- Pointer
- Posts
- Issue #646
Issue #646
Essential Reading For Engineering Leaders
Tuesday 2nd September’s issue is presented by WorkOS
Most AI apps never make it past the prototype phase. The teams that succeed follow clear patterns: invest early in access control, auditability, and identity integration.
This guide shows how they move fast without cutting corners, using enterprise features like SSO, SCIM, RBAC, and Audit Logs enabled by WorkOS APIs and tools.
— James Stanier
tl;dr: “This articles reflects a new and expected management style for the current era: more hands-on, detail-oriented, and direct. This shift is driven by tighter economic conditions and the push for AI-driven efficiency.”
Leadership Management
— Everton Marcelino
tl;dr: “A few days ago, I had the surreal experience of joining a private fireside chat with Werner Vogels, the CTO of Amazon, during Startup Summit 2025 in Florianópolis. It's one thing to follow his talks online – it's another to be in the same room, listening to him unpack two decades of lessons from building some of the most critical infrastructure on the internet. Werner is sharp, real, and doesn't sugarcoat. What follows is a distillation of the ideas that hit me hardest.”
Leadership Management
tl;dr: If you're still hard-coding roles or managing access ad hoc, you're creating technical debt and pulling your team away from core product work. WorkOS RBAC gives you an enterprise ready foundation for per-org access control, with developer-friendly APIs and a powerful dashboard to manage roles, permissions, and SSO or SCIM-based access syncing.
Promoted by WorkOS
Management Tools
— Matheus Lima
tl;dr: “I recently read “Good Inside” by Dr. Becky Kennedy, a parenting book that completely changed how I think about this. She talks about how the most important parenting skill isn’t being perfect — it’s repair. When you inevitably lose your patience with your kid or handle something poorly, what matters most is going back and fixing it. Acknowledging what happened, taking responsibility, and reconnecting.”
Leadership Management
“The best performance improvement is the transition from the nonworking state to the working state.”
— James Somers
tl;dr: Instead of trying to write a report and think at the same time, you break the process into four mechanical stages: (1) Gather: Collect all your “raw facts” up front (notes, conversations, metrics, 1:1 insights, incident reports). (2) Bucket: Sort those facts into themes or categories (team morale, delivery speed, quality, architecture debt). (3) Structure: Arrange the buckets into a logical flow (planning a roadmap, sequencing dependencies, or structuring a design doc). (4) Draft: Only once the structure is set do you write—turning pre-organized notes into clear narrative.
CareerAdvice
tl;dr: Did you know each LLM has its own coding “personality?” Sonar’s new report unveils five unique LLM archetypes, and analyzes the quality of code produced by each. Download now to see core strengths and challenges of each model, along with insights for integrating AI into your SDLC effectively.
Promoted by Sonar
LLM AI
— Chris Penner
tl;dr: Instead of repeatedly writing long, complex SQL queries with lots of joins to answer basic debugging questions, you should create debug views (SQL VIEWs). These: (1) encapsulate all the messy joins once. (2) Surface human-readable info (like project names and branch names) alongside IDs. (3) Make future queries much shorter and easier to type. (4) Don’t take up extra storage since views are just saved query definitions.
Database
— Bernard Kolobara
tl;dr: “I recently ran into an issue that got me thinking and ultimately inspired me to write this post. I needed to wrap a structure into a mutex, because it was being accessed concurrently. To access the internal structure, you first need to acquire a lock on the mutex.”
Rust
— Stefan Marr
tl;dr: “Most research on programming language performance asks a variation of a single question: how can we make some specific program faster? Sometimes we may even investigate how we can use less memory. This means a lot of research focuses solely on reducing the amount of resources needed to achieve some computational goal. So, why on earth might we be interested in slowing down programs then?”
AI Debugging
Most Popular From Last Issue
My Desk Setup In 2025 - Will Larson
Notable Links
500 AI Agents: Curated collection of use cases across industries.
Epicenter: Local-first, OS apps.
System Prompts Leaks: Instructions for various publicly deployed chatbots.
Windows: Windows inside a Docker container.
WrenAI: Queries any database in natural language.
How did you like this issue of Pointer?1 = Didn't enjoy it all // 5 = Really enjoyed it |