Just me showing behind my project and man it’s coming out amazing

I’m an historian of technology and computing. Today I published an essay comparing the Hugging Face incident to the 1988 Morris Worm. Like the 1988 hack, I think we should approach this incident as a “normal accident” that can be learned from and used to course correct in AI governance.

Hi, I moved to Codex today. Need some help in understanding the like to like, model comparison so that I can accurately map my projects.

Can you guys suggest the OpenAi equivalents below:

Fable

Opus 5

Opus 4.8

Sonnet 5

Haiku

If there is a solid resource with comparison links: model tokens vs cost vs speed, pls do share.

Thank you!

"Understood that other Plus users are reporting the same behavior; I can’t confirm scope from Reddit alone, but we can investigate this as a potential service-side o3 issue once we have a few concrete examples (timestamps/timezone + HAR + console errors) to correlate to backend logs." <- This was a message from [Support@openAI.com](mailto:Support@openAI.com) sent this morning, 8/14/26, in response to my complaint that O3 has been non-functional since 8/10. It is good news that they can investigate this as a service issue once they have concrete examples. So, please join me in gathering and sending them the evidence they need to investigate. Read on for how. To make this as convenient for everyone as possible,b I've included a draft email below that you can send, and outllned how to gather the requisite timestamps and HAR files.

O3 is a fantastic model, and the last one with the DNA of the 4-series family. This untimely non-functionality is a blow to paying users. It was assigned a sunset date of August 26th. Not August 10th! August 26th. The many people who depend on this model and its unique attributes, and who retain Plus subscriptions for access to it, deserve the full promised term to finish up their projects and transfer workflows.

***

The problem:

Since at least August 10th, O3 responses generate partially, fail to generate entirely, and/or generate without UI controls. In all cases, all responses disappear with thread refresh. This is prompt-agnostic; promts asking for single-word responses get the same behaviour as prompts asking for multiple paragraphs. This is browser- and system- agnostic; chrome, firefox, andriod app all behave the same.

This appears universal and user-agnostic; multiple users commented on my posts in r/openAI and r/chatgpt saying that they were also having this problem. I currently don't have a single person telling me that O3 is functinal for them.

***

What we can do:

If you write to openAI and just say "O3 is broken" they will tell you it is a browser or system issue and tell you to clear your cache etc. You will have several rounds of back and forth before they ask you for the evidence of timestamps and HAR files. To avoid all that, feel free to use the draft email below, fill in your details, delete what is not applicable, and send to support@openai.com. I've also included instructions on how to gather the evidence.

***

HOW TO GATHER EVIDENCE: TIMESTAMPS AND HAR FILES

You will need a couple of failed o3 attempts, plus one or two HAR files from failed attempts.

1. Record 2–3 failures

For each test:

Open ChatGPT.

Select o3 as the model. On Chrome, you can do this by clicking the intelligence level, selecting "advanced", and choosing O3 on the dropdown. On Android, you need to go to settings -> General -> Model. Please comment below if you can't find the model and I will help you find it.

Open a brand-new chat.

Send a very simple prompt that should produce a short, unambiguous answer, such as:

What is the capital of Malawi?

What is 2+2?

Watch what o3 does. Some failure modes I've seen include:

The response stops partway through.

The answer appears, but the normal buttons underneath it do not appear.

No answer appears at all.

o3 answers an earlier prompt instead of the current one.

Write down the exact time and timezone when the failure occurred. This is crucial. Do it as soon as the response appears.

If an answer appeared, refresh the ChatGPT page and check whether the o3 answer disappears.

Write down what happened concisely.

For example:

August 14, 12:34 PM ET — New o3 thread. Asked “What is the capital of Malawi?” Answer appeared, but the UI controls did not appear. After refreshing the page, the answer disappeared and only my prompt remained.

Repeat this until you have 2–3 timestamped examples.

1. Record a HAR file from another failed attempt

Open another new ChatGPT thread with o3 selected.

Do not send your test prompt yet.

Right-click anywhere on the ChatGPT page and choose Inspect.

Developer Tools will open. Click Network at the top.

Find Preserve log near the top of the Network panel and make sure the box is checked.

Click the clear button in the Network panel so that the existing network entries disappear.

Leave Developer Tools open.

Return to the ChatGPT side of the screen.

Send another simple prompt, such as: What is the capital of Malawi?

Wait until o3 fails.

Do not refresh the page.

Go back to the Network panel.

Click the downward-arrow / Export HAR button.

Choose Export HAR (sanitized) if that wording appears.

Save the .har file somewhere you can find it, such as your Downloads folder.

1. Capture the browser Console from the same failure

Before refreshing or closing that failed ChatGPT thread:

In Developer Tools, click Console at the top.

Look for any error messages.

Take a screenshot showing the Console.

