Build a FlashAttention‑2 Engine from Scratch in Triton
A hands‑on guide to implementing FlashAttention‑2 in Triton, complete with tiled kernels, causal masking, and PyTorch autograd wiring — perfect for a standout CV project.
Tutorial
Your First 7 Days with OpenClaw: A Field Guide for Backend Engineers
Seven days of hands-on notes on OpenClaw: what to install, what to configure, how to model real workloads, and the operational habits that separate a successful rollout from a stalled one.
Elasticsearch Zero to Hero: A Complete, Practical Guide
Elasticsearch has become the de-facto standard for search and analytics in modern applications. Whether you’re building a search bar for your product, analyzing logs at scale, or powering real-time dashboards, Elasticsearch is likely on your shortlist. This “zero to hero” guide is designed to take you from no prior knowledge to a solid, practical understanding of how Elasticsearch works and how to use it effectively in real-world systems. Along the way, you’ll get code examples, architectural explanations, and curated learning resources. ...
Zero-to-Hero with the vLLM Router: Load Balancing and Scaling vLLM Model Servers
Introduction vLLM has quickly become one of the most popular inference engines for serving large language models efficiently, thanks to its paged attention and strong OpenAI-compatible API. But as soon as you move beyond a single GPU or a single model server, you run into familiar infrastructure questions: How do I distribute traffic across multiple vLLM servers? How do I handle failures and keep latency predictable? How do I roll out new model versions without breaking clients? This is where the vLLM Router comes in. ...
A Deep-Dive Tutorial on Small Language Models (sLLMs): From Theory to Deployment
Introduction Small Language Models (sLLMs) are quickly becoming the workhorses of practical AI applications. While frontier models (with hundreds of billions of parameters) grab headlines, small models in the 1B–15B parameter range often deliver better latency, lower cost, easier deployment, and stronger privacy—especially when fine‑tuned for a specific use case. This tutorial is a step‑by‑step, implementation‑oriented guide to working with sLLMs: What sLLMs are and why they matter How to choose the right model for your use case Setting up your environment and hardware Running inference with a small LLM Prompting and system design specific to sLLMs Fine‑tuning a small LLM with Low‑Rank Adaptation (LoRA) Quantization and optimization for constrained hardware Evaluation strategies and monitoring Deployment patterns (local, cloud, on‑device) Safety, governance, and risk considerations Curated learning resources and model hubs at the end All code examples use Python and popular open‑source tools like Hugging Face Transformers and PEFT. ...