2d ago · 11 min read · LangGraph is becoming the default framework for teams building agentic AI workflows. That is both a good thing and a problem. The good part: it has real production pedigree, is actively maintained, an
MMateo and 2 more commented5d ago · 11 min read · When I first started exploring AI development, I kept running into these four names everywhere. Blog posts, YouTube tutorials, GitHub repos; LangChain this, LangGraph that. And honestly? For a while,
Join discussion
6d ago · 13 min read · Agent PoCs often work surprisingly fast. You call an LLM. You connect a tool. You ask for structured output. You add tracing. The demo works. Then the system gets slightly more complex, and a differen
Join discussionMay 22 · 77 min read · The landscape is already saturated with "AI wrappers" and hype-driven demos, I been building something that reflects true Systems Engineering principles. For the last few months—working before and aft
Join discussion
May 20 · 17 min read · 🙋 I’m Luhui Dev, a developer who has been breaking down Agent engineering and exploring how AI can be applied in education. I focus on Agent Harness, LLM application engineering, AI for Math, and th
Join discussionMay 20 · 9 min read · An AI on-call agent is only as good as what it watches. That's what the rules system is for. Nightwatch's Watcher doesn't hardcode what to alert on. Instead, ops teams define rules in a single YAML f
Join discussion
May 17 · 10 min read · It's 3:07am. Your phone is screaming. The dashboard is red. You don't even remember what service checkout-orchestrator-v2 does. Welcome to on-call. Every backend engineer has a war story like that on
Join discussion
May 12 · 6 min read · Most production AI failures are not model failures. They are retrieval failures. If you want to understand why your Retrieval-Augmented Generation (RAG) system is hallucinating, stop looking at your p
DDigit commented
