Pointer Newsletter Digest

• Key Activities For Sustainable Engineering Team Issue #731 · Dunya Kirkali

  “Sometimes you need to fly high: looking at strategy, direction, and the broader system around the team. Sometimes you need to fly low: joining the details, removing friction, and helping the team through turbulence. The hard part is knowing when to change altitude.”

• So You Want to Fix Your All Hands Issue #730 · Michael Lopp

  “I will now explain why at least 50% of your team finds your current All Hands to be a waste of their time. They believe: (1) This is information they can easily find elsewhere. (2) This is about you, not them. (3) This is boring. Now, I will describe how to build an All Hands that will exceed their expectations.”

• How Is AI Adoption Affecting Team Motivation? Issue #730 · Lizzie Matusov

  “Ask an engineer why they got into the field, and you’ll likely hear about building things, untangling hard problems, the specific satisfaction of shipping something they designed. But as the tools take over more of the building, the day-to-day shape of the job is shifting in ways that may cut in the opposite direction. This week we ask: how is AI adoption affecting team motivation, and what is actually driving the decline?”

• Three Bad Managers Issue #729 · Michael Lopp

  “I worked for each of these humans. They either hired or promoted me. I worked for them for many years. As is my way, I’ve vastly altered the details of each human, but the core issue I describe is the core issue. Also, each of these humans is very smart. No dummies.”

• Who Will Be The Senior Engineers Of 2035? Issue #728 · James Stanier

  “We’ll start by looking at the pipeline we used to have: how senior engineers traditionally emerged through years of mistakes, mentorship, and low-stakes learning. Then we’ll examine what’s replacing it, and hypothesise whether AI will actually fill the gap. We’ll explore three possible scenarios for 2035, and finish with what this means depending on where you sit in the industry.”

• How Do I Build A Powerful Internal Network? Issue #727 · Andi Roberts

  “What separates the leaders who create momentum from those who spend their time managing friction is rarely authority or technical expertise. It is network. Not the size of it. The quality, the positioning, and the deliberate investment that went into building it before it was needed.”

• Revised Rules Of Engineering Leadership Issue #726 · Will Larson

  “This post documents the new rules I’ve revised my approach to engineering leadership around, and then talks through the specific projects I’ve worked on over the past year that caused me to believe in these rules.”

• Don't Stack Weaknesses Issue #726 · Yue Zhao

  “The real damage happens when you stack weaknesses - when a leader is weak at something and their direct reports are weak at the same thing. This is a disaster, and it happens far more often than you’d think. Part of the reason for this is that leaders tend to hire people with the same skills they have. They’ll dress it up as hiring for “values” instead of “skills” so it doesn’t look like they’re cloning themselves - but people ultimately search for and evaluate the things they’re good at.”

• Your Team Reviews AI's Code Now. What Should Block A Merge? Issue #726

  Most pull requests on your team no longer start with a human author - but the review bar was built for when they did. AgentField's engineering write-up lays out the four jobs code review has always done quietly, the three that get heavier the moment AI writes the first draft, and how to decide what blocks a merge vs. what ships. A read for eng leaders setting the standard, not rewriting the checklist line by line.

• How Does GenAI Change When And How Teammates Talk To Each Other? Issue #724 · Lizzie Matusov

  “Most discussion of AI coding tools centers on how they change an individual’s output, but they are also quietly rewiring how people on a team turn to one another. This week we ask: how does GenAI change when and how members of a software development team interact?”

+271 more articles in this topic.

• Key Activities For Sustainable Engineering Team Issue #731 · Dunya Kirkali

  “Sometimes you need to fly high: looking at strategy, direction, and the broader system around the team. Sometimes you need to fly low: joining the details, removing friction, and helping the team through turbulence. The hard part is knowing when to change altitude.”

• Three Bad Managers Issue #729 · Michael Lopp

  “I worked for each of these humans. They either hired or promoted me. I worked for them for many years. As is my way, I’ve vastly altered the details of each human, but the core issue I describe is the core issue. Also, each of these humans is very smart. No dummies.”

• Cross-Team Pragmatics Issue #729 · Phil Calçado

  “I’ve been in this industry for twenty-five years and have seen all sorts of companies and projects. You know what I have never heard? Someone saying, “You know what I really like about working here? Cross-team collaboration.””

• Riding Technology Waves Issue #728

  “A lot of senior leaders, even “leaders” at tech companies, are fundamentally insecure about technology. They’re either not technical by training or so out of practice that they don’t know how their products work. This is especially true in fields where projecting confidence is a key part of the job: Sales, Marketing, Human Resources, and arguably even Security are all examples.”

• Who Will Be The Senior Engineers Of 2035? Issue #728 · James Stanier

  “We’ll start by looking at the pipeline we used to have: how senior engineers traditionally emerged through years of mistakes, mentorship, and low-stakes learning. Then we’ll examine what’s replacing it, and hypothesise whether AI will actually fill the gap. We’ll explore three possible scenarios for 2035, and finish with what this means depending on where you sit in the industry.”

• What Being “Inspiring" Actually Means Issue #728 · Wes Kao

  “Being inspiring is not about how passionate your delivery is. You don’t have to get up in front of everyone, or try to cosplay what a bold leader sounds like. You can sound like yourself. Perhaps the most articulate and clear-eyed version of yourself, but still yourself.”

• How Do I Build A Powerful Internal Network? Issue #727 · Andi Roberts

  “What separates the leaders who create momentum from those who spend their time managing friction is rarely authority or technical expertise. It is network. Not the size of it. The quality, the positioning, and the deliberate investment that went into building it before it was needed.”

• Revised Rules Of Engineering Leadership Issue #726 · Will Larson

  “This post documents the new rules I’ve revised my approach to engineering leadership around, and then talks through the specific projects I’ve worked on over the past year that caused me to believe in these rules.”

• Why Is Meta Destroying Its Engineering Organization? Issue #726 · Gergely Orosz

  “In this article, we walk through what’s happened, and ask what’s going through the minds of leadership who are reducing software engineering there from the profit center that it was between 2004 until very recently, to the disdained cost center that it has become in just a few weeks.”

• How Do I Build Influence Across Functions And Departments? Issue #725 · Andi Roberts

  “Research consistently supports this. People are influenced not only by the strength of an argument but also by trust, credibility, relationship, and perceived relevance. Before people commit to an initiative, they need confidence in both the message and the messenger. They need to feel understood before they feel persuaded. And they need to feel ownership before they feel commitment.”

+229 more articles in this topic.

• [Webinar] 8 Levels Of Context Maturity In AI-Native Engineering Issue #731

  AI is in your engineering workflow. While the token spend shows it, the throughput doesn't. The human is very much still in the loop, and that's a context problem. This free webinar maps the 8 levels of context maturity: where most teams are stuck, what the ceiling looks like at each stage, and what it actually takes to make the most out of your agents. Join live July 23 (FREE).

• Some New Agentic Patterns Issue #731 · Jesse Vincent

  “I want to tell you a bit about how we've started shipping some changes to internal production with an "agentic user in the loop." Letting an agent work directly with the implementer building its runloop and tooling is getting us higher-quality tools and experiences for the agent without a human in the middle of a game of telephone.”

• Understanding Is The New Bottleneck Issue #731 · Geoffrey Litt

  “Hot take: I think it's still important to understand the code that our agents write! In this talk I'll explain why that's the case, and show some ideas for how to efficiently understand code. Alright, let's dive in.”

