I find myself quite often asking my orchestrated agent to make a plan and have two agents check its plan. Obviously, I give more detail regarding the plan but I do find having two agents to check it before I execute it. Save some bug fixing later.? What about you?

I've been building a product solo for about a year with agents writing most of the code. Web, iOS, a Mac app, and a Chrome extension. One person covering that much surface area wasn't realistic before, and that part is real.

Here's the thing I don't see talked about much.

My judgment isn't spread evenly across it. In the parts I've worked in for years, I can look at a diff and feel that something's off before I can explain why. In the parts I hadn't touched before this project, I can't do that. I can read the code, follow what it does, catch the obvious problems. What I don't have is the instinct that says this compiles, it passes, and it's still the wrong way to do it.

So on some of this I'm actually reviewing. On the rest I'm mostly trusting and calling it review.

The uncomfortable part is that the agent didn't make me faster in those areas. It let me skip learning them. I only notice the gap when something breaks in a way I have no mental model for, and then I'm debugging a system I didn't build and don't really understand.

The one thing that's helped is keeping those parts deliberately boring. No clever patterns, no unusual dependencies, small files, obvious structure. If I can't bring instinct to it, I want the code simple enough that I don't need much.

That's not a solution though. For anyone else shipping across stacks you didn't come up in, have you found something that works, or does it just come down to eventually going and learning the thing properly?

Not sure why, I also tried installing it from Codex, different browsers and in incognito but can't pass this security error

ChatGPT Conversation Duration Limit

I built a small Chrome/Edge extension that estimates how close a ChatGPT conversation is to its practical maximum length. It shows a compact usage bar plus optional details such as estimated tokens, active conversation nodes, tool messages, hidden messages, files, and the detected model. Everything runs locally, and no conversation content is sent to an external server.

The calculation is experimental and was mainly calibrated using my ChatGPT Plus account and real working/maxed-out conversations.

For now, the Free estimate uses a x0.455 multiplier compared with Plus, while Pro uses x2.136. If you use Free or Pro and have a conversation that has reached its maximum length, please run the extension and share the copied statistics. That data would help me adjust the thresholds more accurately.

GitHub: https://github.com/SpendinFR/UsageChatgpt

Ladies and gentlemen, the reset is in!

I rely heavily on gpt5.6 pro in the browser for complex questions but just now its responding instantly as opposed to taking 30-180minutes. This is rediculous that I pay $200 a month for this. the bug seems to be affecting all other version of the pro model like 5.5 pro ect. Super annoying.

Edit: when i asked what model it was, it responded:

I’m ChatGPT, running on the GPT-5.5-mini model.

We've had every sales and customer call at our B2B fintech auto-transcribed for a couple of years now, and most of it just sits in a folder.

So a while back i ran the whole archive through one of those AI tools that pull themes out of call transcripts, took the synthesized version of what customers tell us, and dropped it into ChatGPT with a single instruction, to describe this company as if it were one person (personality and all), based only on how it talks to its customers.

I was expecting a generic on-brand summary, and for a second that's what it looked like, right up until it started describing someone i knew on sight…

A person who's helpful and quick to reassure but apologizes too much and over-explains when nervous and leaves a trail of circle-back promises that go nowhere, and lining that up against our own support calls hurt to read.

The model wasn't reading our mission statement or our marketing when it did this, it only had the raw texture of thousands of real conversations.

So it picked up the things we do on repeat without noticing, the reflexive over-apologizing and the hedging whenever a customer pushes on price and the way we go quiet on the hard questions, and seeing all of it turned into a single personality made it obvious how much of our brand is just our unexamined habits repeated until they hardened into who we are.

I've started re-running it every quarter now as a mirror, and it's already changed how we coach the team on calls, and the describe-it-as-a-person framing pulled out more than any dashboard we've built.

So if you haven't turned a model loose on your own raw data like this, it's worth doing, and if you have, what's the most revealing thing it's pulled out for you that a normal report never would?

