InApp : Tech Radar

In today's tech landscape, where major corporations often dominate markets with their sheer size and resources, it's refreshing to see smaller players challenging conventional wisdom and carving out unique niches. One such example is Japanese companies, known not only for their meticulous craftsmanship but also for their innovative spirit. What's intriguing isn't just that these firms are thriving in unexpected areas—from technology to food processing—but how they do so by redefining what it means to be a market leader. This approach challenges the widely held belief that larger is always better. By exploring industries where size might seem like an obstacle, Japanese companies demonstrate that innovation and adaptability can transcend traditional business models. It's as if they are saying: "Size doesn't define success; creativity does." This defiance of expectations not only highlights Japan's economic prowess but also offers valuable les...

What if your Linux desktop didn’t hide the terminal — but made it the star of the interface? We’ve spent years rebuilding Flipper One from scratch, not because we wanted another desktop environment, but because we kept hitting the same wall: powerful CLI tools buried under layers of GUI abstraction that pretend they’re making things easier. So we flipped it. Instead of wrapping the terminal in a desktop, we built a desktop around the terminal — using thin, smart wrappers around existing CLI utilities to give them a graphical face without losing their soul. It’s been brutally hard. Financially, we’ve bootstrapped every step. Technically, we’re fighting to maintain full mainline Linux kernel support on ARM while building a GUI framework that doesn’t just sit on top of the system but feels like a natural extension of it. This isn’t another theme or launcher — it’s a rethink of what a Linux desktop could be when you stop hiding the power and start putting it front and center. If ...

When an OpenAI language model quietly disproved a long-standing conjecture in discrete geometry, it didn’t just win a math puzzle — it revealed a new kind of reasoning that doesn’t look like human intuition at all. The conjecture, which had resisted proof for over a decade, wasn’t about neural nets or transformers — it was about packing shapes in high-dimensional space, a problem so abstract even specialists struggle to visualize it. Yet the model didn’t simulate human-like spatial reasoning. It didn’t draw diagrams or rely on geometric intuition. Instead, it explored a vast, alien space of symbolic manipulations — patterns that made no immediate sense to us but consistently led to contradictions in the conjecture’s assumptions. When it finally found a counterexample, the proof wasn’t elegant in the way mathematicians admire. It was cluttered, indirect, and built on layers of abstraction that felt more like machine logic than insight. I’ve spent years watching AI tackle proble...

Most LangChain demos feel like toy examples — a prompt here, a tool call there, and suddenly you’ve got a chatbot that can tell you the weather. It’s clean, it’s tidy, and it barely scratches the surface of what real-world AI applications actually need to do. The truth is, building agents that work outside of a controlled notebook is messy. Tools fail. Models hallucinate. Ambiguity creeps in at every turn — whether it’s a vague user request, an incomplete API response, or a file system that doesn’t behave like you expect. LangChain gives you the primitives to handle that, but most tutorials skip straight to the happy path. Deep Agents changes that. It’s not another abstraction layer for the sake of it — it’s a opinionated set of patterns baked into LangChain for the stuff you actually wrestle with: planning when the goal isn’t clear, delegating to subagents when one model can’t do it all, and safely poking around in a file system without bringing down your whole app. If you’ve...

Most tutorials show you how to call pipeline() — this is what happens when you do. It’s not magic, but it feels like it. One line, and suddenly you’re running a BERT classifier, a Stable Diffusion model, or a Whisper speech recognizer. Behind that call, Transformers is doing a lot: downloading weights, setting up tokenizers, configuring the right inference backend, handling device placement, and wrapping it all in a clean, familiar API. You don’t see any of that unless you look under the hood. I’ve seen beginners treat pipeline() like a black box — and honestly, for quick prototyping, that’s fine. But if you’re trying to debug why your model is slow, or why it’s not using your GPU, or how to swap in a custom tokenizer, you’ll hit a wall fast. The abstraction is helpful until it isn’t. And that’s where most people get stuck: they know how to use it, but not how it works. What I want to show you isn’t how to call pipeline() again. It’s what happens inside when you do — and h...

Discover how to harness yt-dlp’s powerful filtering features while navigating its complex licensing landscape — without getting caught off guard. It’s a tool a lot of people love for downloading videos and audio from all over the web. You can grab stuff from YouTube, TikTok, Instagram, and way more sites than you can count. But here’s the thing: yt-dlp itself is under the Unlicense, which sounds super open and free. And, pretty much all the core code is yours to use however you want. But, kind of like a patchwork quilt, some parts inside it come from other projects with different rules. Those bits might have their own licenses, and you gotta pay attention to that if you’re sharing or modifying things. So, think of yt-dlp like a Swiss Army knife with tools borrowed from different friends. Each tool works great together, but you don’t own all of them the same way. Using its filters to pick exactly what you want — like only 1080p videos or English subs — is super handy. Just know wher...