.NET

A Practitioner's Guide to Graphs - Tim Ainge, Good Collective

Jul 18, 2026 · 14:18

Tim Ainge from Good Collective presents a practitioner's guide to graphs, covering extraction from unstructured text, schema-first design, and graph-native algorithms to make AI applications smarter, cheaper, and more reliable. He demonstrates that giving extractors a schema (e.g., recipe with ingredients and steps) yields more meaningful graphs, and using ontology instructions standardizes units and ingredient names. Embedding models solve the potato–potato problem by flexibly matching duplicate nodes. Personalized PageRank, inspired by Pinterest's Pixie paper and HIPORAG, finds authoritative nodes in dense graphs, e.g., identifying Miranda v. Arizona as a landmark case not directly cited. Shortest path algorithms reduce tool calls by 40% in code search by retrieving intermediate nodes missed by vector search. Subgraph matching detects software design patterns like the decorator by querying graph shape without specific node details.

From Vibe Coding To Vibe Engineering – Kitze, Sizzy

Dec 14, 2025 · 25:28

Kitze, founder of Sizzy, argues that "vibe engineering"—actively steering AI agents with technical knowledge—trumps passive "vibe coding" for building real software. He shares how Cursor's Composer 1 let him port Benji.so and Glink to Next.js 16 with Monorepo in under a week, reviving near-dead projects. Kitze insists LLMs excel at React because humans are bad at it, and that repetitive code is fine since agents don't care. He warns against giving AI tools to juniors without oversight, and predicts the bottom of the job market will thin as agents replace interns. The talk ends with a pitch for "vibe code fixers" as a new role, maintaining legacy systems like cowboy coders.