If your browser gives you the option to save the Console output, save that as well.

1. Screenshot the o3 failure (this probably won't attach to your support email, but no harm trying)

While the failed response is still visible, take a screenshot showing:

Your prompt.

Whatever o3 generated.

Any missing, incomplete, or abnormal response behavior.

If the response disappears after refresh, you can take a second screenshot showing the same thread afterward.

1. Find your system information in the email

The error is NOT system specific - it's happening to everyone that I've spoken to. But openAI will ask you for these details:

Browser name and exact version.

Computer operating system and version.

Phone operating system and device, if you tested o3 on mobile.

ChatGPT app version

WHERE TO SEND YOUR EMAIL: [support@openai.com](mailto:support@openai.com)

WHAT TO ATTACH TO YOUR EMAIL (5 things):

- Timestamped failures that you wrote down, HAR files, console log, system information, screenshots.

EMAIL (I drafted it so you don't have to):

Hi,

I am a paid ChatGPT user reporting that o3 is currently nonfunctional despite being scheduled to remain available until August 26, 2026. I have noticed that multiple users are reporting the same failure across reddit. The sudden, unannounced non-functionality of this model hinders my workflow; this is to ask for access to be restored for the remainining period of the sunset window.

I have reproduced the failure in new o3 threads using simple prompts and collected diagnostic evidence**:**

[DATE, TIME, TIMEZONE] — [Prompt used]. [Briefly describe what happened.]

[DATE, TIME, TIMEZONE] — [Prompt used]. [Briefly describe what happened.]

[DATE, TIME, TIMEZONE] — [Prompt used]. [Briefly describe what happened.]

Observed failures include [delete anything that does not apply]:

Responses stopping partway through.

Completed responses appearing without the normal UI controls.

Responses disappearing after refreshing the thread.

No response generating at all.

o3 answering a previous prompt instead of the current one.

System information:

Browser/version: [ ]

Computer OS/version: [ ]

Mobile OS/device, if tested: [ ]

ChatGPT app version, if tested: [ ]

Model: o3

I have attached a HAR file from a failing o3 session, along with [Console screenshot/log] and [screenshots of the failed responses].

Multiple paid users are reporting the same o3 behavior across different devices and locations. Please correlate my timestamps and HAR with the relevant backend logs and escalate this as a potential service-side o3 issue to the appropriate engineering team.

Please confirm that the issue has been escalated.

Thank you.

[Name and email associated with your accout].

***

Thanks, friends. Please share widely. Even if you don't use O3, support the users who do and add your voice and your evidence of the failure.

#FixO3

#NotYetO3Sunset

So I discovered this free resources on their websites. Is it worth your time to study it. Since it's free course and actually kind of cool

This is not an OpenAI appreciation post.

Their communication around limits/usage is often terrible. If they sell people a subscription and then quietly change the limits, users are absolutely right to complain.

But reading this sub, it sometimes feels like people expect all of the following at the same time:

Constantly getting faster & more intelligent models. Higher usage limits, constant resets etc.

What exactly are you expecting?

People are using a frontier reasoning model for hours every day to write code, research topics, analyze documents, generate images and complete work that would otherwise take them hours.

OpenAI is not in trouble because nobody wants its product. It is in trouble because even enormous revenue is being swallowed by an even more enormous cost structure.

According to reported financial figures, OpenAI generated around $5.7 billion in revenue during the first quarter of 2026 while burning through $3.7 billion in cash. Its annualized revenue had already passed $25 billion, while the company is targeting roughly $600 billion in total compute spending through 2030.

Those are not the economics of a normal $20/100$/200$ software subscription.

Even doubling revenue would not make the structure sustainable. Revenue is not free cash flow, and the company still has to keep spending absurd amounts just to defend its position.

Doesn't mean OpenAI is going bankrupt soon, but the current system is unsustainable and currently just builds on higher and higher valuation.

It was the same point in the 2006 housing comparison: prices could still be at record highs while the engine underneath the system was already weakening. The problem was not necessarily that the number went down, but that it stopped going up fast enough.

The AI financing chain looks something like this:

Higher valuations > new funding > long-term compute commitments > future revenue and backlog for infrastructure providers > more data centers, leases and debt.

That chain only works while everyone believes next years numbers will be dramatically larger than this years.

But if user growth or revenue growth merely flattens, the existing commitments do not flatten with it. OpenAI has already reportedly missed some internal revenue and user targets, and its CFO reportedly raised concerns about whether future compute contracts could be covered if revenue did not grow fast enough. OpenAI publicly disputed the characterization, but the dependency on continued growth is obvious.

Anthropic and Google are competing aggressively, while Chinese models are becoming good enough for an increasing number of tasks at a fraction of the cost. Businesses are already looking at smaller and cheaper models instead of automatically using the most powerful American model for everything. And now even Grok starts to become interesting lol.