• How Is AI Adoption Affecting Team Motivation? Issue #730 · Lizzie Matusov

  “Ask an engineer why they got into the field, and you’ll likely hear about building things, untangling hard problems, the specific satisfaction of shipping something they designed. But as the tools take over more of the building, the day-to-day shape of the job is shifting in ways that may cut in the opposite direction. This week we ask: how is AI adoption affecting team motivation, and what is actually driving the decline?”

• A Return To Two-Pizza Culture Issue #730 · Werner Vogels

  “Coding agents are compressing the time between defining the problem and having something real in our hands to evaluate. It is time to amend the way we think about the process that’s brought us this far. “

• How High-Performing QA Teams Are Adapting To AI Issue #730

  The real challenge with AI isn't getting a demo to work. It's building quality workflows that teams can trust in production. At the QA Leadership Summit, engineering leaders and directors share how they're integrating AI into testing, measuring its impact, and creating reliable systems that scale. Virtual event, July 22.

• A Coding Agent Is Six Functions In A Trenchcoat Issue #730 · Hadley Wickham

  “Of the six tools above, only three are really essential for a basic coding agent. To prove it, I’m going to build a tiny coding agent in R with ellmer. We’ll start ruthlessly minimal — with just read file, write file, and run command — and then work our way up. We’ll lose some niceties, but in exchange we get something you can read in one sitting.”

• Local Reasoning For Global Properties Issue #730 · Laurence Tratt

  “In the last couple of years, I’ve increasingly been asked questions that boil down to: will AI benefit from new kinds of programming languages? My answer has been “probably not” and, so far at least, that answer has held up well: AI is now able to generate large quantities of code in just about any programming language you or I can think of. Now that the technology has advanced, and its characteristics have started to become clearer, my answer has changed.”

• Review The Actual Change, Not The File List. Issue #729

  "AI writes more code than ever. Reviewing it shouldn’t mean scrolling forty files in alphabetical order. CodeRabbit Review reorganizes any pull request from a flat file list into a structured, layer-by-layer walkthrough - the logical reading order of the change, not the order your platform happens to sort it. Every range gets its own plain-language summary, with sequence diagrams, state machines, and ERDs generated inline wherever a visual earns its place. (1) Cohorts group related files and chunks so you review one idea at a time. (2) Layers order them so foundational changes - data shapes, contracts - come before the code that depends on them. (3) Code Peek lets you click any variable, function, class or type to see its definition and usages without leaving the tab. (4) Semantic Diff cuts past formatting noise to show what actually changed. From the team that pioneered AI code reviews. 2M reviews every week. 6M repos. 15K customers. Free during early access.

• Clustering Billions Of Products For Agentic Commerce Issue #729 · Mariya Mansurova

  Shopify's global Catalog organizes billions of products across millions of merchants into a single, searchable intelligence layer. As part of their Spring '26 Edition, Shopify Engineering breaks down the LLM-powered pipeline behind product clustering: how it matches related products across merchants into a unified catalog that AI agents can intelligently search and map to buyers’ preferences.

+203 more articles in this topic.

• Ready To Teleport? Issue #730
• Review Your Next PR With CodeRabbit Review Today Issue #729
• Build Your Tech Career At Fidelity Issue #728
• Take The Diagnostic → Issue #726
• Build Your Agent’s Context Layer For 95% Less → Issue #725
• Save Your Seat Issue #724
• Save Your Seat Issue #722
• Explore Roles That Build The Future Of Tech At Fidelity Issue #721
• Lead Tech Forward At Fidelity Issue #720
• Save Your Seat Issue #718

+79 more articles in this topic.

• I’m An Introvert. This Is How I Get Myself To Speak Up. Issue #704 · Wes Kao

  I often get readers who ask, “Wes, if I’m not naturally loud, how do I speak up or talk about my accomplishments? How do I gain more visibility, either internally in my company or externally online?”

• Nobody Is Coming To Save Your Career Issue #703 · Steve Huynh

  “Most people assume someone will tap them on the shoulder when it’s time. In my experience, that assumption is the single biggest reason high performers get stuck. Here are three truths about who owns your career, and what to do if you realize that you’re trapped on the wrong side of them.”

• What About Juniors? Issue #703 · Marc Brooker

  “In any time of change, the people who can learn and adapt fastest are most likely to succeed. The senior’s advantage is that they have a lot of knowledge and context. The junior’s advantage is that they come in knowing that they need to learn, adapt, and change.”

• Things I Believe Issue #702 · Lee Robinson

  (1) Shipping fast beats the best strategy. (2) You have no career ceiling. (3) Be ruthlessly truth seeking. (4) Communication is the job. (5) Education is the best form of developer marketing. (6) Leadership means owning outcomes beyond the org chart. (7) Work can also be your hobby. (8) Demos > memos. (9) Hiring is what separates good leaders from great. (10) Always try to assume good intent.

• My Heuristics Are Wrong. What Now? Issue #702 · Marc Brooker

  “We’ve seen this play out in small ways before. Over the last decade, I’ve frequently been frustrated by experienced folks who didn’t update their system design heuristics to match the cloud, to match SSDs, to match 100Gb/s networks, and so on. But this is the biggest change I’ve seen in my career by far. An extinction-level event for rules of thumb.”

• Willingness To Look Stupid Is A Genuine Moat In Creative Work Issue #701 · Sharif Shameem

  “It feels like there's something like a conservation law at work here: the amount of stupidity you're willing to tolerate is directly proportional to the quality of ideas you'll eventually produce.”

• How To Handle Overly Confident And Combative Colleagues Issue #699 · Yue Zhao

  “Working with this type of coworker is draining, mentally and emotionally. However, they are able to behave this way because they are in the good graces of senior leadership. This makes collaboration with them an unfortunate necessity. Here are some ways to tackle this situation.”

• Yegge's Developer-Agent Evolution Model Issue #699 · Justin Abrahms

  “In Welcome to Gas Town , Steve Yegge generated a list of the 8 stages of agentic workflow evolution. I find myself wanting to deep link to it, so I’m replicating it here.”

• The Reason Most People Are Terrible Communicators (And How to Fix It) Issue #698 · Steve Huynh

  “I’m going to teach you three techniques that changed how I communicate at work. You can use them individually, but for the best results, you want to use all three together. And I’m going to use each technique to explain itself, so you can see them working in real time.”

• Things I've Done With AI Issue #698 · Jerred Shepherd

  “Effectively using AI required fundamental shift in how I thought about my projects. Why did I care about types? Why do we have design patterns? Why does code need to be maintainable or “well written”. For hobby projects, it can be a source of pleasure to write and see beautiful code.”

+67 more articles in this topic.

• [Webinar] 8 Levels Of Context Maturity In AI-Native Engineering Issue #731

  AI is in your engineering workflow. While the token spend shows it, the throughput doesn't. The human is very much still in the loop, and that's a context problem. This free webinar maps the 8 levels of context maturity: where most teams are stuck, what the ceiling looks like at each stage, and what it actually takes to make the most out of your agents. Join live July 23 (FREE).

• Some New Agentic Patterns Issue #731 · Jesse Vincent

  “I want to tell you a bit about how we've started shipping some changes to internal production with an "agentic user in the loop." Letting an agent work directly with the implementer building its runloop and tooling is getting us higher-quality tools and experiences for the agent without a human in the middle of a game of telephone.”

