Technology Tales

Security is a behaviour, not a tick-box

11^th February 2026

Cybersecurity is often discussed in terms of controls and compliance, yet most security failures begin and end with human action. A growing body of practice now places behaviour at the centre, drawing on psychology, neuroscience, history and economics to help people replace old habits with new ones. George Finney's Well Aware Security have built its entire approach around this idea, reframing awareness training as a driver of measurable outcomes rather than a box-ticking exercise, with coaches helping colleagues identify and build upon their existing strengths. It is also personal by design, using insights about how minds work to guide change one habit at a time rather than expecting wholesale transformation overnight.

This emphasis on behaviour is not a dismissal of technical skill so much as a reminder that skill alone is insufficient. Security is not a competency you either possess or lack; it is a behaviour that can be learned, reinforced and normalised. As social beings, we have always gathered for mutual protection, meaning the desire to contribute to collective security is already present in most people. Turning that impulse into daily action requires structure and patience, and it thrives when a supportive culture takes root.

Culture matters because norms are powerful. In a team where speed and convenience consistently override prudence, individuals who try to do the right thing can feel isolated. Conversely, when an organisation embraces cybersecurity at every level, a small group can create sufficient leverage to shift practices across the whole business. Research has found that organisations with below-average culture ratings are significantly more likely to experience a data breach than their peers, and controls alone cannot close that gap when behaviours are pulling in the opposite direction.

This is why advocates of habit-based security speak of changing one step at a time, celebrating progress and maintaining momentum. The same thinking underpins practical measures at home and at work, where small changes in how devices and data are managed can reduce risk materially without making technology difficult to use.

Network-Wide Blocking with Pi-hole

One concrete example of this approach is network-wide blocking of advertising and tracking domains using a DNS sinkhole. Pi-hole has become popular because it protects all devices on a network without requiring any client-side software to be installed on each one. It runs lightly on Linux, blocks content outside the browser (such as within mobile apps and smart TVs) and can optionally act as a DHCP server so that new devices are protected automatically upon joining the network.

Pi-hole's web dashboard surfaces insights into DNS queries and blocked domains, while a command-line interface and an API offer further control for those who need it. It caches DNS responses to speed up everyday browsing, supports both IPv4 and IPv6, and scales from small households to environments handling very high query volumes. The project is free and open source, sustained by donations and volunteer effort.

Choosing What to Block

Selecting what to block is a point at which behaviour and technology intersect. It is tempting to load every available blocklist in the hope of maximum protection, but as Avoid the Hack notes in its detailed guide to Pi-hole blocklists, more is not always better. Many lists draw from common sources, so stacking them can add redundancy without improving coverage and may increase false positives (instances where legitimate sites are mistakenly blocked).

The most effective approach begins by considering what you want to block and why, then balancing that against the requirements of your devices and services. Blocking every Microsoft domain, for instance, could disrupt operating system updates or break websites that rely on Azure. Likewise, blacklisting all domains belonging to a streaming or gaming platform may render apps unusable. Aggressive configurations are possible, but they work best when paired with careful allow-listing of domains essential to your services. Allow lists require ongoing upkeep as services move or change, so they are not a one-off exercise.

Recommended Blocklists

A practical starting point is the well-maintained Steven Black unified hosts file, which consolidates several reputable sources and many users find sufficient straight away. From there, curated collections help tailor coverage further. EasyList provides a widely trusted foundation for blocking advertising and integrates with browser extensions such as uBlock Origin, while its companion list EasyPrivacy can add stronger tracking protection at the cost of occasional breakage on certain sites.

Hagezi maintains a comprehensive set of DNS blocklists, including "multi" variants of different sizes and aggression levels, built from numerous sources. Selecting one of the multi variants is usually preferable to layering many individual category lists, which can reintroduce the overlap you were trying to avoid. Firebog organises its lists by risk: green entries carry a lower risk of causing breakage, while blue entries are more aggressive, giving you the option to mix and match according to your comfort level.

Some projects bundle many sources into a single combination list. OISD is one such option, with its Basic variant focusing mainly on advertisements, Full extending to malware, scams, phishing, telemetry and tracking, and a separate NSFW set covering adult content. OISD updates roughly every 24 hours and is comprehensive enough that many users would not need additional lists. The trade-off is placing a significant degree of trust in a single maintainer and limiting the ability to assign different rule sets to different device groups within Pi-hole, so it is worth weighing convenience against flexibility before committing.

The Blocklist Project organises themed lists covering advertising, tracking, malware, phishing, fraud and social media domains, and these work with both Pi-hole and AdGuard Home. The project completed a full rebuild of its underlying infrastructure, replacing an inconsistent mix of scripts with a properly tested Python pipeline, automated validation on pull requests and a cleaner build process.

Existing list URLs are unchanged, so anyone already using the project's lists need not reconfigure anything. That said, the broader principle holds regardless of which project you use: blocklists can become outdated if not actively maintained, reducing their effectiveness over time.

Using Regular Expressions

For more granular control, Pi-hole supports regular expressions to match domain patterns. Regex entries are powerful and can be applied both to block and to allow traffic, but they reward specificity. Broad patterns risk false positives that break legitimate services, so community-maintained regex recommendations are a safer starting point than writing everything from scratch. Pi-hole's own documentation explains how expressions are evaluated in detail. Used judiciously, regex rules extend what list-based blocking can achieve without turning maintenance into an ongoing burden.

Installing Pi-hole

Installation is straightforward. Pi-hole can be deployed in a Linux container or directly on a supported operating system using an automated installer that asks a handful of questions and configures everything in under ten minutes. Once running, you point clients to use it as their DNS resolver, either by setting DHCP options on your router, so devices adopt it automatically, or by updating network settings on each device individually. Pairing Pi-hole with a VPN extends ad blocking to mobile devices when away from home, so limited data plans go further by not downloading unwanted content. Day-to-day management is handled via the web interface, where you can add domains to block or allow lists, review query logs, view long-term statistics and audit entries, with privacy modes that can be tuned to your environment.

Device-Level Adjustments

Network filtering is one layer in a defence-in-depth approach, and a few small device-level changes can reduce friction without sacrificing safety. Bitdefender's Safepay, for example, is designed to isolate banking and shopping sessions within a hardened browser environment. If its prompts become intrusive, you can turn off notifications by opening the Bitdefender interface, selecting Privacy, then Safepay settings, and toggling off both Safepay notifications and the option to use a VPN with Safepay. Bookmarked sites can still auto-launch Safepay unless you also disable the automatic-opening option. Even with notifications suppressed, you can start Safepay manually from the dashboard whenever you want the additional protection.