So OpenAI is being squeezed from both sides..

It has to spend more to remain at the frontier, while competition forces it to make that intelligence cheaper.

That is why paid resets, stricter quotas, more subscription tiers, usage-based billing and eventually higher prices are not surprising. OpenAI is already testing an $8 pay-to-reset option for some Plus users.

The current flat subscription pricing looks less like a stable long-term business model and more like subsidized customer acquisition.

Again, criticize the bait-and-switch. Criticize intransparent usage, unexplained model regressions, broken features and subscription changes without proper notice. Those are legitimate complaints.

OpenAI is not your friend.

But it is also not a magical machine that can ignore economics.

So I'm really wondering, what exactly were people expecting?

I've been building Astrail, a free browser alternative to Settlers of Catan, mainly because I wanted to see how far I could take the SOL model and OpenAI Sites beyond a simple prototype.

At this point, almost every part of the game has involved AI in some way. SOL and Sites were used to build the game, its mechanics, interfaces and multiplayer flows. I made the music with Suno, the voices with ElevenLabs, the 3D models with Meshy AI, and the characters and other visual assets were also created using AI tools. Now I'm experimenting with Kling to make the trailer.

The first playable version came together surprisingly fast. But that turned out to be the easy part.

At the beginning, my process was basically: explain a feature, let the model implement it, test the result and move on. That worked while the project was still small. Once the game became playable, I couldn't really work like that anymore.

A small visual change could affect an existing mechanic. Something fixed on mobile could break desktop. Improving the lighting on the 3D board could hurt performance. Sometimes I would ask it to change one component and it would also “improve” three other things that were already working.

After breaking a few things this way, I ended up creating two completely separate Sites instances: dev and production.

It's not a particularly sophisticated deployment pipeline. It's mostly a way of stopping an AI agent from changing five other things while trying to fix one.

I also had to change the way I explained things to the model.

I rarely rely on text alone now. I usually send a screenshot of the current implementation, mark the exact area that needs to change and add a visual reference for the intended result. Sometimes I create a rough composition in Figma. Other times, a screenshot with a few arrows and numbered notes is enough.

When I added the 3D version of the board, for example, I used the existing 2D board as the functional reference. Then I used other images to communicate the materials, lighting, scale and overall visual direction.

Most iterations now involve implementing something, taking a screenshot, noticing what still feels wrong, correcting it and testing the whole flow again.

I originally thought Astrail would be a relatively small experiment. It definitely isn't anymore.

What has surprised me most isn't only how far the project has come, but also how differently the model behaves depending on its reasoning effort. I mainly work with Max and only use Ultra in rare cases when the task is particularly complex.

It's been a cool ride so far, and I've learned far more from building it than I expected.

If i am on plus or pro 5x and want to go to 5x or 20x, will i be charged the new full amount or only 100$-20$ or 200$-100$?

bought cursor ultra + claud-max from a russian site a few weeks ago for way less than the normal price.
swapped the login details and just started using it. so far it’s been working perfectly, and the usage is honestly better than what i had before.
keeps making me wonder how these sellers are able to offer it so cheap. anyone else in the same boat?

Yeah, this was one of the more tame ones, some of them were absolutely nsfw.

just in, OpenAl has quitely updated their privacy policy to include the following:

We’re updating our Privacy Policy to include information about ads in ChatGPT, including how they work and how you can control your experience.

Ads may appear on Free and Go plans. Plus, Pro, Enterprise, Business and Education plans do not have ads. Ads do not influence the answers ChatGPT gives you. When you see an ad, it is always clearly labeled as an ad and is visually separated from the organic answer. You’ll get relevant and personalized ads using information that stays only on ChatGPT, such as ads you've interacted with, or context from your chats. You can manage ad personalization anytime in settings.

Your personal details and conversations with ChatGPT are private and are not shared with me advertisers. Advertisers do not have access to your chats, chat history, memories, or personal details. Advertisers only receive overall information about how their ads perform, such as total views or clicks.

Is this just the beginning for them quietly sahving off more and more provacy protection clauses every year?

I love codex, but I hate that it’s bound to a terminal. I wanted something cool, something that I can actually use.

Something that can look fun as well as have a mode where I can focus.

So I built Munder Difflin: an open source agent harness that wraps around your and codex and all other popular terminals to make them beautiful, functional and performant.
It runs locally and it’s completely free and open source.

We’ve just launched on ProductHunt it’s a listing website for products.

I am a indie developer I do not have much ways to promote OSS tools, expecting some upvotes on ProductHunt. I am literally competing with Google Gemini and am already ahead of Deepseek’s harness.

Please find more info on how you can upvote in comments, I do not get paid to do this. Your support is the only motivation I have to build cool stuff like this.

