Kabir's Tech Dives
I'm always fascinated by new technology, especially AI. One of my biggest regrets is not taking AI electives during my undergraduate years. Now, with consumer-grade AI everywhere, I’m constantly discovering compelling use cases far beyond typical ChatGPT sessions.
As a tech founder for over 22 years, focused on niche markets, and the author of several books on web programming, Linux security, and performance, I’ve experienced the good, bad, and ugly of technology from Silicon Valley to Asia.
In this podcast, I share what excites me about the future of tech, from everyday automation to product and service development, helping to make life more efficient and productive.
Please give it a listen!
Kabir's Tech Dives
DeepSeek: Efficient LLM Token Generation
DeepSeek's Multi-Head Latent Attention (MLA) offers a novel solution to the memory and computational limitations of Large Language Models (LLMs). Traditional LLMs struggle with long-form text generation due to the growing storage and processing demands of tracking previously generated tokens. MLA addresses this by compressing token information into a lower-dimensional space, resulting in a smaller memory footprint, faster token retrieval, and improved computational efficiency. This allows for longer context windows and better scalability, making advanced AI models more accessible. The approach enhances performance without sacrificing quality, benefiting various applications from chatbots to document summarization.
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