For example, if i ask a question regarding an implementation decision while it is implementing a plan, it answers (or not) and immediately proceeds to make changes it assumes i want. Other models switch to chat mode, or ask for the best course of action.

Once this is said, i am not blaming Anthropic For that one, because IMHO the OP has taken a lot of risks and failed to design a proper backup and recovery strategy. I wish them to recover from this though, this must be a very stressful situation for them.

I do not think that anyone who read my comment understood it differently. But I grant you this point, this is just my opinion based on my personal experience not the result of a scientific study.

Once this is said, i wasn't submitting a scientific paper for preprint, just posting my opinion on an internet forum.

Not sure why you are making such a big deal out of it, especially for something for which people can decide within minutes if it works for them or not. And I haven't seen you nitpick on other people saying that all Chinese models are garbage incapable of doing even the most basic task, without quoting any study. This kind of scrutiny tends to be one-sided.

Edit: and regarding what the z.ai team is saying about their models, just check their Discord and the articles they link there. They themselves say that their latest models have leading performance on a number of aspects. It is misleading to suggest that the authors of the model are not proudly saying that their models have best in class performance.

My only goal is to encourage people to try it out so they can see if it moves the needle for them, because there are fair chances that it will. I am not trying to start a flamewar or something.

I also use Claude premium daily for another client, and i use Codex. and i can tell you that GLM5 is at this point much more capable than Claude and Codex for complex backend end work, complex feature planning, and long horizon tasks. One thing i've noticed is that it is particularly good at following instructions and guidelines, even deep into the execution of a plan.

To me the only problem is that z.ai have had trouble with inference : the performance of their API has been pretty poor at times. It looks like this is an hardware issue related to the Huawei chips they use rather than an issue with the model itself. The situation has been substantially improving over the past few weeks.

GLM5.1, GLM5-Turbo and GLM5v are at this point better than Opus, Codex, Gemini and other claude source models. We have reached a major turning point. To me, the only closed source model still in the game is codex as it is much faster at executing simple tasks and implementing already created plans.

Try GLM5v for your PDF work, it's their last generation vision model that has been released a couple of days ago.

As adoption increases, liquidity (which may be what you allude to when you talk about coordination) will be even better.

I'd also note that facilitating liquity can indeed be a complex problem, but there are people specialized in making money out of it and who therefore have an incentive to continually keep the market liquid, read about "market makers".

We are over 8 billion on earth, even if as little as 1% of the population rely on it directly or indirectly due to some form of oppression real or perceived due to political opinions, a long investment thesis on bitcoin can make sense.

I am not a bitcoin holder once this is said. Arrived at the party too late IMHO, and i have better investment options for now from a risk-adjusted perspective.

However if i had more cash to invest than i know what to do with, i'd definitely take a long position on bitcoin.

This kind of problem happens frequently when users add repositories such as Packman on Linux providing dependencies versions different from the ones used by the base system of the distro.

Experienced people know how to avoid these mistakes, but this whole class of problem does not exist on FreeBSD.

For a while even used it on the desktop, but was too much trouble due to specific tools we need that weren't supported properly. so we're using Linux on the desktop.

FreeBSD is stable, lightweight, gets out of the way, and without drama.

But it isn't a need for most use cases, unless you want to do mobile development and meet app store policies. But even then, mature F# frameworks like Fable transpile your F# code to React & Cie.

No test suite, plenty of regressions, and data loss bugs on core code paths that should have been battled tested after so many years. There are many moving parts, which is both its strength and its weakness as anything can break - and does break. Even Erasure Coding/Decoding has had problems, but a guy from Proton has contributed a lot of fixes in this area lately.

One of the big positive in my opinion, is the maintainer. He is an extremely friendly and responsive gentleman. Seaweedfs is also the most lightweight storage system you can find, and it is extremely easy to set up, and can run on servers with very little hardware resources.

Many people are happy with it, but you'd better be ready to understand their file format to fix corruption issues by hand. As far as i am concerned, i realized that after watching all these bugs, the idea of using seaweedfs was causing me more anxiety than peace of mind. Since we didn't need to store billions of files yet, not even millions, we went with creating a file storage API in ASP.NET Core in 1 or 2 hours, hosted on a VPS, that we can replicate using rsync without problem. Since i made this decision, i have peace of mind and no longer think about my storage system. Simplicity is often better, and OSes have long been optimized to cache and serve files natively.

If you are not interested in contributing fixes and digging into the file format when a problem occurs, and if your data is important to you, unless you operate at the billions of files scalability tier Seaweedfs shines at, i'd suggest rolling your own boring storage system.