Issue #427

Issue #427
pointer.io


Friday 30th June’s issue is presented by Speakeasy

Speakeasy's managed SDK pipeline is in GA! Improve your API's usability in minutes. Our platform integrates into your CI/CD to make creation of idiomatic, type-safe SDKs in 7+ languages touch-free.

Failure

— Mike Fisher


tl;dr: "Embracing failure, dissecting it, and learning from it not only builds stronger systems but also fosters an environment of psychological safety, creativity, and continuous improvement." Mike discusses his experiences at Etsy, where it was recognized that system failures are often the result of systemic issues rather than personal failures.


Leadership Management

Estimation Isn’t For Everyone


tl;dr: From the NYTimes Open team, the authors propose focusing on velocity as the primary metric for measuring a team's productivity. Instead of using story points for estimation, they suggest tracking the number of work items completed by the team each week. By focusing on throughput and breaking down tasks efficiently, teams can achieve predictable output without spending excessive time on estimation discussions.


Management

APIs Vs SDKs: Why You Should Always Have Both

- Mateusz Kwaśniewski

tl;dr: This post explains what APIs and SDKs are, exploring the different use cases, examples and best practices for each, and explaining why having both are necessary.

Promoted by Speakeasy

Management UsefulTool

Amir’s 10 Laws Of Tech

— Amir Shevat


tl;dr: “These are my personal observations working in tech for more than 20 years”: (1) A technology that was built for good will eventually be also used for bad. (2) A company’s technology stack will be based on the experience and preferences of the first technical person in that company. (3) The most painful, and least useful projects are migration projects, yet companies will replace technologies every 4 years. And more.


Leadership Management


“The older I get, the more I realize the biggest problem to solve in tech is to get people to stop making things harder than they have to be.”


— Chad Fowler

2023 Engineering Salary Expectations Trends Report

— Sam Graham


tl;dr: Key findings: (1) State of the job market is leading reason engineers are lowering salary expectations. Newly heightened emphasis on non-monetary benefits i.e. career growth, work-life balance, and company culture. (2) Mid-level engineers are reducing salary requirements more significantly than entry- and senior-level peers. (3) Engineers in top tech hubs earn higher salaries than those in other metros, but their salary expectations have fallen more significantly. (4) Gender pay gap is still a big problem.


Salary

Vectors Are In The New JSON In PostgresQL

— Jonathon Katz


tl;dr: “This in itself is an interesting statement, given vectors are a well-studied mathematical structure, and JSON is a data interchange format. And yet in the world of data storage and retrieval, both of these data representations have become the lingua franca of their domains and are either essential, or soon-to-be-essential, ingredients in modern application development. And if current trends continue, vectors will be as crucial as JSON is for building applications.”


PostgreSQL

CLI Tools Hidden In The Python Standard Library

— Simon Willison


tl;dr: “This is a neat Python feature: modules with a if __name__ == "__main__": block that are available on Python's standard import path can be executed from the terminal using python -m name_of_module.” This made Simon: what other little tools are lurking in the Python standard library, available on any computer with a working Python installation?  


Python

Useful DevTools Tips And Tricks

— Patrick Brosset


tl;dr: (1) Zoom DevTools. (2) Delete annoying overlays. (3) List the fonts used on a page. (4) Measure arbitrary distances on a page. (5) Detect unused code. And more.


FrontEnd Tips

Notable GitHub Repos


Gorilla: An API store for LLMs.


GPT Engineer: Specify what you want to build, AI asks for clarification and then builds it.


Roop: One-click deepfake face swap.


TGI: Large language model text generation inference.



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