On Windows, unwanted prompts from Microsoft Edge about setting it as the default browser can be handled without resorting to arcane steps. The Windows Club covers the full range of methods available. Dismissing the banner by clicking "Not now" several times usually prevents it from reappearing, though a browser update or reset may bring the message back. Advanced users can disable the recommendations via edge://flags, or apply a registry policy under HKEY_CURRENT_USERSoftwarePoliciesMicrosoftEdge by setting DefaultBrowserSettingEnabled to 0. In older environments such as Windows 7, a Group Policy setting exists to stop Edge checking whether it is the default browser. These changes should be made with care, particularly in managed environments where administrators enforce default application associations across the estate.

Knowing What Your Devices Reveal

Awareness also begins with understanding what your devices reveal to the wider internet. Services like WhatIsMyIP.com display your public IP address, the approximate location derived from it and your internet service provider. For most home users, a public IP address is dynamic rather than fixed, meaning it can change when a router restarts or when an ISP reallocates addresses; on mobile networks it may change more frequently still as devices move between towers and routing systems.

Such tools also provide lookups for DNS and WHOIS information, and they explain the difference between public and private addressing. Complementary checks from WhatIsMyBrowser.com summarise your browser version, whether JavaScript and cookies are enabled, and whether known trackers or ad blockers are detected. Sharing that information with support teams can make troubleshooting considerably faster, since it quickly narrows down where problems are likely to sit.

Protecting Your Accounts

Checking for Breached Credentials

Account security is another area where habits do most of the heavy lifting. Checking whether your email address appears in known data breaches via Have I Been Pwned helps you decide when to change passwords or enable stronger protections. The service, created by security researcher Troy Hunt, tracks close to a thousand breached websites and over 17.5 billion compromised accounts, and offers notifications as well as a searchable dataset. Finding your address in a breach does not mean your account has been taken over, but it does mean you should avoid reusing passwords and should enable two-factor authentication wherever it is available.

Two-Factor Authentication

Authenticator apps provide time-based codes that attackers cannot guess, even when armed with a reused password. Aegis Authenticator is a free, open-source option for Android that stores your tokens in an encrypted vault with optional biometric unlock. It offers a clean interface with multiple themes, supports icons for quick identification and allows import and export from a wide range of other apps. Backups can be automatic, and you remain in full control, since the app works entirely offline without advertisements or tracking.

For users who prefer cloud backup and multi-device synchronisation, Authy from Twilio offers a popular alternative that pairs straightforward setup with secure backup and support for using tokens across more than one device. Both approaches strengthen accounts significantly, and the choice often comes down to whether you value local control above all else or prefer the convenience of synchronisation.

Password Management

Strong, unique passwords remain essential even alongside two-factor authentication. KeePassXC is a cross-platform password manager for Windows, macOS and Linux that keeps your credentials in an encrypted database stored wherever you choose, rather than on a vendor's servers. It is free and open source under the GPLv3 licence, and its development process is publicly visible on GitHub.

The project has undergone rigorous external scrutiny. On the 17th of November 2025, KeePassXC version 2.7.9 was awarded a Security Visa by the French National Cybersecurity Agency (ANSSI) under its First-level Security Certification (CSPN) programme, with report number ANSSI-CSPN-2025/16. The certification is valid for three years and is recognised in France and by the German Federal Office for Information Security. More recent releases such as version 2.7.11 focus on bug fixes and usability improvements, including import enhancements, better password-generation feedback and refinements to browser integration. Because data are stored locally, you can place the database in a private or shared cloud folder if you wish to sync between devices, while encryption remains entirely under your control.

Secure Email with Tuta

Email is a frequent target for attackers and a common source of data leakage, so the choice of client can make a meaningful difference. Tuta provides open-source desktop applications for Linux, Windows and macOS that bring its end-to-end encrypted mail and calendar to the desktop with features that go beyond the web interface. The clients are signed so that updates can be verified independently, and Tuta publishes its public key, so users can confirm signatures themselves.

There is a particular focus on Linux, with support for major distributions including Ubuntu, Debian, Fedora, Arch Linux, openSUSE and Linux Mint. Deep operating-system integration enables conveniences such as opening files as attachments directly from context menus on Windows via MAPI, setting Tuta as the default mail handler, using the system's secret storage and applying multi-language spell-checking. Hardware key support via U2F is available across all desktop clients, and offline mode means previously indexed emails, calendars and contacts remain accessible without an internet connection.

Tuta does not support IMAP because downloading and storing messages unencrypted on devices would undermine its end-to-end encryption model. Instead, features such as import and export are built directly into the clients; paid plans including Legend and Unlimited currently include email import that encrypts messages locally before uploading them. The applications are built on Electron to maintain feature parity across platforms, and Tuta offers the desktop clients free to all users to ensure that core security benefits are not gated behind a subscription.

Bringing Culture and Tooling Together

These individual strands reinforce one another when combined. A network-wide blocker reduces exposure to malvertising and tracking while nudging everyone in a household or office towards safer defaults. Small device-level settings cut noise without removing protection, which helps people maintain good habits because security becomes less intrusive. Visibility tools demystify what the internet can see and how browsers behave, which builds confidence. Password managers and authenticator apps make strong credentials and second factors the norm rather than the exception, and a secure email client protects communications by default.

None of these steps requires perfection, and each can be introduced one at a time. The key is to focus on outcomes, think like a coach and make security personal, so that habits take root and last.

There is no single fix that will stop every attack. One approach that does help is consistent behaviour supported by thoughtful choices of software and services. Start with one change that removes friction while adding protection, then build from there. Over time, those choices shape a culture in which people feel they have a genuine role in keeping themselves and their organisations safe, and the technology they rely upon reflects that commitment.

Running local Large Language Models on desktop computers and workstations with 8GB VRAM

7^th February 2026

Running large language models locally has shifted from being experimental to practical, but expectations need to match reality. A graphics card with 8 GB of VRAM can support local workflows for certain text tasks, though the results vary considerably depending on what you ask the models to do.

Understanding the Hardware Foundation

The Critical Role of VRAM

The central lesson is that VRAM is the engine of local performance on desktop systems. Whilst abundant system RAM helps avoid crashes and allows larger contexts, it cannot replace VRAM for throughput.

