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https://github.com/ZachGoldberg/Startup-CTO-Handbook
You can buy the book on Amazon or Audible
Hi, thanks for checking out the Startup CTO’s Handbook! This repository has the latest version of the content of the book. You’re welcome and encouraged to contribute issues or pull requests for additions / changes / suggestions / criticisms to be included in future editions. Please feel free to add your name to ACKNOWLEDGEMENTS if you do so.
https://jixuan-fan.github.io/Momentum-GS_Page
https://github.com/Jixuan-Fan/Momentum-GS
A novel approach that leverages momentum-based self-distillation to promote consistency and accuracy across the blocks while decoupling the number of blocks from the physical GPU count.
Every Project I work on I always create a stylization Cheat sheet. Every project is unique but some principles carry over no matter what. This is a sheet I use a lot when I work on isometric stylized projects to help keep my assets consistent and interesting. None of these concepts are my own, just lots of tips I learned over the years. I have also added this to a page on my website, will continue to update with more tips and tricks, just need time to compile it all :)
Guillermo del Toro and Ben Affleck, among others, have voiced concerns about the capabilities of generative AI in the creative industries. They believe that while AI can produce text, images, sound, and video that are technically proficient, it lacks the authentic emotional depth and creative intuition inherent in human artistry—qualities that define works like those of Shakespeare, Dalí, or Hitchcock.
Generative AI models are trained on vast datasets and excel at recognizing and replicating patterns. They can generate coherent narratives, mimic writing or artistic styles, and even compose poetry and music. However, they do not possess consciousness or genuine emotions. The “emotion” conveyed in AI-generated content is a reflection of learned patterns rather than true emotional experience.
Having extensively tested and used generative AI over the past four years, I observe that the rapid advancement of the field suggests many current limitations could be overcome in the future. As models become more sophisticated and training data expands, AI systems are increasingly capable of generating content that is coherent, contextually relevant, stylistically diverse, and can even evoke emotional responses.
The following video is an AI-generated “casting” using a text-to-video model specifically prompted to test emotion, expressions, and microexpressions. This is only the beginning.