• Replacing The Engine Mid-Flight: How A Closed-loop AI Agent Upgraded StarRocks Across 58 Releases With Zero Downtime Issue #731 · Vijay Shekhawat

  TRM Labs built an AI agent that upgraded their StarRocks database across 58 releases with zero downtime. A shadow proxy mirrored production traffic while an autonomous agent diagnosed bottlenecks, optimized queries, and validated fixes - all without human intervention. The result: dozens of endpoints optimized, a critical bug caught, and up to 94% latency improvement.

• A Return To Two-Pizza Culture Issue #730 · Werner Vogels

  “Coding agents are compressing the time between defining the problem and having something real in our hands to evaluate. It is time to amend the way we think about the process that’s brought us this far. “

• A Coding Agent Is Six Functions In A Trenchcoat Issue #730 · Hadley Wickham

  “Of the six tools above, only three are really essential for a basic coding agent. To prove it, I’m going to build a tiny coding agent in R with ellmer. We’ll start ruthlessly minimal — with just read file, write file, and run command — and then work our way up. We’ll lose some niceties, but in exchange we get something you can read in one sitting.”

• Running Local Models Is Good Now Issue #729 · Vicki Boykis

  "“With the most recent releases from Google in the Gemma 4, family, I’ve finally been able to do agentic coding locally and have loops work at about ~75% the accuracy/speed of frontier models, which is incredible.”

• Loop Engineering Issue #728 · Addy Osmani

  “Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. A loop here can be thought of a recursive goal where you define a purpose and the AI iterates until complete. It’s roughly five building blocks and Claude Code and Codex both have all five now.”

• [Webinar] 8 Levels Of Context Maturity In AI-Native Engineering Issue #727

  AI is in your engineering workflow. While the token spend shows it, the throughput doesn't. The human is very much still in the loop, and that's a context problem. This free webinar maps the 8 levels of context maturity: where most teams are stuck, what the ceiling looks like at each stage, and what it actually takes to make the most out of your agents. Join live June 24 (FREE).

• Building Reliable Agentic AI Systems Issue #727 · Sarang Kulkarni

  “In this post, we share that journey — how Bayer's early investment in generative AI has resulted in PRINCE, an agentic AI system built on Agentic RAG. This case study explores the technical architecture, engineering decisions, and lessons learned in transforming preclinical data retrieval from a challenging maze into an intuitive conversational experience.”

• Agentic Code Review Issue #726 · Addy Osmani

  “Stop reviewing everything to the same depth. Spend scarce human attention only where being wrong is costly, and let cheap deterministic gates and AI reviewers handle the rest. The organizing idea is to match review effort to the cost of being wrong, push the cheap deterministic work as early as possible, and reserve human attention for what only humans can do.”

+50 more articles in this topic.

• How To Write An Effective Software Design Document Issue #729 · Michael Lynch

  “I’ve written design docs as a developer at Google, Microsoft, and within my own companies. The specifics vary, but the underlying principles remain the same. A design doc should articulate the hard problems you’re solving and help your teammates give you feedback. Below, I share my approach to creating effective design docs and explain what belongs in a design doc and what does not.”

• Fintech Engineering Handbook Issue #729 · Voytek Pitula

  “This resource aims to describe the most important patterns used in software engineering, where money is the primary focus of the system. It can be read in full to get a comprehensive understanding or in parts when dealing with a particular problem.”

• Your Claude Skills Don't Work The Way You Think Issue #728 · Kaia Colban

  Most people treat a Claude skill like a solid thing that lives somewhere obvious and does what it says. It isn't, and misunderstanding how they work screwed me over a few too many times. Here's what I learned: how a skill's description alone decides whether it fires, how two of my skills quietly cancelled each other out, and how I lost half of them. They say a skill takes 100 hours to master. Save 99 and read this instead.

• Find A New Role - Worksheet Issue #728 · Lara Hogan

  Lara built a worksheet to help you identify: what do I want in a new role? How can I make sure I find the right place? Where do I even start?

• How Claude Code Works In Large Codebases: Best Practices And Where To Start Issue #723

  “This article covers the patterns we've observed that have led to successful adoption of Claude Code at scale. We use “large codebase” to refer to a wide range of deployments: monorepos with millions of lines, legacy systems built over decades, dozens of microservices across separate repositories, or any combination of the above.“

• Life Is Too Short For A Slow Terminal Issue #723 · Mijndert Stuij

  “There was never some big optimization project behind this either; I've just always kept my shell minimal and fast and over the years that turned into a habit. Here's how I go about it, and all of it can be found in my dotfiles.”

• AI Engineering For Developers Issue #721 · Luca Cavallin

  “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.”

• Beyond The Prompt: Claude Code Issue #720 · Arpan Patel

  “The casual user types prompts, accepts suggestions, and treats it like a fancier autocomplete. The daily driver uses it like a programmable agent with memory, custom commands, parallel sessions, and a project setup that compounds over time. This guide is for the second kind of person, assuming you already know what Claude does when you type it in a terminal.”

• Agentic Engineering Patterns Issue #719 · Simon Willison

  “I’ve started a new project to collect and document Agentic Engineering Patterns—coding practices and patterns to help get the best results out of this new era of coding agent development we find ourselves entering.”

• AI Is Writing Code Faster Than Ever, But Who Checks It? Issue #717

  When speed is prioritized over reliability, technical debt and security risks accumulate silently. To realize the full ROI of AI, organizations must shift from manual reviews to an automated verification layer. Evaluate your next vendor with: (1) Six evaluation pillars for modern code review. (2) Strategies to solve the AI productivity paradox. (3) A comprehensive checklist for code health.

+49 more articles in this topic.

• Git's Magic Files Issue #707 · Andrew Nesbitt

  “Git looks for several special files in your repository that control its behavior. These aren’t configuration files in .git/, they’re committed files that travel with your code and affect how git treats your files. If you’re building a tool that works with git repositories, like git-pkgs, you’ll want to ensure you respect these configs.”

• Add Real-Time Web Search To Your AI Apps Issue #706

  AI is smarter when it can search the web. How are AI companies doing it? Meet the worst-kept secret in the AI industry: the SerpApi web search API: (1) Scrape Google and other search engines (including AI overviews, Maps, Shopping, Amazon, and more). (2) Integrate with a dead-simple GET request that any agent can make. (3) Used by NVIDIA, Uber, Adobe, and even the United Nations.

• Eight Years Of Wanting, Three Months Of Building With AI Issue #705 · Lalit Maganti

  “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.”

• The Ultimate Sidekick for Your AI Applications Issue #704

  AI is smarter when it can search the web. How are AI companies doing it? Meet the worst-kept secret in the AI industry: the SerpApi web search API: (1) Scrape Google and other search engines (including AI overviews, Maps, Shopping, Amazon, and more). (2) Integrate with a dead-simple GET request that any agent can make. (3) Used by NVIDIA, Uber, Adobe, and even the United Nations.

• One Prompt. Complete Auth. Issue #702

  Design the perfect auth and onboarding experience for your users with PropelAuth's new Integration MCP Server. Pick and choose which features you need, and seamlessly match your product's look and feel. Don't worry about auth: your agent can handle it while you work on your YC application.

