architecture

Orchestrating Long-Running AI Workflows: Durable Execution, Checkpointing, and Human-in-the-Loop for AI Pipelines

How token-aware rate limiting works for LLM APIs, why request-count limits fail, and implementation patterns across API gateways and proxy layers.

The Stack — Glean

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

Orchestrating Long-Running AI Workflows: Durable Execution, Checkpointing, and Human-in-the-Loop for AI Pipelines

How orchestrating long-running AI workflows differs from traditional job queues — durable execution, checkpointing, human-in-the-loop, and practical…

The Open-Source AI Inference Stack: Ollama, vLLM, llama.cpp, LiteLLM, and the Tooling That Makes Self-Hosted Inference Practical

How the open-source AI inference stack fits together: Ollama, vLLM, llama.cpp, LiteLLM, and the tooling that makes self-hosted models practical in 2026.

The Stack — Vercel v0

A technical teardown of Vercel v0: the models, infrastructure, and engineering decisions behind the product.

AI Agent Authentication and Identity: From Shared API Keys to Workload Identity

Every major AI authentication pattern explained: API keys, OAuth 2.0, workload identity, DPoP tokens, and scoped credentials for agent-to-API…

Token-Based Rate Limiting for LLM APIs

How token-aware rate limiting works for LLM APIs: algorithms, implementation patterns, sliding windows, and production architectures across API gateways.

The Stack — Midjourney

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

AI Guardrails for Production Systems

How AI guardrails work in production: input validation, output filtering, PII detection, hallucination detection, and the architecture that ties them together.

Semantic Caching for LLM Applications

Technical breakdown of semantic caching for LLM apps: exact vs embedding-based matching, similarity thresholds, cache invalidation, and the real tradeoffs.

The Stack — Replit Agent

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

LLM Gateway Architecture: Managing Multiple AI Providers Through a Unified API Layer

How LLM gateways work: provider abstraction, fallback routing, load balancing, cost tracking, and credential management across OpenAI, Anthropic,…

The GPU Landscape for AI Inference: H100 vs A100 vs L40S vs Consumer GPUs, Cloud Pricing, and the Self-Host vs API Decision

Every major AI provider handles GPU memory, batching, and model serving differently. This is how the inference stack works from HTTP request to matrix…

The Stack — Otter.ai

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

Prompt Engineering from First Principles

How prompt engineering techniques work at the mechanical level — tokenization, attention patterns, sampling parameters, and why chain-of-thought…

The Stack — Claude Code

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

Fine-Tuning vs RAG vs Long Context: Decision Framework with Real Tradeoffs

Every major fine-tuning approach compared against RAG and long-context prompting — with architecture patterns, cost math, latency profiles, and a…

Multi-Modal AI in Practice: Vision, Audio, and Document Understanding Across Every Major Provider

A complete technical guide to multi-modal AI: vision, audio, and document understanding across Claude, GPT, Gemini, and open models, with architecture…

The Stack — Intercom Fin

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

Streaming, SSE, and Real-Time AI: How Streaming Responses Work and How to Build Responsive AI UIs

Every major AI provider handles streaming differently. This guide covers SSE, WebSockets, HTTP chunked transfer, and the implementation details that…

LLM Evaluation and Benchmarks: What They Measure, What They Miss, and How to Evaluate for Your Use Case

How LLM evaluation benchmarks actually work, what they measure, what they miss, and how to build evaluation that matters for your specific application.

The Stack — GitHub Copilot

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

The Stack — Harvey AI

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

The Stack — Gamma

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

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.