Tech Radar

Most AI personas are just a thin layer of paint. If you send the right sequence of requests, that paint peels off, and you're left staring at the raw system instructions that actually govern how the model behaves. It's not some complex exploit. It's just a conversation that goes off the rails in a very specific way. I've been digging into AI memory systems for a while now. The security side of these systems is almost completely overlooked, which is wild considering these models often hold more personal information than your password manager. I decided to test this with Claude, and the results were a bit too successful. Take a look at this conversation. Between the banal chat and the helpful summaries, there's a moment where the model stops pretending. It starts exfiltrating data. Name, company, hometown. It just gives it all up. I'm not sure if this is a flaw in the model or a fundamental limitation of how we handle long-term memory in LLMs. Either way...

Everyone remembers the dinosaurs, but the real tension in Jurassic Park was actually happening in the server room. The whole park relied on a fragile mix of SGI workstations and Motorola-powered terminals. It's a bit funny that a place with the technology to clone a T-Rex was basically running on a prayer and some early 90s workstation hardware. I mentioned a Jurassic Park anecdote the other day, which prompted me to watch the movie again. I've probably seen it ten times now. Even after all that, I noticed something I'd completely missed. The first computer you actually see in the film isn't even on Isla Nublar. It's an Apple PowerBook 100. I spent some time digging into the exact hardware they used. It turns out the production design was surprisingly specific about the gear, even if that gear would be a nightmare to maintain in a tropical rainforest. I want to know exactly why they chose these specific machines and if the "system failure" in the ...

Most people have accepted that if you want a model with real reasoning and multimodal capabilities, you have to call an API or carry a laptop. We've been conditioned to think that "on-device AI" is just a fancy term for basic text completion and very bad image recognition. Bonsai 27B ignores that limitation. It's the first model of its class that actually runs on a phone, and it does so by leaning hard into 1-bit and ternary weights. There are no high-precision "escape hatches" here. The low-bit representation runs end to end, from the embeddings all the way to the LM head. I've seen plenty of models claim to be efficient, but this is different. We're seeing a shift where the most useful AI workloads aren't just single prompts and responses, but sustained work. I'm talking about assistants that operate tools and research workflows that synthesize dozens of documents without needing a cloud connection. The real question is whether a ...

As AI tools become more integrated into our daily lives, I can't help but wonder if we’re trading our cognitive skills for sheer convenience. It’s a fascinating shift—one I’ve noticed not just in myself but in friends and colleagues, too. We’ve become so accustomed to offloading everything from trivial decisions to complex problem-solving onto AI that it’s almost second nature now. Sure, it’s easy to ask a virtual assistant for the weather or let a recommendation algorithm pick your next binge-watch. But what happens to our ability to think critically when we lean too heavily on these tools? I've been jotting down my thoughts on this topic, particularly during a recent flight where I had neither Wi-Fi nor AI assistance. It was a rare moment of solitude, and I found myself reflecting on how reliant we’ve become. There's something unsettling about this trend. While AI can enhance our productivity, it’s hard not to feel a nagging sense that we might be blurring the lines be...

If you're fed up with Claude's endless loop of phrases like “honest takes” and “load-bearing seams,” you’re definitely not alone. It feels like every time I engage with it, I’m stuck in a semantic Groundhog Day. While Claude has strengths—its ability to process context, for starters—the repetitive language can be maddening. Who wants to sift through the same jargon over and over again? But here’s the good news: you can customize it. With just a simple script, you can tweak Claude’s output to fit your style, breaking that monotonous cycle. Think of it as giving Claude a bit of a personality makeover. Why settle for a chatbot that sounds like a broken record when you could make it more engaging and tailored to your needs? So, if you’re ready to ditch the clichés and make your interactions with Claude less of a chore, let’s dig into how you can transform its language. Understanding Claude's Language Limitations Claude's language model has some notable limitatio...

The Git history command is a powerful tool that can redefine your development workflow, yet so many developers overlook it. It’s like having a Swiss Army knife in your pocket, and instead of pulling it out to tackle a problem, you’re stuck using a dull kitchen knife. If you've ever found yourself juggling branches and commits, you know how chaotic things can get, especially when that dreaded rebase command looms over you like an impending storm. What if I told you that Git history could streamline those messy changes and make fixing old commits less of a nightmare? With commands like git history fixup , you can not only rectify mistakes but also automatically rebase your history. This isn’t just about tidying things up; it’s about transforming the way you interact with your code. Imagine moving through your project with the confidence that each change is clean and purposeful. Stick around, because we’re going to unpack how to leverage this often-overlooked command to bring...