10 popular fonts that can make your edits look more cinematic and professional. The good part? All these fonts are free and can be downloaded from DaFont, Google Fonts, and other free font resources.

Mistral built Leanstral to do something most AI models don't attempt, write formal mathematical proofs that a compiler can verify as correct. Not "pretty sure this is right" correct. Mechanically, provably, no-exceptions correct. That's a narrow use case, and the audience for it is small. What nobody expected was that a model trained on IMO-level math problems and abstract algebra benchmarks would end up running against open-source codebases and finding bugs that testing and fuzzing had both missed. Five of them previously unreported on GitHub.

AI conversations can feel private, but they may not have the legal protection you assume. Here’s how ChatGPT, Claude and other AI chats can end up in court.

It’s been a little over a month since Sam Altman admitted something you don't often hear from the CEO of the company that helped kick off the generative AI boom. “We did not have our best last 12 months ever, which is mostly my fault.” Then he raised the stakes. Altman said OpenAI is about to have its best 12 months to date. A month later, that promise is worth looking at more closely because OpenAI isn't simply trying to release a better model. It's spending enormous amounts of money on compute, pushing deeper into AI agents and products, competing with increasingly capable rivals, and making bets on infrastructure that could shape how the company operates for years. So what changed? And more importantly, what exactly is Altman betting on to turn a year he largely blames himself for into OpenAI's best one yet?

My journey with on-device AI started with small moments like upscaling a blurry photo while waiting for my coffee, transcribing a quick note with no signal, or chatting with an AI assistant without going online. That’s when it clicked, this is what it feels like when technology works for you, not the other way around.

IBM just released Granite 4.1, a family of open source language models built specifically for enterprise use. Three sizes, Apache 2.0 licensed and trained on 15 trillion tokens with a level of pipeline obsession that's worth understanding. But there's one result in the benchmarks I keep coming back to. The 8B model. Dense architecture, no MoE tricks, no extended reasoning chains. It matches or beats Granite 4.0-H-Small across basically every benchmark they ran. That older model has 32B parameters with 9B active. This one has 8 billion. Full stop. That result is either very impressive or it means the old model was underbuilt. Probably both. Here's how they built it, what the numbers actually say, and whether any of it matters for your use case.

Amazon didn't hold a press event for this. Just a quiet update to the Shopping app, tap the Alexa icon, describe what you want on a T-shirt, watch it appear. Add to cart. Prime shipping handles the rest. That's it. That's the whole barrier now. For years, turning an idea into a physical product meant either learning design tools, hiring someone who had, or finding a platform that made it slightly less painful. Print-on-demand services like Redbubble and Fourthwall built real businesses around that problem. Amazon just solved that problem too.