Ai agents

MCP UI: Extending the frontier — Liad Yosef and Ido Salomon, MCP Apps

MCP UI: Extending the frontier — Liad Yosef and Ido Salomon, MCP Apps

MCP Apps transform tools into interactive UI inside hosts like ChatGPT and VS Code. This summary covers the core architecture, the paradigm shift towards a 'new web' of composable UI, and the future of distributing applications in an agent-first world.

Security for Mythos-Era Agentic Risks — with Rubrik’s Anneka Gupta and Cal Al-Dhubaib

Security for Mythos-Era Agentic Risks — with Rubrik’s Anneka Gupta and Cal Al-Dhubaib

Rubrik’s Anneka Gupta and Cal Al-Dhubaib discuss the escalating cybersecurity risks posed by powerful AI agents, the necessity of a "zero trust" mindset, and how platforms like Rubrik provide cyber-resilience through advanced visibility, governance, and remediation tools in the new agentic era.

Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next

Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next

Boris Cherny, creator of Claude Code, discusses the future of software development at AI Ascent 2026. He argues that coding is effectively a solved problem, detailing his personal workflow of shipping dozens of PRs daily from his phone. Cherny explores the shift from typeahead to autonomous agents, the rise of cross-disciplinary generalist teams, and uses the printing press as an analogy for the coming democratization of software creation for all.

Skill Issue: How We Used AI to Make Agents Actually Good at Supabase — Pedro Rodrigues, Supabase

Skill Issue: How We Used AI to Make Agents Actually Good at Supabase — Pedro Rodrigues, Supabase

A deep dive into building, testing, and iterating on Agent Skills to improve AI agent performance. This workshop covers the core concepts of progressive disclosure, eval-driven development, and practical application using a real-world Supabase and PostgreSQL security scenario.

CLI vs MCP: How AI Agents Choose the Right Tool for the Job

CLI vs MCP: How AI Agents Choose the Right Tool for the Job

AI agents can interact with the world through either the Command Line Interface (CLI) or the Model Context Protocol (MCP). This summary explores the trade-offs between the two, highlighting CLI's efficiency for tasks the model is trained on, versus MCP's power of abstraction and governance for more complex, high-level operations.

Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked

Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked

A practitioner's guide to building a context engine, the reasoning layer that provides AI agents with the necessary organizational context to generate effective and appropriate code. The talk debunks common myths about RAG and large context windows, outlines core requirements for a robust context engine, and shares lessons learned from production.