In 2026, 5% of LLM production spans fail and 60% of all LLM errors are rate limits (Datadog). Here's how to set up OpenTelemetry tracing for LLM apps — from a single API call to a full agent graph — with real code and gen_ai.* conventions.
Loop engineering is three weeks old. The pattern behind it — Anthropic's evaluator-optimizer workflow — is eighteen months old. Here's what's actually new, and what really works.
In 2026, Stanford's Digital Economy Lab found agentic coding tasks can burn up to 1,000x more tokens than a single chat completion — with 30x cost variance on identical runs. Here's where agent spend actually goes, and the five techniques cutting it back down.
Enterprise LLM API spend hit $12.5 billion in 2025. Without an LLM gateway handling routing, caching, and failover, you're paying full price on every call and one provider outage takes your app offline. Here's the architecture that fixes both.
Context engineering determines 80% of agent performance variance. Learn how to design, compress, and manage AI agent context windows — with data from arXiv, Datadog, and Chroma's context rot study.
Prompting, RAG, or fine-tuning? The wrong call early costs weeks of rework. Here's a plain-language decision tree to pick the right LLM customization approach — the first time.
In Feb 2026, 60% of all LLM errors in production were rate-limit errors (Datadog). Here are the five patterns that actually fix it — token-aware buckets, priority queues, jitter math, model fallback, and cache-as-throughput.
Two techniques — prompt caching and model routing — can realistically cut your LLM API bill by 60–80% without touching your product's quality. Here's how to implement both.
MCP has crossed 97 million monthly SDK downloads and 41% enterprise production adoption — yet most technical writing is still hype. Here's the engineer's guide to how the protocol actually works, when to choose it over function calling, and how to ship a server in 20 minutes.
90% of developers use AI tools at work, but most are still prompting ad-hoc. Here's the six-step end-to-end workflow — from spec to deployed code — that turns scattered AI use into compounding productivity gains.
Engineers who've shipped RAG and are now adding agents hit the same design wall: when does the agent retrieve, when does it remember, and when does it need both? Here's the decision framework.
Benchmark scores look great. Your agent breaks in production. Here's why most LLM agent evals are misleading — and how to build ones that actually catch failures before your users do.
Most AI agents fail in production — not because the models are bad, but because the systems around them are built wrong. Here's the architectural guide senior engineers wish they had before they started.
A viral open-source tool forces your AI coding agent to talk like a prehistoric human — and it turns out that's the most reliable way to stop burning money on verbosity.
Vibe coding an entire app in one shot feels fast — until maintenance hits. There's a better model: build atoms first, let AI fill them, and own every layer of your system.