Dec 26, 2025

How Claude Code Works - Jared Zoneraich, PromptLayer

An unofficial deep dive into the architecture of modern coding agents like Claude Code. Jared Zoneraich of PromptLayer explains the shift towards simpler, model-centric designs, detailing the core components like the master loop, tool calling (especially `bash`), and context management strategies. The talk also contrasts Claude's philosophy with other agents like Codex, AMP, and Cursor, offering practical takeaways for building your own AI agents.

Dec 23, 2025

Developer Experience in the Age of AI Coding Agents – Max Kanat Alexander, Capitol One

Max Kanat-Alexander explores the rapid changes in software engineering driven by AI and identifies 'no-regrets investments' that will benefit development teams regardless of the future. He argues that by focusing on foundational developer experience principles—such as standardizing tools, improving validation, structuring code for testability, and refining the code review process—organizations can create a virtuous cycle of productivity for both human developers and their AI agent counterparts.

Dec 23, 2025

Continual System Prompt Learning for Code Agents – Aparna Dhinakaran, Arize

The talk by Aparna Dhinakaran introduces "system prompt learning" as an efficient alternative to traditional Reinforcement Learning for improving large language model-based coding agents. By leveraging LLM-as-a-judge evaluations to generate English feedback and explanations for code failures, agents can automatically refine their system prompts and rules. This method, demonstrated on Claude and Klein, significantly boosts performance on benchmarks like SWEBench with minimal data, highlighting the critical role of high-quality evaluation prompts.

Aug 25, 2025

Advanced Context Engineering for Agents

Dexter Horthy of Human Layer explains why naive AI coding agents fail in complex software projects and introduces 'Advanced Context Engineering.' He details a spec-first, three-phase workflow (Research, Plan, Implement) designed to manage context intentionally, keeping utilization below 40% to maximize model performance. This approach uses subagents and frequent compaction to turn AI from a prototyping tool into a production-ready system for large, brownfield codebases.

Aug 05, 2025

AI Coding Agents Change Software Development Forever

A discussion on the promise and limitations of coding agents, covering key challenges like verification and debugging, and exploring how they can support developers through improved abstraction, collaboration, and handling long-term tasks.