A human look at the AI future
Honest reflections on the uncertainty, excitement, and opportunities of the agentic era.
30 stories
Honest reflections on the uncertainty, excitement, and opportunities of the agentic era.
I turned my personal side project into a real-world testbed for our internal software factory implementation. Over a few weeks, the factory created and wired 21 flags for me—and changed how I ship.
What LaunchDarkly showed live on the Control Panel: how to see what's happening in production, act on it in real time, and test on data you already trust.
You can now install LaunchDarkly from the Vercel Marketplace and evaluate your first flag in minutes. Start on our free Developer plan and upgrade when you need to, all managed from Vercel.
Containerized AI applications require sophisticated deployment infrastructure to manage Docker images.
The vast majority of teams working on large language models (LLMs) and machine learning (ML) systems diligently track hyperparameters.
Learn how MLOps experiment tracking supports reproducibility, lineage, governance, CI/CD integration, and safe model promotion in production ML systems
Last summer, I shipped what I thought was a routine cleanup to production. It turned out to be a bug. But before the vast majority of users ever saw it, our AI software factory caught it and rolled back my change automatically.
On a recent episode of the MonkCast, Marek Poliks spoke with James Governor about why governing agents from the outside leaves teams perpetually one step behind.
Agent Optimization, now in beta in AgentControl, automatically searches for a better agent configuration against criteria you define.
We pointed coding agents at our oldest, most business-critical frontend. Here’s what it taught me about what a healthy AI software factory actually looks like.
A new capability on the LaunchDarkly MCP server offers a practical look at what an automated software factory could look like in practice.
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