Diffusion models are rapidly stealing the spotlight from large language models in everything from code to images and even videos by using a clever “denoise-it-until-it’s-perfect” trick. Instead of predicting the next word or pixel, they start with pure noise and iteratively refine it into coherent output—offering a surprisingly flexible, high-quality alternative to token-based LLMs.
In the video you’ll see a quick rundown of how diffusion’s internal mechanism works, how those magic vectors are generated, and what it all means for the future of AI engineering—culminating in a punchy conclusion and some thought-provoking opinions on where this tech could take us next.
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