When it comes to simple inference tasks, my locally-hosted large language models are reliable enough to deliver accurate results. But everything falls apart as soon as I start prompting my LLMs about complex errors, new topics, or frequently updated information. By default, the models I run on llama.cpp can only access their training data, meaning they’re completely isolated from real-time information from the Internet.

Getting the TV I want with the right specs means settling for whatever operating system it runs. However, the OS that ships on smart TVs is typically slower and more limited than any modern streaming device I plug in. TVs come with a locked-down home screen, ads, and a limited curated app store from the manufacturer.

PDF editors are often too basic to be useful or overloaded with features, pop-ups, and subscription prompts. I wanted something lightweight that could handle the everyday jobs I actually care about, whether I was on a Windows desktop, MacBook Pro, or tinkering with a Linux machine in my home lab.

Most smart displays come with a fixed set of screens, and you get whatever the team behind the firmware happened to think of. Usually that's a clock, the weather, a calendar, and after that you're stuck with it. I've used a few of these over the years and the pattern's always the same; they're mostly cookie-cutter devices, nothing more and nothing less.

There's a simple question you might've asked before heading into this article. "What's actually wrong with Google Photos?" For many years, it was the storage limits that kept getting tighter, the shrinking free tiers, and the added monthly cost of paying for one subscription after another. Today, there's an even more compelling reason to leave the ad giant's ecosystem.

When I first got into PC gaming, the only performance metric I really cared about was average FPS. As someone who switched from a PS3 back then, I just wanted AAA titles to average around 50FPS on my GTX 680 at 1080p. I never even knew what 1% lows or frame times were until years later, when I upgraded to the GTX 1070 and started chasing higher frame rates in fast-paced shooters like Battlefield 1 and Titanfall 2.

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on GTA VI and the state of the video game industry, follow Andrew Webster. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here.How it startedFour years ago, fans got a long-awaited look at Grand Theft Auto VI with a huge cache of videos that showed off Rockstar's latest open-world epic. There were 90 videos in total, providing a glimpse at the next-generation take on Vice City, introducing the game's new dual protagonists, and generally showing off the crime-fueled mayhem the series is known for. …Read the full story at The Verge.

Whenever something gets a lot of attention online, scammers and malware distributors are always quick to capitalise on it. When news broke that a leaker had gotten their hands on a full, working copy of GTA 6, people started to ask questions. For instance, has the leaker uploaded the game files somewhere that everyone can download?

You might think the best option when you hit a Wi-Fi dead zone at home is to invest in an expensive multi-node mesh system to ensure full coverage. However, there are downsides to adding consumer mesh nodes around the house, including wireless backward congestion, doubled latency hops, roaming packet drops, and overlapping co-channel interference.

A little while ago, Google confirmed that it was scrapping its affordable range of Chromebook laptops. The goal was to pivot more toward on-board AI processing with a new brand of laptops called the Googlebook. At the time, it sounded like Google no longer wanted to support the budget-friendly range, but we didn't know just how budget-unfriendly the Googlebook would be.

Qwen 3.8 27B was one of the most highly-anticipated open-weights releases that I've seen in a long time, and like many others, I immediately got to work testing it out and playing with it when it dropped. I'm running it on a single Lenovo ThinkStation PGX, the compact workstation built on Nvidia's GB10 Grace Blackwell chip, packing 128 GB of unified memory and 273 GB/s of bandwidth. Out of the box, it manages a fairly dull 15 to 30 tokens a second, but with an SGLang, NVFP4, and DFlash2 speculative-decoding setup that's become the standard recipe for this hardware, it can reach around 50 tokens a second on code and reasoning.

We're getting to the point with LLMs where we can squeeze them onto an ESP32. Granted, it's not going to take down the bigger, stronger LLM models running on powerful hardware anytime soon, but as a fun thing you can make at home to handle simple queries, they can be a lot of fun. You can even hook up an external LLM to one, too; our own Adam Conway connected a local LLM to a $30 ESP32 display, and it designs a new screen for every question he asks.