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.

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.

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.

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.

Forward Deployed Engineers embed inside customer teams to turn messy enterprise data into shipped AI systems. Here’s how the role emerged, what FDEs actually do, and why it matters now.