If I’m not going far, not driving or not planning on spending much then yes, wallet stays at home.

Otherwise, if I’m driving then I’m taking my driving license with me (you never know), if I’m making a purchase over the contactless limit then I need to take my actual card with me anyway, or if I’m away for a longer period then I might need cards that I don’t have linked to my phone (like my corporate card - like fuck am I putting that in my phone).

And for all that, I’d rather have a wallet instead of just a loose amalgamation of plastic in my pockets.

Apart from the times that the US applied export controls to encryption software.

You really gonna do the Blades dirty like that?!

There’s a lot to cover here but I’ll try to touch on each point:

The key requirement is fast memory that can be addressed by your GPU, and ideally a lot of it - hence the insane cost of this hardware right now.

Remember that you need space for the model’s weights (think of this as its ‘knowledge base’) and the context window, which is basically the data needed for the LLM to keep track of your current conversation with it (effectively its short term memory).

With smaller pools of VRAM (8-16gb) you will have to compromise and either have a more capable model that will lose context quickly and start hallucinating, or a less capable model that can maintain a session for a bit longer but overall less ‘smart’.

For software - there are a couple of options for running the LLM itself, Llama.cpp is one of the more popular tools and is the one that I use. It has a web UI with the usual chat interface, and also exposes an API that you can plug other tools (e.g. opencode) into, depending on your use case.

In terms of hardware recommendations, at 20GB+ of VRAM you do have a bit more headroom compared to more consumer grade GPUs, but to be honest the most cost effective way to get a shitload of VRAM is likely not with a dedicated GPU but actually using a system based around a recent APU.

I got a Minisforum MS-S1 last year for exactly this purpose. It is based on AMD’s Strix Halo platform which it has in common with the Framework Desktop and a couple of other similar devices.

It has 128gb of unified RAM which can be divided between the GPU and CPU however you like, so plenty of capacity for even fairly chunky models. It also uses a tiny amount of power compared to a more traditional system with a dedicated GPU, while also giving really reasonable performance for most AI workloads, more than enough for use in a homelab.

For cloud rental - doable, but pricing is a factor, and of course this will not actually be running locally.

Usability - manage your expectations, but overall for a lot of use cases and of course depending on the model that you are running and the resources you throw at it, it can be comparable with especially older iterations of ChatGPT, Gemini etc.

But remember, you are not a Google or an Anthropic and do not have an infinite pool of compute to throw at your model, nor do you have access to the specific models they are using.

Same, I set it up a few years ago and both me and my partner have been using it since then with no issues at all, it’s completely replaced Google Photos for us.

We’ve also set up immich-frame and repurposed an old Google Nest hub to use as a digital photo frame.

This might not actually be the first one, but one of the earliest games I definitely remember actually buying with my own money was Geoff Crammond’s Grand Prix 2. I would have been around 7 or so.

Definitely worth it, great game and the demos on the CD introduced me to Transport Tycoon, and the XCom and Worms franchises - and things kind of snowballed from there!

You are assuming that they have a personality to begin with.

Similar to others, maybe 2 or 3 years old. I was “helping” (probably hindering) my mum paint my bedroom. I distinctly remember waving the paint roller around.

I have witnessed companies make this exact mistake before - they have a legacy system written in $LanguageA that they either cannot find developers to maintain, believe is badly written, or does not support some new feature they want to implement (or some combination of the three) - and decide to solve this by taking the existing codebase and porting/transpiling it to $LanguageB (which is more modern, performant, is easy to hire developers for, etc) - without actually rewriting or rearchitecting anything.

What they are actually doing is substituting one kind of tech debt for another. The existing code that was poorly written and/or not well understood is now just bad code written in a different language. Fixing bugs or implementing new features now takes just as long, if not longer to account for the idiosyncrasies of how the code was ported.

And now this is being done by AI with even less oversight than usual? Recipe for a maintenance disaster.

There are only two industries that call their customers ‘users’…

The main issue was a catastrophic failure of the VC_FRONT module which is one of the critical onboard computers that manages things like the 12v battery and low voltage power distribution (basically a “smart” fuse box). Without it the car is bricked and cannot be driven.

That took several weeks and some back and forth around the extended warranty to resolve, and then even after that module was replaced, on my first drive after the repair it went straight into limp mode and then spent another week at the service centre having that diagnosed.

During this time I decided it might be time to start looking for a new car, ended up selling it a few months later and took delivery of a new Polestar 2.

I’m not sure why anyone expected a new facelift would improve sales. It’s clear the overall decline is associated with Musk going full mask-off fascist, given this, driving around in a car that looks unlike any previous Model Y just makes it completely obvious that you knew this and decided to buy one anyway. If they want to bolster sales, maybe they should have kept producing the pre-facelifted versions for a while.

Full disclosure, I used to own a Model 3. I had it for 5 years and was generally very happy with it - it was a great daily driver, cost very little to run and maintain, and (aside from a few issues later in my ownership, which was one of the reasons I decided to sell it) in general it was very easy to live with.

There are clearly some very skilled engineers at Tesla who know how to build a great product. It is a shame their efforts are being undermined by a fascist lunatic with a narcissist complex.

Old? Check. Male? Check. Hung out on Epstein island? Check.

Seems like a perfect match to me.

Well, I’m currently writing a service and frontend, both in C# (Blazor for the UI), and using docker-compose to build and deploy them to a Raspberry Pi running Linux. So not only cross-platform, but cross-architecture as well.

This is not a new thing either. Since .NET Core was released almost 10 years ago, it has supported cross platform development.

2017: covfefe

2025: cvefefe

Right now none of the native clients support SSO. It is a frequently requested feature but, unfortunately, it doesn’t look like it will be implemented any time soon. As with many OSS projects it is probably a case of “you want it, you build it” - but nobody has actually stepped up.

For web access, stick it behind a reverse proxy and use something like Authentik/Authelia/SSO provider of your choice to secure it.

For full access including native clients, set up a VPN.

Oh fuck me, HOW in like 25 years did I not get that pun?!

We refer to it as kew-bee-cuttle