• What Comes After Your IDE? Issue #702

  Stop herding your AI agents across terminals and branches. Intent bundles each task into a single workspace with a living spec, agent notes, and full change visibility. Orchestrate agents like a system, not a swarm: direct specialists, keep work aligned, and ship without copy-pasting context. Free with your existing Augment / Claude Code / Codex / OpenCode subscription.

• Your AI Agents Are Missing Context Issue #701 · Dennis Pilarinos

  Coding agents rarely produce mergeable code on the first try. Even with rules, skills, and MCP connections, you still end up babysitting each session, supplying missing context, fixing wrong assumptions, and burning tokens. What do we need to do to actually give AI tools the understanding they need?

• Spec Driven Development With Agents Issue #701

  Are you still prompting one agent at a time? That's so 2025. Intent is a developer workspace for orchestrating multiple agents from a living spec. Go from idea to PR without juggling terminals, repo copies, or stale prompts. It's free with Claude Code / Codex / OpenCode.

• Better, Faster, And (Even) More Issue #701 · Michael Lopp

  “I’ve never built more interesting, random, and useless scripts, tools, and services than I have in the last six months. The cost to go from “Random Thought” to “Working Something” has never been lower... The following is a set of tools and practices I’ve gathered over the last 90 days, which continue to accelerate my process and give me daily joy.”

• Cut Your QA Cycles Down From Hours To Minutes With Automated Testing Issue #699

  If slow QA processes bottleneck your engineering team and slow down releases, you need QA Wolf. Their AI-native service gets engineering teams to 80% automated E2E test coverage, helping them ship 5x faster by reducing QA cycles from hours to minutes. With QA Wolf, you get: (1) Unlimited parallel test runs for web and mobile. (2) 24-hour maintenance and on-demand test creation. (3) Human-verified bug reports sent directly to your team. (4) Zero flakes guarantee. Trusted by Drata, Cohere, AutoTrader, and many more.

+37 more articles in this topic.

• A Peek Into Reddit's Anti-Spam Internals Issue #730 · Lyra Rebane

  “I was a moderator of a few fairly small subreddits that’d from time to time get posts automatically removed for spam. However, when I went to actually look at the removed spam, I saw something I was never meant to see. I saw Reddit’s anti-spam internals.”

• Loop Engineering Issue #728 · Addy Osmani

  “Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. A loop here can be thought of a recursive goal where you define a purpose and the AI iterates until complete. It’s roughly five building blocks and Claude Code and Codex both have all five now.”

• How's Linear So Fast? A Technical Breakdown Issue #720 · Dennis Brotzky

  “A few milliseconds is all it takes to update an issue in Linear. A traditional CRUD app doing the same thing takes about 300ms. How do they do it? There's no secret silver bullet to performance. The reality is that it's built from the ground up on the right foundation, then improved by countless decisions. My goal is to walk through some of the techniques that make Linear feel the way it does and help you implement the same.”

• The Production Version Of Wiring Claude Code Into Your Pipeline Issue #719 · Santosh Kumar Radha

  If your team has chained Claude Code to write, then to review, you already know the pattern. Harness orchestration is the architectural discipline behind it, a first look at how Claude Code, Codex, and Gemini compose as primitives. Read the breakdown — written for engineering leaders evaluating the agent layer.

• The Production Version Of Wiring Claude Code Into Your Pipeline Issue #718 · Santosh Kumar Radha

  If your team has chained Claude Code to write, then to review, you already know the pattern. Harness orchestration is the architectural discipline behind it, a first look at how Claude Code, Codex, and Gemini compose as primitives. Read the breakdown — written for engineering leaders evaluating the agent layer.

• Learning Software Architecture Issue #717 · Alex Kladov

  Alex argues that the real skill isn't technical design but understanding how social incentives shape code. He illustrates this with rust-analyzer. Your codebase will mirror your organization's incentive structure whether you design for it or not.

• AI Adoption Is Repeating The Microservices Mistake Issue #716 · Amy Carrillo Cotten

  Fifteen years ago, conference talks sold engineering leaders on microservices as a universal fix. The same hype cycle is now running on AI. What microservices got wrong - and why recognizing the pattern early is how you avoid paying for it later.

• Databases Were Not Designed For This Issue #711 · Arpit Bhayani

  “Traditional database architecture rests on assumptions that agentic AI workloads systematically violate: deterministic callers, intentional writes, brief connections, loud failures, and schema as a developer contract. Each of these assumptions held because a human was always somewhere in the loop. Agents remove that guarantee.“

• The Vertical Codebase Issue #710 · Dominik Dorfmeister

  “What we’d want is low coupling and high cohesion. A structure where it’s clear what our code interdependencies are and where modules are as focused as possible. Arguably, the horizontal split creates the opposite: No clear boundaries between modules and code living together that is only loosely related. We need an alternative.”

• Managing Context In Long-Run Agentic Applications Issue #709 · Dominic Marks

  “In complex, long-running agentic systems, maintaining alignment and coherent reasoning between agents requires careful design. In this second article of our series, we explore these challenges and the mechanisms we built to keep teams of agents working productively over long time spans. We present a range of complementary techniques that balance the conflicting requirements of continuity and creativity.”

+35 more articles in this topic.

• How Can Engineering Leaders Calculate The Return On Their AI Investments? Issue #714 · Lizzie Matusov

  “AI usage has crossed the line from experiment to default. As that usage scales, the conversation among engineering leaders has shifted from “should we adopt” to “what is all of this actually worth,” and ROI has become a regular topic in staff meetings, board prep, and budget reviews. This week we ask: what does it take to realize ROI from AI-assisted software development?”

• Construct With Collaborators, Call With Work Issue #714 · Shahar Roth

  "A design guideline from Google: inject long-lived dependencies through the constructor and pass per-call data as method parameters. The separation promotes reusability, testability, cleaner APIs, and predictable behavior — though what counts as a 'collaborator' versus 'work' depends on the object's identity and lifetime."

• 10 Lessons For Agentic Coding Issue #714 · Drew Breunig

  “I’ve been keeping a running list of tips for agentic coding: guidelines or rules one might give to someone just getting started with Codex, Claude Code, Pi, or any other agent. Ideally each tip is generalizable guidance, relevant to any agentic programming. I’m also looking for durable lessons that will stick around as models and harnesses improve.”

• 3 Constraints Before I Build Anything Issue #713 · Jordan Lord

  “I’ve been a builder for 10 years, and I've built products that went nowhere because they were either too complex or had no identity. These are the constraints that I landed on after making those mistakes.”

• Drunk Post: Things I’ve Learned As A Senior Engineer Issue #712 · Kirill Bobrov

  “A few years ago, a data engineer got drunk and wrote down everything he learned in 10 years of engineering. The original account is deleted, but the post captures something real — the kind of honesty you only get after a few glasses of wine. Preserving it here, typos and all.”

• How I Use AI To Code Issue #712 · Chris Parsons

  A practitioner's guide to AI-assisted coding in 2026. Senior engineers should shift from reviewing AI-generated diffs to training the harness that produces them via CLAUDE.md files, skill files, and feedback loops. The bottleneck has moved from code generation to verification.

• Structured-Prompt-Driven Development Issue #712 · Wei Zhang, Jessie Jie Xia

  “Instead of relying on ad hoc chats, SPDD turns prompts into assets that can be: version controlled, reviewed, reused, and improved over time. Teams use structured prompts to capture requirements, domain language, design intent, constraints, and a task breakdown. Then the LLM generates code within a defined boundary, so output becomes more predictable and easier to validate.”

