Adnan Masood, PhD.·Aug 31Prompt Caching in Production AI Systems: Architecture, Economics, and GovernanceA practical guide for technical leaders running LLMs in software development and agentic workloads.
Adnan Masood, PhD.·Aug 30From Prompt to Graph: How the Unit of LLM Engineering Moved Up the StackHow LLM engineering moved from prompt engineering to context engineering, harnesses, loops, and agent graphs, and when each layer is worth…A response icon1A response icon1
Nisha·Aug 12Speculative Decoding in LLM Inference: Why Wasting Compute on Wrong Guesses Makes AI FasterYou are paying your GPU to generate words it fully expects to throw away — and that is exactly why your inference bill is about to get…
Chinmay Sahoo·Aug 11What Building an AI Data Room Taught Me About Inference EconomicsThe first version of our data room reviewer worked exactly as you’d expect: take the question, take the documents, send everything to a…
Doran Gao·Mar 17AI Agents Are Easy to Demo. The Bill Comes Later.Why token economics — not just model capability — will decide what survives the AI bubble
Michael Hannecke·Dec 1, 2025LLM Prompt Caching: What You Should KnowPrompt caching can cut your LLM inference costs by up to 90% and slash latency dramatically. But understanding what’s actually happening…