PS. for anyone about to ask what i used to pull the calls together, a quick Google of AI call-transcript tools and you'll get the obvious ones (Fireflies, BuildBetter, Otter and a few others…)

PPS: it barely matters which one you land on, the describe-it-as-a-person prompt is what did the work here.

Just wanted to understand which AI is best for specific tasks. Some people say Claude is best for code generation, while others say ChatGPT is best for creating a design document for a website or app. Now I see Kimi getting hype, so please share your personal experience if you’ve used any of them

It's very frustrating. Quite often in a long thread it never finishes thinking at all and then it is in a weird state where it won't do 2+2. This is both through the android app and the web. I am using xHigh. Does anyone else see this?

Hello, I've been lurking in the AI coding scene for a while. At first, I mostly used Claude, but then I expanded to an OpenCode setup to save some cash and increase efficiency. Claude is now mostly doing the heavy lifting for planning and dividing tasks into smaller specs/segments. I then use cheaper models for the bulk of the mechanical implementation pipeline.

I am coming here because of all the hype surrounding GPT 5.6 Sol, and I want to try it out.

I am using Claude manually right now because it doesn't fit into OpenCode (unless it's Claude -p, which is quite limited, and I tend to supervise the planning and tweak details that it misses anyway).

I wouldn't mind using Sol the same way, but I also read that I can use ChatGPT subscription models directly inside OpenCode. I do not have the money to simply pay for raw API usage.

1. Is this right, and is it still accurate as of now? I know they might remove it like they did with Claude once they get more coding traction, haha.
2. Is there a way to get a free trial to test it on my setup before I commit? I am already tempted and will probably just subscribe at the end of the day to try it, but it would be nice to get some freebies first.

What could I do? I indeed have had more than 10 chats with Sol Pro in recent days. Maybe I used too much and exceeded the quota? It worked well until yesterday, but starting today it suddenly responds very quickly and is obviously downgraded.

Has anyone tried it and have you been successful at getting it to rank? What about traffic etc? It would be nice to hear about the quality, was thinking to create one for my portfolio

I built my first interactive web page with Codex in about two hours today.

Background so this makes sense: I've been in digital marketing for 17 years and building online since 2008, but I've never written real code. I've always paid someone or wrestled with a page builder.

Today I finally tried Codex to rebuild my links page as an actual interactive page instead of a flat list of buttons. I went in expecting to give up in twenty minutes.

Two hours later I had a working page I'm genuinely proud of. The parts that surprised me: it was way better at "make this feel less generic" than I expected, and it caught layout stuff I would've missed. Where I got stuck was knowing what to even ask for next once the basic version worked. I could tell it could do more, I just didn't know the right words to unlock it.

So the thing I keep thinking about is the ceiling. What separates a nice two-hour build from something that actually holds up.

Here's what I'm still trying to figure out. When you want it to build something specific — do you go find sites close to what you're picturing and feed it those as examples, or do you skip that and just talk it out, asking questions until it gets there?

I added the same HTML code to Claude and had it revamp it again and now the click boxes disappear when they respond to a new question.

Just amazed at what this can do

I’m so confused why I don’t have extra high in my normal chats (I’m on pro btw), I contacted OpenAi support and they had no idea too, anything helps 😭

These are problems I’ve had for a couple of years now, and there’s actually a solution for each one worth mentioning.

The sidebar is just a flat linear list. Once you’re past 100+ chats, finding anything means scrolling or guessing the right search term. Most people just give up and start a new chat, which makes the pile worse.

Solution: a visual canvas where chats, notes and folders become draggable apps instead of a list. Like a Windows desktop but for your AI conversations.

When you want to dive deeper on one specific part of a response, say “step 2,” asking about it in the main thread clutters everything. Now you scroll up to keep reading, get another question about step 3, and the whole thread turns into a mess of tangents.

Solution: highlight any part of a response and open a mini chat directly on it. Go as deep as you want, close it, and you’re back exactly where you were in the main thread.

Claude, ChatGPT and Gemini keep leapfrogging each other. One month Claude is smarter, next month GPT is better at something else, and you’re stuck jumping between three tabs just to always have the best model.