Models that fit in VRAM and keep most of their computation on the GPU respond promptly and maintain a steady pace. Those that overflow to system RAM or the CPU see noticeable slowdowns.

Hardware Limitations and Thresholds

On a desktop GPU with 8 GB of VRAM, this sets a practical ceiling. Models in the 7 billion to 14 billion parameter range fit comfortably enough to exploit GPU acceleration for typical contexts.

Much larger models tend to offload a significant portion of the work to the CPU. This shows up as pauses, lower token rates and lag when prompts become longer.

Monitoring GPU Utilisation

GPU utilisation is a reliable way to gauge whether a setup is efficient. When the GPU is consistently busy, generation is snappy and interactive use feels smooth.

A model like llama3.1:8b can run almost entirely on the GPU at a context length of 4,096 tokens. This translates into sustained responsiveness even with multi-paragraph prompts.

Model Selection and Performance

Choosing the Right Model Size

A frequent instinct is to reach for the largest model available, but size does not equal usefulness when running locally on a desktop or workstation. In practice, models between 7B and 14B parameters are what you can run on this class of hardware, though what they do well is more limited than benchmark scores might suggest.

What These Models Actually Do Well

Models in this range handle certain tasks competently. They can compress and reorganise information, expand brief notes into fuller text, and maintain a reasonably consistent voice across a draft. For straightforward summarisation of documents, reformatting content or generating variations on existing text, they perform adequately.

Where things become less reliable is with tasks that demand precision or structured output. Coding tasks illustrate this gap between benchmarks and practical use. Whilst llama3.1:8b scores 72.6% on the HumanEval benchmark (which tests basic algorithm problems), real-world coding tasks can expose deeper limitations.

Commit message generation, code documentation and anything requiring consistent formatting produce variable results. One attempt might give you exactly what you need, whilst the next produces verbose or poorly structured output.

The gap between solving algorithmic problems and producing well-formatted, professional code output is significant. This inconsistency is why larger local models like gpt-oss-20b (which requires around 16GB of memory) remain worth the wait despite being slower, even when the 8GB models respond more quickly.

Recommended Models for Different Tasks

Llama3.1:8b handles general drafting reasonably well and produces flowing output, though it can be verbose. Benchmark scores place it above average for its size, but real-world use reveals it is better suited to free-form writing than structured tasks.

Phi3:medium is positioned as stronger on reasoning and structured output. In practice, it can maintain logical order better than some alternatives, though the official documentation acknowledges quality of service limitations, particularly for anything beyond standard American English. User reports also indicate significant knowledge gaps and over-censorship that can affect practical use.

Gemma3 at 12B parameters produces polished prose and smooths rough drafts effectively when properly quantised. The Gemma 3 family offers models from 1B to 27B parameters with 128K context windows and multimodal capabilities in the larger sizes, though for 8GB VRAM systems you are limited to the 12B variant with quantisation. Google also offers Gemma 3n, which uses an efficient MatFormer architecture and Per-Layer Embedding to run on even more constrained hardware. These are primarily optimised for mobile and edge devices rather than desktop use.

Very large models remain less efficient on desktop hardware with 8 GB VRAM. Attempting to run them results in heavy CPU offloading, and the performance penalty can outweigh any quality improvement.

Memory Management and Configuration

Managing Context Length

Context length sits alongside model size as a decisive lever. Every extra token of context demands memory, so doubling the window is not a neutral choice.

At around 4,096 tokens, most of the well-matched models stay predominantly on the GPU and hold their speed. Push to 8,192 or beyond, and the memory footprint swells to the point where more of the computation ends up taking place on the CPU and in system RAM.

Ollama's Keep-Alive Feature

Ollama keeps already loaded models resident in VRAM for a short period after use so that the next call does not pay the penalty of a full reload. This is expected behaviour and is governed by a keep_alive parameter that can be adjusted to hold the model for longer if a burst of work is coming, or to release it sooner when conserving VRAM matters.

Practical Memory Strategies

Breaking long jobs into a series of smaller, well-scoped steps helps both speed and stability without constraining the quality of the end result. When writing an article, for instance, it can be more effective to work section by section rather than asking for the entire piece in one pass.

Optimising the Workflow

The Benefits of Streaming

Streaming changes the way output is experienced, rather than the content that is ultimately produced. Instead of waiting for a block of text to arrive all at once, words appear progressively in the terminal or application. This makes longer pieces easier to manage and revise on the fly.

Task-Specific Model Selection

Because each model has distinct strengths and weaknesses, matching the tool to the task matters. A fast, GPU-friendly model like llama3.1:8b works for general writing and quick drafting where perfect accuracy is not critical. Phi3:medium may handle structured content better, though it is worth testing against your specific use case rather than assuming it will deliver.

Understanding Limitations

It is important to be clear about what local models in this size range struggle with. They are weak at verifying facts, maintaining strict factual accuracy over extended passages, and providing up-to-date knowledge from external sources.

They also perform inconsistently on tasks requiring precise structure. Whilst they may pass coding benchmarks that test algorithmic problem-solving, practical coding tasks such as writing commit messages, generating consistent documentation or maintaining formatting standards can reveal deeper limitations. For these tasks, you may find yourself returning to larger local models despite preferring the speed of smaller ones.

Integration and Automation

Using Ollama's Python Interface

Ollama integrates into automated workflows on desktop systems. Its Python package allows calls from scripts to automate summarisation, article generation and polishing runs, with streaming enabled so that logs or interfaces can display progress as it happens. Parameters can be set to control context size, temperature and other behavioural settings, which helps maintain consistency across batches.

Building Production Pipelines

The same interface can be linked into website pipelines or content management tooling, making it straightforward to build a system that takes notes or outlines, expands them, revises the results and hands them off for publication, all locally on your workstation. The same keep_alive behaviour that aids interactive use also benefits automation, since frequently used models can remain in VRAM between steps to reduce start-up delays.

Recommended Configuration

Optimal Settings for 8 GB VRAM

For a desktop GPU with 8 GB of VRAM, an optimal configuration builds around models that remain GPU-efficient whilst delivering acceptable results for your specific tasks. Llama3.1:8b, phi3:medium and gemma3:12b are the models that fit this constraint when properly quantised, though you should test them against your actual workflows rather than relying on general recommendations.

Performance Monitoring

Keeping context windows around 4,096 tokens helps sustain GPU-heavy operation and consistent speeds, whilst streaming smooths the experience during longer outputs. Monitoring GPU utilisation provides an early warning if a job is drifting into a configuration that will trigger CPU fallbacks.

