Articles
In-depth technical articles that go beyond our YouTube videos — fresh research, benchmarks, and production insights.
LLM Gateway Architecture in 2026: Routing, Caching, and the Cost Math
One company cut its LLM bill from $47K to $12.7K a month with a gateway. Here's the architecture, the latency tradeoffs, and which gateway to actually pick.
Context Engineering: Why a Bigger Context Window Makes Your Agent Worse
Million-token windows don't fix agents — they break them. The context rot data, the 32K cliff, and the three techniques that actually work in 2026.
LLM Fine-Tuning vs RAG: When to Use Each in Production
OpenAI is shutting its fine-tuning platform on May 8, 2026. Here's the real cost math, the benchmark data, and how to decide between RAG and fine-tuning.
Designing Netflix's Recommendation Engine With AI Agents
Netflix replaced 30+ recommendation models with one foundation model in March 2025. Here's where AI agents fit — and where they break the 200ms latency budget.
Google's Agent2Agent (A2A) Protocol — Multi-Agent Interoperability in 2026
How A2A v1.0, signed Agent Cards, and 150+ org adoption made cross-vendor agent communication a real production layer in 2026.
Building RAG on Amazon Bedrock — Knowledge Bases, Guardrails, and Agents in 2026
S3 Vectors killed the OpenSearch tax. Guardrails dropped 80%. Here's how to actually ship RAG on Bedrock in 2026 without the $350/month trap.
Amazon Bedrock Pricing Deep Dive — Real Costs at 1M, 10M, and 100M Tokens
Real Bedrock costs at scale: Sonnet vs Nova, the OpenSearch trap, and three discounts that cut bills in half. Numbers the marketing page hides.
Kubernetes for AI Workloads in 2026 — GPU Scheduling, Autoscaling, and the 5% Problem
How DRA, KAI, Kueue, Karpenter, and vLLM cut your Kubernetes GPU bill 50–70% in 2026 — and why most clusters still run at 5% utilization.
Self-Hosting LLMs in 2026 — When It Makes Sense and When It Doesn't
The break-even is 500M tokens a day. Below that, APIs win. Here's the actual math, the hidden costs, and the four conditions that justify your own GPUs in 2026.
AI Coding Agents Compared 2026 — Cursor vs GitHub Copilot vs Claude Code vs Windsurf
Cursor 3.0, Copilot agent mode, Claude Code Opus 4.7, Windsurf SWE-1.5. Real benchmarks, real pricing, real picks for May 2026.
Vibe Coding in 2026 — What It Actually Means for Engineering Teams
The term Karpathy coined is already obsolete. Here's what vibe coding does to engineering teams in 2026 — adoption, productivity, security, the playbook.
Amazon Bedrock AgentCore: From Idea to AI Agent in Minutes
AgentCore is AWS's modular agent platform — Runtime, Memory, Gateway, Identity, and Observability you can adopt one piece at a time. Here is what it actually does.
Amazon Bedrock vs Google Vertex AI vs Azure AI — The Real Architecture Difference
The architectural choices behind the three big enterprise AI platforms — and the trade-offs every team hits in production.
MCP: The Complete Developer Guide to Model Context Protocol
How Model Context Protocol actually works under the hood — primitives, transports, security, and the production patterns nobody warns you about.
Vector Databases at Scale: pgvector vs Pinecone vs Qdrant
The real trade-offs between pgvector, Pinecone, and Qdrant — benchmarks, cost at 1M/10M/100M vectors, and the scaling walls that hit at 3 AM.
GPT-5.5 vs DeepSeek V4: The Real Cost Gap Nobody Talks About
A 10 QPS RAG system costs $15K/month on OpenAI. The same workload on self-hosted DeepSeek V4 runs at $2,500. Here is what actually changes.
Multi-Agent AI: How Teams of Agents Replace Single Models
Why single-agent AI fails at complex tasks and how production multi-agent systems work — orchestrators, specialized agents, tools, shared memory, and routing.
LLM Inference: How to Cut Your GPU Bill from $60K to $6K
Five production techniques that reduce LLM serving costs by 90% — continuous batching, KV cache management, quantization, model parallelism, and intelligent routing.
Building Efficient RAG Pipelines with Vector Databases
The five stages of a production RAG pipeline — and the chunking, embedding, and retrieval mistakes that silently kill accuracy.