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- Issue #757
Issue #757
The Briefing For Engineering Leaders
Tuesday 6th October issue is presented by WorkOS
Every fake account on your free tier burns real credits. Abusers rotate emails and devices faster than any blocklist can track, so your team ends up maintaining rules instead of shipping.
WorkOS Radar scores each signup on device, email, and network signals. Block the obvious abuse, challenge the suspicious, and see the evidence behind every decision.
— Camille Fournier
tl;dr: “This talk forced me to boil the book down to a few recurring themes of focus areas for managers; namely, Technical Skills, Signal Processing, and People Skills. What are these things and how have they started changing with the current state of AI?”
Leadership Management AI
— Andi Roberts
tl;dr: Better decisions under uncertainty start with better sensemaking. The risk is often not a lack of information, but settling too quickly on one explanation of what is happening. Three trialectics from Dave Snowden’s work can help leaders examine how they perceive, know, and reason before deciding how to act.
Leadership Management DecisionMaking
tl;dr: Every fake account on your free tier burns real credits. Abusers rotate emails and devices faster than any blocklist can track, so your team ends up maintaining rules instead of shipping. WorkOS Radar scores each signup on device, email, and network signals. Block the obvious abuse, challenge the suspicious, and see the evidence behind every decision.
Promoted by WorkOS
DevEx AI Security
— Steve Huynh
tl;dr: “I’m a recovering productivity junkie. I’ve learned there isn’t a secret method that unlocks more hours in the day, and that most of what we call “time management” or “productivity systems” is a trap. The solution isn’t a new app or a more complex scheduling system.”
Productivity Wellbeing
“Stories are the single most powerful weapon in a leader’s arsenal”
— Andrew Moffat
tl;dr: “When reviewing large amounts of AI-generated code, I often find that the LLM chooses terms for abstractions that do not always map to my own choices.” Andrew shares his approach.
CodeReview AI DevEx
tl;dr: Recently, Google, OpenAI, and Anthropic have each disclosed AI security incidents, making "guardrails" a new buzzword. OpenAPPA provides deterministic guardrails that are 100% resistant to data exfiltration caused by prompt injection or model hallucination and are the first of their kind that don't break agents. It sits between an AI agent and its tools, checks tool calls against security rules, and can sanitize data or run parts of a task in isolation so the agent can keep working safely.
Promoted by Archestra
OpenSource Agents Security
— Christopher Kennelly, Nimit Khandelwal
tl;dr: “Unless designing for flexibility, even trivial choices can quickly harden into painful one-way doors. To maintain high decision velocity safely, use deliberate architectural patterns to engineer reversibility into your code. Instead of coding yourself into a corner, actively design your code with built-in escape hatches. By consciously building flexibility into your systems up front, you can eliminate analysis paralysis and keep your team executing both fast and safely.”
TechDebt DecisionMaking Architecture
— Blake Scholl
tl;dr: “Burnout is not what it presents: it’s not about working too hard for too long, burnout is about working in the face of a goal that seems too far out, too unattainable, too abstract.”
Management Wellbeing
— Sebastian Raschka
tl;dr: While Jev aims to classify things, it’s easy to dismiss Jev as “just a classifier,” and my own view of Jev has evolved quite a bit over the past few days. In particular, my thoughts went from “classifiers used to be my bread & butter; I can easily build this myself” to “wow, this actually works better than I thought.”
Tools+Setup ML AI
Editorial
Two Builders on the Future of Engineering Management
Last month I sat in on a public conversation between Gergely Orosz and Michael Grinich of WorkOS about what AI is doing to engineering management. Five things stuck:
Roles are redrawing. Leaders are back in the code with agents, engineers are making more product calls, yet WorkOS hired more PMs due to work volume.
The bottleneck moved downstream. More PRs means CI / CD strain and review that doesn't scale. Raise output without redesigning review and you've shifted the bottleneck.
Engineering's remit is expanding to enabling everyone to build. Non-engineers are shipping internal agents and prototypes on engineering platforms.
Your own learning environment is a career issue. Leaders are leaving AI-resistant orgs; some are building their own companies and others taking smaller roles to experience hands back on.
Reliability might become the dividing line between fast and effective. Higher change volume means more ways to break prod. Reliability is becoming a bigger concern with the increased speed and scale.
AI isn't eliminating engineering management. It's changing what you have to be hands-on. Less coordinating the work, more designing the systems and teams that can absorb the output.
Most Popular From Last Issue
10 Career Traps (Thoughtful People Fall Into) - John Cutler
Notable Links
Laya: System 1 decision engine.
Livenerf: Benchmark for tracking model capability.
PhotoCraft: OS reimplementation of Photoshop.
REA: Reverse engineer anything with agents.
Router: Model router for agentic systems.
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