Sep 10, 2025

AI Assistance for Software Teams: The State of Play • Birgitta Böckeler • GOTO 2024

Birgitta Böckeler from Thoughtworks provides a comprehensive overview of the current AI tooling landscape for software delivery. The talk focuses on the architecture and capabilities of modern coding assistants, differentiating between the underlying model and the crucial role of tooling in providing context. It explores what works today, the promising but challenging frontiers like testing and refactoring, and offers a practical guide to the major tools in the ecosystem.

Sep 07, 2025

Ask the Experts: Gen AI, Cybersecurity, & AI Agent Questions Answered

Experts Martin Keen and Jeff Crume differentiate between Generative and Agentic AI, delve into the nature of AI hallucinations, and explore critical cybersecurity topics like the permanence of the dark web and the dangers of zero-click attacks.

Sep 04, 2025

Predictability Beats Accuracy in Enterprise AI

Anant Bhardwaj, CEO of Instabase, presents a pragmatic guide for building enterprise AI. He argues that AI agents are best used during the 'design-time' to create predictable workflows, rather than for autonomous 'runtime' operations. Bhardwaj also debunks the hype around RAG, highlighting its dependency on data quality, and explains why trust in AI systems stems from predictability, not just accuracy.

Sep 03, 2025

Build Hour: Built-In Tools

Built-in tools like web search, file search, and code interpreter allow developers to extend model capabilities out-of-the-box. This summary covers the concepts, compares them to function calling, and details a demo of building a data exploration dashboard using multiple tools in concert.

Sep 01, 2025

7 AI Terms You Need to Know: Agents, RAG, ASI & More

A deep dive into seven essential AI concepts shaping the future of intelligent systems, including Agentic AI, RAG, Mixture of Experts (MoE), and the theoretical frontier of Artificial Superintelligence (ASI).

Aug 25, 2025

Conext Engineering for Engineers

Jeff Huber of Chroma argues that building reliable AI systems hinges on 'Context Engineering'—the deliberate curation of information within the context window. He challenges the efficacy of long-context models, presenting a 'Gather and Glean' framework to maximize recall and precision, and discusses specific challenges and techniques for AI agents, such as intelligent compaction.