Issue #398

Friday 17th March's issue is presented by QA Wolf

Larger dev and product teams spend hours per sprint on QA. Get that time back. Let QA Wolf’s in-house experts get you to 80% automated end-to-end test coverage in 4 months with zero effort on your part. Fully automated testing, done for you, integrated with your CI/CD. Written in JS on Playwright (no lock-in).

Teams Of 10+ Devs Can Start With A Risk Free Pilot

Simple Explanations For Complex Phenomena

- Paulo André

#Leadership #Management

Paulo discusses the conflict between our desires - as engineers - to “crave certainty, order, and structure” and the constantly changing scope of our work. “If you’ve done it before, requirements are known. If someone else has done it before, requirements are knowable. If it’s never been done before by anyone, requirements will change.” and most of our work falls in this last space. Paulo believes this requires a “momentous shift in mindset” with how we approach our work with “less predicting & planning, more probing, sensing & responding.”

Getting More From Your Team Health Checks

- Justin Kotzé, Fiona Siseman

#Leadership #Management

Leaders at Spotify run health checks as a source of data to support - and not judge - teams. Checks usually follow the following pattern: (1) Ask predefined questions in the areas of tech health, team health, and product health. (2) Run debrief workshops where members of a squad discuss their results and decide on a set of actions. (3) Aggregate results into a multi-team-level visualisation, so that patterns and trends can be observed and addressed. This article discusses how they’re run.

QA Wolf Exits Stealth With An End-To-End Service For Software Testing

#Management #IndustryNews

QA Wolf has exited stealth, securing $20 million for its platform that automates and ostensibly streamlines the process of testing web-accessible software apps.

Promoted by QA Wolf

The Case Against Relying Solely On DRY

- Ashley Peacock

#ThoughtPiece

Instead of being applied when needed, Ashley believes DRY is thrown around anytime duplication is spotted, and this leads to worse code in the long run. Ashley walks through why duplication is not the root of all evil, and why it’s perfectly fine to repeat yourself sometimes.

“One of the best programming skills you can have is knowing when to walk away for a while.”

- Oscar Godson

Online Gradient Descent Written In SQL

- Max Halford

#SQL #ML #Algo

Max implements a ML algorithm within a relational database, using SQL. Some databases allow doing inference with an already trained model. Training the model in the database would remove altogether the need for a separate inference / training service. Max attempts to do this with the Online Gradient Descent algorithm.

How Virtual Environments Work

- Brett Cannon

#Python

“There are two parts to virtual environments: their directories and their configuration file. As a running example, I'm going to assume you ran the command py -m venv --without-pip .venv in some directory on a Unix-based OS.”

Docker Is Deleting Open Source Organisations - What You Need To Know

- Alex Ellis

#IndustryNews #Docker

“Yesterday, Docker sent an email to any Docker Hub user who had created an "organisation", telling them their account will be deleted including all images, if they do not upgrade to a paid team plan. The email contained a link to a tersely written PDF (since, silently edited) which was missing many important details which caused significant anxiety and additional work for open source maintainers.” Alex discusses the implications of this.

Why Some GitHub Labels Illegible

- Moritz Firsching

#GitHub

On GitHub, it’s possible to select the color of a label when assigning it to a pull request or an issue. When using GitHub with the Light default theme, the color of the text on those labels depends on the brightness of the color used: The darker the color of the label the brighter the color of the text of the label and vice versa. Moritz discusses shows why this is problematic.

Notable GitHub Repos

• Codon: High-performance, zero-overhead, extensible Python compiler using LLVM.

• Evals: Framework for evaluating OpenAI models and an OS registry of benchmarks.

• Stanford Alpaca: Code & docs to train Stanford's Alpaca models, and generate the data.

• SQL Translator: Converting natural language queries into SQL code using AI.

  
  

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