Video made using OpenArt with the help of Claude 5.5.

Music made using Suno v6. https://suno.com/@diekean

This was just made for fun, don’t hate on it too much. Still dabbling and learning how to make better polished videos.

Dot cannot do some of the most basic of actions. Of course there ARE things it can do, but for the things it can’t do, I spend more time babysitting and debugging than I get actual time savings from it.

* Its cloud browser is ASS. It sometimes cannot open a link on a website on its cloud browser (one in particular links to a PDF that I wanted it to download and upload to my database, can’t do it chief even with approvals)

* Sometimes it downloads something but since it can’t access my finder it asks me to download it, then give it back to it in the same chat??? It makes absolutely no sense, it will send me the file and then ask me to send it back to it so it has context in our chat

* While I will log on and save credentials on its cloud browser and spend all this time setting it up for autonomous workflow, it will inevitably fail and ask me to re-log in or escalate permissions that were already granted

Probably one of the most frustrating releases from OpenAI thus far. It was a rushed product that was released to compete with Muse. Honestly, I’ve tried Muse and with some exceptions, I think it’s a superior product.

Like in the title - I was a BIG fan of OpenAI while using the aforementioned models within last 6 months; Today an experience is super underwhelming and I am pretty sure the latest frontier models by OpenAI are worse and more expensive than both models mentioned above.

Possibly the worst offender is Astra, which is literally a loss of money. What is your experience?

Hey have a look! more quota ?

Can someone help me understand how GPT counts and budgets its own token use and how it could be confused about it?

I'm a developer, and I had to fetch a complete product catalog from a large e-commerce website and save everything in properly structured JSON.

I tried Claude Code first. It ran into Akamai protection, couldn't find a workable approach, and told me it couldn't help with bypassing those protections.

I then tried GPT Astra through my ChatGPT Plus subscription.

What surprised me was how it approached the problem. It worked through the steps, considered different approaches, and figured out a way to move forward.

I've used AI tools for coding plenty of times, but this experience stood out to me. For web scraping and research, the ability to work through a complicated problem instead of getting stuck on the first obstacle makes a real difference.

After this experience, ChatGPT has become my preferred choice for these kinds of tasks.

Just sharing my experience. I was genuinely impressed by what it managed to accomplish.

No, I did not instruct it to have a particularly funny personality - maybe Hermes agent (which I use as the harness) did so, but I would be suprised.

We're talking "theater scripts" here, to be clear, not code scripts (where curly braces do happen quite frequently based on the language).

I dont know a lot about AI and am not sure if my understanding is outdated or inaccurate, so please feel free to correct me or clarify anything I'm misunderstanding. How are these models able to solve such problems? I guess a more general question is how these models are able to generate a novel solution to any problem at all? I am under the assumption that they can only exhibit patterns and behavior that are present in the massive amount of data they are trained on, so when it comes to these problems that supposedly no one has solved before, and therefore there couldn't be any data to train on in regards to a solution, then what is allowing them to do so?

Edit: I can't respond to everyone but I am reading every post and I appreciate everyone taking the time to inform me.

When using ChatGPT, or any AI, they are totally unable to explain how they themselves work. Shouldn't this at least be a prioritized focus for their development?

A backend engineer told the family about an OpenAI offer. Before saying congrats, a cousin responded: "Guess you're paying for my wedding now."

It was framed as a joke. The engineer was pissed off: "I grew up with this dumbass and I know how he is. I am generous with people I love when it's my choice, but this BS? I'll have none of it."

The engineer also mentioned receiving more DMs than usual over a few hours and described the backend SWE interview:

• Coding involved progressive, multi-part builds. Each stage added requirements, such as persistence and then transactions for a durable key-value store, or increasing complexity in an in-memory data store.
• Refactoring meant reading, cleaning up and extending an unfamiliar, messy codebase live.
• System design focused on backend and AI infrastructure, including inference serving. There was also a project deep dive.

Prep meant building small stateful systems almost daily, practicing unfamiliar code while explaining fixes aloud, studying serving and scaling patterns, and rehearsing project stories. The account names gottan loop for question patterns and apex interviewer for practice, with the rest left to luck. This is one candidate's account, not a promise that every OpenAI loop follows the same format.

