Deep Dives
Comprehensive technical articles on AI systems, architecture, infrastructure, and production ML.
How Large-Scale LLM Inference Actually Works
A comprehensive deep dive into the mechanics of production LLM serving.
KV Cache: The Memory Behind Modern LLMs
Understanding attention caching and its impact on inference performance.
Continuous Batching: Keeping GPUs Busy
Dynamic batching strategies for maximizing throughput in LLM serving.
vLLM Internals: Paged Attention and Memory Virtualization
Deep dive into vLLM's innovative memory management techniques.
Speculative Decoding: Generating Tokens Faster
How speculative decoding accelerates LLM inference without quality loss.
Architecting Reinforcement Learning for LLMs
RLHF, PPO, DPO, reward modeling, and alignment workflows explained.
Agentic AI Stack: What Breaks and Where
Failure modes, reliability patterns, and observability for multi-agent systems.
Demystifying AI Compute
GPUs, TPUs, accelerators, memory hierarchy, and the economics of AI infrastructure.
LLM Evaluation in Production
Frameworks and patterns for evaluating LLM quality in production systems.
Building Multi-Agent AI Systems
Architecture patterns for reliable multi-agent orchestration.