I have been using Ollama to run local LLMs on my Mac, and it has been working just fine. However, my Mac's overall performance took a hit because local LLMs are resource-hungry. I have a MacBook Air M5 with 16GB of RAM. It's probably not the most powerful machine for this kind of workload, but it's been good enough to run models with fewer than 7 billion parameters.

For many other smart home users and me, a smart home assistant should be a local deterministic traffic controller rather than an analytical cloud companion. By turning a simple light toggle into a multi-second LLM processing loop that constantly passes ambient household conversations and continuous Nest camera history, Google isn't making your home smarter by introducing Gemini for Home. They are introducing massive network latency, a recurring subscription tax, and a violation of the telemetry boundary into your family's private life.

It feels like there wasn't a single computer manufacturer that didn't get negatively hit by the RAM price hikes. We saw the Steam Deck get an increase, console manufacturers announced some price spikes, and even Framework had to slowly inch its hardware prices up.

Prime Day is coming to a close, but that doesn't mean the deals are done quite yet. If you're looking for a new phone, we can highly recommend the Pixel 10. Not only does it deliver when it comes to its design and features, but the price is also quite good right now too.

Local LLMs are at a point where I can use them for most of my coding tasks, but moving up to larger models comes with some caveats. If you've got unified memory like on a Mac, you're golden, but I've been running models on an Nvidia RTX 5090 and noticed the model seems to get worse over time.

Steve Jobs thanking Bill Gates for helping Apple still feels wrong, yet it's exactly what happened in 1997. At the time, Apple was battered, yet Microsoft was the company that Mac users loved to hate, and the MacWorld Expo Boston crowd at the time wasn't exactly waiting for a reason to cheer for Bill Gates.

Claude Code itself is great, but have you used subagents? They make Claude Code even better. I've been using five Claude Code subagents, each staying in its own context window, handling a specific task, and returning only a short summary along with some metadata.

I've never written a console emulator before, but I can imagine getting the netplay working is one of the harder parts. Games usually ship with special netcode designed to only speak with a specific server, and part of the emulation process involves emulating that system so that people can play online.

If you're finally ready to ditch all the cloud services you pay for, or if you're just someone who wants a better way to back up all your data, this Ugreen NAS is going to be a good way to go. Right now, for a very limited time, you can score the Ugreen DH2300 2-bay NAS for its best price at $175. This price won't last long, so you'll want to be quick.

If you have got a new laptop in the last few years, you must have noticed that most modern models lack repairability, something which was largely the norm until a decade ago. I still have a 12-year-old HP laptop (HP ProBook 640 G1), and it's clearly more repairable than both of my newer upgrades: a Dell 2-in-1 and a MacBook Air. And that's perhaps the only reason I still have it.

For as long as I've built PCs, the graphics card was meant to be the crown jewel of your build. It's likely to be the most expensive component in a build, and probably the part you spent the most time thinking about. It dictates what resolution you play at, how high the settings go, and in a lot of scenarios, how high that FPS counter climbs. In terms of PC components, it's by far the most romanticized piece of kit, and when you're considering a single component to that degree, the details matter.

Google's AI tools weren't the first to market, or the most advanced for long after they launched, but these tools thrive on their ecosystem integration. While other AI forerunners, such as Cursor, Claude, and ChatGPT are out raising billions to grab eyeballs and engage users with task-optimized models, Google is counting on its AI tools to thrive because users with a Gmail account have already invested around two decades of their lives into other Workspace products like Calendar, Drive, Docs, Photos, and others.