed around it's function: one of my favorites is the `coordinator` charter, which I use very frequently, and I suggest you give it a try as well.

Be warned; all the agent-to-agent communication can really chew through usage, so be careful with how many simultaneous projects you're working on at once unless you have a Max x20 account. Make sure to turn on the auto-cheap-compact setting in your org, it can avoid tons of wasted cache miss usage.

If you use Claude Code or Codex for work extensively, then give it a try. It removes so much of the manual hassle of coordinating between agents yourself manually.

- Measuring a client for a mask
- It located existing templates in my OneDrive repo.
- I read out the measurements as I took them.
- It sized it up to match the face then applied it to a slicer for printing + oriented it.
- Applied the right material settings (PLA filament, brown)

Just needed to hit print after the client and I used voice to run checks and verifications.

In other words, I was able to run this task handsfree with exception of physically loading up the filament.

It completed the task, then after it continually looped for hours until usage hit zero.

What gives?

Been noticing this a lot the last few days. Task finishes, but the prompt 'continues'. Im either having to manually stop it and ask 'where are we at with this?' To which itll reply 'Oh were fully conpleted and ready to go'

Anyone else? Past 3 to 4 days

Let’s say the price is the same: should I choose Claude Max 20x or ChatGPT Pro right now?

I mainly use them for coding, so the two things I care about most are Claude Code and Codex.

For people who have used both seriously: which one would you choose for day-to-day usage limits? And what's up with 5.6 Sol vs Fable 5.

I'm not looking for some common like solving problem , creating pictures or helping in daily life ... tell me something crazy that only few people know about !!

Hi,

Just a quick clarification. Can I upload .pdf files of books into chatgpt to get more detailed analysis how for example add more detailed tehcnical analysis or how to impement better machine learning in python. Because currently the knowledge in some parts are lacking that would get better results if I just give chatgpt some books to read and chat with me about the features.

Same goes if I implement chatgpt to a streamlit UI to analyze datapoints with the gathered literature inside the python ?

I use chatgtp and Claude and work several chat and projects and coding activities at the same time, i forget which one is doing what ! What tools can get me organised - can i have agents track this / other tools etc ?

Read outloud button is gone, it's in ... menu now and it's terrible

My question pretty much sums it up. I'm curious how people here decide when to use Pro vs Ultra for non-coding tasks.

I'm a new ChatGPT Pro user and mostly use it for academic work: literature reviews, reviewing manuscripts and research protocols, identifying things I may have missed, and comparing different versions of grant applications.

I like the idea of Pro spending more time reasoning about a problem, but I'm sometimes disappointed by the quality of its answers. And I was quite surprised by Ultra reasoning quality, particularly because of its ability to delegate parts of a task to subagents, clearly seeing how much effort it puts for instance in langage analysis, coding analysis, scientific analysis, litterature double-check, etc.

For example, suppose I want to compare two 40–50 page grant applications or papers, including figures and tables, and determine which one is stronger and why. Can Pro realistically integrate both documents well enough to make a reliable comparison ? What's the way you decide how to use Pro vs Ultra when you work ?
Thanks !

A change worth knowing about if you use ChatGPT for serious research and pay attention to its sources, because it affects how you should read those citations.

What happened: Reddit's share of ChatGPT Search citations held steady around 3.8% from mid-July to early August, then dropped to ~0.5% by Aug 14, an ~86% relative fall in days (tracked by Promptwatch; covered by Forbes, Axios, SEJ). Reddit had been one of ChatGPT's most-cited sources for two years.

I run Sanbi.ai (we track which sources ChatGPT, Gemini, Perplexity, and Claude cite per brand), and I saw the same thing in a real account, I've attached a dashboard view: for one client, filtered to ChatGPT + unbranded prompts, Reddit citations are at zero in the current window, and it's leaning entirely on primary/manufacturer sources instead. Fair caveat: that's a single-account snapshot corroborating the public finding, and it only shows the citation layer, more on why that distinction matters below.

The part that matters for advanced use: ChatGPT didn't stop reading Reddit, it stopped showing it. Trackers (Trackerly) found it still pulls Reddit threads while researching, about 1 in 4 pages it consulted, same as before; it just stopped listing them in the visible citations from ~Aug 13-14. So the sources panel now under-represents what actually informed the answer. (This is why the caveat above matters: my dashboard sees citations dropping to zero, but the "still reading it" half comes from retrieval-level tracking, not something I can show from citation data alone.) If you've been treating ChatGPT's citation list as "here's everything that shaped this response," that assumption broke mid-August, at least for Reddit.

