WorkshopWaitingRoom

The Mastering AI Observability Workshop repository is a hands-on workshop resource for building a trace-backed support-agent improvement workflow with Braintrust. It provides a local Python workshop app, setup instructions, a support agent using tool calls, seeded evaluation cases, production-like trace generation, Braintrust datasets and evaluations, hosted scorers, Topics facets, sample-trace imports, and optional draft evaluation cases derived from traces. The workflow connects tracing and online analysis to offline evaluations, custom scoring, failure investigation, regression cases, and reviewable code changes. The repository includes a public WORKSHOP.md guide, environment setup through uv and Make commands, local database seeding, a web interface, and configuration for Braintrust Gateway model calls.

A GitHub-hosted hands-on workshop repository for wiring AI-powered UI verification workflows. It covers a podcast landing-page challenge with easy and medium paths, using a Chrome extension for deterministic visual annotation, a coding agent, and the Nova Act SDK to find and fix rendered UI discrepancies and verify design expectations and user flows. The annotation workflow lets participants select elements, draw on a page, or drop pins and export structured feedback; the verification workflow crawls a site, checks the resulting interface, and generates a report. The repository includes workshop instructions, setup prerequisites, integration code, and a Bee Wearable reverse proxy that surfaces completed-conversation feedback as sidebar cards with Apply and Dismiss actions.

Workshop Studio is a hosted Jupyter notebook environment with pre-created AWS accounts for building and testing an agent. In the described workshop, it hosts notebooks for developing a customer-service agent with Strands Agents, testing tool-call controls, adding skills and steering handlers, and preparing deployment to Amazon Bedrock AgentCore.

Langfuse Workshop is a step-by-step learning resource about applying Langfuse across the AI engineering loop. It uses a small TypeScript Dad IT Support Agent, named Specs, to teach tracing agent steps, managing prompts, monitoring production behavior, building datasets, running experiments, and evaluating changes. The workshop includes self-guided learner lessons, instructor notes, preparation materials, checkpoint branches, a runnable sample application, and helper scripts; its material is maintained in the public langfuse/langfuse-workshop repository. The sample agent uses an OpenAI tool-calling loop with two local tools, and the monitoring exercises cover user disagreement and all-caps frustration. The workshop is licensed under MIT and recommends Langfuse Cloud because self-hosted instances may not match the features used in the lessons.

Agent Context Workshop is a hands-on workshop resource for building and evaluating context layers for coding agents. It uses selected crates from the Qdrant Rust codebase to extract syntax with tree-sitter, resolve symbols and relationships with rustdoc JSON, store the resulting code knowledge graph in Neo4j, and expose graph tools such as search, blast-radius analysis, and implementor lookup to an agent. The workshop compares graph-based agents with grep-style agents using gold-verified questions, deterministic scoring, cached evaluation results, tool-adoption measurements, and trace inspection. Additional notebooks cover static Python resolution with Griffe and an optional Fenic-and-LLM semantic layer. The GitHub repository maintains the workshop through ordered Jupyter notebooks, typed extraction and graph code, evaluation harnesses, tests, committed Qdrant seed data, and a committed evaluation cache; it is licensed under Apache-2.0.