• The 20 Software Engineering Laws Issue #711 · Dr Milan Milanović

  “Some of these laws are sixty years old. They still apply to software development in 2026, and they will still apply in 2036 because they are not really about software. They are about people working together to build things under time pressure (basically, a lot of them are just laws of human nature).”

• How I Use Claude Code Issue #711 · Boris Tane

  “I’ve been using Claude Code as my primary development tool for approx 9 months, and the workflow I’ve settled into is radically different from what most people do with AI coding tools. Most developers type a prompt, sometimes use plan mode, fix the errors, repeat.“

• Threat Modeling Guide For Software Teams Issue #710 · Gayathri Mohan, Jim Gumbley

  “Rather than conducting security analysis as a separate or upfront activity, teams should integrate threat modeling into their development process through small, regular activities. The article helps teams get started and develop their practice using different approaches across application development, and infrastructure.”

+26 more articles in this topic.

• Be The Idiot Issue #731 · Kirill Bobrov

  “I’ve developed a mental checklist of “stupid” questions that have saved me more times than I can count.” The author shares some of these with the understanding that most engineering failures happen because someone was afraid to look stupid.

• Saying The Obvious Thing Issue #730 · Sean Goedecke

  “Stating the obvious is surprisingly useful. Most of your knowledge lives below the threshold of conscious awareness, so it’s possible for a piece of writing to remind you of what you already know. It’s common to know you don’t like something without being quite sure why, and reading an obvious statement can help clarify why you find certain things distasteful.”

• The 80/20 Rule For Your Whole Life Issue #729 · Yew Jin Lim

  “The best investment framework isn’t the one that maximizes returns. The best career strategy isn’t the one that eliminates risk. The best contemplative practice isn’t the one that promises instant transformation. The best framework is the one you’ll stick with. For me, that’s been a boring foundation with a laboratory on top.”

• Riding Technology Waves Issue #728

  “A lot of senior leaders, even “leaders” at tech companies, are fundamentally insecure about technology. They’re either not technical by training or so out of practice that they don’t know how their products work. This is especially true in fields where projecting confidence is a key part of the job: Sales, Marketing, Human Resources, and arguably even Security are all examples.”

• Who Will Be The Senior Engineers Of 2035? Issue #728 · James Stanier

  “We’ll start by looking at the pipeline we used to have: how senior engineers traditionally emerged through years of mistakes, mentorship, and low-stakes learning. Then we’ll examine what’s replacing it, and hypothesise whether AI will actually fill the gap. We’ll explore three possible scenarios for 2035, and finish with what this means depending on where you sit in the industry.”

• Keep Building Your Tech Career Issue #728

  Tech moves fast, but we ensure you stay ahead. At Fidelity, we don't just use the latest tech - we give our technologists the dedicated time, backing, and platforms to explore and adopt it at scale. Want to champion new stacks and grow your career with our support? See how we invest in you at Tech.FidelityCareers.com

• 5 Lessons At 50 Issue #728 · Murat Demirbas

  (1) Caution is warranted. (2) Realism helps you understand how the world runs (3) Competence is bliss (4) Do what you like (5). Your attitude determines your success.

• Find A New Role - Worksheet Issue #728 · Lara Hogan

  Lara built a worksheet to help you identify: what do I want in a new role? How can I make sure I find the right place? Where do I even start?

• How Do I Build A Powerful Internal Network? Issue #727 · Andi Roberts

  “What separates the leaders who create momentum from those who spend their time managing friction is rarely authority or technical expertise. It is network. Not the size of it. The quality, the positioning, and the deliberate investment that went into building it before it was needed.”

• Note To My Younger Self Issue #727 · Yew Jin Lim

  “These lessons came through trial, error, and observing countless brilliant people. Simple doesn’t mean easy, and knowing doesn’t mean doing. Youth, as they say, is wasted on the young.”

+25 more articles in this topic.

• When Was the Last Time You Did Just One Thing? Issue #724 · Steve Huynh

  “You don’t need another productivity system, or more hours in the day. All you need is to choose to do one thing at a time, to be fully in it, especially when something in you is itching to be somewhere else.” Steve provides actionable things you can do to reach this goal.

• Weird Projects I Shipped With AI Issue #722 · Sean Goedecke

  “I definitely would not have done all of these by hand. I might have found the time to do one or two of them, but based on my pre-AI track record they would probably have stayed in the “GitHub repo with a few commits” stage. This list is a kind of existence proof: a bunch of weird projects, useful to at least some people, that would not have existed without AI assistance.”

• Fast Is Better Than Slow Issue #721 · Patrick Dubroy

  “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."

• Most AI Productivity Claims Are Anecdotal Issue #721

  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.

• How I Use LLMs As A Staff Engineer In 2026 Issue #717 · Sean Goedecke

  “A year ago I would very occasionally ask an agent to make changes to a single file if it was a simple change I couldn’t be bothered typing out. Sometimes I would copy a function I wrote into a LLM chat window for feedback. But now I start every single change by asking an agent to solve the problem, and usually push the PR after a single editing pass.”

• Interrogatory LLM Issue #717 · Martin Fowler

  "When we need an LLM to perform a complex task, we often need to feed it a lot of context. Coming up with a design for a new feature requires descriptions of how we want the feature to appear to the user, guidelines on how it should be implemented, information on external systems to consult, and so on. All this can be several pages of markdown. The obvious way to do this is for a human to write this context, but an alternative is to use an LLM to write this context after interviewing a human."

• Using An Engineering Notebook Issue #717 · Nicole Tietz-Sokolskaya

  “One of my core software engineering practices is writing, by hand, in a physical notebook. It's one of the most important things I do to remain productive and effective. Maybe the single most important. And it's a practice that I see very few others using!”

• How To Work And Compound With AI Issue #716 · Eugene Yan

  “How can we work effectively with AI? What’s the workflow, how does it scale, and how do we improve our systems over time? And ideally, it should compound. Every finished artifact—code, docs, analysis, decisions—becomes context for the next session. And each correction updates a config that reduces future errors. While I’m still learning, I’ve repeated my answers often enough that I’m writing it here so the next time I’m asked I can share a link instead.”

• Finishing Things Issue #711 · Dave Gauer

  Dave reflects on how he finishes personal projects despite life getting in the way. He uses a physical Post-It stack to stay focused on one thing at a time, treat habits like spinning plates worth protecting, and argue that keeping projects barely alive through tiny daily progress beats letting them go cold and having to restart from scratch.

• Hurry Up And 10x: The Path To Real AI Productivity Issue #709

  AI promised 10x productivity. Most teams got faster code generation and the same deployment bottlenecks. Platform experts from OpenAI and ACI Worldwide explain why CI/CD maturity determines whether AI actually works — and what systemic roadblocks need removal before engineering leaders see ROI.

+18 more articles in this topic.

• A Coding Agent Is Six Functions In A Trenchcoat Issue #730 · Hadley Wickham

  “Of the six tools above, only three are really essential for a basic coding agent. To prove it, I’m going to build a tiny coding agent in R with ellmer. We’ll start ruthlessly minimal — with just read file, write file, and run command — and then work our way up. We’ll lose some niceties, but in exchange we get something you can read in one sitting.”

