Attention Residuals are a brilliant reminder: Transformers still have untapped levers beyond just scaling. Depth-wise attention is basically giving each layer memory over its predecessors- like unrolling the network through time, but SMARTER

Myth vs Reality: AI Chatbots Myth: “AI understands everything you ask.” Reality: LLMs predict the next token based on probability. That’s why most template chatbots break in real-world conversations: • hallucinations • inconsistent responses • weak context The solution

Most people think better AI = better models. The truth? System design decisions like these make the real difference. This is what separates production-ready AI from a demo. If you’re building real-world AI systems, this layer is everything. Test our custom chatbot here:

What happens when AI stops treating audio and video separately? Perceptron Pulse - Issue #3, coming this April, explores exactly that. Not just visuals. But sound generated with it; in perfect sync. -> LTX-2 and dual-stream audio-visual models -> How sync between sound + video

This was honestly frustrating to debug. The model was good.. but the experience felt off. Slow replies. Weird lip sync. Just not usable. We didn’t have to change the model. But everything improved. Before: • 3 - 5 s lag (kills conversation) • lip sync felt off •

Most people are still using LLMs to answer questions. Andrej Karpathy is using them to build knowledge that improves itself. The workflow is almost boring in its simplicity: dump raw research, let the model organize it into a structured wiki, and keep querying it. But every query

One of our designers put LTX-2 to the test, and this is exactly the kind of hands-on perspective we value. Beyond the hype, it’s about how these tools actually perform in real creative workflows. Saad's take: promising progress for open-source AI video, especially in