I've been using my Galaxy Z Flip 5 for about three years now, so it's easy to blame its age whenever it feels a little slower than it used to. But there's a difference between a phone actually taking longer to do something and Android simply taking its sweet time showing you that it did it. I was recently reminded of that when I started messing around with the animation scale settings in Developer options.

When one of our D&D group members couldn't make it to the session, we decided to make a night of it anyway. We headed over to a local pub, only to be told that they were about to start a pub quiz and that we were welcome to join. We had never done a pub quiz before, but we thought we'd give it a go.

There is a major hardware contrast between my $2,500 custom gaming PC that features an RTX 40-series GPU, a high-end 4K HDR display, and a Gigabit fiber connection, and a $30 Amazon Fire TV Stick plugged into the same monitor. Yet somehow my PC delivers a blurry, artifact-ridden 720p or low-bitrate 1080p Netflix stream, and the streaming stick offers crisp, instantaneous 4K Dolby Vision with zero user configuration.

I used to think of upscaling as a fairly simple trade-off. You give up some of the internal resolution of the image, and in return, DLSS or FSR would give you enough performance to push higher settings or a higher output resolution. That is still its biggest selling point, but after using DLSS upscaling ever since it arrived back in 2020, I've realized that there's another benefit hiding in plain sight.

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.

I’ve tried several tools for running AI models locally, but I keep returning to Ollama and LM Studio. Both make it easy to download a model and start chatting without building a complicated environment first. I can change the system prompt or connect the model to another app through an API. The model itself, however, remains exactly as I downloaded it.

I have been a long-time Notion user. I have used it to take notes, prepare documents, write my articles, and even present things to people. I stuck around primarily because it has a dark mode, which Google Docs doesn’t have on desktop, and I found features like rich embeds helpful.

Even though you have a modern Wi-Fi 6, 6E, or 7 router and high-spec endpoints capable of gigabit wireless throughput, your network might still suffer from random latency spikes, package jitter, and sluggish page loads. This is down to airtime starvation. Wi-Fi is a shared half-duplex medium. It doesn't matter how fast your gaming rig or laptop is if the wireless radio spectrum is tied up waiting for slow legacy transmissions.

My very first smartphone was a Google Nexus One, which my dad bought me when I was 16. Back then, I would install APKs from outside the Play Store all the time, mainly because some of them weren't available in my region. Even though Android has come a long way since the early 2010s, this part of the experience has largely stayed the same. Yes, the OS will warn you about the risks when you try to install an app from an unknown source, but overriding that warning and going ahead has always been easy.

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.

I’ve invested enough time in my Jellyfin library that losing it would be more than a storage problem. The media files matter, obviously, but so do everything I’ve done around them: folder organization, metadata, artwork, watched status, collections, and all the little choices that make the server behave the way I expect. None of those things sound especially painful to rebuild on their own. Put them all together after a drive or server failure, though, and I’d be looking at hours of tedious work I’ve already done once.

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.