May 04, 2026

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.

May 04, 2026

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.

May 04, 2026

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.

May 03, 2026

TLMs: Tiny LLMs and Agents on Edge Devices with LiteRT-LM — Cormac Brick, Google

Cormac Brick from Google's AI Edge team details the dual trends of on-device AI: large, system-level models like Gemma 4 enabling complex agent skills, and fine-tuned tiny LLMs for high-performance, in-app tasks. The summary covers the architecture of on-device function calling, the engineering trade-offs for edge deployment, and the practical workflow for fine-tuning and deploying models under 1B parameters on platforms like Android and iOS.

May 03, 2026

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.

May 02, 2026

Human-in-the-Loop Automation with n8n — Liam McGarrigle

Liam McGarrigle demonstrates how to build secure, observable, and controllable AI agents in n8n. The workshop covers creating a human-in-the-loop workflow for managing Gmail and Google Calendar, focusing on n8n's visual system for tool configuration, prompting strategies, and implementing essential approval steps to prevent unintended actions.