Oct 21, 2026

Does Code Quality Still Matter? It Depends on This — Max Kanat-Alexander, Capital One

Max Kanat-Alexander reframes the familiar "it depends" answer around two factors that govern all software work: understanding a system and safely changing it. He explains why code quality matters for durable software but not for a new, rapidly growing category of disposable software created by AI, and shows how to make systems more disposable and make rigor cheap through standardization, inward-flow tooling, easier testing, and maximum strictness.

Oct 09, 2026

Generation Is Cheap, Review Is Expensive: How to Stop Shipping AI Slop — Gabriel Martinez, G2i

Gabriel Martinez of G2i argues that slop is not AI-written code but work that looks finished before the thinking behind it is done. He connects it to missing judgment, code as a liability, misaligned incentives that reward merging speed over review, oversized PRs and documents that push understanding onto reviewers, and the case for conventions, diagrams as review surfaces, and small changes. He closes by explaining why G2i built ORC to keep humans in control of agent-generated work.

Aug 28, 2026

AI-Native Organisations Run on Skills: How to Structure and Scale Them — Imad Touil, QuantumBlack

Imad Touil explores the critical need for governing AI skills within organizations, asserting that skills represent the true repository of enterprise know-how. He contrasts simplified coding agent loops with complex, real-world product lifecycles, demonstrating how ungoverned skills lead to technical debt—including duplication, quality degradation, security risks, and lack of discoverability. Proposing a microservices-inspired approach, Touil outlines a centralized skills platform and human-led governance model essential for achieving deterministic workflows, boosting productivity, and mitigating risks in AI-native enterprises.

Aug 08, 2026

Benchmarking Coding Agents on New vs Legacy Codebases — Denys Linkov, Wisedocs

Denys Linkov details Wisedocs' journey of refactoring a complex, distributed ML pipeline into a monorepo, benchmarking the process against evolving AI coding tools. He honestly audits whether the six-month effort was justified, or if waiting for more advanced AI would have been better, ultimately concluding that the significant social and technical benefits made the refactor a successful strategic move despite current LLM limitations.

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 20, 2026

In the Land of AI Agents, the Verifiers Are King — Tariq Shaukat, Sonar

This talk addresses the critical challenge of verification in AI agent development, moving beyond generation to ensure correctness. It highlights the problem of "AI slop" and the "productivity paradox" of AI coding agents, where initial velocity gains are offset by increased technical debt and quality issues. The speaker introduces the AC/DC (Agent-Centric Development Cycle) framework comprising three stages: Guide (providing context and constraints), Verify (zero-trust, multi-layered verification using both algorithmic and agentic methods), and Solve (active code maintenance to control technical debt). This systems-level approach, integrating verification across agentic, CI, and code maintenance loops, significantly reduces issues and transforms AI into a reliable enterprise asset.