• p99 0 ms* Autocomplete For 240 Million Domain Names Issue #727 · Ruurtjan Pul

  “There are a ton of sites that offer this (growing faster than ever thanks to vibe coding), so I need a way to stand out. I picked tool quality / usefulness and UX. The autocomplete is the main way to navigate Wirewiki, so it should be as complete, accurate and fast as possible. I want it to be instant. Like, next frame instant.”

• SQLite Improving Performance With Pre-Sort Issue #726 · Anders Murphy

  “The defining characteristic of a B+ Tree is that it is ordered. Sequential writes are fast. Random data works against this thrashing pages, causing page splits and tree re-balancing. It's not a good time. But, we are batching the data already. So what if we sort it before we insert?”

• How LLMs Actually Work Issue #724

  “Most modern LLMs share the same transformer-family skeleton. The differences come from what each one was trained on, the scale and configuration choices, and the post-training done on top. By the end, you should be able to read many modern LLM papers or model cards and know which piece of the architecture each section is talking about.”

• You Don't Love Systemd Timers Enough Issue #723 · Tyler Langlois

  “A systemd timer is a type of unit that schedules other units on a particular schedule. Timers are effectively a functional replacement for a traditional cron daemon (though you could conceivably run both), and timer calendar settings offer some similarities to help bridge the gap from traditional cron-like expressions.”

• The Last Technical Interview Issue #721 · Steve Yegge

  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.

• Training An Agentic Search Model To Be 20x Faster Than GPT-5 Issue #720 · Sam Dauncey, Max Rumpf

  Frontier LLMs are incredible generalists, but expensive specialists. As token usage comes under scrutiny, there’s an emerging trend toward small models trained for specific tasks. SID-1 is an agentic search model that runs complex retrieval tasks 20x faster than frontier LLMs with 99% fewer tokens - at double the recall of traditional RAG pipelines. Here’s how it was trained.

• How's Linear So Fast? A Technical Breakdown Issue #720 · Dennis Brotzky

  “A few milliseconds is all it takes to update an issue in Linear. A traditional CRUD app doing the same thing takes about 300ms. How do they do it? There's no secret silver bullet to performance. The reality is that it's built from the ground up on the right foundation, then improved by countless decisions. My goal is to walk through some of the techniques that make Linear feel the way it does and help you implement the same.”

• Always Be Blaming Issue #718 · Alex Kladov

  Alex walks through his advanced git blame workflows in service of understanding code history and the "why" behind code changes. He describes this as a 4D approach to reading code.

• The Two Abstractions Of System Design: Hide Or Reduce Issue #715 · Murat Demirbas

  There are two kinds of "abstraction" conflated under the same umbrella term. (1) Modularity abstraction: This is the traditional abstraction taught in CS curricula as ADTs, APIs, layered design, etc. It is all about encapsulation, drawing boundaries, and hiding internals. (2) Modeling abstraction: This is the same sense of abstraction mathematicians and physicists when building models for thinking and reasoning. The goal is to find the minimal and most elegant description that preserves the property you care about.

+17 more articles in this topic.

• [Webinar] 8 Levels Of Context Maturity In AI-Native Engineering Issue #731

  AI is in your engineering workflow. While the token spend shows it, the throughput doesn't. The human is very much still in the loop, and that's a context problem. This free webinar maps the 8 levels of context maturity: where most teams are stuck, what the ceiling looks like at each stage, and what it actually takes to make the most out of your agents. Join live July 23 (FREE).

• How High-Performing QA Teams Are Adapting To AI Issue #730

  The real challenge with AI isn't getting a demo to work. It's building quality workflows that teams can trust in production. At the QA Leadership Summit, engineering leaders and directors share how they're integrating AI into testing, measuring its impact, and creating reliable systems that scale. Virtual event, July 22.

• Free Gauntlet AI Night School: Planning, Specs, And Decision Logs: What Separates AI Engineers From Vibe Issue #724

  In this Night School, Ash Tilawat breaks down why AI-native development is moving toward a document-driven workflow. He'll show how specs act like contracts with AI agents, why planning is becoming the most important part of the build, and how decision logs create a shared constitution for architecture and product choices. What You'll Learn: (1) How the software development lifecycle is changing inside AI-native teams. (2) Why planning is becoming one of the most important bottlenecks in AI-assisted development. (3) How to use specs as clear contracts for coding agents. (4) How decision logs track architecture choices and guide future work. (5) What separates a true AI engineer from someone who is just vibe-coding.

• 8 Levels Of Context Maturity In AI-Native Engineering Issue #723

  AI shows up in 60% of engineering work. Only about a fifth of it can be handed off without someone babysitting the output. That’s because agents are still missing the context you have. Join live June 24 (FREE) to find out how teams pulling ahead are using a context layer to level up.

• Free Gauntlet AI Night School: AI-First Product Development: Planning, Prompting, and Shipping With AI - Wednesday June 10 5CT Issue #722

  Join Derek Peters as he breaks down how engineers and product managers can use AI-first workflows to plan more clearly, prompt more intentionally, and move from idea to execution without losing control of the product. In this session, you’ll learn how to: (1) Understand how AI is changing the role of engineers and product managers. (2) Move beyond vibe coding by taking a more intentional approach to planning and prompting. (3) Use AI tools to support product plans, roadmaps, use cases, go-to-market strategy, and milestones. (4) Think like the “CEO” of the product by directing AI tools instead of letting them lead the process.

• Gauntlet AI Night School: Why System Design Is So Critical With AI And How To Learn It - Wednesday 5/27 6ET Issue #718

  Join Byron Mackay as he breaks down why system design is becoming one of the most important skills for software engineers in an AI-first job market. In this session, you’ll learn how to: (1) Understand why system design is becoming a critical skill for software engineers in an AI-first market. (2) Think more deeply about the technical decisions that impact security, performance, and long-term maintainability. (3) Recognize key system design topics to study, including databases, data flow, authorization, and schema design.

• Gauntlet AI Night School: Building Braid on Claude Managed Agents - Wednesday 5/20 at 5CT Issue #716

  Join Gauntlet instructors as they provide insight into Infrastructure for multi-agent applications. You bring the front and backend, and Anthropic handles the agentic sessions and environments. We're going to build a web-based multi-agent multimedia UI for creatives. Stitching together prompts, models and Gen AI sessions with memory.

• How Engineering Leaders Are Actually Using AI To Improve Software Quality Issue #712

  Most teams aren't struggling with whether to use AI, they're struggling with how to use it well. At Breakpoint, speakers like Jason Huggins (creator of Selenium), Keith Klain, Ashley Hunsberger, and many more cover the real decisions quality engineering leaders are facing: how to scale AI across your org, where agentic and autonomous QA workflows actually deliver, and what it takes to move past the pilot. If you're figuring out where AI fits in your engineering process, this is a practical starting point, May 12–14.

• Measuring The Impact Of AI On Your Engineering Team Issue #709

  Most engineering orgs have adopted AI coding tools. Few can tie them to delivery speed, defect rates, or ROI. In this fireside chat, Vinay Perneti (VP Eng, Augment Code) and Stephen Barrett (CTO, Milestone) break down how leading teams pair AI-native dev workflows with rigorous measurement — where AI saves real time in the SDLC, how to correlate usage with code quality, and what governance looks like at scale.

