The AMS has always been one of Bambu Lab’s best ideas, but also one of its messiest workflows. It can swap materials, keep spools ready, and turn a single printer into a much more flexible machine. Yet the software side has never quite felt as polished as the hardware promised. For a system built around loading multiple spools at once, keeping track of those spools has often been weirdly manual.

Anthropic has now built persistent memory into Claude Code. It works in two ways. First, there's Auto Memory, where Claude automatically saves useful project context, patterns, and preferences. Second, there's the CLAUDE.md file, which stores project instructions and context that get loaded into future sessions.

The local models that get talked about most tend to sit in the 7B to 12B range, which is also where most setups land if you've got decent hardware. Anything smaller usually gets written off as a toy before it gets a fair try. But not everyone has 16GB+ of VRAM to work with, and the really tiny models, the under-2B crowd, are getting more capable than their size suggests, and I wanted to see if they're worth poking at despite being able to run the mid-size ones.

"People are going to use more and more AI." The words of Jensen Huang have become more relevant by the day, and anyone in a vibe-coding, programming, or creative workflow already knows exactly what the Nvidia CEO meant.

If you’ve worked on DIY computing projects in the late 2010s, you’ve definitely heard of the Raspberry Pi, if not own a few single-board computers belonging to this family. After all, their tiny form-factor, affordable price tags, and solid compatibility with popular Linux distros (and packages) made them the perfect tinkering companions. But that’s all in the past now.

Hi, friends! Welcome to Installer No. 129, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, come on you Gunners, and also you can read all the old editions at the Installer homepage.) This week, I've mostly been sick, which has meant nearly a full rewatch of Parks and Recreation while alternately napping and feeling bad for myself. But I've also been reading about Nick Fuentes and clowns, listening to old episodes of Short History Of, testing the NextSense Smartbuds while I sleep, writing in the Outerline Markdown app beta, and eagerly looking for things to do with the upcoming Flipper One. Today' …Read the full story at The Verge.

One of the biggest benefits of using Linux is that it's a very much an operating system that gets out of your way. As such, when something on Linux doesn't respect people's choices and keeps bothering them, the developers are sure to know about it. And as much as I love KDE Plasma, there is one thing that irks me when managing the clipboard: the constant asking if I want to clear starred items.

In an ideal world, you'd be able to use any controller on any device. Different controller designers bring different things to the table, and having the option to pick your favorite and use it on any console or PC you own would be a huge benefit. At the very least, it would stop me needing to re-learn where the A and B buttons are when I go between an Xbox and a Switch controller.

Of all the apps you can misclick in, hitting the wrong button during an online meeting is one of the worst. You can accidentally show your webcam, hang up on the call, stick your hand in the air, and even leave the call altogether, just from a simple aiming problem. And when they do happen, you don't forget about it for weeks after.

Lossless Scaling has built up a reputation in some corners of the PC gaming community as the secret weapon that makes DLSS and FSR irrelevant. To put it bluntly: it's not. If I'm playing a game with a proper DLSS 4 transformer-model implementation, that's what I'm reaching for, and FSR 4 on supported AMD hardware is close enough that I won't pretend otherwise. Lossless Scaling's LS1 upscaler isn't winning that fight, and LSFG, while remarkable for what it is, doesn't beat native DLSS Frame Generation paired with Reflex on latency or motion clarity.

Just a stuffed deer having the time of his life. | Image: Gemini / The Verge Last year I deepfaked my kid's stuffed animal to make it look like his plush deer was on vacation.It was an experiment to see if I could re-create the events depicted in a Gemini ad Google was running, and I never showed the videos of Buddy the deer on his adventures to my four-year-old. But it was a revealing exercise that made me think a lot about the difference between some harmless fun with generative AI and full-on slop. Maybe that Venn diagram is a perfect circle! Maybe not. But what I know for sure is that the tools to make realistic videos are surprisingly good, requiring surprisingly little effort and know-how. And that trend is c …Read the full story at The Verge.

For the longest time, the conversation around local AI models revolved around quality. They were either too slow, too dumb, too small, or too incapable to match what the titans over at OpenAI, Anthropic, and Google are doing with ChatGPT, Claude, and Gemini, respectively. That gap, however, is shrinking a lot faster than most people realize, even though it does exist in some areas. For the most part, though, modern local models have become genuinely impressive, and are capable of writing, summarizing, coding, and reasoning on capable hardware, of course.