- Pointer
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- Issue #604
Issue #604
Essential Reading For Engineering Leaders
Friday 4th April’s issue is presented by WorkOS
Today’s bots can easily bypass traditional detection — executing JavaScript, storing cookies, rotating IPs, and even solving advanced CAPTCHAs.
Their attacks are advanced by the day, fueled by growth in AI agents. So how do you block these bad actors? The answer is WorkOS Radar.
A single JS script is all it takes to instantly protect your signup flow. Whether it’s brute force attacks, leaked passwords, or throwaway emails, WorkOS Radar can catch it all, keeping your real users safe from abuse.
— Abi Noda
tl;dr: Abi discusses the following: (1) Avoid single-metric models. (2) Measure all outcomes you care about, and capture multiple metrics for each outcome. (3) Be mindful of incentives created by measurement. (4) Measure different facets of productivity. (5) Use system-based and self-reported data together.
Leadership Management
— Gergely Orosz, Stefan Mai, Evan King
tl;dr: It’s been widely reported that the tech hiring market is much cooler than in 2020-2022; the number of software engineering job openings is down internationally in all major regions and the number of full-remote roles is in steady decline. Meanwhile, other metrics indicate that tech hiring is starting to recover – at least for senior engineers. This article is an attempt to get clarity about how tech interviews are changing, by focusing on what the engineers who take interviews are seeing.
CareerAdvice
— Zack Proser
tl;dr: “Cursor Rules allow you to codify the foundational decisions in your codebase, to reduce hallucinations across agentic composer and chat sessions. By placing these rules in special files, you can tailor Cursor’s suggestions or completions to match your team’s coding style and best practices.”
Promoted by WorkOS
BestPractices AI
— Sean Goedecke
tl;dr: “Empathetic managers care. They are emotionally invested in their employees as human beings, and actively campaign to support their employees’ needs. Ruthless managers are there to do their job. They aren’t necessarily assholes, but they see their main role as communicating the company’s needs to their engineers and vice versa. They will almost never go out on a limb on an employee’s behalf.”
Leadership Management
“I’m not a great programmer. I’m just a good programmer with great habits.”
— Nolan Lawson
tl;dr: “So this is where I’ve landed: I’m using generative AI, probably just “dipping my toes in” compared to what maximalists like Steve Yegge promote, but even that little bit has made me feel less excited than defeated. I am defeated in the sense that I can’t argue strongly against using these tools (they bust out unit tests way faster than I can, and can I really say that I was ever lovingly-crafting my unit tests?), and I’m defeated in the sense that I can no longer confidently assert that brute-force statistics can never approach the ineffable beauty of the human mind that Chomsky described.”
AI ThoughtPiece
— Brian Morrison
tl;dr: In this article, you'll learn how Supabase Auth is implemented before comparing how Clerk can be leveraged using the same defense in depth technology (RLS) while providing enhancements such as beautifully designed, drop-in UI components, one-click social sign on connections, and a suite of B2B tools to easily add multi-tenancy to your Supabase-powered applications.
Promoted by Clerk
Guide
— Mark Manley, Palak Bansal
tl;dr: The team at Google compares two functions and highlights how one is easier to follow due to its consistent level of abstraction, providing a top-down narrative of the code’s logic. createPizza is a high-level function that delegates the preparing, baking, and boxing steps to lower-level specialized functions with intuitive names. Those functions, in turn, delegate to their own lower-level specialized functions (e.g., heatOven) until they reach a function that handles implementation details without needing to call other functions.
BestPractices
— Ying Yang, Zheng Hu, Zhe Mai
tl;dr: Previously, the team at Doordash relied on humans to transcribe and update restaurant menus manually, which is costly and time-consuming. The rapid improvement of large language models, or LLMs, creates an opportunity for a big stepwise change, allowing AI to transcribe information from menu photos. However the diverse menu structures restaurants use pose a challenge for an LLM to do an accurate job at scale. In this blog, we will discuss how we built a system with a guardrail layer for LLMs leveraging traditional ML techniques.
LLM ML
— Cameron Kaiser
tl;dr: “Everyone should pull one great practical joke in their lifetimes. This one was mine, and I think it's past the statute of limitations. The story is true. Only the names are redacted to protect the guilty.”
Entertaining
Most Popular From Last Issue
Tactical Work In The Age Of Layoffs — Sean Goedecke
Notable Links
Bknd: Lightweight Firebase alternative built to run anywhere.
Browser Use: Make websites accessible for AI agents.
GitDiagram: Interactive diagrams for GitHub repos.
Graft: OS transactional storage engine.
Hatchet: Run background tasks at scale.
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