Everything AI, rated by the people who use it.

It's astonishing to see Apple settle for DALL-E 3 (or worse?! these remind me of the Bing samples) for the image generator part. Hasn't the incredible extent of mode-collapse and the horrible DALL-E 3 style become universally known and disliked yet?

The only thing "clear" about that License agreement is it contradicts all their other marketing about Copilot. So either that document is fraudulent or everyone else at Microsoft is committing fraud daily. Examples from the first search result: https://support.microsoft.com/en-us/topic/microsoft-365-copi... Support page with ~25 tutorials provided by Microsoft about how to "Create a document with Copilot" or "Create a branded presentation from a file" or "Start a Loop workspace from a Teams meeting". Do you actually believe that creating branded presentations (from Microsoft's own examples) is something people do for "entertainment purposes"?

There is a major misunderstanding in the general public and especially in the news about what ChatGPT really does. This misunderstanding is not really new, there has been confusion about ML techniques for a long time. Imho a large part of the issue comes from the term "Artificial Intelligence", which covers everything in this area supposed to represent some form of intelligence. Clearly most people would like to believe that there is some real intelligence at play (sci-fi, etc.). The fact that many companies or institutions have an interest in keeping the ambiguity alive (e.g. "smart cars", "smart home", etc.) and that some experts support it greatly contributes to the confusion. But as far as I know, from the point of view of ML experts, a system like ChatGPT is more like a highly complex calculator than an intelligence. Its calculations cannot be simulated manually (well technically they can, but it would take a lot of people and a very long time) so it gives the impression of something smart. It's also worth noting that the evolution of ML has been to give less and less direct human supervision to the models, i.e. they have more autonomy in which way and what they learn. What this kind of system is trained to do is to simulate giving an answer. The more the answer looks real, the better. It is not trained to give a correct answer or give any importance to the truth, because this is not easily measurable. The Chinese Room argument explains why such a system does not comprehend anything of what it says, it just applies a lot of complex rules/calculations based on its training. One could say that the goal of a system like ChatGPT is to create the illusion that there is some intelligence behind it, like the goal of a magician is to make you believe whatever they are showing. As a consequence, it is well known that ChatGPT answers are meaningless in general: see for instance this article or the ban on StackExchange.

I have been checking organic and inorganic chemistry skills in ChatGPT pro and it is absolutely, laughably bad. But it sounds good, plausible but it comically wrong in so many ways. Maybe you should think twice about whether the health issues advice it is giving you is legitimate.

ran into this same problem and ended up rolling my own markdown-based memory files instead of claude-mem. works for my setup but the 46k stars are telling - most people want zero config.

worth checking out. ideogram has the best creation of english text out there. Nightcafe (https://creator.nightcafe.studio/) sturck a deal with them recently and added support for the ideogram model in their tool also, if you want to try that, which I also highly recommend for general SD online image creation services.

OpenAI in my estimation has the habit of dropping a model's quality after its introduction. I definitely recall the web ChatGPT 5.2 being a lot better when it was introduced. A week or two later, its quality suddenly dropped. The initial high looked to be to throw off journalists and benchmarks. As such, nothing that OpenAI says in terms of model speed can be trusted. All they have to do is lower the reasoning effort on average, and boom, it becomes 40% faster. I hope I am wrong, because if I am right, it's a con game. Starting off the ChatGPT Plus web users with the Pro model, then later swapping it for the Standard model -- would meet the claims of model behavior consistency, while still qualifying as shenanigans.

RooCode is a better version of ClaudeCode than ClaudeCode. No affiliation, just a fan.

I have read comments about this on X, here, and other places, yet I have ever seen there be proof this is an actual productivity boost. I use Claude Opus (4.5, 4.6) all the time and catch it making making subtle mistakes, all the time. Are you really being more productive (let’s say 3x times more), or just feel that way because you are constantly prompting Claude? Maybe I’m wrong, but I don’t buy it.

Yeah. I've found out that you can start out with o3-mini-high and then switch over to o1-pro, or the other way around. Helps to iterate a bit faster.

If on Gemini Advanced you say > Did you know that there is no country in Africa that starts with the letter "k"? The answer is > Kenya is a country in Africa that begins with the letter "k." So, in Google search is using another model?

For inference, Nvidia/AMD/Intel/Apple are all generally on the same tier now. There's a post on github of a madman who got llama.cpp generating tokens for an AI model that's running on an Intel Arc, Nvidia 3090, and AMD gpu at the same time. https://github.com/ggml-org/llama.cpp/pull/5321