Insights on software development, engineering practices, architecture patterns, and the craft of building robust, scalable systems.
Learn how to build agentic workflows with LangChain and LangGraph.
Understand the mathematical foundations of LLM fine-tuning with clear explanations and minimal prerequisites. Learn how gradient descent, weight updates, and Transformer architectures work together to adapt pre-trained models to new tasks.
A comprehensive guide to choosing the right approach for your LLM project: using pre-trained models as-is, enhancing them with context injection and RAG, or specializing them through fine-tuning. Learn the trade-offs, costs, and when each method works best.
Learn the foundational concepts of LLM workflows - connecting language models to tools, handling responses, and building intelligent systems that take real-world actions.
Learn how to use Monte Carlo simulation to model and analyze stock market returns, estimate future performance, and understand the impact of randomness in financial forecasting. This tutorial covers the fundamentals, practical implementation, and interpretation of simulation results.
A comprehensive guide to understanding AI agents, their building blocks, and how they differ from agentic workflows and agent swarms.
A deep dive into how MCP makes tool use with LLMs easier, cleaner, and more standardized.
An exploration of why setting temperature to zero doesn't eliminate all randomness in large language model outputs.
This is a test notebook for testing the admin editing functionality.
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