Issue #768

The Briefing For Engineering Leaders

November 13th, 2026

Friday 13th November issue is presented by Buildkite

Buildkite, the CI orchestration platform for AI.

We provide programmable primitives you co-ordinate, running only what changes, across any cloud, any chip, your secrets, your infra. Control without compromise.

Build faster, more securely, more reliably, with the CI engineered for frontier scale, used by the teams setting the pace.

You Deserve Better CI. Start With Our 30-Day Trial. Kick The Tires →

— Yue Zhao

tl;dr: “The kernel of a strategy contains three elements: a diagnosis, a guiding policy, and coherent action. This definition resonates with me, maybe because now I’m not lost on strategy expectations. Now that we know what Strategy is, we need to adapt this to Engineering Strategy.”

Leadership AI

— Mike Fisher

tl;dr: “With the different changes going on with AI, we thought that it was timely to put together some thoughts on how team structures and responsibilities are changing at a very tactical level. These takeaways are derived from managing teams, investing in startups, advising companies, and our own network discussions.”

  Leadership OrgDesign AI

tl;dr: Buildkite, the CI orchestration platform for AI. We provide programmable primitives you co-ordinate, running only what changes, across any cloud, any chip, your secrets, your infra. Control without compromise. Build faster, more securely, more reliably, with the CI engineered for frontier scale, used by the teams setting the pace.

Promoted by Buildkite

DevOps Tools+Setup

— Lenny Rachitsky

tl;dr: Here’s what we’re seeing right now: (1) PMs seem to be winning in the AI era. (2) Engineering demand has stalled. (3) Design is at a record low. (4) Everybody now needs to AI. (5) Tech jobs are concentrating in the Bay Area. (6) Remote is at a record low. (7) The tech job market is growing fast, but mostly outside of software (gasp!)

“A common mistake that people make when trying to design something completely foolproof is to underestimate the ingenuity of complete fools.”

— Douglas Adams

tl;dr: The sections that follow explain why context quality has become an operational concern, define what is meant by context and where it sits in the larger AI system, describe each CAFE(S) property - Clarity, Actionability, Fidelity, Efficiency, Security - examine common failure patterns, and offer practical guidance for individuals and teams.

  Guide AI Agents

tl;dr: Join Honeycomb and AWS on November 19th or December 10th for a virtual workshop exploring how better telemetry helps AI agents investigate application issues and uncover root causes. Deploy a microservices application, investigate real performance issues, and build your own AI agent. All you need is a free Honeycomb account to get started. Register for the Workshop.

Promoted by Honeycomb

Event AI Observability

— Marc Gauthier

tl;dr: “A few months ago, I wrote about my AI workflow, which is basically forever ago given how fast things are moving nowadays. I figured I’d write an update with a few things I changed over the months. It’s worth noting that most of my work nowadays is on my boxing app, which is a fairly small codebase where I’m the only one merging code in… so if you’re at a large company, it probably won’t translate very well.”

Tools+Setup Productivity AI

— Kevin Kelly

tl;dr: “In the past, books were the primary unit of knowledge. When computers came along, knowledge was combined with action, and the shareable unit became software code. The web made the page and the link the unit, and for twenty years knowledge spread hyperlink by hyperlink. Code itself became shareable as packages and libraries that anyone could snap together. Later, mobile phones refined the knowledge unit into the “app.” Tremendous amounts of potential knowledge and action are compressed into a tiny app, which can be shared, bought, and sold.”

— Lyra Rebane

tl;dr: “So that is the end of my fun little story. I doubt it was particularly surprising to anyone, but I think it’s a good case study on how both social media platforms and people’s reliance on AI can be abused to distribute disinformation and cause harm.”

AI Ethics CaseStudy

Frictionmaxx

In collaboration with Cassidy Williams, Senior Director of DevEx @ GitHub, hand-drawn by Manu.

Anatomy: Claude skill that explains an idea by building it. 

AWS Emulator: Local emulator of the AWS APIs.

Diagrm: Design, validate & optimize cloud and AI infrastructure before deployment.

Improve: Audit your codebase and write plans for cheaper models.

Modern Web Guidance: Skills that embed web platform expertise into your agents.

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