Tech: Blog

How I attempted to sync applications between Macs using Homebrew and Brewfiles.

Open source projects dominated by a single vendor are a hallmark of "open source in name only." Rather than filling the traditional role of open source fostering innovation and decision-making from a diverse community, "open source in name only" projects are often used as marketing tools for proprietary platforms. These projects are also seen as riskier than community-driven projects because a single vendor is more apt to abruptly terminate long-term support, restrict contributions, or switch from an open-source license to a more restrictive one (forcing some previous contributors to pay for the project they helped build). In these projects, critics claim that investments are often lopsided and heavily skewed toward onboarding, marketing, and brand-related support. As a result, technical contributions are frequently less developed, opaque, undocumented, or lacking in real substance, often manifesting merely as a superficial "ease of entry and onboarding." Because of these underlying gaps in documentation and codebase depth, developers are routinely forced to reverse-engineer functionality simply to get the tools to work correctly.

Al has blindsided cloud native infrastructure management, rendering established platform engineering woefully inadequate. Original platform engineering often suffers from a developer-only focus, but a platform that serves only one persona in a multi-persona organization ultimately delivers a shrinking fraction of its potential enterprise value. Autonomous agents are already beginning to write, review, test, and deploy code or fixes with or without human intervention. This shifts the primary organizational bottleneck from writing code to delivering it safely and quickly. The gap requires a new model: Platform Engineering 2.0.

IN 2005, NOKIA SOLD its billionth mobile phone, a budget-friendly device that went to a customer in Nigeria. By then, the company, based in Espoo, Finland, was making one of every three cellphones globally. But just nine years later, the mobile-device maker offloaded its entire handset division to Microsoft for pennies on the dollar, compared to what it had been worth at its peak.

Finally, after a lot of work over a long period of time, I have a new non-fiction book out, co-written with Ines Akrap. It’s [“Building the Sustainable Web”, out with Apress now](/books/building-the-sustainable-web).

Today I am 45, so I am releasing music for the first time in about 15 years. A look at my musical journey and the new EP I am releasing.

AI tools are transforming how we approach complex problems by automating tasks, enhancing decision-making, and fostering collaboration. This article explores the impact of AI on my software development work, ethical considerations, and the future of problem-solving in the AI-driven era.

macOS has always lacked a built-in way to route and mix audio on a per-application basis. Windows users have had per-app volume control for years, but on macOS, you need third-party tools to get anything close. I had been using a combination of SoundDesk, the Stream Deck MIDI plugin, and a Stream Deck plus with dials to solve this. The setup worked, but configuring it was fiddly. Then Elgato released Wave Link 3, and everything changed.

TypeBoost is an AI personal assistant that helps boost writing productivity by allowing you to apply prompts to text in any application. In this post, I test the tool to see how well it performs and how much it improves my productivity.

Opera Neon is a new browser with an AI assistant that interacts with web pages on your behalf, completing tasks that typically require manual clicking, typing, and navigation. In this post, I test the browser with real tasks to see how well it performs.

If bare metal provides the best raw performance, why do hyperscalers still insist on running their own infrastructure on virtual machines? The answer reveals what the companies running the world’s most complex infrastructure really think about cloud architecture.

CleanMyMac has long been a go-to tool for macOS maintenance, but its recent addition of cloud storage cleanup is worth examining. In this post, I test the new feature to see how well it helps manage cloud storage across services like iCloud and Google Drive, and whether it's a useful addition or just a gimmick.

Four years into a full-scale invasion and a decade after Russia’s annexation of Crimea, Ukraine’s tech ecosystem continues to flourish at home and in exile around the world. After celebrating its 10th anniversary in 2024, Ukraine’s premier tech event, IT Arena, returned to the Arena Lviv Stadium for the first time since 2019.

Surrounded by digital devices, it's all too easy to use any number of them to access a world of distractions, rather than focus on doing what you need to get done. In a strange, ironic twist, many of the devices and operating systems now offer tools to help block and filter these distractions.

Have you ever tried programming with a language that uses musical notation? What about a language that never runs programs the same way? What about a language where you write code with photographs? All exist, among many others, in the world of esoteric programming languages, and Daniel Temkin has written a forthcoming book covering 44 of them, some of which exist and are usable to some interpretation of the word “usable.” The book, Forty-Four Esolangs: The Art of Esoteric Code, is out on 23 September, published by MIT Press.

The blanket statement that bare metal is superior to containers in VMs for running containerized infrastructure, such as Kubernetes, no longer holds true. Each has pros and cons, so the right choice depends heavily on specific workload requirements and operational context. Bare metal was long touted as the obvious choice for organizations seeking both the best compute performance and even superior security when hosting containers compared to VMs. But this disparity in performance has slowly eroded. For security, it is now hard to make the case for bare metal’s benefits over those of VMs, except for very niche use cases.