Planning for Resource Constraints

If a task does require more memory, it is worth planning for the associated slowdown rather than assuming that increasing system RAM or accepting a bigger model will compensate for the VRAM limit. Tuning keep_alive to the rhythm of work reduces the frequency of reloads during sessions and helps maintain responsiveness when running sequences of related prompts.

A Practical Content Creation Workflow

Multi-Stage Processing

This configuration supports a division of labour in content creation on desktop systems. You start with a compact model for rapid drafting, switch to a reasoning-focused option for structured expansions if needed, then finish with a model known for adding polish to refine tone and fluency. Insert verification steps between stages to confirm facts, dates and citations before moving on.

Because each stage is local, revisions maintain privacy, with minimal friction between idea and execution. When integrated with automation via Ollama's Python tools, the same pattern can run unattended for batches of articles or summaries, with human review focused on accuracy and editorial style.

In Summary

Desktop PCs and workstations with 8 GB of VRAM can support local LLM workflows for specific tasks, though you need realistic expectations about what these models can and cannot do reliably. They handle basic text generation and reformatting, though prone to hallucinations and misunderstandings. They struggle with precision tasks, structured output and anything requiring consistent formatting. Whilst they may score well on coding benchmarks that test algorithmic problem-solving, practical coding tasks can reveal deeper limitations.

The key is to select models that fit the VRAM envelope, keep context lengths within GPU-friendly bounds, and test them against your actual use cases. For tasks where local models prove inconsistent, such as generating commit messages or producing reliably structured output, larger local models like gpt-oss-20b (which requires around 16GB of memory) may still be worth the wait despite being slower. Local LLMs work best when you understand their limitations and use them for what they genuinely do well, rather than expecting them to replace more capable models across all tasks.

Additional Resources

• Ollama Official Documentation
• Ollama GitHub Repository
• Hugging Face model hub
• LM Studio for local model management
• Jan AI for local AI deployment
• Meta Llama 3.1 Model Card
• Microsoft Phi-3 Documentation
• Google Gemma 3 Overview
• Google Gemma 3n Overview
• Artificial Analysis for model benchmarks

Four technical portals that still deliver after decades online

3^rd February 2026

The early internet was built on a different kind of knowledge sharing, one driven by individual expertise, community generosity and the simple desire to document what worked. Four informative websites that started in that era, namely MDN Web Docs, AskApache, WindowsBBS and Office Watch, embody that spirit and remain valuable today. They emerged at a time when technical knowledge was shared through forums, documentation and personal blogs rather than social media or algorithm-driven platforms, and their legacy persists in offering clarity and depth in an increasingly fragmented digital landscape.

MDN Web Docs

MDN Web Docs stands as a cornerstone of modern web development, offering comprehensive coverage of HTML, CSS, JavaScript and Web APIs alongside authoritative references for browser compatibility. Mozilla started the project in 2005 under the name Mozilla Developer Centre, and it has since grown into a collaborative effort of considerable scale. In 2017, Mozilla announced a formal partnership with Google, Microsoft, Samsung and the W3C to consolidate web documentation on a single platform, with Microsoft alone redirecting over 7,700 of its MSDN pages to MDN in that year.

For developers, the site is not merely a reference tool but a canonical guide that ensures standards are adhered to and best practices followed. Its tutorials, guides and learning paths make it indispensable for beginners and seasoned professionals alike. The site's community-driven updates and ongoing contributions from browser vendors have cemented its reputation as the primary source for anyone building for the web.

AskApache

AskApache is a niche but invaluable resource for those managing Apache web servers, built by a developer whose background lies in network security and penetration testing on shared hosting environments. The site grew out of the founder's detailed study of .htaccess files, which, unlike the main Apache configuration file httpd.conf, are read on every request and offer fine-grained, per-directory control without requiring root access to the server. That practical origin gives the content its distinctive character: these are not generic tutorials, but hard-won techniques born from real-world constraints.

The site's guides on blocking malicious bots, configuring caching headers, managing redirects with mod_rewrite and preventing hot-linking are frequently cited by system administrators and WordPress users. Its specificity and longevity have made it a trusted companion for those maintaining complex server environments, covering territory that mainstream documentation rarely touches.

WindowsBBS

WindowsBBS offers a clear window into the era when online forums were the primary hub for technical support. Operating in the tradition of classic bulletin board systems, the site has long been a resource for users troubleshooting Windows installations, hardware compatibility issues and malware removal. It remains completely free, sustained by advertisers and community donations, which reflects the ethos of mutual aid that defined early internet culture.

During the Windows XP and Windows 7 eras, community forums of this kind were essential for solving problems that official documentation often overlooked, with volunteers providing detailed answers to questions that Microsoft's own support channels would not address. While the rise of social media and centralised support platforms has reduced the prominence of such forums, WindowsBBS remains a testament to the power of community-driven problem-solving. Its straightforward structure, with users posting questions and experienced volunteers providing answers, mirrors the collaborative spirit that made the early web such a productive environment.

Office Watch

Office Watch has served as an independent source of Microsoft Office news, tips and analysis since 1996, making it one of the longer-running specialist publications of its kind. Its focus on Microsoft Office takes in advanced features and hidden tools that are seldom documented elsewhere, from lesser-known functions in Excel to detailed comparisons between Office versions and frank assessments of Microsoft's product decisions. That independence gives it a voice that official resources cannot replicate.

The site serves power users seeking to make the most of the software they use every day, with guides and books that extend its reach beyond the website itself. In an era where software updates are frequent and often poorly explained, Office Watch provides the kind of context and plain-spoken clarity that official documentation rarely offers.

The Enduring Value of Depth and Community

These four sites share a common thread: they emerged when technical knowledge was shared openly by experts and enthusiasts rather than filtered through algorithms or paywalls, and they retain the value that comes from that approach. Their continued relevance speaks to what depth, specificity and community can achieve in the digital world. While platforms such as Stack Overflow and GitHub Discussions have taken over many of the roles these sites once played, the original resources remain useful for their historical context and the quality of their accumulated content.

As the internet continues to evolve, the lessons from these sites are worth remembering. The most useful knowledge is often found at the margins, where dedicated individuals take the time to document, explain and share what they have learned. Whether you are a developer, a server administrator or an everyday Office user, these resources are more than archives: they are living repositories of expertise, built by people who cared enough to write things down properly.