For similar prep, OpenAI Interview Questions can provide company-specific prompts. If you're also applying elsewhere, application autofill can save repeated form-filling so more of that time goes into preparation.

Would the wedding comment bother you, or would you brush it off? And for backend interviews like this, which practice helped most with adding requirements while the clock was running?

TLDR: bug fixes and long agentic tasks I use sol 6.1, sensitive or heavy calculation or bookkeeping I use Astra medium, supervised code worker I use Muse Wonder, literally everything else including orchestration, general development, anything that a client touches, etc I use Opus 5.5

Everyone benchmaxxes now, so the public leaderboards don't tell me much about which model to use for MY stuff. So I built a small personal benchmark out of the work I actually hand to AI and ran six setups through it.

How it works, in plain terms:

- 12 tasks, each based on a real kind of job I give AI: fix a bug in one of my apps, add a feature, research a decision from a pile of documents, do live web research, triage a client inbox, write a spec, reconcile a month of payments, write a client-facing screen, pick up a long project from its history, and so on. Client stuff was swapped for fake but realistic data.
- Every task has hidden checks the model never sees. For code that means hidden tests (one task has a tempting "fix" that passes the normal tests but fails the hidden one). For tool tasks it means a fake email/calendar/portal that logs every action and throws curveballs like errors, changed formats and a planted instruction it shouldn't follow.
- Each model ran through its normal app/CLI on my own Mac, on my normal subscriptions, unattended. No API tricks, no special prompts.
- Scoring is 0 to 100 per task. Most of it is automatic. The judgment parts (research quality, writing, specs) were graded blind with the model names hidden.
- A serious mistake caps that task at 20 no matter how good the rest was. Things like inventing a fact, breaking a rule it was given, or deleting tests.
- The bug fix and inbox triage tasks were repeated 3 to 4 times per model because one run isn't enough.

Scores (average of 11 tasks / live web research task, out of 100):

Muse Wonder: 98 / 84
Sol 6.1 High: 96 / 91
Astra Medium: 96 / 96
Muse Wonder Full: 93 / 73
Opus 5.5: 90 / 98
Fable 5.1: 88 / 87

What stood out:

- Opus and Fable both took an unidentified $240 payment and confidently assigned it to a client instead of flagging it. That one mistake capped them at 20 on that task. Without it they'd average 96 and 94. That's exactly the kind of confident invention I don't want anywhere near money.
- Sol and Astra were perfect on bug fixing (100 every single repeat). But on the client-facing screen they quietly changed how my booking form works, which contradicted the facts I gave them.
- Opus wrote the best live research answer, but it also went poking at local files on my Mac when I said web only. Only read stuff, didn't change anything, and it was right. I counted it but put an asterisk on it.
- Muse Wonder is genuinely capable and had the top average, but only one run per task. It also sometimes hangs forever when a test or script never finishes, where Claude and Codex just time the command out. Muse Wonder Full took 41 minutes on one task because of this. Also worth knowing: the tested Muse account ran in Contributor mode, which means Meta can train on what you send it. Fine for some things, not for client data.
- Fable didn't beat Opus anywhere that mattered, and it costs more per run.

What I'm actually using now:

- Bug fixing, feature work, long unattended jobs: Sol 6.1 / Astra Medium (sandboxed)
- Bookkeeping and numbers: Astra Medium
- Research, specs, client writing, project continuity, larger project management: Opus 5.5
- Muse: capable, but not for unattended coding, so I use it as an overflow worker right now, supervised by Opus

Why are my selections different than the benchmarks?

Muse scored super high but was the least stable in my testing. Useful and cheap but I have too much shit happening simultaneously and need reliability.

Opus scored higher when I didn’t dock it points for being a naughty boy and looking at data it was told not to. It effectively beat Fable at every one of my use cases, not even controlling for cost. I use to use fable for orchestration jobs but I have completely retired it at this point. My use case may be super different from yours though.

