Your AI Needs a Brain, Not a Better Model
A map of the 14 memory, retrieval and knowledge tools building the AI brain layer. Why temporal knowledge graphs beat vector-only RAG, and how to pick the right stack for your agents in production.
In-house LLM translation pipeline at Beekeeper
Replaced a vendor translation contract with a GitHub Actions and OpenAI pipeline in 6 weeks.
Python OpenAI GitHub Actions Evals
Problem
Translation was a recurring vendor line item with multi-week turnaround that slowed every release. Cost scaled with the number of locales and strings. Product waited on an external gatekeeper for every new string.
What I built
I designed and shipped a pipeline on GitHub Actions and the OpenAI API: locale-specific prompts, glossary enforcement, and automated evaluation against the old vendor output. It hooks into the PR flow, so new strings translate on merge. Shipped in 6 weeks. Over 12,000 key-value pairs translated since January 2024 with negligible regression.
What I learned
The eval harness sold the project, not the model. I compared LLM output with vendor output on real strings before I asked anyone to switch. Quality held, turnaround dropped from weeks to a PR, and the pipeline paid for itself many times over.
Forward Deployed Engineer Is the Title. Outcome Engineer Is the Job.
Forward deployed engineer is the hottest title in AI. If you want it, aim at the part that never goes stale: owning the outcome. What the title means, why it got hot, and what every posting actually asks for.
External 
Towards AI
Towards AI Drafted, not sent: how I built the second half of an AI outbound agent
A rep told me I had built half the product. So the agent now drafts the full 10-touch sequence and drops it into Outreach, ready for the rep to review and send. Nothing goes out on its own.