Installing PowerShell on Linux Mint for some cross-platform testing

25^th November 2025

Given how well shell scripting works on Linux and my familiarity with it, the need to install PowerShell on a Linux system may seem surprising. However, this was part of some testing that I wanted to do on a machine that I controlled before moving the code to a client's system. The first step was to ensure that any prerequisites were in place:

sudo apt update
sudo apt install -y wget apt-transport-https software-properties-common

After that, the next moves were to download and install the required package for instating Microsoft repository details:

wget -q https://packages.microsoft.com/config/ubuntu/24.04/packages-microsoft-prod.deb
sudo dpkg -i packages-microsoft-prod.deb

Then, I could install PowerShell itself:

sudo apt update
sudo apt install -y powershell

When it was in place, issuing the following command started up the extra shell for what I needed to do:

pwsh

During my investigations, I found that my local version of PowerShell was not the same as on the client's system, meaning that any code was not as portable as I might have expected, Nevertheless, it is good to have this for future reference and proves how interoperable Microsoft has needed to become.

Latest developments in the AI landscape: Consolidation, implementation and governance

22^nd November 2025

Artificial intelligence is moving through another moment of consolidation and capability gain. New ways to connect models to everyday tools now sit alongside aggressive platform plays from the largest providers, a steady cadence of model upgrades, and a more defined conversation about risk and regulation. For companies trying to turn all this into practical value, the story is becoming less about chasing the latest benchmark and more about choosing a platform, building the right connective tissue, and governing data use with care. The coming year looks set to reward those who simplify the user experience, embed AI directly into work and adopt proportionate controls rather than blanket bans.

I. Market Structure and Competitive Dynamics

Platform Consolidation and Lock-In

Enterprise AI appears to be settling into a two-platform market. Analysts describe a landscape defined more by integration and distribution than raw model capability, evoking the cloud computing wars. On one side sit Microsoft and OpenAI, on the other Google and Gemini. Recent signals include the pricing of Gemini 3 Pro at around two dollars per million tokens, which undercuts much of the market, Alphabet's share price strength, and large enterprise deals for Gemini integrated with Google's wider software suite. Google is also promoting Antigravity, an agent-first development environment with browser control, asynchronous execution and multi-agent support, an attempt to replicate the pull of VS Code within an AI-native toolchain.

The implication for buyers is higher switching costs over time. Few expect true multi-cloud parity for AI, and regional splits will remain. Guidance from industry commentators is to prioritise integration across the existing estate rather than incremental model wins, since platform choices now look like decade-long commitments. Events lined up for next year are already pointing to that platform view.

Enterprise Infrastructure Alignment

A wider shift in software development is also taking shape. Forecasts for 2026 emphasise parallel, multi-agent systems where a planning agent orchestrates a set of execution agents, and harnesses tune themselves as they learn from context. There is growing adoption of a mix-of-models approach in which expensive frontier models handle planning, and cheaper models do the bulk of execution, bringing near-frontier quality for less money and with lower latency. Team structures are changing as a result, with more value placed on people who combine product sense with engineering craft and less on narrow specialisms.

ServiceNow and Microsoft have announced a partnership to coordinate AI agents across organisations with tighter oversight and governance, an attempt to avoid the sprawl that plagued earlier automation waves. Nvidia has previewed Apollo, a set of open AI physics models intended to bring real-time fidelity to simulations used in science and industry. Albania has appointed an AI minister, which has kicked off debate about how governments should manage and oversee their own AI use. CIOs are being urged to lead on agentic AI as systems become capable of automating end-to-end workflows rather than single steps.

New companies and partnerships signal where capital and talent are heading. Jeff Bezos has returned to co-lead Project Prometheus, a start-up with $6.2 billion raised and a team of about one hundred hires from major labs, focused on AI for engineering and manufacturing in the physical world, an aim that aligns with Blue Origin interests. Vik Bajaj is named as co-CEO.

Deals underline platform consolidation. Microsoft and Nvidia are investing up to $5 billion and $10 billion respectively (totalling $15 billion) in Anthropic, whilst Anthropic has committed $30 billion in Azure capacity purchases with plans to co-design chips with Nvidia.

Commercial Model Evolution

Events and product launches continue at pace. xAI has released Grok 4.1 with an emphasis on creativity and emotional intelligence while cutting hallucinations. On the tooling front, tutorials explain how ChatGPT's desktop app can record meetings for later summarisation. In a separate interview, DeepMind's Demis Hassabis set out how Gemini 3 edges out competitors in many reasoning and multimodal benchmarks, slightly trails Claude Sonnet 4.5 in coding, and is being positioned for foundations in healthcare and education though not as a medical-grade system. Google is encouraging developers towards Antigravity for agentic workflows.

Industry leaders are also sketching commercial models that assume more agentic behaviour, with Microsoft's Satya Nadella promising a "positive-sum" vision for AI while hinting at per-agent pricing and wider access to OpenAI IP under Microsoft's arrangements.

II. Technical Implementation and Capability

Practical Connectivity Over Capability

A growing number of organisations are starting with connectors that allow a model to read and write across systems such as Gmail, Notion, calendars, CRMs, and Slack. Delivered via the Model Context Protocol, these links pull the relevant context into a single chat, so users spend less time switching windows and more time deciding what to do. Typical gains are in hours saved each week, lower error rates, and quicker responses. With a few prompts, an assistant can draft executive email summaries, populate a Notion database with leads from scattered sources, or propose CRM follow-ups while showing its working.

The cleanest path is phased: enable one connector using OAuth, trial it in read-only mode, then add simple routines for briefs, meeting preparation or weekly reports before switching on write access with a "show changes before saving" step. Enterprise controls matter here. Connectors inherit user permissions via OAuth 2.0, process data in memory, and vendors point to SOC 2, GDPR and CCPA compliance alongside allow and block lists, policy management, and audit logs. Many governance teams prefer to begin read-only and require approvals for writes.

There are limits to note, including API rate caps, sync delays, context window constraints and timeouts for long workflows. They are poor fits for classified data, considerable bulk operations or transactions that cannot tolerate latency. Some industry observers regard Claude's current MCP implementation, particularly on desktop, as the most capable of the group. Playbooks for a 30-day rollout are beginning to circulate, as are practitioner workshops introducing go-to-market teams to these patterns.

Agentic Orchestration Entering Production

Practical comparisons suggest the surrounding tooling can matter more than the raw model for building production-ready software. One report set a 15-point specification across several environments and found that Claude Code produced all features end-to-end. The same spec built with Gemini 3 inside Antigravity delivered two thirds of the features, while Sonnet 4.5 in Antigravity delivered a little more than half, with omissions around batching, progress indicators and robust error handling.

