AI content generation is about feeding your AI models with semantic and contextual information. The result is a platform that can ‘understand’ what an item is, and how it should be used. AI creates content using semantic knowledge in any form of content including video, 3d, VR and more.
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I converted some Half Life 1/2 screenshots into real life with the help of Klein 4b!
I know that there are AI video generators out there that can do this 10x better and image generators too, but I was curious how a small model like Klein 4b handled it… and it turns out not too bad! There are some quirks here and there but the results…
Everything You Need to Know About How Python Manages Memory
In languages like C, you manually allocate and free memory.
DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation
Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence. The global planning and iterative refinement features of dLLMs are particularly useful for code generation. However, current training and inference mechanisms for dLLMs in coding are still under-explored. To…
How Thomson Reuters built an Agentic Platform Engineering Hub with Amazon Bedrock AgentCore
This post was co-written with Naveen Pollamreddi and Seth Krause from Thomson Reuters. Thomson Reuters (TR) is a leading AI and technology company dedicated to delivering trusted content and workflow automation solutions. With over 150 years of expertise, TR provides essential solutions across legal, tax, accounting, risk, trade, and media…