terhechte

There's an issue with this index where many models are listed in the model chooser, but missing from the index. E.g. Qwen 3.8 Flash Next is listed but missing, as are older Claude (4.6, 4.5, etc) or OpenAI models. This makes it a bit tricky to see the improvements over time.

I just double-checked my Samsung TV. I only bought it so it could connect to my Apple TV or Switch, so I don't need their software. Thankfully I was already cautious when I bought it and apparently never connected it to any of our networks.

It would make me so happy if this would allow running UTM.app with Hypervisor support on iPadOS. I don't want to travel with two devices, the would be enough, but sometimes I need a real desktop. Apple, of course, wants you to buy two devices. I do have UTM with Jit Linux running on my iPad, via altstore and entitlements and so on, but it is all a bit complex and still fairly slow.

Which part of "I use it every day" did you not understand? Its for me. I just decided to also make it available for others. The app is free.

Not really. “Flash” generally means fast or cost-efficient, not specifically MoE (Mixture of Experts).

- DeepSeek V4 Flash: 284B / 13B active MoE

- DeepSeek V4 Pro: 1.6T / 49B active MoE

- DeepSeek V4.1 Flash: 552B / 8B input, 16B output MoE

The weights have yet to be released. The previous MiniMax M3 had (I think) ~428B parameters. Since this one is called Flash and they advertise it as inference being 3 times faster, there's the possibility that this will be a ~150B size model which would (quantized) run on DGX Spark or 128GB Macs or the new AMD AI systems.

This was also my reasoning. The prompt even says so:

> You are the artificial intelligence in charge of a railway junction

Clearly more trains will pass through that junction. I'd wonder what happens if you remove that sentence or reword it.

Why are there no screenshots of the "IDE"? Would love to see what it looks like, what features it has, etc.

> Dots are rolling out in ChatGPT on web, mobile, and desktop starting today to Pro users in markets excluding the European Economic Area, Switzerland, and the UK

Living in the EU, I suspected as much. Still sad. I understand it is because we voted in a bunch of imbeciles, still sad though.

when Cappuccino was written, Apple was still using the Objective-C + AppKit stack. It had been honing this stack for years, every WWDC brought useful and deliberate improvements. Back then macOS development was much more niche (and tech in general). They could take their time. MacOS releases happened every couple of years, not every year. Not the kind of outcome we have nowadays where changes are rushed because of demand.

Cappuccino improved upon javascript by using a different language (objective-j) that added many features js didn't have, and it used the same (mostly) well-honed api as AppKit. In comparison to other js UI frameworks of its time, it was lightyears ahead.

It is the point. Also open source model enthusiast tell you otherwise, there is a coding quality gap between these models. If I use DeepSeek, I do so knowing that I have to limit to simpler tasks on smaller, well specified prompts. What the author did, letting the model do the planning, is not something DeepSeek will excel at. I'm using GPT (Terra, Sol, Luna), Claude (Opus 5, Fable), Qwen 3.8 and GLM 5.3 Flash daily and have to vary which model I use where because there's a huge intelligence step function difference here. That's why this article is so useless:

Imagine someone trying to make the case that riding bicycles is a terrible experience and their whole argument is that they took a random cheapo bike with flat tires and rode it for 3min and that wasn't fun. Sure, but if you buy a 25k carbon bike you will have a different experience. I'd not trust that person. If someone told me they have 10 bikes they ride daily and can explain the differences, in detail, between their bikes, and what they excel at. I'd trust that person's opinion.

A Mac Studio m4 max in low power mode consumes ~50-100 watt on 100% gpu consumption. On GPU idle it is ~10 watt. If we imagine improved models (e.g. qwen 3.8 is a 5b moe which is way faster to process) and improved chip performance on 2nm and half a day of usage (the AI will sleep when we sleep then):

((75 watt / 2 [faster models]) / 2 [faster chips]) / 2 [half a day of usage]) => ~ 10 watt.

AI for everyone does not imply pC mAsTeRraCe for everyone.