Jun 26, 2026

Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI

Paul Iusztin and Louis-Francois Bouchard unveil their AI Research OS, a powerful system that transforms notes, documents, videos, and code repositories into a dynamic, personalized memory for AI agents. They detail a three-layered architecture (raw files, index, wiki) that efficiently manages context and enables continuous learning and evolution, addressing the limitations of static research and generic AI tools.

Jun 25, 2026

The Miranda Hypothesis: How Hamilton Poisoned Persona Evals - Jacob E. Thomas, Results Gen

This talk exposes "Miranda distortion," a critical flaw in AI personas where models, influenced by modern cultural narratives, produce convincing but anachronistic outputs. Current evaluations fail to detect this, prioritizing fluency over fidelity. The speaker proposes "epistemic simulation"—a new paradigm grounded in corpus-bounded, temporally-anchored, and expert-evaluated reasoning—and introduces the "Prism Experiment." This rigorous, pre-registered protocol uses Abraham Lincoln to demonstrate how a weighted rubric, created by historians and de-emphasizing rhetorical fluency, can detect anachronism. It advocates for the "humanist in the loop" as a technical requirement to ensure AI personas are true to their documentary records, not just convincing.

May 16, 2026

Connecting the Dots with Context Graphs — Stephen Chin, Neo4j

Stephen Chin of Neo4j argues that traditional RAG is insufficient because AI agents lose the reasoning behind past decisions. He introduces Context Graphs as a solution to capture the 'why' behind decisions, creating a queryable system of precedent that provides grounded, explainable, and auditable results.

May 08, 2026

Agentic Search for Context Engineering — Leonie Monigatti, Elastic

Leonie Monigatti from Elastic provides a practical guide to agentic search, arguing that effective context engineering is not just a retrieval problem, but a search problem. The workshop explores the trade-offs between specialized tools (like semantic search) and general-purpose tools (like shell and SQL execution), offering a "low floor, high ceiling" framework for building a robust and efficient retrieval stack for AI agents.

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

How RAG, GraphRAG, and Context Engineering Improve AI Performance

Martin Keen explains that context, not model intelligence, is the biggest bottleneck in AI. He introduces Context Engineering, its four pillars (Connected Access, Knowledge Layer, Precision Retrieval, Runtime Governance), and advanced techniques like GraphRAG to build more reliable, context-aware AI systems.