I can actually use and enjoy Linux. The "year of the desktop" never came for me, but instead I got the "year of the cli".
For 20 years I've used Linux in one form or another, but I've felt like I was kneecapped for the most basic things. Just trying to plug in an external drive or a second display meant hours of stack overflow and pasting commands I didn't understand.
Now I'm using several Linux machines for Steam, NAS, local LLM, development, and what used to derail a weekend project now amounts to a coffee break while Claude figures it out.
It's fine for dense models where you need them in VRAM, less so for MoE where you're offloading layers to ram. But 32/32 is pretty good for both in the popular ~30b range right now.
- You can send any amount of money to anyone in the world very quickly and cheaply, and nobody can stop you.
- No government can dilute it or limit its supply.
Stuff like that. Maybe that matters to you, maybe not, but BTC was created because that didn't exist. And even if you don't use it, you're living in a world where financial institutions have to live alongside an alternative that does these things, for whatever that's worth.
This is where a lot of the crypto scammers on x have moved, selling ai/bots to do this. It seems like the odds slip significantly after these large bets are filled.
For the huge percentage of devs using vscode, switching to Cursor was essentially adding a new color theme and a chat window. The CLI switch was far more radical.
- Apple depends on chipset and memory. Sweet spot would be 128gb M3 Ultra, probably $6-8k but admittedly haven't been tracking closely. New M5 might come in the fall. You can get a new 128gb M5 Max laptop for ~5-6k today.
- a 4x3090 rig would take $5-6k
Every platform has tradeoffs, but it's mostly ecosystem, memory bandwidth, and power consumption. They're all slow. The best option is likely to rent hardware on Runpod. The RIO on self-hosting is very low unless you have a specific need or you're ok treating it as a hobby.
Why this entire tool chain instead of building within something like pi code?
I've been exploring this area and a project like https://github.com/itayinbarr/little-coder (not my work) lets me mix and match with my current setup or any plugins built for pi.
You can fine-tune a model, but there are also smaller models fine-tuned for specific work like structured output and tool calling. You can build automated workflows that are largely deterministic and only slot in these models where you specifically need an LLM to do a bit of inference. If frontier models are a sledgehammer, this approach is the scalpel.
A common example would be that people are moving tasks from their OpenClaw setup off of expensive Anthropic APIs onto cheaper models for simple tasks like tagging emails, summarizing articles, etc.
Combined with memory systems, internal APIs, or just good documentation, a lot of tasks don't actually require much compute.
OpenClaw had a huge viral marketing campaign. It wasn't a coincidence everyone on twitter was talking about it at the same time suddenly. To its credit, it also executed well enough in a few areas that captured people's imagination. Most of the concepts are ideas people have been toying with for years, though.
What kind of small tasks do you find it's good at? My non-coding use of agents has been related to server admin, and my local-llm use-case is for 24/7 tasks that would be cost-prohibitive. So my best guess for this would be monitoring logs, security cameras, and general home automation tasks.
If you're cool with that, this appliance isn't for you.