Agent observability, aka AgentOps, has emerged as a vital ecosystem of tools for keeping an eye on what AI agents and LLMs ...

Learn how to improve AI agent performance using better prompting, memory management, RAG, evaluation metrics, multi-agent systems, and proven optimization strategies to build faster, more accurate, ...

A multi-institution research team from NVIDIA, the University of Michigan, the University of Illinois Urbana-Champaign, UC Berkeley, and Carnegie Mellon University released ASPIRE on June 29, 2026 — a ...

Spend enough time in the self-hosting and smart home hobby, and even the most ardent smart home enthusiast eventually reaches a moment where the excitement of adding new gadgets gives way to the ...

Cleaning massive, disorganized spreadsheets or parsing through thousands of lines of raw server logs is annoying. You can do it yourself, make a program to do it, or you can just give it to Claude and ...

Student focused on web development and programming. I write about debugging, coding challenges, and improving skills. Your function looks right. The logic checks out. You've traced it three times on ...

To open a debugger 'post mortem', after an error hits the top level: When running interactively: call patdb.debug() after the error has returned you to the REPL. (Or ...

Observability startup Raindrop AI’s new open source, MIT Licensed "Workshop" tool, launched today, gives developers something that they've likely wanted, perhaps subconsciously, since the agentic AI ...

A modern agent project depends on at least four services. There is the LLM proxy that fronts your model providers. There is the vector store that holds your embeddings. There is the observability ...

Debugging an AI agent that runs for dozens of steps: reading files, calling APIs, writing code, and revising its own output, is not like debugging a regular function. There is no single stack trace to ...

Excel is my database, Python is my brain.