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Thinking & Reasoning Models

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Nikita Namjoshi 's video " How do thinking and reasoning models work? " from the Google for Developers channel explains the concepts behind "thinking models" or "reasoning models," such as Gemini, and how they use more computation at inference time to achieve better results in complex tasks. Video Summary The video focuses on how Large Language Models (LLMs) can be improved to handle complex tasks like coding, advanced mathematics, and data analysis by utilizing more compute power during the generation phase (inference or test time). The Problem: LLMs generate responses by predicting one token at a time. When solving complex problems, the model has to figure out the entire solution in a single pass to generate the correct final answer, which is difficult. The Solution: Chain of Thought (CoT): CoT prompting is a technique where the model is prompted to generate a series of intermediate steps (a "chain of thought") that lead to the final answer...
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Kai-Fu Lee on China-US AI Race - Q&A Transcript from a Bloomberg Interview

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Kai-Fu Lee, Chairman of Sinovation Ventures and author of  AI Superpowers and  AI 2041: Ten Visions for Our Future on the China-US AI Race Q: There's still a lot of challenges when it comes to the whole monetization of AI, according to his perspective. In the last three years, though, how have you looked at this whole evolution of China AI versus the US, and what are the advantages and challenges for China? A: Yeah, so firstly, I disagree on the prospects for the USA. USA because yes, you look at OpenAI, it's making half billion spending 40 billion. It looks like a bad balance sheet, but most of that 40 billion is spent for future revenues. And if you believe there are 2x, 3x, 5x growth for the next three years, it's going to justify that valuation at some point. The bubble is merely that it's gotten ahead of itself, not the likelihood of growth in the future. Not saying it's worth its price, right, but there's absolute substance under the thumb. Now back to t...
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This Week I Learned - Week #1 2026

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This Week I Learned -  *  Rishikesh Sreehari has created NPSNAV.in  website that hosts India's NPS Fund Database & API. You can access the latest NPS NAV and complete historical data for free. You can use the API programmatically or integrate live fund values into Google Sheets, Excel, or other tools. I needed an easy way to check the 52-week high and low of some schemes, so I vibe-coded a sample using the NPSNAV.in API which can be re-used for any NPS scheme. * Similarly, I used the free MFAPI.in API by Yuvaraj Loganathan to visualize the multi‑year NAV history of any Indian mutual fund scheme. The code sample shows seasonal patterns and the 52‑week high/low levels for any MF scheme at a glance. * These ChatGPT prompts can help you learn a new language. *  Nitter.net is an open-source alternative Twitter front-end   *  Invidious is an open source alternative front-end to YouTube   *  Adafruit IO is a cloud service by Adafru...
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