The orgs best positioned for AI agents aren't the ones with the best tooling. They're the ones with the strongest human writing cultures.

Everyone's watching humanoid robots. But if you think something is revolutionary, variants probably exist. The robotics community is vast. Here's what i'm learning as a software engineer jumping in.

Org theory applies to agent swarms. I've lived three different org structures—here's how they map to multi-agent patterns.

CAP theorem gives you a lens for distributed systems. There's a missing dimension for infrastructure decisions: whether your system is latency-bound or throughput-bound. Most performance advice...

Three weeks into a new role, agent orchestration finally clicked. Not just using AI tools—actually managing complexity with them.

Real production failures from running Ray at scale: lost training runs, enterprise network disasters, and cascade outages the documentation never warns you about.

I built an automated Twitter feed scraper that triages 100 tweets daily, analyzes community sentiment with Claude, and delivers a 5-minute digest to Slack.

I chose Telegram over Slack for my AI assistant setup, then realized I was making decisions based on old baggage instead of current evidence.

Memory fragmentation, throughput cliffs, and quantization accuracy issues that only show up in production—lessons from running vLLM at scale for conversational AI.

How I eliminated context bleeding in my AI assistant using openclaw's multi-agent features and Telegram forum topics.

Patterns and tradeoffs from building RAG infrastructure in regulated environments.