We just saved $2M/year and nobody noticed

You deployed a neuro-symbolic system that routes financial transactions through a rule engine before LLM approval. It caught 500 compliance violations that an LLM-only system would have missed.

Your hybrid system is now too slow for production

You built a beautiful neuro-symbolic system: LLM → knowledge graph → constraint solver → LLM explanation. Works great in testing.

Your LLM approved a transaction that violates policy

You deployed an LLM to approve or reject loan applications. It uses RAG to fetch relevant policies, and it’s remarkably accurate — 95% approval rate matches your historical baseline.

Measuring Developer Experience: From Friction Logs to Dashboards

TL;DR

Developer Experience (DevEx) is about how easy and satisfying it is for developers to get work done. Measuring it means combining qualitative signals (surveys, friction logs) with quantitative ones (build times, pull request cycle time). Tools like Apache DevLake help automate data collection so teams can track and improve DevEx systematically.

Your agent asked a question and then couldn’t find the answer it needed

You built an agent that uses a knowledge graph. The agent thinks: “I need to find the customer’s purchase history.” It should query the graph for [Customer] –purchase–> [Product]. But instead it searches for “customer purchases” in the vector database and gets back 47 documents.