architecture

Cost Optimization for LLM Applications

The Stack — Granola

A technical teardown of Granola: the models, infrastructure, and engineering decisions behind the product.

LLM Observability in Production

CI/CD for AI Applications

The Stack — Klarna's AI Assistant

A technical teardown of Klarna's AI Assistant: the models, infrastructure, and engineering decisions behind the product.

Structured Output from LLMs

Building Reliable RAG Systems

The Stack — Notion AI

A technical teardown of Notion AI: the models, infrastructure, and engineering decisions behind the product.

AI Agent Orchestration Patterns

How Vector Databases Actually Work

The Stack — Perplexity AI

A technical teardown of Perplexity AI: the models, infrastructure, and engineering decisions behind the product.

Create collection with scalar quantization

Embeddings in Practice: Every Major Model Compared

The Stack — Cursor

A technical teardown of Cursor: the models, infrastructure, and engineering decisions behind the product.

Prompt Injection Prevention in Production

Taxonomy of prompt injection attacks and the layered defenses — input validation, output filtering, guardrails — that actually work at scale.

The Inference Stack Top to Bottom

What happens between your API call and a streamed token — routing, batching, KV cache, quantization, and speculative decoding explained.

MCP, Tool Use, and Function Calling: How Agents Actually Work in 2026

A comprehensive rundown of function calling, Model Context Protocol, agent frameworks, and the patterns that actually work in production — across every major provider.