On May 23rd 2025, the MySQL database celebrated its 30th anniversary. Look at the usage trends for databases on DB-engine and MySQL and its owner since 2010, Oracle's own product occupy the top two spots. However, the same rankings show that the popularity of most of the top four is declining, especially for MySQL. It has been in slow decline since the era of "cloud computing" began, but like many other areas of technology, it is the demands of and for artificial intelligence (AI) that are causing the most pressure. As it celebrates its 30th year, what is the project doing to remain relevant and competitive in the new AI age? With rivals hot on its tail and some of its biggest competitors offering fully compatible, free alternatives, how does it plan to survive for another 30 years?

Much like "googling" has become a generic term for searching, Grammarly has become almost synonymous with a general grammar and style checker, outside of those provided by tools like Word or Google Docs. I have used the pro version for several years, and while its advice is not always correct and inconsistent, it's generally a good aid in giving ideas and pointers for writing. However, every year, that subscription comes up for renewal, and I start thinking about alternatives and features I'd like to have that Grammarly doesn't offer. In this post, I look at the disappointingly few alternatives that exist.

KubeVirt offers a bridge between virtual machines and containerized environments. As an open-source project, its standout feature is the ability to run VMs and containers side by side. But while the concept is promising, several caveats remain for organizations that need to support critical at-scale VM workloads. The CNCF project also reflects how containers are not going to replace VMs, while the reverse may be true in the long term for many use cases.

Many organizations work with clients and infrastructure around the world and face significant challenges ensuring they follow privacy regulations as their application data flows across borders. This post looks at how you can use Bacalhau to handle distributed cross-border processing and anonymize data with Microsoft Presidio to help meet some of these requirements.

API documentation is generally predictable, follows common patterns, and is one of the least interesting tasks in a documentation project. It's also a task with a degree of pre-existing automatic generation tools and practices. It sounds like a perfect use case for AI-assistive tools! In this post, I look at general and specialized tools for generating API documentation from code and text-based prompts. I also cover potential problems and pitfalls in generated docs and how to test the generated output to ensure its accuracy.

The Opera Web browser, first introduced 30 years ago, has over its long tenure helped to pioneer features that would later become commonplace among all Web browsers—including tabs, sync, and built-in search. Opera was among the first to introduce a built-in AI assistant (Aria) as well as the ability to use locally running models with its developer version. Now, Opera aims to be the first to offer a new kind of AI agent–based browsing, with a feature called Browser Operator.

Machine Learning requires vast amounts of resources, and distributing these resources across multiple devices and regions helps with cost, speed, and data sovereignty. Bacalhau is an open-source distributed orchestration framework designed to bring compute resources to the data where and when you want, drastically reducing latency and resource overhead. Instead of moving large datasets around networks, Bacalhau makes it easy to execute jobs close to the data’s location, reducing latency and resource overhead.

The move to the cloud promised to save users money and give them insights into their usage and costs. However, the opposite happened. A 2025 report from AAG stated that around 82% of respondents found cloud spending challenging. A cloudzero report from 2024 states that more than 20% of respondents had no clear idea of their cloud costs, with reports for large users sometimes consisting of thousands of rows of hard-to-read usage data.

Welcome to a new newsletter/post/radar/term-yet-to-be-defined from me that I have been planning for ages. I intend it to be something of a round up of tools and services i’ve been trying recently, plus also industry analysis and trends. To begin with it’s just the tools and service round up as the analysis part requires more thought that I haven’t had time for yet.

Autocomplete has existed on macOS in some form for years, and it's always been inconsistent. I tried Cotypist, an AI-powered autocomplete tool that works across almost every app on the Mac, to see if it does a better job.

With many applications that rely on data warehouses, you need to keep data sources in different locations. This could be due to privacy or regulatory reasons or because you want to keep processing close to the source. However, there are still times when you want to perform analysis on and across these data sources from one location but not move the data. This post uses Bacalhau to orchestrate the distributed processing and DuckDB to provide the SQL storage and querying capacity for some mock sales data based in the EU and the US.

When the modern-day internet began emerging in the early 2000s, finding hosting services and resources to run the new wave of dynamic web applications was hard. You needed a database to store application data. These were slow, expensive, and unreliable, regularly bringing applications to a grinding halt when a single instance failed. You needed a server to run interpreted languages like PHP, Python, or Ruby. These were equally expensive, often needed configuration, had security issues, and frequently ran out of memory or CPU resources, again bringing applications to a grinding halt.

A while ago I made a video on Mermaid, the markdown-style syntax for creating diagrams and charts. It's still one of my more popular videos, and it needs an update. In the meantime, the people at Mermaid Chart, a new commercial company formed by some of Mermaid's original creators, reached out to show me their platform. So in this post, I take a look at what it adds on top of the default Mermaid syntax.

For those of you in the Bazel ecosystem, when you hear "Dagger", you probably think of the dependency injection framework for Java, Kotlin, and Android. And for good reason. Dependencies are frequently part of a build process. But there's another Dagger in the wild, a tool and platform for ephemerally building and testing multi-language projects. Sound familiar? In this post, I look at what (the new) Dagger is, how it works, and how it compares to Bazel.