Learn coding in Python, Go and Rust from Serdar Yegulalp, software dev specialist and senior writer at InfoWorld.
Encryption is one of the hardest things to do well in computing, if only because there are so many ways to get it wrong. This video explores some common pitfalls with using encryption in software development, and how to avoid them. (First hint: Don't ever make your own encryption algorithm!)
When does it make sense to build a desktop app with native or semi-native UI components (such as GTK or QT), or to go the web-UI route and use toolkits like Electron? https://www.infoworld.com/article/3547072/electron-vs-tauri-which-cross-platform-framework-is-for-you.html https://www.infoworld.com/article/2240075/easy-cross-platform-app-dev-with-githubs-electron.html
Python programs aren't easy to bundle up into click-to-run installable packages. No native way to do this exists, and the various third-party solutions like PyInstaller have major shortcomings and quirks. The PyApp project, written in Rust, aims to make it easy to build standalone executables for Python apps, although you need to be familiar with the Rust toolchain to use it. This video shows a simple example of PyApp in action, and details some of its features and limitations.
AWS's Kiro IDE uses an agent-powered workflow to iteratively transform user prompts into working code with test suites. Watch a hands-on demo of Kiro, as we take a simple prompt and make a full program from it — and see close-up both the power and the limitations of code-generating AI.
Astral's uv utility simplifies and speeds up working with Python virtual environments. But it has some other superpowers, too: it lets you run Python packages and programs without having to formally install them, all by way of one simple command. This video shows off how this can work for anything from a simple one-file program to a full project with a pyproject.toml file. Featuring Serdar Yegulalp: https://www.linkedin.com/in/serdar-yegulalp-136a483/
Cutting-edge AI systems, like generative AI, use models trained on immense amounts of data. But is that also the path to general artificial intelligence? There are good reasons to believe it isn't. In this video I talk about why data-model generative-AI systems are built on top of assumptions about intelligence and thinking that may not hold — and what other approaches might lie beyond them.
Microsoft has outfitted the Visual Studio Code editor with many extensions to make it a prime place to write Python code. The latest, albeit experimental, extension in this vein helps you manage the virtual environments you create for Python projects. In this video we'll walk through how it works in its early incarnation, and note some pitfalls you want to know about if you intend to try it on your own projects.
With the new free-threaded or "no-GIL" versions of Python, it's now possible to run Python code with full parallelism on multiple CPU cores. It also means "data race" bugs are easier than ever to create, where it's hard to guarantee the sequence in which a given object will be altered by code in different threads. One of the discussions at the recent Python Language Summit confronted this problem, and in this video I unpack their proposed solution in detail, and what implications it has for the rest of the language. For details about the talk itself: https://pyfound.blogspot.com/2025/06/python-language-summit-2025-fearless-concurrency.html
When we say something is "deterministic", we mean it delivers the same outputs for the same inputs. In theory LLMs should do that, as they're just mathematical models. But we don't get consistent results from LLMs even with the same inputs — and there's a slew of reasons for that. In this video I dive into why LLMs are designed to introduce randomness into the results, and why even without that feature it's hard to get them to deliver the exact same results from the exact same prompts.
Up until now, Python type annotations were evaluated "eagerly" - you needed to have an object defined before you could use it as an annotation. With Python 3.14, the default behavior has changed: Type annotations are now evaluated "lazily", as needed, for better performance and more flexibility. This video compares 3.14's new behavior with the way Python's handled annotations in previous versions, with recommendations for how to adapt to the new behavior.
AI agents are LLM-powered interfaces designed to help people accomplish specific tasks. Creating and deploying them can be complex. Google's newly released Agent Development Kit for Python (and Java) takes the boilerplate and heavy lifting out of writing AI agents, and gives you frameworks to write many common workflows used with such agents. InfoWorld article references: https://www.infoworld.com/article/3988205/google-i-o-2025-news-and-analysis.html https://www.infoworld.com/article/3818503/google-rolls-out-cheaper-ai-model-as-industry-scrutinizes-costs.html
Python has long had the "f-string" feature, for creating formatted strings from variable data. Now, with Python 3.14, there's a "template string" type — an f-string-like construction which gives you far more control over how the data is formatted and outputted. In this example we show how t-strings (as they're called) can be used to create templates that safely format text for HTML output.
Python 3.14 has rolled out a new official tool for adding, removing, and maintaining various versions of Python on Microsoft Windows. It also eclipses the existing \\\\\\\"py\\\\\\\" tool on that platform. This video explores how it works, how it replaces existing tooling, and what to expect when using it with pre-existing Python installations.
The Jupyter notebook system gives Python users a visual, interactive way to run code, display the results, and share the source with others. But Jupyter notebooks have limited interactivity. Marimo, a new competitor for Jupyter notebooks, lets you create notebooks that are more interactive, more powerful, and even easier to share with others.
Console apps are, by default, pretty plain-looking, but modern consoles offer powerful color and text stylization features. The trick is knowing how to use them. The \\\"rich\\\" library in Python gives you tools to easily add striking text stylization to console text output, including advanced interactive features like progress bars or elaborate layouts like tree views.
The Poetry and UV projects both give you high-level tools for creating and managing Python projects, including tracking dependencies and creating lockfiles from them. But their workflows, and the demands they make on the user and on your project, are very different. This video lays out how those differences affect you, and where it makes sense to opt for one solution over the other.
WebAssembly gives you a way to run code across platforms, including in a web browser, at near-native speeds, and by way of a broad range of languages. Google's Go language compiles directly to WebAssembly with minimal changes to one's code. In this video we show how a simple WASM module can be created in Go, and how to use Go's standard library to interact with JavaScript objects (for instance, to read form data or change DOM elements). https://www.infoworld.com/article/2264559/webassembly-tutorial-get-started-with-webassembly.html https://www.infoworld.com/article/2255892/what-is-webassembly-the-next-generation-web-platform-explained.html
Python packages can be installed in an "editable" or "in-place" installation mode, where instead of copying the package's code into a virtual environment, there's a pointer to the source directory instead. This allows any changes made to the package source to be reflected instantly in every virtual environment where the package is installed this way. This video demonstrates how useful and powerful this can be for Python developers, as it becomes far easier to evaluate changes made to a package without having to reinstall it each time you change it.
WebAssembly gives you a way to run code across platforms, including in a web browser, at near-native speeds, and by way of a broad range of languages. Rust has first-class support for WebAssembly as a compilation target. This video demonstrates how to create a simple WASM module for the web in Rust, which includes all the components you need to have your WASM module work in a web application.
WebAssembly apps can run at near-machine-native speeds in a browser — and outside of it, too, with the Wasmer runtime. In this video you'll see how Wasmer can run WebAssembly apps from the command line, download pre-packaged apps from the wasm.io online package registry, and work with local WebAssembly binaries you've already downloaded or built. What is WebAssembly? The next-generation web platform explained | InfoWorld https://www.infoworld.com/article/2255892/what-is-webassembly-the-next-generation-web-platform-explained.html Wasmer takes WebAssembly server-side | InfoWorld https://www.infoworld.com/article/2262266/wasmer-takes-webassembly-server-side.html Wasmer melds WebAssembly, Posix with WASIX spec | InfoWorld https://www.infoworld.com/article/2338550/wasmer-melds-webassembly-posix-with-wasix-spec.html
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