CLIMB

The Unreasonable Effectiveness of Prompt Learning – Aparna Dhinakaran, Arize

Dec 23, 2025 · 10:56

Aparna Dhinakaran, co-founder of Arize, argues that prompt learning—applying RL techniques to prompts rather than model weights—can continuously improve coding agents by auto-tuning system prompts from runtime feedback. She details a process using Claude Code and CLIMB on the SWE-bench dataset: agents generate patches, unit tests yield results, then an LLM-as-a-judge eval produces natural-language explanations of failures. These explanations feed a meta prompt that iterates on the system prompt rules. On 150 SWE-bench examples, this approach improved Claude Code's issue-resolution rate by 5% and CLIMB's by 15%. She contrasts their method with DSPy's GEPA optimizer, noting theirs required fewer loops due to carefully engineered eval prompts.

Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic

Mar 1, 2025 · 1:44:12

Mahesh Murag of Anthropic presents the Model Context Protocol (MCP) as an open standard that replaces fragmented integrations with a single protocol for connecting AI systems to data sources, enabling context-rich AI applications and agentic experiences. He explains MCP's philosophy, inspired by APIs and LSP, and its three interfaces: tools (model-controlled), resources (application-controlled), and prompts (user-controlled). Murag highlights adoption with over 1,100 community-built servers and official integrations from companies like Cloudflare and Stripe. He demonstrates building agents with MCP using the MCP-Agent framework, showing how agents can use tools dynamically and composably across hierarchical systems. Future plans include remote server support with OAuth 2.0, a centralized registry for discovery and verification, and enabling agents to self-evolve by dynamically finding new capabilities via registry search.