AI

Link to the article: https://www.anthropic.com/research/building-effective-agents One Paragraph Summary Agents are systems where Large Language Models (LLMs) dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. They can start with a command or interactive discussion, and then gain "ground

Yuval Noah Harari's Nexus offers a compelling new lens for understanding human history and our current predicament: the analysis of information networks. Harari argues that understanding how information connects people and things, rather than focusing solely on technological advancements, is key. He emphasizes that information's power

When I decided to create a Slack bot to remind me of my due Anki flashcards, I didn’t find the concept intimidating. The steps—accessing Anki’s data, posting to Slack, and setting up a cron job—were all within my capability. However, I’m not a seasoned software

AI tools have opened up a world of possibilities, enabling us to accomplish tasks more efficiently and creatively. Here are some of the exciting things I've tried recently with AI: 1. Create a Webpage with Just a Chatbox Using tools like Cursor, you can: * Build a webpage by

In essence, "Low Training Error + Sufficient Data Relative to Model Complexity → Low Test Error". Supervised learning must work on the SAME distribution. Key Concepts * Training Error (\(\text{Train}_S(f)\)): The error of a model \(f\) evaluated on the training dataset \(S\). * Test Error (\(\text{Test}_D(f)

UC Berkeley's CS285 course: lecture 4. The goal of reinforcement learning is to find an optimal policy \(\pi^*\) that maximises the cumulative rewards over time. Define the trajectory \(\tau = (s_0,a_0,s_1,a_1,...) \), which is a sequence of states and actions. The probability under