Issue #721

Essential Reading For Engineering Leaders

June 2nd, 2026

Tuesday 2nd May’s issue is presented by Fidelity Careers

Join a team where the roadmap is built for the future. At Fidelity, we do fintech differently. Being privately held means we can pursue new technologies and follow our curiosity as we build tools and products that make real impact. We empower our technologists to imagine and create the next generation of tech, offering:

  • 💡 Collaborative teams: bring your skills to the table as you build for the long-term

  • 🏗️ Modern craft: work with new and emerging technologies

  •  🚀 Impact: build tools that impact millions - with resources that startups can’t match

— Patrick Dubroy

tl;dr: “About 10 years ago, I realized all the best programmers I had worked with had something in common: they were fast. By that I mean that they moved quickly: we’d discuss a problem and an hour or two later they’d already have a patch ready or a prototype to show off."

CareerGrowth Productivity

— Wes Kao

tl;dr: “Your message isn’t just the content of what you say. When your facial expressions and body language don’t match your message, you diminish your ability to convey your point. The impact is you might come across as insincere. When you’re mindful of your body language, you help set the emotional tone, which leads to more productive conversations.” Wes shares her tactics.

Leadership Communication

tl;dr: Rethink what it means to work in fintech. At Fidelity, we’re building the next generation of tools and products. Discover a vibrant tech community where your work impacts millions. It’s the energy of a startup with the resources of an industry leader. Bring your curiosity and your craft to a team that’s proactively shaping what’s next in finance. Learn more about growing your tech career at Fidelity and visit Tech.FidelityCareers.com

Promoted by Fidelity Careers

CareerGrowth

— Steve Yegge

tl;dr: Steve draws on 35 years of conducting technical interviews at Amazon and Google to argue the process is "bordering on pseudoscience." He offers some alternative approaches, including replacing interviews with "campfires" - short paid stints doing real work on real codebases - where candidates walk away with a portable record of what they built, whether or not they get the offer.

DeepDive Hiring

“A leader is best when people barely know he exists, when his work is done, his aim fulfilled, they will say: we did it ourselves.”

— Lao Tzu

— Camille Fournier

tl;dr: “As companies adopt AI tools, a lot of time is spent on thinking about AI policies from a security, compliance, or even cost-focused angle. But many leaders are neglecting to address how their teams should work with AI in the context of the team as a whole. This creates a lot of unresolved tension, and it’s time for leaders to step up and set some guidelines not just for how to use AI in an “approved” sense, but how to use it respectfully.”

Leadership Management AI

tl;dr: Developers ship faster. PRs close quicker. But org-level throughput? Deployment frequency? Business value? Flat. StackUp is a free 10-minute diagnostic that shows you what's actually moving and what's stuck in your systems. See how you compare to your peers and learn the pattern that explains what changes will have the most impact. Run the Diagnostic.

Promoted by Uplevel

Productivity AI

— John Charter

tl;dr: Jon walks through lesser-known tricks - why column order in composite indexes matters more than you think, how functional indexes save you from wrapping every query in LOWER(), when partial indexes can drastically shrink your index size, and how covering indexes let Postgres answer queries without ever touching the table.

Database PostgreSQL Tips

— Tomasz Tunguz

tl;dr: Tomasz breaks the harness into seven disciplines: context & memory, tools & action, orchestration, state & persistence, sandboxing, observability, and cost optimization. "What happens when every company has access to the same model?"

SystemDesign AI

— Luca Cavallin

tl;dr: “This post is what I wish someone had handed me the first time I had to ship an AI feature. I spent fifteen years writing backends, operating Kubernetes clusters, debugging Terraform, and arguing about API design. Then LLMs landed in production and a lot of the rules I trusted stopped applying. The system is now non-deterministic by default, the input is a string of natural language, and your unit tests cannot tell you whether the output is good.”

Guide AI

A fascinating read about why Japanese companies do so many different things. David Oks argues that diversification is not random in Japan companies: it comes from a unique model (lifetime employment, horizontal coordination, broad training) and different corporate incentives - long term survival vs immediate shareholder value. This allows Japanese companies e.g. Toto, Yamaha to manufacture high-precision products across unrelated categories. David explains why this model is hard to replicate in the US, and why American companies cannot simply copy one practice from it, and how the system struggles with new category innovation.

Awesome Harness: Resources, patterns, and templates for building agent harnesses.

Eagle: Frontier vision-language models.

Odysseus: Self-hosted AI workspace.

Markitdown: Convert documents to Markdown.

Re_gent: Version control for agents.

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