Programmer Weekly (Issue 166 August 3 2023)

Programmer Weekly - Issue 166

August 03, 2023

Programmer Weekly

Welcome to issue 166 of Programmer Weekly. Let's get straight to the links this week.

Quote of the Week

 

"One of the main causes of the fall of the Roman Empire was that lacking zero, they had no way to indicate successful termination of their C programs." -  Robert Firth

Reading List

In this post, Andy Warfield, VP and distinguished engineer over at S3 covers three distinct perspectives on scale that come along with building and operating a storage system the size of S3.

If you remove the first word from the string "hello world", what should the result be? This is the story of how we discovered that the answer could be your root password!

This post is about practical patterns for integrating large language models (LLMs) into systems and products. We’ll draw from academic research, industry resources, and practitioner know-how, and try to distill them into key ideas and practices.

After a four-decade career of toggling back and forth between big and small, Lightspark SVP of Engineering James Everingham shares his playbook for scaling down your leadership as you go from an org of hundreds or thousands to a handful of direct reports.

Choosing to use a block range index (BRIN) to query a field with high correlation is a no-brainer for the optimizer. However, under some easily reproducible circumstances, a BRIN index can result in significantly slower execution even when the indexed field has very high correlation. This article describes how using a BRIN index in presumably "ideal circumstances" can result in degraded performance, and suggest a recent new feature of PostgreSQL as a remedy.

mondayDB is the new in-house data engine we crafted at monday.com. It shifted the entire organization’s data paradigm, and is by far the most challenging and rewarding project I’ve had the pleasure of working on. In this post, you’ll get a glimpse of the complexities we tackled when implementing mondayDB, and a drill-down into the creative solutions we crafted in response.

The article describes four common mistakes that people make when using Redis. The mistakes can lead to data loss, performance problems, and security vulnerabilities.

Presto is a free, open source SQL query engine. We’ve been using it at Meta for the past ten years, and learned a lot while doing so. Running anything at scale - tools, processes, services - takes problem solving to overcome unexpected challenges. Here are four things we learned while scaling up Presto to Meta scale, and some advice if you’re interested in running your own queries at scale.

Embedding Scalable Vector Graphics (SVG) can expose websites to code injection. This article explores how SVGs work, the risks they pose, and how to mitigate them.

Serve-side JSX with Ajax is a PHP? or a new stack?

We took on the challenge of migrating all of our Premium Plan Subscriptions to a new system. This was an extremely sensitive migration because we were dealing with our “bread and butter” paying customers. In this article, you will learn about some of the challenges we encountered, how we solved them, and some of the lessons learned.

What is the next big idea in cloud security after identity? Using software fundamentals, the author speculates about what could be the next valuable abstraction in access management and analyzes the rise of Okta and identity to predict how that abstraction could get adoption.

A small team within Mozilla’s innovation group recently undertook a hackathon to build a trustworthy internal chatbot prototype.

Want to really understand how large language models work? Here’s a gentle primer.

Using Prometheus to eliminate metrics loss, scale up metrics usage, standardize metrics labels, and significantly lower overall costs.

Which graph database is faster? Which one is easier to use? What can GUAC use?

Watch and Listen

ML systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In this course we’ll discuss how researchers can shape the process that will lead to strong AI systems and steer that process in a safer direction. We’ll cover various technical topics to reduce existential risks (X-Risks) from strong AI, namely withstanding hazards (“Robustness”), identifying hazards (“Monitoring”), reducing inherent ML system hazards (“Alignment”), and reducing systemic hazards (“Systemic Safety”). At the end, we will zoom out and discuss additional abstract existential hazards and discuss how to increase safety without unintended side effects.

The talk provides insights into measuring and enhancing developer productivity, offering practical tips and strategies for teams to optimize their development processes effectively. It emphasizes data-driven approaches to foster continuous improvement in software development workflows.

Interesting Projects, Tools and Libraries

BlazingMQ is an open source distributed message queueing framework, which focuses on efficiency, reliability, and a rich feature set for modern-day workflows.

Send emails from your domain through Cloudflare for free. Self host on your account.

Compozify is a simple (yet complicated) tool to generate a docker-compose.yml file from a docker run command.

Open-source solution to programmatically send and receive SMS using your own SIM cards.

Lightweight Kubernetes. Production ready, easy to install, half the memory, all in a binary less than 100 MB.

Quickly deploy preview environments to the cloud!

Open source API management platform.

Smaller & Faster Single-File Vector Search Engine.

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