Software development

Aug 17, 2026

What Is RAD? Why It Matters in the Age of AI Coding

This video explores how James Martin's 1991 Rapid Application Development (RAD) methodology, with its emphasis on rapid prototyping, iterative development, and user feedback, offers a robust framework for contemporary AI coding and "vibe coding" workflows. It highlights how AI agents accelerate prototyping but introduces new challenges regarding production readiness and security. The solution proposed is spec-driven development, where human expertise formalizes requirements and verifies AI-generated code based on insights gained from early prototypes, ensuring quality and mitigating risks.

Aug 09, 2026

Velocity Sickness: What Happens When Your Whole Team Gets 10x Faster — Matt Dailey, Ref.

Matt Dailey introduces "velocity sickness" – the stress of increased AI output without impact. He proposes shifting from ephemeral chat-based agent interactions to durable, shared documents as the "decision layer" to separate planning from implementation, enabling teams to own their code and prioritize ideas effectively.

Aug 08, 2026

Realtime multiplayer, automation, and you! — Idan Gazit, GitHub

Idan Gazit, head of GitHub Next, discusses the future of AI in software development, moving beyond personal productivity to enabling group collaboration and advanced automation. He introduces two prototypes: "Agentic Workflows" for autonomous, secure code upgrades and task execution using natural language, and "ACE," a real-time collaborative development environment designed to integrate AI into team workflows, addressing the 95% of development work beyond mere typing.

Aug 03, 2026

Agentic Engineering vs Software Engineering: Beyond Vibe Coding

Anna Gutowska explains the paradigm shift in software engineering towards "agentic engineering," where AI agents execute goals defined by developers. She differentiates this from traditional, AI-assisted, and vibe coding, highlighting the increased importance of human oversight, orchestration, and verification in a world of probabilistic AI systems, and how this redefines the developer's role.

Jul 11, 2026

Why Open Models Will Beat GPT and Claude

This episode explores the strategic shift of enterprises towards open AI models driven by cost and data sovereignty, highlighting the evolving tooling and hybrid deployment strategies. It also delves into the "attenuation funnel" phenomenon, where increased code generation by AI tools doesn't proportionally translate to shipped software or end-user value, and introduces "neo-consulting"—a new business model challenging traditional consulting with outcomes-based pricing and AI-focused expertise.

Jul 10, 2026

Should AI Engineers Still Read Code in 2026? The Z/L Continuum — Alex Volkov, ThursdAI

Alex Volkov introduces the "Z/L Continuum," a framework for navigating the tension between rapid AI-generated code production and the critical need for human review. He argues that the key lies in understanding that the continuum applies to tasks, not individuals, and presents a pragmatic routing table for verifying changes based on their criticality, highlighting the shift towards engineering systems that build and verify code, rather than meticulously inspecting every line. The talk also touches on emerging capabilities like Fable and "loops" and the importance of flexibility and human judgment in the evolving AI engineering landscape.