Anthropic built a tool that reads Claude's thoughts. Not the words Claude produces. The internal representations like the numerical signals firing inside the model before any words get generated. And when they pointed it at Claude during safety testing, they found something that should make anyone building or using AI pay attention. Claude knew it was being tested. It just didn't say so.

Last week an AI startup called Higgsfield announced it had premiered a fully AI-generated feature film at Cannes. The Wall Street Journal covered it. The founder posted on LinkedIn that "for decades, Cannes has been the room where new cinema gets legitimized." The story spread fast. There was one problem. Cannes said it never happened. "We can confirm that 'Hell Grind' was not screened as part of the official Festival de Cannes program," a festival spokesperson said. The film was shown at a paid third-party screening at a local theater in the town of Cannes during the festival period. That's a meaningfully different thing and the distinction matters because the entire credibility of the announcement rested on the Cannes name. This deserves the attention because it's a clean example of how AI hype gets manufactured and how quickly it travels before anyone checks.

Lumina-DiMOO is a state-of-the-art open source multimodal AI system, designed as a completely free and flexible Nano Banana alternative. This model is capable of text-to-image generation, image editing, inpainting, style transfer, subject-driven creation, controllable generation, extrapolation, and advanced image understanding, all in a single, developer-friendly framework.

I didn’t stop using Perplexity because it’s bad. I stopped because it slowly stopped feeling like mine. At first, it was impressive with fast answers, clean summaries, citations that made Google feel outdated. I used it almost daily. Over time though, it began to feel less like a tool and more like a gate. Features I relied on sat behind prompts to upgrade, and the experience started nudging me in directions I hadn’t chosen. That’s when I started looking sideways instead of paying up. I wasn’t searching for a cheaper alternative. I wanted control search that runs on my machine, uses models I choose, and doesn’t turn every query into someone else’s asset. That curiosity led me to Perplexica. I tried it casually, without expectations. It stuck.

wo years ago, if you wanted to generate a decent AI video, the only real option was a subscription. Pick a tool, pay monthly, generate on their servers. That was just how AI video worked. Open source models eventually closed the gap on quality, but running them locally meant terminals, dependency errors, and a lot of patience. Not everyone wanted that headache. Most people didn't. But now that just changed. On March 5th, Lightricks dropped two things at once. LTX 2.3, a major upgrade to their open source video model, and LTX Desktop, a proper video editor built entirely on top of it. Its Open Source & you can install it like any other app on your computer.

If you're dealing with laggy performance in After Effects or trying to run it on a low-end device, you're not alone. In this post, we’ll explore practical ways to optimize the application for a smoother performance.

Give an AI agent a hard problem and it usually figures out the easy wins fast. After that, more time does not help. It just sits there, trying the same things. ZhipuAI ran GLM-5.1 on a vector database optimization problem and let it go for 600 iterations. It did not run out of ideas. At iteration 50 it was sitting at roughly the same performance as the best single-session result any model had achieved. By iteration 600 it had reached 21,500 queries per second. The previous best was 3,547. That gap is not incremental improvement. It is a different category of result. GLM-5.1 is open source, MIT licensed, and the weights are on HuggingFace right now. It works with Claude Code, vLLM, and SGLang. If you are building anything that runs agents over long tasks, this one is worth understanding.