Kabir's Tech Dives

This episode discusses the emergence of "1-bit LLMs," a new class of large language models (LLMs) that use a significantly reduced number of bits to represent their parameters. These 1-bit LLMs, specifically the "BitNet" model, use only three values (-1, 0, and 1) for their weights, dramatically reducing computational cost, memory footprint, and energy consumption compared to traditional 16-bit or 32-bit LLMs.
This reduction in bit representation works through quantization, where the original weight values are mapped to these three values. This simplification leads to significant performance gains in terms of latency and memory usage while maintaining comparable accuracy to traditional LLMs. The video also highlights the potential of this technology to revolutionize the field of AI and make LLMs more accessible and efficient.

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The Notebook LM from Google is simply a game changer. It gives me reasons to believe that Google is ahead of everyone in the AI race. Sometimes, we techniques who know a thing or two about AI companies and their widgets think that Google and Apple dropped the ball when Microsoft led the race by joining forces with OpenAI. I was shocked in the early days—about two years ago—that OpenAI used Google's transformer technology to beat Google to its own tech dominance game.
Google has had many failures with AI. I do not use any of their AI products directly. I tried all the goodies like Gemini Advanced to Google Docs AI integration, and they all sucked severely.
However, a day ago, I discovered a killer app from Google: Apple Grade and Beyond. The poorly named product in the experimental stage—Notebook LM—is a game changer.
It allows any creator, like me, to validate what we have been doing with something we are passionate about. For example, I am building many tools for founders, from taking public notes to using AI to manage projects most passively. Users who have no idea where to find these tools must validate them. However, I can use Notebook LM to dive deeply into my product data/contents and see how the AI sees my offerings.
This will allow creators, founders, and marketers to have a third-party, deep-dive analysis of their offerings, from marketing content to actual product videos and pre-usage validation, which will allow more tweaking and refinement of the offering. This is a game-changer!
No, this is not just to paste my PDF to ChatGPT and get a read from it. It is beyond that. Google got the mojo!
Google: Go collect $200
OpenAI: DO NOT PASS GO. DO NOT COLLECT $6B
Microsoft: WAIT FOR OPEN AI
Apple: Sorry!

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Delegation is more than just assigning tasks—it's a strategic tool for building trust, optimizing processes, and achieving impactful results. In this episode, we dive into a game-changing framework from MIT Sloan Management Review that categorizes delegation styles into four trust-based approaches: Engage, Educate, Engineer, and Empower. Learn how to align your delegation style with trust levels in individuals and processes to avoid common pitfalls and maximize effectiveness. We'll explore the role of accountability, open communication, and how to tailor delegation strategies to your unique team dynamics. Perfect for leaders aiming to elevate their delegation game and foster high-performing teams!

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The text is a series of posts and comments from the r/podcasting subreddit, an online community for podcasters to discuss industry news, share advice, and seek assistance with technical issues. The posts highlight a variety of concerns, including finding the best equipment for audio recording, using specific software for editing and production, and navigating the complexities of podcast hosting, advertising, and video integration. The subreddit also serves as a platform for promoting podcasting-related products and services, with several members actively soliciting feedback on their offerings.

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I wanted to see how Google Notebook LM does with videos. Knowing Google is behind both Notebook LM and YouTube, I figured it would have an easy time with this task. It did. It did an excellent job of analyzing the video.
The video is mostly Bengali, which was nothing for Google Notebook LM. It figured out the deep-down details of the family video and discovered the tonality of the video so well that it is shocking!
Here is the video:
https://www.youtube.com/watch?v=82qQOQXwIHQ
Many of my friends—about 39 of them—were on a week-long group trip to Yellowstone. I shot the video and narrated the ending but used Bengali songs (generated by AI), and the Notebook LM just nailed my deep thoughts, which were embedded in the video.
I can not tell you how good of a job it did! It was awesome!

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