Python LLM applications often start small: a FastAPI route, a call to an LLM provider, some prompt engineering, and you’re done. Then traffic grows, latencies spike, and your CPUs sit mostly idle while users wait seconds—or tens of seconds—for responses. What went wrong? One of the most common and least understood culprits is thread pool starvation. This article explains what thread pool starvation is, why it’s especially dangerous in Python LLM apps, how to detect it, and concrete patterns to avoid or fix it. ...

Introduction Docker has revolutionized application deployment by enabling developers to package applications and their dependencies into lightweight, portable containers. However, containers don’t exist in isolation—they need to communicate with each other, with the host system, and with external networks. This is where Docker networking comes in. Understanding how Docker networking works is essential for building scalable, secure, and efficient containerized applications. Docker networking is the system that enables containers to communicate with each other, the host, and external networks[3]. It defines how data flows between containers and provides the infrastructure necessary for multi-container applications to function seamlessly. Whether you’re running a simple web application or a complex microservices architecture, mastering Docker networking is crucial for success. ...

Table of Contents Introduction What Are Vector Databases? Why Vector Databases Matter for LLMs Core Concepts: Embeddings, Similarity Search, and RAG Top Vector Databases Compared Getting Started: Installation and Setup Practical Python Examples Indexing Strategies Querying and Retrieval Performance and Scaling Considerations Best Practices for LLM Integration Conclusion Top 10 Learning Resources Introduction The explosion of large language models (LLMs) has fundamentally changed how we build intelligent applications. However, LLMs have a critical limitation: they operate on fixed training data and lack real-time access to external information. This is where vector databases enter the picture. ...

Context engineering is the systematic discipline of selecting, structuring, and delivering optimal context to large language models (LLMs) to maximize reliability, accuracy, and performance—far beyond basic prompt engineering.[1][2] This zero-to-hero tutorial equips developers with foundational concepts, advanced strategies, practical Python implementations using Hugging Face Transformers and LangChain, best practices, pitfalls, and curated resources to build production-ready LLM systems.[1][7] What is Context Engineering? Context engineering treats the LLM’s context window—its limited “working memory” (typically 4K–128K+ tokens)—as a critical resource to be architected like a database or API pipeline.[2][5] It involves curating prompts, retrievals, memory, tools, and history to ensure the model receives the right information at the right time, enabling plausible task completion without hallucinations or drift.[1][4][6] ...

As an expert AI and PyTorch engineer, this comprehensive tutorial takes developers from zero PyTorch knowledge to hero-level proficiency in building, training, fine-tuning, and deploying large language models (LLMs). You’ll discover why PyTorch dominates LLM research, master core concepts, implement practical code examples, and learn production-grade best practices with Hugging Face, DeepSpeed, and Accelerate.[1][5] Why PyTorch Leads LLM Research and Deployment PyTorch is the gold standard for LLM development due to its dynamic computation graph, which enables rapid experimentation—crucial for research where architectures evolve iteratively. Unlike static-graph frameworks, PyTorch’s eager execution mirrors Python’s flexibility, making debugging intuitive and prototyping lightning-fast.[5][6] ...