• AI Isn’t Waiting For Your Infrastructure To Catch Up Issue #708

  Join us at IaCConf and get practical advice from engineers who've actually figured out what it takes to let AI touch your infrastructure safely. These speakers are walking through it live & sharing the hard-won lessons that took years to earn.

+11 more articles in this topic.

• Review The Actual Change, Not The File List. Issue #729

  "AI writes more code than ever. Reviewing it shouldn’t mean scrolling forty files in alphabetical order. CodeRabbit Review reorganizes any pull request from a flat file list into a structured, layer-by-layer walkthrough - the logical reading order of the change, not the order your platform happens to sort it. Every range gets its own plain-language summary, with sequence diagrams, state machines, and ERDs generated inline wherever a visual earns its place. (1) Cohorts group related files and chunks so you review one idea at a time. (2) Layers order them so foundational changes - data shapes, contracts - come before the code that depends on them. (3) Code Peek lets you click any variable, function, class or type to see its definition and usages without leaving the tab. (4) Semantic Diff cuts past formatting noise to show what actually changed. From the team that pioneered AI code reviews. 2M reviews every week. 6M repos. 15K customers. Free during early access.

• Your Team Reviews AI's Code Now. What Should Block A Merge? Issue #726

  Most pull requests on your team no longer start with a human author - but the review bar was built for when they did. AgentField's engineering write-up lays out the four jobs code review has always done quietly, the three that get heavier the moment AI writes the first draft, and how to decide what blocks a merge vs. what ships. A read for eng leaders setting the standard, not rewriting the checklist line by line.

• Agentic Code Review Issue #726 · Addy Osmani

  “Stop reviewing everything to the same depth. Spend scarce human attention only where being wrong is costly, and let cheap deterministic gates and AI reviewers handle the rest. The organizing idea is to match review effort to the cost of being wrong, push the cheap deterministic work as early as possible, and reserve human attention for what only humans can do.”

• The Open-Source Code Reviewer Built For AI-Written Diffs Issue #723

  AI now writes a large share of your team's pull requests - and AI-written code fails differently than code review tooling expects. AgentField open-sourced pr-af, a multi-agent reviewer that designs a review lens per PR instead of running a fixed checklist. Your team sets what blocks a merge vs. what ships. Runs on open or closed models, cents per review. One-click GitHub install - plus their write-up on how code review is evolving when humans aren't writing the diffs.

• Using AI To Write Better Code More Slowly Issue #720 · Nolan Lawson

  “A lot of people seem convinced that the point of AI coding is to write low-quality code as fast as possible. Spew out barely-passable slop, open massive PRs, and merge them unvetted. Ship it! But the thing is, LLMs are very flexible. And you can use them just as effectively to write high-quality code more slowly.”

• Maintainability Sensors For Coding Agents Issue #718 · Birgitta Böckeler

  “Internal quality problems affect AI agents in similar ways that they affect human developers. An agent working in a tangled codebase might look in the wrong place for an existing implementation, create inconsistencies because it has not noticed a duplicate, or be forced to load more context than a task should require. In this article, I describe my experimentation with various sensors that help us and AI reflect on the maintainability of a codebase, and what I learned from that.”

• AI Is Writing Code Faster Than Ever, But Who Checks It? Issue #717

  When speed is prioritized over reliability, technical debt and security risks accumulate silently. To realize the full ROI of AI, organizations must shift from manual reviews to an automated verification layer. Evaluate your next vendor with: (1) Six evaluation pillars for modern code review. (2) Strategies to solve the AI productivity paradox. (3) A comprehensive checklist for code health.

• How DoorDash Built An AI code Reviewer Engineers Actually Listen To Issue #716 · Adam Rogal, Adam Yarger

  Over the last few months, we've been rolling out a code review agent across DoorDash's engineering org. The central challenge turned out to be attention: helping the agent focus on the parts of a change that deserve review, and stay quiet when it has nothing useful to add. The bar we set for ourselves wasn't "does it find things." It was: (1) Do engineers actually change their code when it comes up with a comment? (2) Does it preserve enough trust that teams keep it enabled?

• Code Review Responses: Add Context When It Counts Issue #716 · Saicharan Nimmala

  “When responding to code review comments, responses like “Done,” “Updated,” or “Fixed” are commonly used to indicate addressing a suggestion. However, sometimes, a little extra context adds a lot of clarity.”

• How To Scale Code Review When AI Writes Code Faster Than You Can Understand It Issue #705

  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.

+10 more articles in this topic.

• Be The Idiot Issue #731 · Kirill Bobrov

  “I’ve developed a mental checklist of “stupid” questions that have saved me more times than I can count.” The author shares some of these with the understanding that most engineering failures happen because someone was afraid to look stupid.

• Why I Stopped Arguing With People Issue #731 · Cong Wang

  “Over the years I’ve slowly stopped arguing. Not because I stopped caring about being right, but because I finally understood what an argument actually is, and what it can and cannot do. Here is what changed my mind.”

• So You Want to Fix Your All Hands Issue #730 · Michael Lopp

  “I will now explain why at least 50% of your team finds your current All Hands to be a waste of their time. They believe: (1) This is information they can easily find elsewhere. (2) This is about you, not them. (3) This is boring. Now, I will describe how to build an All Hands that will exceed their expectations.”

• Saying The Obvious Thing Issue #730 · Sean Goedecke

  “Stating the obvious is surprisingly useful. Most of your knowledge lives below the threshold of conscious awareness, so it’s possible for a piece of writing to remind you of what you already know. It’s common to know you don’t like something without being quite sure why, and reading an obvious statement can help clarify why you find certain things distasteful.”

• Cross-Team Pragmatics Issue #729 · Phil Calçado

  “I’ve been in this industry for twenty-five years and have seen all sorts of companies and projects. You know what I have never heard? Someone saying, “You know what I really like about working here? Cross-team collaboration.””

• What Being “Inspiring" Actually Means Issue #728 · Wes Kao

  “Being inspiring is not about how passionate your delivery is. You don’t have to get up in front of everyone, or try to cosplay what a bold leader sounds like. You can sound like yourself. Perhaps the most articulate and clear-eyed version of yourself, but still yourself.”

• Make It Memorable Issue #722 · Molly Graham

  “It sounds almost insultingly simple: make things memorable. But I’ve watched smart leaders at good companies invest enormous energy into goals and values that don’t actually have any effect on behavior — not because they got the strategy wrong, but because they made things too complex for it to stick.”

• EQ Is Not Just About What You Say. It’s Your Body Language Too. Issue #721 · Wes Kao

  “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.

• Get To The Core Of The Thing Issue #720 · Shreyas Doshi

  “In every product conversation, the framing decides the discussion. People rise to whatever level of abstraction the question opens up. Board conversations especially. But it’s the same with executive staff conversations and team conversations.”

• Be A Thermostat, Not A Thermometer Issue #720 · Lara Hogan

  We are easily influenced by the mood of those around us — “one person’s behavior change can cause others to change their behavior,” and by setting the whole temperature for the room, they’re being a thermostat. As leaders, Lara advises us to pick up on these negative mood changes early and become the person that sets the the new temperature of the room in a positive and healthy way. She illustrates how to do so here.

+9 more articles in this topic.

