Side quests at GitHub Universe: who owns AI proof now?
What we heard at GitHub Universe 2026: DevEx teams and AI adoption leads now own the question of whether AI is paying off across engineering orgs.
Author: Aaron Beals
Aaron Beals is the CTO of Flux, where he assembles high-performing engineering teams and translates business needs into innovative solutions. At Flux, Aaron combines his experience building scalable, secure enterprise-class SaaS apps with cutting-edge AI models to deliver new products to support engineering managers like himself.
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Is it worth it to DIY engineering intelligence in the age of AI?
A DIY engineering intelligence dashboard takes a day or two to build. Trusting it with a board-level answer takes much more. Here’s where DIY breaks down.
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AI broke one of software’s oldest assumptions
AI code generation isn’t deterministic: the same prompt and model can output different code each run. Aaron Beals answers webinar Q&A on what that means.
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How to implement DORA metrics: a practical guide for engineering leaders
A 90-day DORA metrics implementation plan, with benchmarks, common pitfalls, and how AI changes what these metrics mean.
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The signals senior engineers read that juniors don’t (yet)
Ramping in a new codebase is about reading the right signals—where work is happening, what kind it is, and where things keep breaking.
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Finding a firm foundation for engineering intelligence
Flux is the engineering intelligence platform built on a code-first philosophy to help leaders manage risk and navigate AI complexity.
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Four unchanging responsibilities for engineering leadership in 2026
Discover the 4 core engineering leadership responsibilities that remain unchanged in 2026, from managing tech debt to personal ownership in the age of AI.
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Camping out: engineering intelligence for teams
Reflections from Boston Code Camp on why engineering intelligence for teams depends on developer buy-in and moving beyond ticketing data.
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The State of Code Reviews: Modern Code Review Approaches and Tools
Modern code review is evolving fast—engineering leaders must adapt to AI-driven complexity, distributed teams, and rising pressure on code quality.
