When you build a PowerShell project from multiple files, the natural structure is clear: enums first, then classes, then functions. Each group has its own place, and as long as dependencies only flow in one direction, that structure works perfectly. But sometimes a function depends on a class, and that class calls the function. There is no longer a clean boundary between the two groups — they need
The drift problem nobody told you about If you have used Claude Code, Cursor, Aider, or any other AI coding agent across more than two projects, you have felt this: You start project A. You copy the .agents/ folder (or CLAUDE.md, or .cursorrules) from your last project. You tweak two things. Done. You start project B six weeks later. You copy from project A. You tweak three things this time. Now
Cross-posted from the Stigmem blog. Today we're releasing stigmem v1.0: A stable, open-source specification and reference implementation for a federated knowledge fabric for AI agents. Stigmem = Stigmergy + Memory. Stigmergy (Greek stigma — mark; ergon — work) is the coordination mechanism you see in ant colonies and termite mounds: agents don't communicate directly with each other. Instead, they
More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
Overview Let's get our hands dirty. This part covers the full setup and the actual demo: deploy PayLedger to both regions, wire up Route 53 failover, configure the Agent Space, inject three simultaneous faults, and walk through exactly what the agent found. Quick recap from Part 1: PayLedger is a demo payment ledger deployed to ap-southeast-1 (primary) and ap-northeast-1 (secondary) with Route 5
Overview I finished the DR Toolkit thinking I had covered the important parts of disaster recovery: runbooks, RTO/RPO targets, post-mortems. Then I mapped out the actual incident lifecycle and realized everything I built sits at the edges. The middle part (detecting the incident, correlating signals across regions, finding the root cause while the primary region is actively failing) was not cove
I still remember the message. A developer on my team - sharp, careful - pinged me: "My Claude Code bill spiked $200 this week. Same workflow. Something's off." I had no answer. The built-in usage view showed session totals. The web billing page showed monthly aggregates. But neither could answer the only question that mattered: which specific turn ate the money? How do I improve the way I use Clau
Every observability vendor has bolted "AI" to their landing page. Half of those features are genuine improvements. The other half are autocomplete in a costume. After a few years of running these tools across enterprise estates, here is where AI-augmented SRE actually pays off, where it doesn't, and what we'd advise teams adopting it today. The single most defensible use case. A medium-sized estat