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- Issue #733
Issue #733
Essential Reading For Engineering Leaders
Tuesday 14th July’s issue is presented by Atono
Nine out of ten engineering teams adopted AI for delivery. One in three actually believe they're ready to hand work off to it.
That gap isn't a tooling problem. When production becomes cheap, the constraint doesn't stay there - it moves upstream.
Inside the guide:
Why the assumption Agile was built on no longer holds with AI in the loop
How leverage is reshaping at every level—from product leaders to managers to engineers
The "Handoff Test" - how to measure if your org runs on conversations or Product Knowledge
Why this is infrastructure, not documentation, and why that distinction actually matters
— Will Larson
tl;dr: “When you talk about growing an organization, the conversation usually leads to hiring. While I believe hiring is a very important approach to growing organizations, I believe we reach for it too often. In order to prioritize hiring for scenarios where it’ll do the most good, over the past year I’ve developed a loose framework for reasoning about what a given team needs to increase performance.”
Leadership Management
— Brian Houck
tl;dr: “Each paper tackles a different question. Some measure the productivity impact of AI coding assistants. Others examine how those gains propagate through the software delivery process, explore what developers actually want from future AI systems, or reconsider the kinds of debt we should be paying attention to in an AI-assisted world. Despite coming from different research groups and using very different methodologies, they all seem to be converging on the same underlying story.”
Management AI
tl;dr: Nine out of ten engineering teams adopted AI for delivery. One in three actually believe they're ready to hand work off to it. That gap isn't a tooling problem. When production becomes cheap, the constraint doesn't stay there - it moves upstream. Inside the guide: (1) Why the assumption Agile was built on no longer holds with AI in the loop. (2) How leverage is reshaping at every level - from product leaders to managers to engineers. (3) The "Handoff Test" - how to measure if your org runs on conversations or Product Knowledge. (4) Why this is infrastructure, not documentation, and why that distinction actually matters.
Promoted by Atono
Management AI Product
— Steve Huynh
tl;dr: “After participating in many such conversations, both through my podcast and during my time at Amazon, I have recognized a handful of patterns that have held over the decades and across different companies. What follows is my running list of those patterns, the ones that keep surfacing whenever I sit down with someone elite. There are five of them, and I explain the gap between what they do and where most other people stop.”
CareerGrowth
"It's easy to win forgiveness for being wrong; being right is what gets you into real trouble"
— Arvind Narayanan, Sayash Kapoor
tl;dr: “In this essay, we argue that there is enough evidence to reject the narrative that once AI capabilities reach a certain threshold, it will cause mass layoffs. Given that this is true even in a sector with very few regulatory barriers, most other professions are likely to be even more cushioned.”
IndustryTrends AI
tl;dr: Every integration gives your product more context, and most of it lives in your users' other apps like GitHub, Slack, Google, and Salesforce. Pipes connects them. Users authorize once through a drop-in widget, one call returns a valid, scoped token, and credentials stay off your stack. The more context your agents can reach, the more useful your product becomes.
Promoted by WorkOS
Tools+Setup
— Tomasz Tunguz
tl;dr: “Anthropic spends 2.3x its payroll on compute — $515k per engineer per year at today's $224k fully-loaded salary. The top 1% of software companies spend $89k, the median $137. Three 2029 scenarios bracket how that gap closes.”
Management
— Abraham Thomas
tl;dr: “Data quality. We love it, we want it, we praise it, we aspire to it. Even in these benighted and degenerate times, if there’s one belief that unites all sensible individuals, it is the belief that data quality is a Good Thing. It’s a pity, then, that nobody seems to know what data quality is.”
DataEngineering Architecture
— Dr Andrew Leigh
tl;dr: “Dr Andrew Leigh reflects on 25 years of professional experience as a software architect and offers his insights and a reading list of recommendations.”
Books Architecture
Most Popular From Last Issue
Software Design In The Agentic Age: Place Your Bets - Mathias Verraes
Notable Links
Ant: JS runtime built from scratch.
Background Agents: OS background agents coding system.
DCG: Block dangerous git & shell commands by agents.
OpenKnowledge: Markdown editor with integrations.
PentesterFlow: Agentic offensive-security in your terminal.
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