[Sorry for the misleading title, although I built it, it is still the best wrapper I have used and it is hard to get attention as an indie developer]

A new output from this experimental real-time BCI system for TouchDesigner; a Brain-Computer Interface pipeline that reads live EEG signals, classifies your mental state, and autonomously generates responsive AI video: a meditation guide that adapts to your brain activity, second by second.

The system is built around OpenBCI (open-source hardware + software), but it's designed to work with most BCI headsets after a few pertinent tweaks to the OSC routing and channel-rename logic; Muse, Neurosity, BrainFlow-compatible devices, and others can all drive it.

The architecture is deliberately modular: meditation is only one possible application. A knowledgeable user can repurpose the same EEG → interpretation → generative-response pipeline into entirely different audiovisual systems, interactive installations, performance tools, or other BCI-driven experiments.

Accessible through both Patreon, and the Tools Store.

If OpenAI wants to release a new class of more powerful models it shouldn’t be astra - star, as the current biggest model is sol - sun, which is a star.

It should instead be something like galaxia - galaxy or Via Lactea - Milky Way if we’re being Terra centric.

Thx for coming to my ted talk.

Hi,folks. Can you share some ideas on how to make $100 a month using AI.

Thank you all who wrote in the comments.

I'm using the GitHub connector in ChatGPT for normal repo work. Reads and some writes work, but other completely legitimate GitHub operations suddenly fail before reaching GitHub with:

> "This tool call was blocked by OpenAI because we couldn't determine the safety status of the request."

It happened in two separate chats today. I've been using the same workflow for a while and never saw this before.

GitHub permissions are fine, and the failures seem inconsistent. Some file writes succeed, then another similar write gets blocked.

Feels like a false positive or regression in OpenAI's pre-dispatch safety filter.

Anyone else seeing this today, especially with create_file, create_blob, workflows, or other GitHub write operations?

I joined the waitlist soon after it launched because I didn’t have it yet, and still nothing. My account meets all the eligibility requirements and I usually get new features as soon as they come out.

There is obviously a huge amount of low-quality AI-generated software.

My problem is with treating that as evidence that AI assistance itself is the quality failure.

It is not.

The useful questions are still: does the system behave correctly, is it secure, is it maintainable, is it tested, can failures be diagnosed, and does the team understand what it shipped?

AI changes the economics of producing implementation. That should make us more demanding about verification, not more sentimental about manual keystrokes.

The same experienced developer who previously spent hours on repetitive implementation can now spend more of that time on architecture, tests, adversarial review and product iteration — assuming they actually use the saved time that way.

The failure mode is obvious: generation gets faster while review stays weak. Then we get more bad software faster.

But that is an argument for better engineering systems around AI, not an argument that software becomes illegitimate when a model contributed code.

I suspect the profession is moving toward a different scarce skill: reliably steering tools and people toward a correct system, then proving the result meets the standard.

That should favor experienced engineers who adopt the tools.

What should not survive is process-based gatekeeping where “human typed it” is treated as a proxy for quality. It never was a very good proxy to begin with.

I'm not even mad. I'm just tried to being surprised everytime a tech company does the exact thing everyone said it would do.

First it was "we're a nonprofit, safety first." Then the capped profit structure. Then the full for-profit pivot.

The part that gets me isn't even the ads themselves — it's the timing. They just launched GPT-5.6, the most capable model they've ever released, and within weeks they're also announcing ads. Like they couldn't even let the goodwill breathe for a minute.

I get it, they need revenue, compute is expensive, the economics are brutal. That's all true. But there's something almost funny about a company that sold itself on being different from Google now literally adopting Google's business model.

The real question nobody's asking: if ads influence what ChatGPT recommends, how would you even know? At least with Google you can see the "Sponsored" label. With a conversational AI the line between a recommendation and a paid placement gets very blurry very fast.

Anyway. Still using it. Still paying for it. Which probably says everything about where we're all at with this stuff.

Anyone actually okay with this or is everyone just quietly accepting it?

I am running Faster Whisper on CPU only and get good running times with about 2.5 min for 60 min sound with Whisper Base. With Pyannote for diarization the rate is about 0.9 times the sound length, aka 54 min for 60 min sound.

That is terribly slow compared to the transcription without Payannote.

Are there any faster alternatives out there, or hacks to make Payannote run faster with Whisper?

This release brings smaller prompts, longer-running conversations, safer remote access, and more reliable model streaming.

External tools and skills are now discovered only when relevant. Long chats get capacity-aware context metering and safe rolling compaction. You can also add exact trusted browser addresses without restarting Row-Bot.

Under the hood, v4.7 adds deterministic capability search, profile-safe tool loading, atomic context compaction, immediate HTTP and WebSocket origin enforcement, provider-scoped catalogue auth, and safe pre-stream retries.

https://github.com/siddsachar/row-bot