Most people think getting better AI results is all about writing smarter prompts. I used to think the same thing. But after spending months working with Claude, I realized the biggest difference was not the prompts; it was the relationship. The moment I stopped treating Claude like a chatbot and started treating it more like a co-worker, the quality of responses changed dramatically. Conversations became more useful, ideas became sharper, and the entire experience felt more collaborative.

On the surface, PC building is about choosing 8–9 components that fit within your budget and provide the performance you have in mind. The intricacies of cores, VRAM, frequencies, timings, DRAM, and cooling are where things start to get complicated. Many enthusiasts and veteran PC builders recommend adhering to several PC-building best practices to craft the perfect build. The problem is that most of these tips don't really apply to most people who just want a decent gaming rig without major compromises. Buying decked-out motherboards, cutting-edge storage, and "future-proof" graphics cards may seem logical to enthusiasts, but they are counterproductive for the average user. Most people don't have insane budgets to buy the latest and greatest components. More importantly, overspending on PC hardware often doesn't yield the desired benefits.

It's a conflicting time to be a tech enthusiast, or even just to need a new laptop for work. Stratospheric pricing has made it tricky to recommend anything with onboard memory or SSD storage, and terms like "budget" and "good value" all carry caveats about how they relate to the overall market. Until today. The Lenovo IdeaPad Slim 5x Gen 11 is a fantastic value, with a snappy processor, good screen options and a premium feel.

Ever since DLSS 4.5 became available earlier this year, I've been spending more time experimenting with this feature on my RTX 4090 than I have in years. While that's partly because upscaled footage is almost indistinguishable from the native, it's also because Nvidia has somehow managed to make DLSS more complicated than ever. What was once a simple choice between Quality, Balanced, and Performance modes now comes with two additional model presets that many gamers don't fully understand.

Look at those cute little guys. | Image: Record Club There isn't really a solid equivalent to Goodreads or Letterboxd for music lovers, but Record Club is aiming to change that. Yes, we have Rate Your Music, but its interface is crowded, and it feels more geared towards longer-form reviews than cataloging your listening habits and connecting with other fans. Record Club is clean and modern, with a streamlined interface that's quite similar to Letterboxd. The basic features you'd expect from such a site are all there. You can rate and review records or mark them as listened to. You can also see what your friends are listening to and see what albums are trending with other users. There's a …Read the full story at The Verge.

Roger Linn lets his accomplishments do the talking. | Image: Roger Linn Roger Linn is a legend in the world of musical instruments. He's been at the cutting edge of music technology for decades. He created the LM-1, the first drum machine to use samples, and its successor, the LinnDrum, is one of the most iconic drum machines of all time. They were used on countless records in the 1980s, including hits by Tom Petty, Queen, and Tears for Fears. But the most notable fan was probably Prince, who used them extensively on Purple Rain and 1999.Somehow, those are not his greatest contributions to the music world. That would, undoubtedly, be the MPC. Linn partnered with Akai to create one of the most popular and impor …Read the full story at The Verge.

PC gamers waited for Grand Theft Auto V for over a year after its console launch, and they'll also be waiting for Grand Theft Auto VI for an unspecified amount of time. Even console gamers have done their fair share of waiting for this behemoth of a game to finally see the light of day, but recent remarks from the GTA 6 boss have reignited the same old outrage cycle across Reddit and social media.

A few months before Windows 10 went into extended support, I knew I wanted to move to Linux. I wasn't a fan of having to jump through hoops to keep using the OS that I love, and regardless of how long I held on for, Microsoft was inevitably going to boot me off Windows 10 and funnel me into 11. And I really did not want to use Windows 11.

Smart homes can quickly transcend into a typical morning feeling frustrating. Let's say you say a voice command, and then your smart assistant responds with "Sorry, I'm having trouble connecting to the internet right now." Or your connection does work, and your smart switch still takes five full seconds to turn on a bulb which is two feet away. When your partner or guests can't turn on a lamp because the physical switch was flipped off or the app logged them out, all this does is build up even more pent-up rage. The smart home was supposed to reduce friction, but now you're acting as a full-time IT help desk for your own living room.

LM Studio has been my default runner for as long as I've been running local LLMs, which is more than long enough now to call it part of my daily flow rather than just something I'm experimenting with anymore. The appeal of LM Studio is pretty much that it has a GUI, one-click installs, and no command-line stuff to wade through. Development is not my domain, even using the terminal isn't, so a user-friendly runner was important to me and is what got me comfortable with self-hosting AI in the first place.

The Linux desktop debate has been ongoing for a couple of decades now. The narrative changes every few months with the release of a new kernel, architectural improvements or integrations, but the conclusion remains quite the same: "It's promising, but not quite there." Sometimes it's due to too many compatibility gaps, a departure from traditional professional workflows, or just plain and simple usability. That conversation remains ongoing, but the problems aren't being resolved anytime soon.

Prompting an LLM requires a different approach than typing a search query into Google, yet it's common for people to treat them the same. The process often goes something like this: type a sentence, get a response, then ask refining questions or supply follow-up context to guide AI toward the information you're after. If you're only asking for a simple answer, then simple questions usually work fine. But for tasks involving deeper context or multiple steps, you're normally two or three exchanges deep before the model begins to understand what you actually wanted.