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TLDR: A pretrained LLM is a generalist. Fine-tuning makes it a specialist. Supervised Fine-Tuning (SFT) teaches it your domain's language through labeled examples. LoRA does the same with 99% fewer tr

• PagedAttention & KV-Cache Optimization: How vLLM Handles Large Scale Inference
• ANN Index Types Explained: When to Choose Flat, HNSW, IVF, or IVF-PQ
• RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)
• Fine-Tuning LLMs with LoRA and QLoRA: A Practical Deep-Dive
• Build vs Buy: Deploying Your Own LLM vs Using ChatGPT, Gemini, and Claude APIs

TLDR: Stale reads return superseded data from replicas that haven't yet applied the latest write. Cascading failures turn one overloaded node into a cluster-wide collapse through retry storms and redi

• System Design: Designing a Financial Ledger with Double-Entry Constraints
• High-Level Design: Building a Real-Time Ad Click Aggregator at Scale
• High-Level Design: Scaling a Concert Ticket Booking System under Flash Load
• System Design: Designing an Autonomous AI Coding Agent (Devin at Scale)
• System Design for Agentic AI Systems: From Distributed Systems Principles to Production

TLDR: LLMs are stateless — every API call starts fresh. LangChain memory classes (Buffer, Window, Summary, SummaryBuffer) explicitly inject history into each call, and RunnableWithMessageHistory is th

• LangChain Tools and Agents: The Classic Agent Loop
• LangChain RAG: Retrieval-Augmented Generation in Practice
• LangChain Memory: Conversation History and Summarization
• LangChain 101: Chains, Prompts, and LLM Integration
• From LangChain to LangGraph: When Agents Need State Machines