Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto pattern for building knowledge‑aware language‑model applications. At its core, RAG couples a large language model (LLM) with a vector store that holds dense embeddings of documents, passages, or other pieces of knowledge. When a user asks a question, the system first retrieves the most relevant vectors, converts them back into text, and then generates an answer that is grounded in the retrieved material. ...

Table of Contents Introduction From Stateless Functions to Stateful Serverless 2.1 Why State Matters 2.2 Traditional Approaches to State Event‑Driven Microservices: Core Concepts 3.1 Events as First‑Class Citizens 3.2 Loose Coupling & Asynchronous Communication Building Blocks of a Stateful Serverless Architecture 4.1 Compute: Functions & Containers 4.2 Persistence: Managed Databases & State Stores 4.3 Messaging: Event Buses, Queues, and Streams 4.4 Orchestration: Workflows & State Machines Practical Patterns and Code Samples 5.1 Event Sourcing with DynamoDB & Lambda 5.2 CQRS in a Serverless World 5.3 Saga Pattern for Distributed Transactions Scaling Characteristics and Performance Considerations 6.1 Auto‑Scaling at the Event Level 6.2 Cold Starts vs. Warm Pools 6.3 Throughput Limits & Back‑Pressure Observability, Debugging, and Testing Security and Governance Real‑World Case Studies 9.1 E‑Commerce Order Fulfillment 9.2 IoT Telemetry Processing 9.3 FinTech Fraud Detection Challenges and Future Directions Conclusion Resources Introduction Serverless computing has matured from a niche “run‑code‑without‑servers” novelty into a mainstream paradigm for building highly scalable backends. The original promise—pay‑only‑for‑what‑you‑use—remains compelling, but early serverless platforms were largely stateless: a function receives an event, runs, returns a result, and the runtime disappears. ...

Introduction Large Language Models (LLMs) such as GPT‑4, LLaMA, and PaLM have transformed natural language processing, enabling capabilities ranging from code generation to conversational agents. However, the sheer size of these models—often exceeding tens or even hundreds of billions of parameters—poses a formidable challenge when it comes to inference latency. Users expect near‑real‑time responses, especially in interactive applications like chatbots, code assistants, and recommendation engines. Achieving low latency while maintaining high throughput requires a deep integration of software optimizations and high‑performance computing (HPC) architectures. ...

Table of Contents Introduction Fundamentals of Multi‑Agent Systems 2.1. Key Characteristics 2.2. Common Use Cases Why Low‑Latency Event‑Driven Architecture? 3.1. Event Streams vs. Request‑Response 3.2. Latency Budgets in Real‑Time Domains Serverless Functions as Orchestration Primitives 4.1. Stateless Execution Model 4.2. Cold‑Start Mitigations Designing an Orchestration Layer 5.1. Event Brokers and Topics 5.2. Routing & Filtering Strategies 5.3. State Management Patterns Communication Patterns for Multi‑Agent Coordination 6.1. Publish/Subscribe 6.2. Command‑Query Responsibility Segregation (CQRS) 6.3. Saga & Compensation Practical Example: Real‑Time Fleet Management 7.1. Problem Statement 7.2. Architecture Overview 7.3. Implementation Walkthrough Monitoring, Observability, and Debugging Security and Governance Best Practices & Common Pitfalls Conclusion Resources Introduction Multi‑agent systems (MAS) have moved from academic curiosities to production‑grade platforms that power autonomous fleets, distributed IoT networks, collaborative robotics, and complex financial simulations. The core challenge is orchestration: how to coordinate dozens, hundreds, or even thousands of autonomous agents while guaranteeing low latency, reliability, and scalability. ...

Table of Contents Introduction Why Run LLMs Locally? WebAssembly as the Execution Engine for Local LLMs 3.1 Wasm’s Core Advantages 3.2 Current Limitations for AI Workloads Garbage Collection in WebAssembly: A Brief History The New GC Proposal and Its Implications 5.1 Typed References and Runtime Type Information 5.2 Deterministic Memory Management 5.3 Interoperability with Existing Languages Performance Bottlenecks in Local LLM Inference 6.1 Memory Allocation Overhead 6.2 Cache Misses & Fragmentation 6.3 Threading and Parallelism Constraints Practical Optimization Techniques Using Wasm GC 7.1 Zero‑Copy Tensor Buffers 7.2 Arena Allocation for Transient Objects 7.3 Pinned Memory for GPU/Accelerator Offload 7.4 Static vs Dynamic Dispatch in Model Layers Case Study: Running a 7B Transformer with Wasm‑GC on a Raspberry Pi 5 8.1 Setup Overview 8.2 Benchmarks Before GC Optimizations 8.3 Applying the Optimizations 8.4 Results & Analysis Best Practices for Developers Future Directions: Beyond GC – SIMD, Threads, and Custom Memory Allocators Conclusion Resources Introduction Large language models (LLMs) have moved from cloud‑only research curiosities to everyday developer tools. Yet, the same cloud‑centric mindset that powers ChatGPT or Claude also creates latency, privacy, and cost concerns for many real‑world use cases. Running LLM inference locally—whether on a laptop, edge device, or an on‑premise server—offers immediate responsiveness, data sovereignty, and the possibility of fine‑grained control over model behavior. ...