Oct 19, 2026

The Model Swap Workshop: GPT-5.5 vs Kimi vs DeepSeek vs Mistral

Pamela Fox runs a guided workshop comparing GPT-5.5, Kimi K2.6, DeepSeek V4 Flash, and Mistral Large 3 on Microsoft Foundry, showing that benchmarks cannot predict how a model will behave in a specific application. The talk covers counting, RAG groundedness, vision, multilingual failures, tool calling, parallel tool calls in agents, string-check and LLM-as-a-judge evals, Microsoft's ASSERT tool for generating test sets, and DSPy prompt optimization that lets a cheap model satisfy a spec.

Oct 19, 2026

Ash Framework: Declarative Elixir Web Apps • Rebecca Le, Zach Daniel & C. Alexander • GOTO 2026

Rebecca Le and Zach Daniel, co-authors of the Ash Framework book, discuss how their Tunez-based guide was built to introduce concepts in the order a real project needs them, how writing it forced the addition of generator-based testing to Ash, and why Ash's extensibility, Cinder's rapid development, and AI-assisted onboarding make its steep but flattening learning curve worthwhile.

Oct 19, 2026

What Is Loop Engineering? Building Better AI Agents

Martin Keen explains loop engineering as the next step beyond prompt engineering: designing systems where AI agents act, observe, adjust, and repeat toward verifiable goals. The discussion covers goal design, triggers, context and persistent state, tools via MCP and APIs, verifier subagents, and why humans must remain in the loop to manage comprehension debt, intent debt, and cognitive surrender.

Oct 19, 2026

Version-Controlled Search: The Web as It Was — Ryan Ahern, Exa

Ryan Ahern of Exa presents Exa Snapshot, a point-in-time web search that lets agents query the web as it appeared on any past date, and shows how teams can backtest predictive signals and financial models without overfitting.

Oct 19, 2026

Backtest Your Agent Against Yesterday's Web — Ryan Ahern, Exa

Ryan Ahern of Exa explains how version-controlled search through Exa Snapshot lets you view the web as it existed on a past date, enabling backtests of AI agents while avoiding look-ahead bias. A hands-on acquisition-prediction exercise illustrates feature tuning and the dangers of overfitting without holdout sets.

Oct 18, 2026

A mind-expanding conversation on how models actually work | Emmanuel Ameisen (Anthropic)

Emmanuel Ameisen, an interpretability researcher at Anthropic, explains how the team looks inside models like Claude to understand how they actually think. The conversation covers why next-token prediction hides real planning and conceptual representation, why models are grown rather than built, how intermediate activations reveal structure, why anthropomorphic framing can be useful, how smooth scaling produces sudden capability thresholds, and how interpretability ties into alignment, safety, and the broader question of what these systems are.