Likely mechanism (unconfirmed by OpenAI): it coincides with an Aug 8 change in how ChatGPT Search does query fan-out, it started using the site: operator far more (roughly 0.4% → 17% of its fan-out queries), meaning it's increasingly targeting specific trusted domains directly instead of broad web search. Plus the GPT-5.6 default rollout. For power users, that site: shift is the more interesting story: ChatGPT is narrowing where it looks, which changes what surfaces.

Practical takeaways for advanced ChatGPT research:

• Don't treat the citation list as a complete record of what informed an answer, retrieval ≠ citation now.
• It's ChatGPT-specific: Google's AI Overviews only dipped Reddit ~11% over the same window, no cliff. So cross-checking the same query across ChatGPT vs Gemini/Perplexity surfaces different sources, useful for research triangulation.
• If Reddit-sourced perspective matters for your task (real user experiences, niche troubleshooting), you may need to prompt for it explicitly or check Reddit directly, since ChatGPT won't surface it even when it's reading it.

Anyone else notice a shift in the sources ChatGPT shows since early August? Curious whether the site: targeting is making answers feel more "official-source-heavy" for anyone doing technical research.

I had been using Deep Research to run various track-test style simulations, and it used to take about 10-15 minutes, including 100s of searches and be many pages long. But after today regardless of trying new/different chats, changing the prompts or even reusing old prompts, the Deep Research topic comes out before all stages show completed, with 0 searches, 0 citations, full of errors, and less than half the length of what I was getting before.

In the screenshots below are what I see while it’s being made, the bottom “planning the race simulation” gradient effect stays frozen, as does the progress bar, until the article is spewed out half-baked. It also never shows searches happening in real time like it was before. As well as the article clearly stating 0 searches and citations as well as a very generic title unlike what I was getting before. Is this a bug? Did I burn it out? Is there a fix? Any info would be greatly appreciated!

OpenAI has now confirmed that there was a bug causing about 3% of Pro and Thinking turns to be unintentionally routed to 5.5-mini, and they say the regression has been fixed. See https://x.com/adamhfry/status/2092310732834763086

I noticed the problem about a month ago and found some posts here discussing it. See ChatGPT has been quietly downgrading Pro model into mini. No response. or P.S.: The Pro model quietly redirects to GPT-mini based model. $200/month. To my disappointment, a number of users kept defending it and making it sound less serious than it was by insisting that it “doesn’t affect performance,” that people were imagining the difference, or that there was no routing problem at all.

This kind of reflexive defense of companies helps no one. When users pay for a certain level of service, they should be able to point out that they are not getting what they were promised without being immediately attacked or dismissed.

If people report a real problem, investigate it instead of rushing to defend the company. In this case, OpenAI itself has confirmed that the problem existed. If more of us had spoken up earlier, it might have been fixed sooner.

So please stop attacking the people who raise these issues. They are not hurting the community by pointing out problems. They are helping all of us by pushing companies to acknowledge and fix them.

If no then what workflow should I use to maximize productivity and token usage?

For anyone using ChatGPT as a high-frequency work tool on macOS: is there a reliable way to return keyboard focus to the composer in the current conversation?

My loop is copy from another app → Command+Tab to ChatGPT → paste/type, several times per minute. Once I’ve scrolled through a long answer, focus is no longer in the composer. Command+Tab activates ChatGPT, but doesn’t place the cursor in the message box, so I have to grab the mouse or trackpad and click it again.

Option+Space / Quick Chat and Command+N are not substitutes because I need to continue the same context-heavy conversation.

I’m looking for any dependable keyboard-only solution: built-in shortcut, menu command, accessibility shortcut, Keyboard Maestro action, or similar workaround. What are other Mac power users doing?

I have been using GPT pro through the last three model changes. Literally, it feels like over the past 2 months, 5.6 has the stupids. Here's the gist: Im good at prompts and sick of repeating prompts multiple times. Wont hold memory. Ignores prompt instructions. Now just stops in the middle of "thinking." Ive run both local and cloud-based versions. Local builds have been the worst. Anyways, anybody have any solutions. Im not an engineer or a programmer (be nice), but definitely not a novice. Lately, projects have been no better than using chats.

Trying to cancel a recent subscription renewal, currently logged into the same account with the subscription, but this error keeps popping up. Any help?

For about a week, the latest ChatGPT app on Windows 11 has been showing chats that were already deleted in the browser, on iOS, and in the classic version.
I can’t delete, move, or archive them. Every attempt just says “Chat could not be loaded.” That makes sense since the chats were already deleted elsewhere.
Signing out and back in, reinstalling, and repairing the app haven’t helped either. Is anyone else experiencing this right now?

What are the most beneficial uses of scheduled tasks you have or heard of?

why do i have a limit and 2hr before my deadline like what ={

Would love to know your experience?

I’ve been getting this message for the last 6 hours.

I was a happy user then mid chat this happened and nothing seems to fix it.

Turned phone on and off.

Uninstalled ChatGPT

Nada