InApp : Tech Radar

We're hitting a wall with LLMs. The idea that we can just "chat" our way through complex workflows is starting to feel like a fantasy. As these interfaces become more common, I've noticed the friction of crafting the perfect prompt often outweighs the actual value of the output. It's a lot of cognitive overhead for a result that frequently misses the mark. I saw this clearly last week when I stumbled upon several GitHub repositories that were actively spreading malware. I tried using an AI agent to help me figure out the best way to report and mitigate the spread, but the response was useless. It gave me generic, high-level advice that didn't help me take any real action. It was the same experience I had years ago working as a developer, asking a business owner a direct question about a task and getting a response that completely bypassed the technical reality of the problem. The tech is getting better at mimicking conversation, but it's still failing ...

The Appeal of Talking to AI The appeal of talking to an AI comes from how quickly it turns a thought into a response. You speak or type a prompt and get an answer back without having to hunt through menus or learn a special syntax. That immediacy feels like having a knowledgeable partner who is always ready to help. Early adopters often mention speed and convenience as the biggest draws. Writers use voice prompts to draft emails or brainstorm story ideas while their hands stay free. Students ask the model to explain a math problem step by step, getting a tutoring session that adapts to their pace. Some people simply enjoy casual chat, finding the AI’s replies a low‑pressure way to pass time or work through thoughts. Under the hood, the system relies on a language model that generates text token by token. Latency depends on model size and the hardware it runs on; a 7‑billion‑parameter model on a modern GPU can return a reply in 200‑400 ms, while larger models may take a second or tw...

ASML’s machines don’t just make chips — they make modern life possible. The Dutch company builds the only tools on Earth capable of etching the impossibly fine patterns onto silicon that power everything from smartphones to AI servers. When a single supplier holds that kind of leverage, it’s not just a business story — it’s a geopolitical fault line. Governments are now treating ASML like strategic infrastructure, not a commercial vendor. Export controls, subsidy negotiations, and quiet diplomatic pressure have turned its headquarters in Veldhoven into an unlikely cockpit of global tech rivalry. The U.S. wants to slow China’s chip ambitions. The Netherlands wants to keep its crown jewel. And China? It’s pouring billions into building alternatives, knowing that without access to ASML’s extreme ultraviolet lithography systems, its semiconductor dreams hit a hard wall. What happens when a company that sells $200 million machines becomes the choke point in a tech cold war? And mor...

The fastest way to write code with AI isn’t letting it generate everything — it’s making it work harder, one deliberate step at a time. I’ve watched too many developers treat AI pair programmers like magic boxes: prompt, paste, pray. They get flashy demos, then spend hours untangling hallucinated logic, debugging invented APIs, or rewriting code that almost works but subtly breaks everything else. It’s not that the models are dumb — they’re shockingly capable — but the assumption that more generation equals more progress is quietly sabotaging productivity. What actually moves the needle isn’t letting the AI write whole functions or modules in one go. It’s using it like a relentless junior engineer who needs constant direction: ask for a single line, verify it, then ask for the next. Break the problem into atomic steps, validate each piece, and let the AI focus on the narrow slice where it excels — pattern completion, boilerplate, syntax — while you keep ownership of intent, s...

After months of fighting digital distractions with apps that failed, I built a simple hardware device that finally let me write without interruption. Most digital wellness tools still live inside the same device they’re trying to tame, and in my experience, that’s usually why they fall apart. The problem isn’t that our phones lack settings — it’s that those settings rarely create enough resistance in the moment that matters. You can toggle focus mode a dozen times a day, but when the urge hits, it’s just one tap away from undoing all your good intentions. So I stepped outside the screen. A physical switch. A separate device. Something that doesn’t rely on willpower or software nudges. It sounds almost too simple to work — and honestly, I wasn’t sure it would. But it did. And now I’m wondering: if the solution to digital overload isn’t more software, but less of it — what else have we been overcomplicating? Why Software Solutions for Distraction Blocking Keep Failing Softwar...

For the first time in decades, the U.S. is requiring certain green card seekers to leave the country before they can even start their application. That’s a reversal of how it’s worked since the mid-20th century — if you were already here legally, you could adjust your status without going home. Now, for some categories, you have to depart first, apply from abroad, and wait for approval before you’re allowed back in. I saw the DHS notice posted outside a San Diego USCIS office in January — a small sign, easy to miss, but the implications aren’t. This isn’t just a procedural tweak. It affects people on H-1Bs, L-1s, even some F-1 OPT holders who thought they were on a path to residency. Now they’re being told to uproot their lives, sometimes with weeks’ notice, to chase a visa stamp in a consulate that might be backlogged for months or years. I’ve covered immigration tech for years — the biometric kiosks, the CASE Act portals, the endless digitization of paper forms — but this fe...