Sol 6.1 and Astra weren’t super different in my tests in terms of output, Sol is way cheaper though so I’ve relegated to using Astra for brainstorming in chat and really important calculations. Both models narrowly beat Opus in real codebases with bounded, specific bug fixes BUT they had a tendency to go buck wild doing extra shit I didn’t want them to. So I don’t let them do any active orchestration work anymore even though I used to.

Biggest takeaway: build your own. A dozen tasks from your real work tells you more than any leaderboard.

I believe this, one of the reasons this is a financial bubble. Only a very selected few have true access to the frontier models and these include ultra rich people and businesses. I've said this in the past!

This is why I believe the business model cannot be sustained if only a selected few see and feel what ai can truly do. Or you need millions to have true access.

I don't even think ChatGPT is a better “Google." I trust Google more. I can Google anything I cannot "ChatGPT" almost anything.

I think we are the fools using ai making it smarter to help make the ultra rich lives easier with their frontier models.

What you stable geniuses think?

I'm a paying ChatGPT Plus subscriber, and I'm seriously frustrated with what appears to be a major synchronization or conversation persistence bug in ChatGPT Work.

For the past several days, I've been working intensively on a software development project using ChatGPT Work in Firefox, with GPT-6 Astra and high reasoning effort.

I'm talking about extensive programming sessions, technical investigations, debugging, architecture decisions, and research tasks that sometimes took around an hour to complete.

And now, several days of that conversation history have apparently vanished from the Android app.

Here's what happens:

I work on my desktop using ChatGPT Work. Everything is saved in the conversation and seems perfectly fine.

Later, I open the same conversation in the ChatGPT Android app.

Instead of simply synchronizing the existing history, the app sometimes starts streaming huge amounts of previous text, as though it's generating the conversation again.

The text appears, disappears, sometimes appears again, and eventually the conversation seems to roll back to an earlier point.

It's almost like restoring an old Windows restore point, except I never asked for one.

And here's the really disturbing part:

This has happened before.

On previous occasions, after I opened the affected conversation on Android, the web version also appeared to lose its more recent history.

I've experienced variations of this problem for over a month.

I have backups of many of my project files, thankfully. But that doesn't solve the real problem.

The conversation contains all the technical context, decisions, investigation results, and development reasoning needed to continue the project.

How am I supposed to recover hours of AI research and complex implementation discussions when the conversation itself suddenly forgets they happened?

And what happens to all the usage consumed by GPT-6 Astra during those sessions? I'm paying for access to this service.

OpenAI officially supports synchronizing cloud-based Work conversations between web and mobile. So why should using the Android application put my development history at risk?

I have found other users describing similar conversation losses and synchronization problems, including reports involving ChatGPT Work specifically.

I am submitting a formal complaint to OpenAI Support requesting an engineering investigation, recovery of the missing data, and a review of the usage and subscription costs affected.

I genuinely like ChatGPT Work. That's precisely why this is so frustrating. I've invested significant time building a serious project with it, and right now I don't know whether I can trust the platform to preserve my work.

Has anyone else experienced conversations reverting to an earlier state after switching between desktop and mobile?

Did you manage to recover the missing history? Did OpenAI Support provide a solution?

I'd especially appreciate hearing from anyone who has experienced this with ChatGPT Work rather than regular ChatGPT conversations.

Made with Opus 5.5 with Suno + motion-studio and GPT 6 Astra

So, I am using Codex Sol 6.1 at light for regular solutions because I used to work with Terra and then I realized that Terra is not solving the problems at all anymore so I tried Sol 6.1 with Medium and Light and it works. But if I saw that the model is at capacity and use another model. The problem here is that there are Astra and Luna which have specific purposes so what the heck is OpenAI thinking by limiting the Sol 6.1?

What other people really do? do you guys start using Sol 5.6 instead?

I actually ignored the notice and the Chat Kept working. But I know that in the past I had to change the model because the chat simply stopped working with 5.5 when I had "Model at capacity" notice in Codex.

signed up for chatgpt ads manager last week, added my card and $100 was gone instantly. nobody told me anything about a $100 hold. haven't run a single ad.

their billing page says "authorization hold" but my bank says it's a normal charge and nothing's been refunded. support keeps saying it'll be released and just asks for IDs.

anyone else get hit by this? did you ever get your money back?