- Pointer
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- Issue #705
Issue #705
Essential Reading For Engineering Leaders
Tuesday 7th April issue is presented by UpLevel
84% of developers use AI tools. Fewer than 3% of organizations have meaningfully changed how they ship — or can show what it's worth to the business.
StackUp is a free 10-minute diagnostic that benchmarks your engineering org against peers across ways of working, alignment, velocity, and environment — and tells you which 2–3 changes will have the biggest impact on your AI ROI.
— Alex Piechowski
tl;dr: “After years of codebase audits, the same five signals keep showing up, so I finally put them into a scoring rubric: The Codebase Drag Audit. Five signals, scored 0 to 2. If you hit 4 or above, the code needs direct investment before anything else will help.”
Leadership Management
— Dave Kellogg
tl;dr: “There’s a question I’ve been mulling for a while now, and I think it’s time to write it down: when is it okay to use generative AI in a given business context, and when does it cross a line? I’ll focus on two specific areas I know well — board work and strategic analysis — but I think the principles generalize.”
Leadership Management
tl;dr: 84% of developers use AI tools. Fewer than 3% of organizations have meaningfully changed how they ship — or can show what it's worth to the business. StackUp is a free 10-minute diagnostic that benchmarks your engineering org against peers across ways of working, alignment, velocity, and environment — and tells you which 2–3 changes will have the biggest impact on your AI ROI.
Promoted by Uplevel
Leadership Management
— Rahul Garg
tl;dr: ““AI coding assistants respond to whoever is prompting, and the quality of what they produce depends on how well the prompter articulates team standards. I propose treating the instructions that govern AI interactions as infrastructure: versioned, reviewed, and shared artifacts that encode tacit team knowledge into executable instructions, making quality consistent regardless of who is at the keyboard.”
Leadership Management
“Too many of us are not living our dreams because we are living our fears.”
— Anton Zaides
tl;dr: “As everybody and their mother thinks they can build great software right now, I decided to help them avoid a bit of pain. Here are 7 laws every engineer has broken at least once, learned the hard way.”
BestPractices
tl;dr: AI is outpacing traditional code review, creating a verification bottleneck. This report breaks down the shift: (1) a growing trust gap: 96% of developers distrust AI output, (2) the move to automated guardrails, and (3) embedding verification directly into the SDLC with a “trusted, but verified” approach.
Promoted by CodeReview
CodeReview
— Lalit Maganti
tl;dr: “There’s no shortage of posts claiming that AI one-shot their project or pushing back and declaring that AI is all slop. I’m going to take a very different approach and, instead, systematically break down my experience building syntaqlite with AI, both where it helped and where it was detrimental.”
Tools Productivity
— Gergely Orosz
tl;dr: “Many engineers use inference daily, but inference engineering is a bit obscure – and an area rich with interesting challenges. Philip Kiely, author of the new book, “Inference Engineering,” explains.”
DeepDive
— William Pliger
tl;dr: “System architecture diagrams are essential tools for documenting complex systems. However, common mistakes in these diagrams can lead to confusion, misinterpretation, and frustration for viewers. Here’s a rundown of seven (more!) common mistakes to avoid.”
BestPractices Architecture
Editorial Note
A friend passed this article on and it stuck with me.
It likens the economics of AI to the subprime crash, arguing that AI labs (OpenAI, Anthropic) obfuscate finances with zero path to profitability and an irreversibly broken business model.
The issue is that it the impending doom of these AI labs becomes our own instability, given our staggering adoption, growing over-reliance and rapid workflow integration.
AI feels like a magical tool to me but, perhaps, part of that magical feeling is that it’s on always on tap? For engineering managers, this raises uncomfortable questions: if code generation isn’t truly free, how should we think about cost, ownership, and quality over time?
To be clear, I’m not convinced this is inevitable - but the argument is worth sitting with.
PS. I’m experimenting with this section to pen personal thoughts. Feel free to hit reply and share any feedback.
Most Popular From Last Issue
What I Learned From Nearly 1,000 Interviews At Amazon - Steve Huynh
Notable Links
Claude How-To: Visual, example-driven guide.
EmDash: TypeScript CMS based on Astro.
Gallery: On-device GenAI use cases.
Goose: OS AI agent that automates engineering tasks.
QMD: Search engine for everything you need to remember.
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