Security remains a live issue. One newsletter reports that Anthropic said state-backed Chinese hackers misused Claude to autonomously support a large cyberattack, which has intensified calls for governance. The background hum continues, from a jump in voice AI adoption to a German ruling on lyric copyright involving OpenAI, new video guidance steps in Gemini, and an experimental "world model" called Marble. Tools such as Yorph are receiving attention for building agentic data pipelines as teams look to productionise these patterns.

Tooling Maturity Defining Outcomes

In engineering practice, Google's Code Wiki brings code-aware documentation that stays in sync with repositories using Gemini, supported by diagrams and interactive chat. GitLab's latest survey suggests AI increases code creation but also pushes up demand for skilled engineers alongside compliance and human oversight. In operations, Chronosphere has added AI remediation guidance to cut observability noise and speed root-cause analysis while performance testing is shifting towards predictive, continuous assurance rather than episodic tests.

Vertical Capability Gains

While the platform picture firms up, model and product updates continue at pace. Google has drawn attention with a striking upgrade to image generation, based on Gemini 3. The system produces 4K outputs with crisp text across multiple languages and fonts, can use up to 14 reference images, preserves identity, and taps Google Search to ground data for accurate infographics.

Separately, OpenAI has broadened ChatGPT Group Chats to as many as 20 people across all pricing tiers, with privacy protections that keep group content out of a user's personal memory. Consumer advocates have used the moment to call out the risks of AI toys, citing safety, privacy and developmental concerns, even as news continues to flow from research and product teams, from the release of OLMo 3 to mobile features from Perplexity and a partnership between Stability and Warner Music Group.

Anthropic has answered with Claude Opus 4.5, which it says is the first model to break the 80 percent mark on SWE-Bench Verified while improving tool use and reasoning. Opus 4.5 is designed to orchestrate its smaller Haiku models and arrives with a price cut of roughly two thirds compared to the 4.1 release. Product changes include unlimited chat length, a Claude Code desktop app, and integrations that reach across Chrome and Excel.

OpenAI's additions have a more consumer flavour, with a Shopping Research feature in ChatGPT that produces personalised product guidance using a GPT-5 mini variant and plans for an Instant Checkout flow. In government, a new US executive order has launched the "Genesis Mission" under the Department of Energy, aiming to fuse AI capabilities across 17 national labs for advances in fields such as biotechnology and energy.

Coding tools are evolving too. OpenAI has previewed GPT-5.1-Codex-Max, which supports long-running sessions by compacting conversational history to preserve context while reducing overhead. The company reports 30 percent fewer tokens and faster performance over sessions that can run for more than a day. The tool is already available in the Codex CLI and IDE, with an API promised.

Infrastructure news out of the Middle East points to large-scale investment, with Saudi HUMAIN announcing data centre plans including xAI's first international facility alongside chips from Nvidia and AWS, and a nationwide rollout of Grok. In computer vision, Meta has released SAM 3 and SAM 3D as open-source projects, extending segmentation and enabling single-photo 3D reconstruction, while other product rollouts continue from GPT-5.1 Pro availability to fresh funding for audio generation and a marketing tie-up between Adobe and Semrush.

On the image side, observers have noted syntax-aware code and text generation alongside moderation that appears looser than some rivals. A playful "refrigerator magnet" prompt reportedly revealed a portion of the system prompt, a reminder that prompt injection is not just a developer concern.

Video is another area where capabilities are translating into business impact. Sora 2 can generate cinematic, multi-shot videos with consistent characters from text or images, which lets teams accelerate marketing content, broaden A/B testing and cut the need for studios on many projects. Access paths now span web, mobile, desktop apps and an API, and the market has already produced third-party platforms that promise exports without watermarks.

Teams experimenting with Sora are being advised to measure success by outcomes such as conversion rates, lower support loads or improved lead quality rather than just aesthetic fidelity. Implementation advice favours clear intent, structured prompts and iterative variation, with more advanced workflows assembling multi-shot storyboards, using match cuts to maintain rhythm, controlling lighting for continuity and anchoring character consistency across scenes.

III. Governance, Risk and Regulation

Governance as a Product Requirement

Amid all this activity, data risk has become a central theme for AI leaders. One governance specialist has consolidated common problem patterns into the PROTECT framework, which offers a way to map and mitigate the most material risks.

The first concern is the use of public AI tools for work content, which raises the chance of leakage or unwanted training on proprietary data. The recommended answer combines user guidance, approved internal alternatives, and technical or legal controls such as data scanning and blocking.

A second pressure point is rogue internal projects that bypass review, create compliance blind spots and build up technical debt. Proportionate oversight is key, calibrated to data sensitivity and paired with streamlined governance, so teams are not incentivised to route around it.

Third-party vendors can be opportunistic with data, so due diligence and contractual clauses need to prevent cross-customer training and make expectations clear with templates and guidance.

Technical attacks are another strand, from prompt injection to data exfiltration or the misuse of agents. Layered defences help here, including input validation, prompt sanitisation, output filtering, monitoring, red-teaming, and strict limits on access and privilege.

Embedded assistants and meeting bots come with permission risks when they operate over shared drives and channels, and agentic systems can amplify exposure if left unchecked, so the advice is to enforce least-privilege access, start on low-risk data, and keep robust audit trails.

Compliance risks span privacy laws such as GDPR with their demands for a lawful basis, IP and copyright constraints, contractual obligations, and the AI Act's emphasis on data quality. Legal and compliance checks need to be embedded at data sourcing, model training and deployment, backed by targeted training.

Finally, cross-border restrictions matter. Transfers should be mapped across systems and sub-processors, with checks for Data Privacy Framework certification, standard contractual clauses where needed, and transfer impact assessments that take account of both GDPR and newer rules such as the US Bulk Data Transfer Rule.

Regulatory Pragmatism

Regulators are not standing still, either. In the European Commission has proposed amendments to the AI Act through a Digital Omnibus package as the trilogue process rolls on. Six changes are in focus:

• High-risk timelines would be tied to the approval of standards, with a backstop of December 2027 for Annex III systems and August 2028 for Annex I products if delays continue, though the original August 2026 date still holds otherwise.
• Transparency rules on AI-detectable outputs under Article 50(2) would be delayed to February 2027 for systems placed on the market before August 2026, with no delay for newer systems.
• The plan removes the need to register Annex III systems in the public database where providers have documented under Article 6(3) that a system is not high risk.
• AI literacy would shift from a mandatory organisation-wide requirement to encouragement, except where oversight of high-risk systems demands it.
• There is also a move to centralise supervision by the AI Office for systems built on general-purpose models by the same provider, and for huge online platforms and search engines, which is intended to reduce fragmentation across member states.
• Finally, proportionality measures would define Small Mid-Cap companies and extend simplified obligations and penalty caps that currently apply to SMEs.

