Deep dives into LLMs, machine learning, prompt engineering, and the tools shaping the future. Written for engineers who build with AI.
The largest single inference speedup of the last three years is also the most invisible to application developers. A small draft model proposes tokens; a big model verifies them in parallel; the math guarantees the output distribution is unchanged. Here is how it actually works — and why your stack probably has it on already.
Most LLM apps send every request to the most expensive model and re-pay for every duplicate question. The LLM Gateway pattern fixes both — with smart routing, semantic caching, and budget guards. Here is the production architecture, with code.
Single-agent architectures hit a wall the moment your task needs planning, research, and execution in parallel. Multi-agent systems solve this — but most tutorials skip the hard parts. This guide doesn't.
Everyone's building with agents, MCP servers, skills, and subagents. Almost nobody can explain when to use which. This is the guide that fixes that — with architecture diagrams, production code, and a decision framework you can apply today.
Most RAG pipelines retrieve garbage, stuff it into context, and pray. Agentic RAG replaces the prayer with a judge, a retry loop, and a routing layer that actually works.
Your agent framework handles the happy path. Erlang's supervision trees handled telecom uptime for 40 years. Here's how to apply the same 'let it crash' philosophy to make AI agents self-healing.
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