Error Handling - TechnoScripts

error handling

Strategies for handling errors gracefully in Groovy applications.

AI Agent Evaluation: Trajectories, Tool Errors, Success

In the LangChain State of AI Agents survey of 1,340 practitioners, quality beat cost and safety as the number one obstacle to shipping agents. AI agent evaluation is how you find out whether the agent you built in posts 225 and 234 actually finishes tasks or just looks busy calling tools. And agent evals are not chat evals: you grade a whole trajectory of tool calls, not one message. Two agents can land on the same reply while one took ... Read More

DL: PyTorch GPU Training, Debugging, and Speedups

A model sitting on the GPU and a batch still on the CPU: that one mismatch kills more first runs than any piece of math. PyTorch GPU training is mostly two habits, keeping everything on the same device and reading error messages instead of panicking at them. This post covers device discipline, CUDA out of memory, shape bugs, NaN loss, and mixed precision speedups. “Neural nets fail silently. The code runs, the loss looks plausible, and the bug just quietly ... Read More

Debugging ML Models in Python: 5 Real Failure Cases

Debugging ML models is a different sport from debugging normal code. Your Python never throws an exception, the pipeline runs green, and yet the model is quietly wrong. This tutorial walks through five real production failures the way a senior reviewer would, each one shown breaking on tested code and then fixed: training loss that diverges, a 99% accuracy that is a lie, a model that shines offline and falls apart online, label leakage caught after deploy, and a loss ... Read More