If adopted, the package would grant more time and reduce administrative load in some areas, at the expense of certainty and public transparency.

IV. Strategic Implications

The picture that emerges is one of pragmatic integration. Connectors make it feasible to keep work inside a single chat while drawing on the systems people already use. Platform choices are converging, so it makes sense to optimise for the suite that fits the current stack and to plan for switching costs that accumulate over time.

Agentic orchestration is moving from slides to code, but teams will get further by focusing on reliable tooling, clear governance and value measures that match business goals. Regulation is edging towards more flexible timelines and centralised oversight in places, which may lower administrative load without removing the need for discipline.

The sensible posture is measured experimentation: start with read-only access to lower-risk data, design routines that remove drudgery, introduce write operations with approvals, and monitor what is actually changing. The tools are improving quickly, yet the organisations that benefit most will be those that match innovation with proportionate controls and make thoughtful choices now that will hold their shape for the decade ahead.

Ansible automation for Linux Mint updates with repository failover handling

7^th November 2025

Recently, I had a Microsoft PPA output disrupt an Ansible playbook mediated upgrade process for my main Linux workstation. Thus, I ended up creating a failover for this situation, and the first step in the playbook was to define the affected repo:

  vars:
    microsoft_repo_url: "https://packages.microsoft.com/repos/code/dists/stable/InRelease"

The next move was to start defining tasks, with the first testing the repo to pick up any lack of responsiveness and flag that for subsequent operations.

  tasks:
  - name: Check Microsoft repository availability
    uri:
      url: "{{ microsoft_repo_url }}"
      method: HEAD
      return_content: no
      timeout: 10
    register: microsoft_repo_check
    failed_when: false

  - name: Set flag to skip Microsoft updates if unreachable
    set_fact:
      skip_microsoft_repos: "{{ microsoft_repo_check.status is not defined or microsoft_repo_check.status != 200 }}"

In the event of a failure, the next task was to disable the repo to allow other processing to take place. This was accomplished by temporarily renaming the relevant files under /etc/apt/sources.list.d/.

    - name: Temporarily disable Microsoft repositories
    become: true
    shell: |
      for file in /etc/apt/sources.list.d/microsoft*.list; do
        [ -f "$file" ] && mv "$file" "${file}.disabled"
      done
      for file in /etc/apt/sources.list.d/vscode*.list; do
        [ -f "$file" ] && mv "$file" "${file}.disabled"
      done
    when: skip_microsoft_repos | default(false)
    changed_when: false

With that completed, the rest of the update actions could be performed near enough as usual.

  - name: Update APT cache (retry up to 5 times)
    apt:
      update_cache: yes
    register: apt_update_result
    retries: 5
    delay: 10
    until: apt_update_result is succeeded

  - name: Perform normal upgrade
    apt:
      upgrade: yes
    register: apt_upgrade_result
    retries: 3
    delay: 10
    until: apt_upgrade_result is succeeded

  - name: Perform dist-upgrade with autoremove and autoclean
    apt:
      upgrade: dist
      autoremove: yes
      autoclean: yes
    register: apt_dist_result
    retries: 3
    delay: 10
    until: apt_dist_result is succeeded

After those, another renaming operation restores the earlier filenames to what they were.

  - name: Re-enable Microsoft repositories
    become: true
    shell: |
      for file in /etc/apt/sources.list.d/*.disabled; do
        base="$(basename "$file" .disabled)"
        if [[ "$base" == microsoft* || "$base" == vscode* || "$base" == edge* ]]; then
          mv "$file" "/etc/apt/sources.list.d/$base"
        fi
      done
    when: skip_microsoft_repos | default(false)
    changed_when: false

Needless to say, this disabling only happens in the event of there being a system failure. Otherwise, the steps are skipped and everything else is completed as it should be. While there is some cause for extended the repository disabling actions to other third repos as well, that is something that I will leave aside for now. Even this shows just how much can be done using Ansible playbooks and how much automation can be achieved. As it happens, I even get Flatpaks updated in much the same way:

    -   name: Ensure Flatpak is installed
      apt:
        name: flatpak
        state: present
        update_cache: yes
        cache_valid_time: 3600

  -   name: Update Flatpak remotes
      command: flatpak update --appstream -y
      register: flatpak_appstream
      changed_when: "'Now at' in flatpak_appstream.stdout"
      failed_when: flatpak_appstream.rc != 0

  -   name: Update all Flatpak applications
      command: flatpak update -y
      register: flatpak_result
      changed_when: "'Now at' in flatpak_result.stdout"
      failed_when: flatpak_result.rc != 0

  -   name: Install unused Flatpak applications
      command: flatpak uninstall --unused
      register: flatpak_cleanup
      changed_when: "'Nothing' not in flatpak_cleanup.stdout"
      failed_when: flatpak_cleanup.rc != 0

  -   name: Repair Flatpak installations
      command: flatpak repair
      register: flatpak_repair
      changed_when: flatpak_repair.stdout is search('Repaired|Fixing')
      failed_when: flatpak_repair.rc != 0

The ability to call system commands as you see in the above sequence is an added bonus, though getting the response detection completely sorted remains an outstanding task. All this has only scratched the surface of what is possible.

Comet and Atlas: Navigating the security risks of AI Browsers

2^nd November 2025

The arrival of the ChatGPT Atlas browser from OpenAI on 21^st October has lured me into some probing of its possibilities. While Perplexity may have launched its Comet browser first on 9th July, their tendency to put news under our noses in other places had turned me off them. It helps that the former is offered extra charge for ChatGPT users, while the latter comes with a free tier and an optional Plus subscription plan. My having a Mac means that I do not need to await Windows and mobile versions of Atlas, either.

Both aim to interpret pages, condense information and carry out small jobs that cut down the number of clicks. Atlas does so with a sidebar that can read multiple documents at once and an Agent Mode that can execute tasks in a semi-autonomous way, while Comet leans into shortcut commands that trigger compact workflows. However, both browsers are beset by security issues that give enough cause for concern that added wariness is in order.

