y42

Sire, wouldst thou perchance elaborate upon this ancient, forgotten technique, long buried in the mists of antiquity and shrouded in mysterious lore?

Still some way to go until we run "it" on our own machines. Even with current "mid to high end" hardware you barely reach the same quality and speed with a free LLM compared to what Opus or Astra delivers. In numbers: A 3k Euro Macbook with 32 GB RAM allows you to run a (hard guess only) up to 16 GB Model. Alone! Not speaking about sub agents here. Because you still need memory for your everday work and other programs, cache, context etc.

From my experience, there is no everlasting digital system, no matter how many fallbacks you add. So I wonder, how can you ensure, this still works after e. g. 1 year?

Yea.."art performance" kind of already catches it. I want to see what people can do with just HTML, JavaScript and CSS. Like back in the days, withouth subscriptions, algorithms, bloat.

It certainly sounds interesting, but then I wonder: After a couple of days or maybe weeks, even with a sophisticated knowledge management strategy, the knowledge of Wilmund is so big, that there won' t be much room left in the context window. Right?

The interesting part is this:

"After losing a bunch of customers to Codex"

Hot take: Right now it's quite simple to loose customers, like customer churn from A to B or back. That's the weakest spot, isn't it? No matter how complex the products grown and how deep they are integrated into our systems, common users can easily switch. Even heavy users, I argue. Just tell Codex to rewrite the existing Claude-instructions into their own. Even that may not be necessary, if you organized your work "agent agnostic".

And I dont see how that could change, that's why they try to offer tools that are even more integrated into our lives. Like "dots". But this also narrows down the use cases and client base, I argue.

When working with a whole bunch of sub-agents, like you probably would do when coding, 2-3 sessions at a time are probably not enough. Like I build very specific sub-agents for code review, ui review and so on. Within one project I work with at least 3 - 4 agents then. Using Opus on every task would not leave room for other everyday-tasks.

good to have a quick overview, though I am missing some kind of depth, like

>There are others — MLX-native runtimes, llama.cpp directly — but Ollama and LM Studio cover 95% of people reading this.

Sure, but... MLX native runtime have a lot of advantages right? and they are not necessarily more complicated like eg ollama. It's worth to mention them, too.

Coming to the next missing part: use cases.

Running it locally competes with dozens of freely available chat bots. And for most simple parts, seriously, you don't need that sophisticated payed AI service. But then: how do local model's perform exactly here: let's start with coding, image generation and more? Like stuff that I probably really want to run locally.

I don't know.... With all those approaches to run AI in your own premise, people need their own hardware. It makes them independent. Of course hyperscalers and AI labs don't like that, but I argue that consumers prefer independence over some costs of operations. If this would be the case, people wouldn't own cars.

I am quite familiar with Resolve..I am not a pro, and it's probably totally overload for my purpose, but I think it's easy to learn and even quite intuitive

I tried many others, but mostly all others are not stable enough or have an even worse UI/UX... so I wonder... is this different?

No rockets, no emoticons? Not real LI cringe.