- Pointer
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- Issue #542
Issue #542
What Current & Future Engineering Leaders Read
Tuesday 20th August’s issue is presented by WorkOS
WorkOS is a modern identity platform for B2B SaaS.
With modular and easy-to-use APIs, integrate complex features like SSO and SCIM in minutes of months.
If you care deeply about design and user experience, WorkOS is a perfect fit. From high-quality documentation to self-serve onboarding for your customers, WorkOS removes all the complexity for your engineers.
User Management is also free up to 1 million MAUs and includes MFA, bot protection, domain verification.
— Christoffer Stjernlöf
tl;dr: Christoffer discusses the following: (1) Use off-the-shelf. (2) Cost and reliability over features. (3) Idea to production quickly. (4) Simple data structures. (5) Reserve resources early. (6) Set maximums. (7) Make testing easy. (8) Embed performance counters.
— Nicola Ballotta
tl;dr: Nicola covers: (1) What a tech specs document is, why it's important, and why it can sometimes be challenging to create one. (2) How to create outstanding tech specs. (3) A Notion system for creating tech specs. (4) Tips from both his own experience as well as the communities.
— Amit Bhojraj, Min Kim
tl;dr: One of the pitfalls of building SSO and SCIM from scratch is the ongoing engineering investment required to scale your solution — supporting more IdPs, dealing with expiring SAML certificates, and standardizing onboarding fragmentation. And this is after spending 3-6 months to develop the initial solution for a handful of providers. When factoring in feature expansion (domain verification, JIT provisioning, custom-mapped attributes, IdP role assignment), which is different from work related to maintenance and scalability, the total cost of ownership multiplies significantly.
Promoted by WorkOS
— Ben Northrop
tl;dr: “After 15 years in industry, I've come to realize that the most defining quality of a developer is his source of motivation. It undergirds all of his decisions and interactions. It predicts what kind of code he'll write, what technologies he'll prefer, and even how he'll succeed on a given assignment. And it's often quite easy to peg for any given developer after just a few days of working with him or her.”
Editor’s Note
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— Birgitta Böckeler
tl;dr: “To get an idea for how well this works and what the potential is, I picked an issue of the open source project Bahmni and tried to understand the issue and what needs to be done with the help of AI. Bahmni is built on top of OpenMRS, which has been around for a very long time. OpenMRS and Bahmni are good examples of very large codebases with a lot of tech debt, representing a lot of different styles and technologies over time.” Birgitta shares her observations about what AI could and could not help with in such a use case.
tl;dr: How much are programmers really using AI? How useful is it actually? We wanted to know for sure. We surveyed software developers to uncover the real impact of LLMs like ChatGPT on their work. From debugging to project planning, the responses revealed surprising strengths and notable gaps. Check out our findings to see where AI shines and where it still falls short.
Promoted by Raygun
— Andrea Bergia
tl;dr: “Over a decade ago, I used to work in a large and crazy C++ code base. The system had a feature that allowed our customers to search for a record based on a referred field’s value. For example, if we had an incident table, you could search by an attribute of the incident’s assignee (which referred to a record of user).”
— Mark Gurevich, Anton Egorov
tl;dr: “Websockets and WebRTC technologies are both excellent options for real-time presence features. Using the combination of WebSockets and a message broker or an in-memory database like Redis, it’s possible to implement such features on a global website with hundreds of thousands of simultaneous collaborative users. This approach enables users to interact seamlessly and synchronously, fostering a sense of connection and community in a digital space.”
— Lean Rada
tl;dr: Sweep-and-prune is my go-to algorithm when I want to quickly implement collision detection for a game. I think it’s an awesome and elegant algorithm, so I wrote a post about it. This post is split into simple and sophisticated versions of the algorithm.
Against Names - Steve Klabnik
Bark: Text-prompted generative audio model.
Dasel: Query & modify data structures using selector strings.
Kardinal: Spin up dev & test environments in Kubernetes.
Mpv: Command line video player.
PyScript: Create Python applications in the browser.
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1 = Didn't enjoy it all // 5 = Really enjoyed it
