Code generation

C++26 for Lazy Programmers • Will Briggs & Todd Montgomery • GOTO 2026

C++26 for Lazy Programmers • Will Briggs & Todd Montgomery • GOTO 2026

Will Briggs and Todd L. Montgomery discuss the impact of AI on programming careers and language learning, the pedagogical approach of Briggs' book "C++26 for Lazy Programmers," and the evolving landscape of C++. They explore how AI requires deeper code fluency, Briggs' method for teaching complex C++ concepts like pointers, and his critiques of C++'s feature proliferation. The conversation concludes with a strong argument for C++'s enduring relevance as a foundational language for aspiring developers.

Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon

Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon

Stefano Ermon, co-founder and CEO of Inception, discusses his company's radical bet on diffusion models to overcome the hardware and latency bottlenecks of autoregressive LLMs. He explains how parallel token generation in diffusion offers superior inference scaling and GPU utilization, expanding beyond images to discrete text and code generation. Ermon shares insights into Inception’s Mercury models, real-world applications like voice agents, the custom software stack required, and why efficiency will define the next era of AI competition.

The Design-Code Roundtrip That Isn't — Jonathan Gordon, ReWeaver AI

The Design-Code Roundtrip That Isn't — Jonathan Gordon, ReWeaver AI

Jonathan Gordon, with three decades in dev tools, unpacks why AI hasn't solved the design-code roundtrip problem. He introduces ReWeaver, a tool that uses deterministic guardrails to detect and manage "drift" between AI-generated code and design, ensuring fidelity and control across dimensions like accessibility and design consistency, ultimately advocating for humans to remain in control.

Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked

Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked

Brandon Waselnuk of Unblocked discusses the critical need for robust context engines in AI-driven software development. He explains how AI agents' lack of company-specific understanding leads to compounding costs like wasted tokens and rework. Waselnuk outlines common pitfalls in providing context and details six essential characteristics of an effective context engine, demonstrating its impact with a 50% token reduction and significant time savings. He also introduces three open-source tools from Unblocked, including a workshop to build a relational context engine, emphasizing that "access to information is not understanding" and that context, not intelligence, is the current bottleneck for AI effectiveness.

Reading Group July 2026 - Loop Engineering

Reading Group July 2026 - Loop Engineering

This session provides an in-depth exploration of Loop Engineering, a paradigm shift from manual AI prompting to designing autonomous systems that orchestrate AI agents. Speakers share practical experiences, from building production-grade platforms with automated code generation and adversarial AI reviews to experimental loop structures and foundational infrastructure layers. Key discussions address challenges like managing token costs, preventing agent chaos, and implementing robust verification mechanisms for industrializing software development.

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

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.