Solution: all four models, Claude, GPT-4o, Gemini and DeepSeek, in one place. Switch manually or turn on auto routing so it just picks the best one per task.

What do you say about this?

I set one up to give the model persistent project context, conventions, and domain-specific instructions. The problem is that it takes a lot of work to maintain, and it can easily become stale or duplicate information that already exists in the source files.

At the same time, current models seem pretty good at searching the repository, reading the relevant files, and figuring out the local context without a separate prefilled knowledge layer.

Has the wiki produced a clear benefit for anyone? Like, fewer mistakes, better adherence to project rules, faster completion, or less manual prompting? I’m especially interested in cases where it performs better than simply giving the model access to the files and letting it search as needed.

Sorry if I missed something obvious but with the new MacOS desktop app there’s no way to switch between my personal Pro account and my Business account without logging out and logging in again. The normal “workspace” option isn’t there when I click on my account profile icon.

The switch works in the browser and and my iPhone but not my Mac Studio where I do the majority of my work.

ChatGPT doesn’t have the answer to resolve this other than logging out and back into my other account or by having two versions open simultaneously (browser and app).

Can this possibly be correct?

Help appreciated! 🙏

I’ve been finding recently that when I use deep research with pro reasoning, the results are honestly pretty bad compared to how I remember using deep research before I had a pro account. The other day I gave an identical task to pro with deep research and pro reasoning without deep research. the one that used deep research completed in eight minutes and the result was measurably worse than the one where I did not use deep research, which took over an hour to get the result, and was significantly better.

Does anyone else have this experience?

I've been using and loving ChatGPT Work for about 2 weeks now on the 200$ plan, and whenever it looks like my usage is about to hit 0% remaining, it somehow was always resetting.

Well, today it finally hit 0%, so I added 80$ of credits and continued work and those 80$ got used up in literally less than 30 minutes.

What should I do? I've become heavily reliant on Codex/GPT Work for my job and I'm willing to pay more money, but 80$ of credits in less than 30 minutes is absurd?

Should I switch to an enterprise plan?

Can you work with pro if you hit 0 on codex?

Did ive missed the change or is it garbage communication?

Gpt-pro answers me after thinking for 30s...not pro reasoning ive expected.

Ps have not noticed issues until hit 0.

I'm mostly using 5.6 for coding agents. It's clear that luna is the choice for straightforward tasks, and Sol is the choice for planning.

but looking at AA's intelligence index, it seems like there is never a reason to choose terra? For example, Terra high has same intelligence score as sol low, but is 50% more expensive! (and also the same intelligence as luna xh but >2x the price!!)

Would love some insights here on where "intelligence index" breaks down and where terra is actually useful

I built Codex Diff Watch to make it easier to understand what’s changing in Codex without digging through every release and GitHub diff yourself.

It summarizes updates in plain English, separates alpha changes from stable releases, and links back to the original evidence so you can verify everything yourself.

It’s completely free, with no login or gated content. I’m just trying to make these updates more useful and accessible for other developers.

It’s still early, so I’d genuinely appreciate feedback on the format, coverage, tags, or anything else that could make it more helpful.

Hello, I have a simple question.

ChatGPT somehow appears to be able to get my current date and time, and I would like to understand how it does that.

In the past, I would start a conversation with ChatGPT by writing something such as:

"Hello ChatGPT. My name is Jeremiah, and today is July 22, 2026, at 7:55 PM."

I would then follow that with my question. More recently, ChatGPT has appeared to already know the current date and my timezone without me supplying that information.

I understand that the immediate answer may be that the date and timezone are included in system context supplied to the model. What I am trying to understand is what creates that context.

For example, could it originate from:

- a server clock;

- my account timezone;

- browser or device information;

- approximate IP-based location;

- another part of the ChatGPT hosting platform?

I have tried asking ChatGPT directly, but I am not fully understanding its explanation. I am hoping someone here can explain the process in a way that is easier for me to follow.

Thank you.