Machine
We track 19 posts about Machine from 14 engineering blogs. Most active: Eric Lippert, Etsy, Chris Wellons. Latest post: Sep 30, 2026.
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Recent posts
When AI agents move at machine speed, banks need machine-speed visibility (opens on the source site)
Elastic ·
As AI agents transform cyber risk, banks need real-time visibility across security and operational data to detect unusual behavior, investigate threats, and respond at machine speed.
Quiz: Setting Up Python for Machine Learning on Windows (opens on the source site)
Test your understanding of setting up a Python machine learning environment on Windows with Miniconda, Conda environments, packages, and channels.
A custom virtual machine for the Stars! 4X game (opens on the source site)
Stars! is a 1995 4X game (explore, expand, exploit, exterminate) for 16-bit Windows 3.1 that I first played ~28 years ago. While Windows is famously backwards compatible, it’s notoriously difficult to play Stars! today. Windows x64 cannot run 16-bit applications, and playing requires either retro hardware or emulation (otvdm, DOSBox), sometimes paired with Wine. My new, exciting solution, Stars!VM, or Stars! Virtual Machine, embeds a custom 80286 emulator and a Win16 to Win32 bridge. As native Win32, the game looks and feels exactly as it did originally, except sporting a modern file chooser…
Agentic Machine Learning Modeling at Instacart (opens on the source site)
Tilman Drerup, Moe Moazzami, Shih-Ting Lin, Greg Reda (and many more)IntroductionAt Instacart, artificial intelligence is fundamentally changing the way our machine learning engineers operate. In a prior blog post, we used one of our teams as a case study to illustrate how the emergence of agents has reshaped what machine learning engineers spend their time on. The post below goes a few levels deeper and zooms in on the machine learning modeling process itself, an area where recent developments in AI-assisted research have opened up exciting new frontiers that we are now actively exploring.…
Machine vs. machine: The new reality of cybersecurity in ANZ (opens on the source site)
Elastic ·
Frontier AI has accelerated cyber threats to machine speed, leaving many ANZ organisations vulnerable. Our latest research reveals how fragmented data and visibility gaps hinder defence and why a unified platform is essential to battle threats.
Packer v1.16.0 brings verifiable provenance to machine images (opens on the source site)
Packer v1.16.0 adds native SLSA provenance generation and verification for machine images, along with new HCL2 features for provisioners and variables.
A custom virtual machine for the Stars! 4X game (opens on the source site)
Stars! is a 1995 4X game (explore, expand, exploit, exterminate) for 16-bit Windows 3.1 that I first played ~28 years ago. While Windows is famously backwards compatible, it’s notoriously difficult to play Stars! today. Windows x64 cannot run 16-bit applications, and playing requires either retro hardware or emulation (otvdm, DOSBox), sometimes paired with Wine. My new, exciting solution, Stars!VM, or Stars! Virtual Machine, embeds a custom 80286 emulator and a Win16 to Win32 bridge. As native Win32, the game looks and feels exactly as it did originally, except sporting a modern file chooser…
A Fast Bytecode VM for Arithmetic: The Virtual Machine (opens on the source site)
In this series of posts, we write a fast bytecode compiler and a virtual machine for arithmetic in Haskell. We explore the following topics: Parsing arithmetic expressions to Abstract Syntax Trees (ASTs). Unit testing for our parser. Interpreting ASTs. Compiling ASTs to bytecode. Disassembling and decompiling bytecode. Unit testing for our compiler. Property-based testing for our compiler. Efficiently executing bytecode in a virtual machine (VM). Unit testing and property-based testing for our VM. Benchmarking our code to see how the different passes perform. All the while keeping an eye on…
Scaling Subscriptions at The New York Times with Real-Time Causal Machine Learning (opens on the source site)
How real-time algorithms and causal ML transformed our digital subscription funnel from static paywalls to dynamic, millisecond decision-makingIllustration by Mathieu LabrecqueThe New York Times became a subscription-first news and lifestyle service with the launch of its paywall in 2011. Since then, our subscription strategy has evolved substantially. Initially, users could access a limited number of free articles per month before they encountered the paywall. In 2019, we began personalizing this number using a Machine Learning (ML) model — The Dynamic Meter. In the past few years, we have…
Deliveroo's Machine Learning Platform: Powering the Future of ML (opens on the source site)
Enter Deliveroo’s ML Platform For the past three years, we have been building Deliveroo’s Machine Learning Platform, or the ML Platform as we like to call it. The ML Platform boosts our model-building and deployment capabilities by standardising ML workflows, streamlining the end-to-end development process and simplifying model deployment. Besides saving software engineering effort through centralising tooling, the ML Platform also reduces the time that our ML engineers spend on infrastructure tasks. As a result, our ML engineers can now iterate their ML models 2-3x faster than before. What…