• Cut Your QA Cycles Down From Hours To Minutes With Automated Testing Issue #699

  If slow QA processes bottleneck your engineering team and slow down releases, you need QA Wolf. Their AI-native service gets engineering teams to 80% automated E2E test coverage, helping them ship 5x faster by reducing QA cycles from hours to minutes. With QA Wolf, you get: (1) Unlimited parallel test runs for web and mobile. (2) 24-hour maintenance and on-demand test creation. (3) Human-verified bug reports sent directly to your team. (4) Zero flakes guarantee. Trusted by Drata, Cohere, AutoTrader, and many more.

• How The New York Times Is Scaling Unit Test Coverage Using AI Tools Issue #699 · Eric Chima, Leonardo Quixada

  “Our goal was to improve the reliability of our web app, but also to evaluate AI products and determine how far we could push them to do work in bulk across our codebase.”

• Can AI Agents Build Real Stripe Integrations? We Built A Benchmark To Find Out Issue #697 · Kevin Ho, Carol Liang

  “We wanted to answer this question: can agents autonomously build complete Stripe integrations? When it comes to businesses running on Stripe, a mostly correct integration is a failure; payments require 100% accuracy. What matters is not just an agent’s ability to generate code, but its capacity to verify, test, and validate that code with the rigor of a human engineer.”

• Using AI To Shift E2E Test Maintenance Left Issue #692 · Uzeyir Abdullayev

  “That was the moment we started asking a different question. Instead of asking how to fix E2E failures faster, we asked if we could help engineers understand failures sooner, while the change context was still accessible.”

• Why Pentests Break Engineering Workflows (And What Actually Works Instead) Issue #685 · Sherif Koussa

  After a few cycles, you begin to notice recurring negative patterns in penetration testing. The severity levels do not align with actual risks, leading to a loss of trust in the process. As a result, the report turns into a static document rather than a useful tool for teams. What should be a valuable exercise instead becomes a routine checkbox that fails to contribute to risk reduction. The most effective tests are those grounded in the app's technology stack, real-world risks, and appropriate fixes.

• Stop Forwarding Errors, Start Designing Them Issue #679

  “We have a name for this. We call it “Error Handling.” But in reality, it’s just Error Forwarding. We treat errors like hot potatoes—catch them, wrap them (maybe), and throw them up the stack as fast as possible.”

• Ceilometer: Uber’s Adaptive Benchmarking Framework Issue #675

  “At Uber, scale and reliability define our infrastructure. Every new server type, kernel upgrade, and configuration change must be rigorously vetted before it touches production. Historically, this qualification process was manual and time-consuming, forcing engineers to stitch together ad hoc benchmarks with limited ability to measure efficiency or ROI. This created delays, added risk, and slowed the adoption of new technologies. To close this gap, we built Ceilometer—an adaptive benchmarking framework that delivers fast, production-like signals on system performance.”

• The A11y Testing Gap: Why 40% Of Issues Still Need Manual Review Issue #670

  Most A11y tools catch the obvious. The tricky, contextual stuff? Still manual. BrowserStack's A11y Issue Detection Agent mimics human intelligence to bridge that gap, identifying decorative versus functional images, checking logical focus order, and more. It’s like a WCAG expert built into your workflow. Run a free scan to see what automated tools miss!

• How We Prepare Shopify For BFCM Issue #670 · Matthew Frail, Kyle Petroski

  “Bimonthly fire drills all year, simulating 150% of last year's BFCM load. Tests so massive we ran them at night and coordinated with YouTube. Each test exposed bottlenecks—Kafka, memory, timeouts—that we fixed and revalidated. We didn't stop until the infrastructure performed under extreme load.“

• Composable Tests Issue #667 · Kent Beck

  Kent explains the difference between isolation and composition in tests. Isolated tests stand alone, but composed tests work together to provide broader coverage with fewer, clearer tests.

+9 more articles in this topic.

• Accelerate Engineering Velocity: Unified, Fast Access To Resources Issue #730

  Your engineers shouldn’t wait hours for access. Your security team shouldn’t have to piece together audit data across multiple systems. Teleport's unified identity layer eliminates these bottlenecks with: (1) Cryptographic identities for all humans, machines, and AI agents. (2) Short-lived privileges issued on just-in-time and that automatically expire. (3) Unified access to servers, Kubernetes, databases, MCP tools, cloud consoles. (4) Centralized audit logging that you can export directly to your SIEM.

• A Peek Into Reddit's Anti-Spam Internals Issue #730 · Lyra Rebane

  “I was a moderator of a few fairly small subreddits that’d from time to time get posts automatically removed for spam. However, when I went to actually look at the removed spam, I saw something I was never meant to see. I saw Reddit’s anti-spam internals.”

• Authentication And Authorization For Internal MCP Servers Issue #727 · Paul Vatterott

  Your internal MCP server can give LLMs access to everything by default, with no auth layer to stop them. This post shows how to lock it down with roles, granular scopes, and Enterprise SSO so employees and their AI assistants only reach the data they're cleared for.

• OAuth 2.1 In Simple Terms Issue #722 · Andrew Israel

  OAuth 2.1 is used in everything from Sign in with Google to integrating with third parties to MCP server authentication. Learn how it works with this article.

• Decoding Common Customer Questions About Enterprise SSO Issue #715 · Victoria Krauchunas

  Enterprise SSO might feel complicated, but it's one of the simplest ways to win trust and close deals faster. Most customers won't even say the words 'Enterprise SSO'. They'll ask if you support Okta, or SAML, or SCIM syncing. This guide decodes what they're really asking, so you can answer with confidence and stop losing deals to acronyms you don't quite recognize.

• Securely Connect AI Agents in Seconds Issue #711

  Giving unknown agents access to your infrastructure? Probably not. Running each agent in an isolated Firecracker VM with built-in identity? That’s the new model. Beams connects to your infrastructure and inference services with zero secrets, zero IAM wrestling, zero standing privileges, and full auditability.

• Threat Modeling Guide For Software Teams Issue #710 · Gayathri Mohan, Jim Gumbley

  “Rather than conducting security analysis as a separate or upfront activity, teams should integrate threat modeling into their development process through small, regular activities. The article helps teams get started and develop their practice using different approaches across application development, and infrastructure.”

• WorkOS FGA: The Authorization Layer For AI Agents Issue #700 · Aaron Tainter, Pavan Kulkarni

  Authentication proves an agent's identity. Authorization defines its blast radius. Most agents today inherit a user's full access token, turning a helpful assistant into a confused deputy that can leak production secrets to a shared Slack channel. This post digs into why that happens and how WorkOS FGA solves this by scoping the blast radius with resource-level permissions.

• How to Ship Fast Without Shipping Risk: The Security Of AI-Assisted Software Development Issue #686 · Sherif Koussa

  The data is clear: AI coding assistants have crossed the chasm from experimental to enterprise-critical. With 90% of Fortune 100 companies now using GitHub Copilot and over 20 million developers adopting AI coding tools as of July 2025, we're witnessing the fastest technology adoption curve in software engineering history. But beneath the productivity gains lies a more complex reality. This guide distills insights from recent security research, incident data, and enterprise deployments to help engineering leaders navigate the security implications of AI-assisted development.

• User Identity Isn’t Complete Without Authorization Issue #680 · Mark Van Oppen

  Engineering teams solved authentication, but authorization has become the bottleneck. Separate AuthN/AuthZ systems create fragmentation and performance issues. FusionAuth's acquisition of Permify unifies identity and authorization, and delivers sub-10ms permission checks with fine-grained control (RBAC, ABAC, ReBAC) that scales to billions of users.

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