Transformer Architectures: 2017 → 2025+
From ‘Attention Is All You Need’ to modern mixture-of-experts and state-space hybrids: how the transformer family has evolved and what working engineers should know about the variants in production today.
Posts
"Attention Is All You Need" — The Paper That Rewrote NLP and Reshaped Modern AI
A practical walkthrough of the 2017 transformer paper that replaced RNNs with self-attention — what the architecture actually does, why the design choices matter in production, and how every modern LLM inherits from it.
AI Engineering Is No Longer Just About Knowing How to Use an LLM
Calling an LLM API is the easy part. Shipping reliable AI features in production demands evaluation pipelines, retrieval layers, observability, cost controls, and guardrails. Here is what real AI engineering looks like in 2026.
NVIDIA Model Optimizer: The Compiler-Style Playbook for Production LLM Inference
A deep dive into NVIDIA’s Model Optimizer toolkit and how its compilation-style approach reshapes the production LLM inference stack, from quantization recipes to KV-cache strategies.
LLM Gateway: One Layer to Manage Multiple AI Models
An LLM gateway centralizes routing, auth, caching, and observability across providers like OpenAI, Anthropic, and self-hosted models. Here’s how it works in production and why most teams end up building one.