fastpaca What happens when LLMs hit production?
I write about memory systems, context management, how to scale agents, and more.
Research · Open Source · Fractional & Advisory
Writing
All posts →Building on a Moving Train
AI infrastructure feels brittle because every primitive beneath it moves. Here's what I learned building and killing infrastructure in a field that reinvents itself every quarter.
Let's Build an AI Assistant That Remembers
A practical walkthrough of building an assistant with persistent memory using Cria.
Ultimate Guide to LLM Memory
How do you add memory to your agent or LLM? What works and what does not? How do you use multiple memory systems at once to cover each others weaknesses?
Design Your LLM Memory Around How It Fails
Not all context is sacred. Design your agent's memory around what happens when critical information gets dropped.
Open Source
GitHub →cria
New models, providers, and memory systems drop. Your prompts break.
Prompt architecture for fast-moving teams—same logic, swap the building blocks underneath when you need to upgrade.
pacabench
Benchmarking agents shouldn't mean wrestling with brittle scripts and lost progress.
Local-first, reproducible benchmarks with isolated execution and persistent state. No SDK lock-in.