Introduction Writing a patent as a programmer may seem intimidating, but with the right process, you can protect your innovations. This guide walks you through coming up with ideas, checking if they exist, and writing a patent in a clear, structured way. Step 1: Understand What Can Be Patented Not every idea is patentable. Generally, you can patent: New algorithms or methods if they are tied to a specific technical solution. Unique software systems or architectures. Novel methods of solving a technical problem in computing. Pure abstract ideas, mathematical formulas, or generic software solutions are usually not patentable. ...

Table of Contents Introduction Understanding the Foundations Setting Up Your Django Project Integrating LLM Models with Django Building Views and API Endpoints Database Design for LLM Applications Frontend Integration with HTMX Advanced Patterns and Best Practices Scaling and Performance Optimization Deployment to Production Resources and Further Learning Introduction Building web applications that leverage Large Language Models (LLMs) has become increasingly accessible to Django developers. Whether you’re creating an AI-powered chatbot, content generation tool, or intelligent assistant, Django provides a robust framework for integrating LLMs into production applications. ...

Introduction This comprehensive guide contains 500 curated LeetCode problems organized for efficient interview preparation. Each problem includes: ✅ Interactive checkboxes for progress tracking 🔥 Priority markers for must-solve problems 🟢🟡🔴 Difficulty indicators (Easy/Medium/Hard) Pattern tags for systematic learning Study Recommendations Beginner Path (3-4 months): Start with Easy problems in Array & Hash Table Master Two Pointers and Sliding Window patterns Build foundation with Trees and Linked Lists Practice 2-3 problems daily Intermediate Path (2-3 months): ...

Why Most RAG Systems Fail Most Retrieval-Augmented Generation (RAG) systems do not fail because of the LLM. They fail because of bad chunking. If your retrieval results feel: Random Hallucinated Incomplete Loosely related to the query Then your embedding model and vector database are probably fine. Your chunking strategy is the real bottleneck. Chunking determines what the model is allowed to know. If the chunks are wrong, retrieval quality collapses — no matter how good the LLM is. ...

As LLM-powered systems have grown more capable, they have also grown more complex. By 2025, most production-grade AI systems no longer rely on a single monolithic agent. Instead, they are composed of multiple specialized sub-agents, each responsible for a narrow slice of reasoning, execution, or validation. Sub-agents enable scalability, reliability, and controllability. They allow systems to decompose complex goals into manageable units, reduce context pollution, and introduce clear execution boundaries. This document provides a deep technical explanation of how sub-agents work, how they are orchestrated, and the dominant architectural patterns used in real-world systems, with links to primary research and tooling. ...