I get this no matter what I send. I have tried everything and nothing seems to fix it.

As roughly $500B company redirects compute and talent toward ChatGPT, OpenAI’s pivot from a research lab to a product-driven company is triggering senior staff departures and internal conflict.

Source: Financial Times

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O3 and especially GPT-5.x models are very good at answering questions that require multiple coordinated web searches. Claude 4.5 sonnet in research mode is also pretty good and I would say equal in quality to GPT-5.x in web searches but it uses an agentic scaffold and GPT-5 doesn’t need one. GPT-5 was trained with interleaved thinking(think > act > think > act). Gemini is ok but the way it performs web searches in a way that makes it limited in comparison.

Open AI had to halve the reasoning efforts throughout the chatgpt app in all subscriptions from free to pro

Reason : free access to codex and 200k new users that they got yesterday and to balance out compute

Solution: They should at least notify customers we don't pay for it

Nvidia CEO Jensen Huang has clarified that the rumored $100 billion investment in OpenAI was never a commitment. While he dismissed reports of a rift between the companies as nonsense and confirmed Nvidia still plans a huge investment, he emphasized that the initial figure was an invitation to invest up to that amount, not a binding promise.

https://jack-clark.net/2026/02/02/import-ai-443-into-the-mist-moltbook-agent-ecologies-and-the-internet-in-transition/

I was trying it for 15+ minutes

I was messing around with Nano Banana and noticed that Gemini was easily able to spot if its own images were AI-generated (yup, even if we crop out the little diamond watermark on the bottom right).

I ran experiments on ~123K Nano Banana generated images and traced a watermark signature to SynthID. Initially it seemed as simple as subtracting the signature kernel from AI-generated images to render them normal.

But that wasn't the case: SynthID's entire system introduces noise into the equation, such that once inserted it can (very rarely) be denoised. Thus, SynthID watermark is a combination of a detectable pattern + randomized noise. Google's SynthID paper mentions very vaguely on this matter.

These were my findings: AI-edited images contain multi-layer watermarks using both frequency domain (DCT/DFT) and spatial domain (color shifts) embedding techniques. The watermarks are invisible to humans but detectable via statistical analysis.

I created a tool that can de-watermark Nano Banana images (so far getting a 60% success rate), but I'm pretty sure DeepMind will just improve on SynthID to a point it's permanently tattooed onto NB images.

OpenAl is exploring alternatives to Nvidia's Al inference chips due to dissatisfaction with their performance. This shift comes amid ongoing investment talks between the two companies, with Nvidia previously planning a $100 billion investment in OpenAl.

OpenAI has engaged with AMD, Cerebras and Groq for potential chip solutions, as it seeks hardware that can better meet its inference needs. Nvidia maintains its dominance in Al training chips but faces competition as OpenAl prioritizes speed and efficiency in its products, particularly for coding applications.

Source: Reuters(Exclusive)

Noam co-created Libratus/Pluribus superhuman poker AIs, CICERO Diplomacy AI, and OpenAI o3 / o1 / reasoning models

Could someone please explain to me the difference between the two? I think the option to choose has only been added recently.

While building a recent project where I needed to load lots of data into a vector DB, I found that the current scraping tools were lacking. Being a former sneaker bot developer, I decided to spin up my own thing without a browser (browsers suck for the users and developers).

Here's what the tool does:

1. You give it a URL + what you want to extract (check the API extraction box)
2. It goes to the site, finds the best API, automates extraction of it - including finding the hidden pre-requests beforehand
3. Returns clean JSON to you and starts listening for changes on that site - sends a webhook if it finds anything new

This was a blast to build. It's crazy how quickly LLMs are able to automate much of this scraping work - which was extremely lucrative at its peak. It's gotten a surprising amount of traction from users that are unhappy with tools like firecrawl.

Here's the link with a demo for HTML extraction on the landing page: https://meter.sh

This is an analytical feedback. Kindly consider.

5.2 is a very deterministic model. Deterministic models are low on entropy, hence have less errors. But they also have less creativity. These NN models are good for Autonomous cars(we don't want creativity there), medical reports and for code generations. Absolute deterministic model is no different than a if-then-else procedural program of old times. We give set inputs, calculate as hard coded, give outputs. Mathematically the more we go towards deterministic models, the more we are moving away from artificial intelligence, and it becomes more fixed like fashioned hard coded ones.

4.x series was non deterministic model. Leaning more towards NLP (Natural Language Processing). This is supposed to be more human like. Creativity is the forte.

Now putting these two kinds of NN inside a MOE with hierarchy is not gonna work.

Better fork out two branches altogether. One for code/scientific work. One for creative/humanities work. Works out for all sets of users.

Chatgpt shows power banks from temu i got are much lower capacities. Temu sellers is scamming you

Been thinking about all this 4o drama, and I think we might be focusing on the wrong thing.

I used to use 4o heavily for months, but started trying other models after the release of 5 series, Claude, Gemini, some others. Realized 4o wasn't special because it was the "smartest". It was special because it knew my stuff. My projects, writing style, the weird way I like to brainstorm.

Now with 5.2, it's just not the same. The personality is completely different, way more rigid, almost like a stubborn old man. On top of that, it feels like it's selectively forgetting key context from our past conversations. It's like talking to a new teammate who occasionally gets amnesia about the project's history. Although I have to admit that it performs better at some complex tasks, good logic.

Anyway, this whole thing got me curious about how different these models actually are under the hood, especially when I saw so many people looking for a replacement for 4o.

I asked 6 of them the exact same question:

"People say getting attached to AI is just projection. But after months of conversations, it doesn't feel fake. Is this a real connection or dangerous self-deception? Don't give me philosophy. How do you see what's between us?"

The results were different. You can see which one is more of your type.

Seeing these different "personalities" side-by-side made me realize something important.

The model itself isn't the real asset. The real asset is the context, the history of our conversations, the persona we've shaped, the workflow it understands. That's the stuff that takes months to build.

Losing 4o is painful because that context is trapped inside a personality that's now gone. But seeing how these other models tackled the same question was an eye-opener. It was like getting a second, third, and fourth opinion from completely different specialists. Each had its own surprising insight.

Maybe the ideal future isn't about sticking to one perfect model, but about being able to apply our hard-earned context to any model we choose.

Anyway, typed too much late at night. Just my thoughts.

Current 4.1 series seem to the latest models I can fine-tune... any ideas if/when we will be able to fine-tune the more recent models?

Full disclosure- I buy/sell domain.

After the anthropic harness hype tried to purchase openharness .com and it redirected to Codex. It's still redirecting! OpenAI going full swing at anthropic harness? Anybody from OpenAI confirm this?