Tech Radar

Most LLMs are essentially frozen snapshots. You train them on a massive pile of data, lock the weights, and hope they stay relevant. Inkling does something different. It's designed for a recursive loop where the AI generates its own training data and updates its own weights in real time. To see if this actually works, the team at Inkling asked the model to fine-tune itself. They didn't go for something boring. They told it to become a lipogram model, which means it had to learn how to communicate without ever using the letter 'e'. The model had to create its own training dataset, define the objective, fine-tune on the Tinker platform, and evaluate the results against the base model before switching to the new weights. It sounds like a parlor trick, but the implications for how we customize models are actually pretty interesting. If a model can autonomously identify its own weaknesses and patch them, we stop treating AI like a static product and start treating it lik...

We've been told for years that eight hours is the magic number for sleep. It's the gold standard we all strive for, even though most of us fail to hit it. But new data suggests we're looking at the wrong metric. It turns out that when you sleep might actually matter more for your lifespan than how long you spend unconscious. I've always been skeptical of rigid sleep quotas. They ignore how different bodies actually work. This shift toward timing over duration is interesting because it moves the goalposts from a simple quantity to a biological rhythm. It's a lot harder to track, but it's also a lot more honest about how our systems function. The real question is whether we can actually optimize our schedules to match these biological windows, or if we're just fighting a losing battle against our alarm clocks. The Duration Myth The obsession with the 8-hour sleep rule is a distraction. Recent 2023 data shows that sleep duration isn't the primary dri...

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...