How Machine Learning Transforms Visual Validation in Game Development: A DevOps Success Story (opens on the source site)
In today’s competitive gaming landscape, visual fidelity can make or break a title’s success. Yet as game worlds grow increasingly complex, traditional methods of ensuring visual quality are breaking down. Manual visual verification has become increasingly impractical. Even when teams scale up testing, small rendering anomalies can introduce severe regressions that go unnoticed until later stages of development, leading to delayed releases and drained resources.Existing image comparison tools, such as Structural Similarity Index (SSIM) from OpenCV, fall short in real-world game development…
Foundations of AI and Machine Learning for Java Developers Course Review (opens on the source site)
Introduction In this article, I’m going to review the Foundations of AI and Machine Learning for Java Developers video course from my fellow Java Champion, Frank Greco. If you are new to AI and ML and want to get a great introduction to these topics, then you should definitely join watch the video lessons created by Frank Greco. And, thanks to LinkedIn Learning’s generosity, until June 20, you can enroll in this video course for free. Video Course Agenda The course provides one hour and thirty-five minutes of video lessons that are... Read More The post Foundations of AI and Machine Learning…
Finding my path: from tech graduate to Machine Learning Engineer (opens on the source site)
I studied computer science in university, but I never expected it to be my full-time job. During my second year, I seriously considered pivoting into consulting. However, I’m glad I stayed the course, as it led to immense growth and the discovery of a renewed passion I had never expected. At the start, I had a general feeling of unpreparedness and lack of direction, as I knew nothing about the tech fields out there. Web Development? Sounds fun, but I’m not much of a UI person. Data Engineering? Sounds complicated. Systems Engineering? Wait, what was that again? Basically, there wasn’t a…
Machine Learning in Content Moderation at Etsy (opens on the source site)
Etsy ·
At Etsy, we’re focused on elevating the best of our marketplace to help creative entrepreneurs grow their businesses. We continue to invest in making Etsy a safe and trusted place to shop, so sellers’ extraordinary items can shine. Today, there are more than 100 million unique items available for sale on our marketplace, and our vibrant global community is made up of over 90 million active buyers and 7 million active sellers, the majority of whom are women and sole owners of their creative businesses. To support this growing community, our Trust & Safety team of Product, Engineering, Data,…
Machine Learning in Content Moderation at Etsy (opens on the source site)
Etsy ·
At Etsy, we’re focused on elevating the best of our marketplace to help creative entrepreneurs grow their businesses. We continue to invest in making Etsy a safe and trusted place to shop, so sellers’ extraordinary items can shine. Today, there are more than 100 million unique items available for sale on our marketplace, and our vibrant global community is made up of over 90 million active buyers and 7 million active sellers, the majority of whom are women and sole owners of their creative businesses. To support this growing community, our Trust & Safety team of Product, Engineering, Data,…
Building Trust in a Digital World: The Role of Machine Learning in Behavioral Biometrics (opens on the source site)
Feedzai ·
In the world of financial services, the bank or financial institution’s relationship with the customer relies on digital trust, which is anchored in two fundamental principles. First, it must ensure the person engaging through digital banking channels is genuinely the individual they claim to be. Second, it must confirm that this person is authorized to complete the intended financial transaction.Addressing these crucial requirements is the core mission of Feedzai’s Digital Trust solution. The solution collects and analyzes comprehensive user behavioral data, scrutinizes device information…
Bean Machine Retrospective, part 9 (opens on the source site)
I wanted to implement concise “pattern matching” in Python, a language which unlike C#, F#, Scala, and so on, does not have any pattern matching built in. Logically a pattern is just a predicate: a function which takes a value … Continue reading →
Bean Machine Retrospective, part 8 (opens on the source site)
Before getting into the details of how my combinator-inspired source code transformation system works, I should say first, what is a general overview of the system? and second, why did I build it at all? In my experience, a typical … Continue reading →
Bean Machine Retrospective, part 7 (opens on the source site)
How do we write a compiler in a typical general-purpose line-of-business OO programming language such as Python, C#, Java, and so on? Compilers are programs, so we could make the question more general: how do we write programs? The basic … Continue reading →
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