The way I star GitHub repositories is pretty much the same as how I like and collect videos on bilibili: I just click them on a whim. Whenever I see a project that looks even remotely interesting, my brain always goes “might be useful someday” or “I’ll take a look later” — and my finger clicks the button before my brain finishes the thought.

That’s how, over the years, my star count quietly climbed past 800. To be honest, I have no idea what those 800+ projects actually are — most of them were starred because they seemed useful back then, and then never opened again.

Remember [that April post agonizing over device choices]? Back then I listed a bunch of candidates, weighed them one by one, and confidently declared I would “stick with the Fydetab Duo.” Well… it turns out humans can’t escape the universal law of zhen xiang (something you dismissed turns out to be great), and in the end I bought the MacBook Neo after all.

Lately I’ve been working on integrating several AI applications, which means I need to switch between different models. That’s when I discovered a problem — the APIs of OpenAI and Anthropic are incompatible.

Recently, I chatted with friends about the devices we each want to buy, and we all shared the same feeling — everything is getting more expensive. Game consoles, graphics cards, RAM, SSDs, even laptops that used to be relatively affordable — the prices now are enough to make anyone hesitate.

Yesterday I got an email from LeanCloud saying they’re shutting down service…. Wait, what?

I had been thinking that when my blog’s total page views hit 50,000 again, I’d write a celebratory post. Little did I expect that the clicks would grow much faster than I imagined — before I could even finish drafting the celebration, the number had already blown past it.

I’ve been living at MoFang Apartment for almost three years. What I got wasn’t a renewal notice — it was news that the company’s funding chain had snapped and they were on the verge of bankruptcy.

I recently got a Mac Mini to use as a server. I initially thought that since macOS is based on Unix, it would be pretty much like Linux — just set it up and go. But once I actually started using it, I ran into quite a few pitfalls. Here’s a record of the main issues.

To do some small-scale model training at work, I eventually recommended buying a Mac Mini. The reasoning wasn’t complicated: I needed it for bioinformatics analysis, running agent deployments, and occasionally training models with modest parameter counts — these scenarios are exactly where M-series chips with unified memory shine. The cost of separate RAM plus a large-VRAM GPU far exceeds the Mac platform; do the math and the choice is clear.

And just like that, I unlocked the achievement of pushing forward multiple devices, multiple platforms, and multiple projects all at once.

Following up on the previous post [From Fydetab Duo to HP Chromebook X11], I have been using the HP Chromebook X11 for a while. The overall experience is quite good, but I encountered two annoying bugs. Here I record the solutions.