To stay up to date in the Artificial Intelligence solutions era, it's critical to become acquainted with the fundamental terms, concepts, and jargon that characterize this fast-developing discipline. This article will help you discover the foundational concepts of neural networks, deep learning, natural language processing, and beyond.
Every Wednesday, we’ll pick one trending repository from the previous week and give it attention, preparing a tutorial, article, or code review—learning from its creators along the way. Today, we’re taking a fresh project from Google engineers to the bench: Google/langextract.
As part of our GitHub All-Stars series, where we examine open-source gems, I stumbled upon a project that strikes at the heart of one of the most fundamental problems in modern AI. After analyzing deepagents and its system-level approach to reasoning, the natural next step was to examine how we solve the problem… of memory.
Test suites have grown in size and complexity. What used to be a quick validation step has become a bottleneck that can significantly impact development velocity. The solution is to distribute test execution across multiple machines, running different test groups in parallel.
I’ve always claimed there’s no better way to learn anything than by building something yourself… and the second-best way is reviewing someone else’s code. From now on, every Wednesday we’ll pick one trending repo from the previous week and give it some attention: a tutorial, an article, or a code review - learning directly from its authors.
If you are using AI coding assistants or agents, they will only be as helpful as their knowledge of the libraries you use. LLMs know a lot about the world from their training set, but that might not include a library's documentation at all or include an older version. Agents can browse the Internet looking for more recent information, but this might not be precise enough. For instance, the web might be littered with outdated examples, and the latest information might only be available in the official docs. Hence, if you are a library author, how do you prepare your documentation for consumption by coding agents?
Java has come a long way on ARM. Runtime improvements, smarter garbage collectors, and better vector performance now make it a solid fit. CI/CD pipelines work without extra effort. This guide walks through: - real-world benchmarks - examples from AWS and Google Cloud - gives hands-on advice for tooling, deployment, and compatibility If you're weighing cost, speed, or sustainability, this will help you judge whether a move to ARM makes sense—and how to pull it off cleanly.
WebAssembly (Wasm) is a binary instruction format that offers a compact and efficient way for executing code across diverse environments, including the web. Previously, Scala couldn’t directly compile to Wasm, but now Scala.js will support Wasm as its new linker backend, thanks to the collaborative efforts of the ScalaCenter and VirtusLab. This development may raise a question: why does Scala aim to compile to Wasm, and how is this achieved?
Scala 2 accumulated multiple amazing features that either improve the readability of the code, make it easier to write and maintain, or reduce the amount of boilerplate. Even though most of them have become an integral part of the language, some were only treated as experimental—their goal was to discover new language capabilities. Still, it was treated as a temporal and prone to change solution.
Kotlin lacks Java’s package-private visibility, exposing everything by default unless explicitly marked, which can bloat APIs and hinder encapsulation. The author praises Java’s manifest-based modular exports for making visibility decisions more conscious and centralized.
A general abstraction extends functions into composable building blocks, enabling the expression of business logic as data flows. This shift transforms side‑effecting, exception‑ridden code into clean, purely functional pipelines.