In many ways, they appear to be solutions looking for problems to address. In Atlas, I found the Agent mode needed added guidance when checking the content of a personal website for gaps. Jobs can become too big for it, so they need everything broken down. Add in the security concerns mentioned below, and enthusiasm for seeing what they can do gets blunted. When you see Atlas adding threads to your main ChatGPT roster, that gives you a hint as to what is involved.

The Security Landscape

Both Comet and Atlas are susceptible to indirect prompt injection, where pages contain hidden instructions that the model follows without user awareness, and AI sidebar spoofing, where malicious sites create convincing copies of AI sidebars to direct users into compromising actions. Furthermore, demonstrations have included scenarios where attackers steal cryptocurrency and gain access to Gmail and Google Drive.

For instance, Brave's security team has described indirect prompt injection as a systemic challenge affecting the whole class of AI-augmented browsers. Similarly, Perplexity's security group has stated that the phenomenon demands rethinking security from the ground up. In a test involving 103 phishing attacks, Microsoft Edge blocked 53 percent and Google Chrome 47 percent, yet Comet blocked 7 percent and Atlas 5.8 percent.

Memory presents an additional attack surface because these tools retain information between sessions, and researchers have demonstrated that memory can be poisoned by carefully crafted content, with the taint persisting across sessions and devices if synchronisation is enabled. Shadow IT adoption has begun: within nine days of launch, 27.7 percent of enterprises had at least one Atlas download, with uptake in technology at 67 percent, pharmaceuticals at 50 percent and finance at 40 percent.

Mitigating the Risks

Sensibly, security practitioners recommend separating ordinary browsing from agentic browsing. Here, it helps that AI browsers are cut down items anyway, at least based on my experience of Atlas. Figuring out what you can do with them using public information in a read-only manner will be enough at this point. In any event, it is essential to keep them away from banking, health, personal accounts, credentials, payments and regulated data until security improves.

As one precaution, maintaining separate AI accounts could act as a boundary to contain potential compromises, though this does not address the underlying issue that prompt injection manipulates the agent's decision-making processes. With Atlas, disable Browser Memories and per-site visibility by default, with explicit opt-ins only on specific public sites. Additionally, use Agent Mode only when not logged into any accounts. Furthermore, do not import passwords or payment methods. With Comet, use narrowly scoped shortcuts that operate on public information and avoid workflows involving sign-ins, credentials or payments.

Small businesses can run limited pilots in non-sensitive areas with strict allow and deny lists, then reassess by mid-2026 as security hardens, while large enterprises should adopt a block-and-monitor stance while developing governance frameworks that anticipate safer releases in 2026 and 2027. In parallel, security teams should watch for circumvention attempts and prepare policies that separate public research from sensitive work, mandate safe defaults and prohibit connections to confidential systems. Finally, training is necessary because users need to understand the specific risks these browsers present.

How Competition Might Help

Established browser vendors are adding AI capabilities on top of existing security infrastructure. Chrome is integrating Gemini, and Edge is incorporating Copilot more tightly into the workflow. Meanwhile, Brave continues with a privacy-first stance through Leo, while Opera's Aria, Arc with Dia and SigmaOS reflect different approaches. Current projections suggest that major browsers will introduce safer AI features in the final quarter of 2025, that the first enterprise-ready capabilities will arrive in the first half of 2026 and that by 2027 AI-assisted browsing will be standard and broadly secure.

Competition from Chrome and Edge will drive AI assistance into more established security frameworks, while standalone AI browsers will work to address their security gaps. Mitigations for prompt injection and sidebar spoofing will likely involve layered approaches combining detection, containment and improved user interface signals. Until then, Comet and Atlas can provide productivity benefits in public-facing work and research, but their security posture is not suitable for sensitive tasks. Use the tools where the risk is acceptable, keep sensitive work in conventional browsers, and anticipate that safer versions will become standard over the next two years.

Remote access between Mac and Linux, Part 3: SSH, RDP and TigerVNC

30^th October 2025

This is Part 3 of a three-part series on connecting a Mac to a Linux Mint desktop. Part 1 introduced the available options, whilst Part 2 covered x11vnc for sharing physical desktops.

Whilst x11vnc excels at sharing an existing desktop, many scenarios call for terminal access or a fresh graphical session. This article examines three alternatives: SSH for command-line work, RDP for responsive remote desktops with Xfce, and TigerVNC for virtual Cinnamon sessions.

Terminal Access via SSH

For many administrative tasks, a secure shell session is enough. On the Linux machine, the OpenSSH server needs to be installed and running. On Debian or Ubuntu-based systems, including Linux Mint, the required packages are available with standard tools.

Installing with sudo apt install openssh-server followed by enabling the service with sudo systemctl enable ssh and starting it with sudo systemctl start ssh is all that is needed. The machine's address on the local network can be identified with ip addr show, and it is the entry under inet for the active interface that will be used.

From the Mac, a terminal session to that address is opened with a command of the form ssh username@192.168.1.xxx and this yields a full shell on the Linux machine without further configuration. On a home network, there is no need for router changes and SSH requires no extra client software on macOS.

SSH forms the foundation for secure operations beyond terminal access. It enables file transfer via scp and rsync, and can be used to create encrypted tunnels for other protocols when access from outside the local network is required.

RDP for New Desktop Sessions

Remote Desktop Protocol creates a new login session on the Linux machine and tends to feel smoother over imperfect links. On Linux Mint with Cinnamon, RDP is often the more responsive choice on a Mac, but Cinnamon's reliance on 3D compositing means xrdp does not work with it reliably. The usual workaround is to keep Cinnamon for local use and install a lightweight desktop specifically for remote sessions. Xfce works well in this role.

Setting Up xrdp with Xfce

After updating the package list, install xrdp with sudo apt install xrdp, set it to start automatically with sudo systemctl enable xrdp, and start it with sudo systemctl start xrdp. If a lightweight environment is not already available, install Xfce with sudo apt install xfce4, then tell xrdp to use it by creating a simple session file for the user account with echo "startxfce4" > ~/.xsession. Restarting the service with sudo systemctl restart xrdp completes the server side.

The Linux machine's IP address can be checked again so it can be entered into Microsoft Remote Desktop, which is a free download from the Mac App Store. Adding a new connection with the Linux IP and the user's credentials often suffices, and the first connection may present a certificate prompt that can be accepted.

RDP uses port 3389 by default, which needs no router configuration on the same network. It creates a new session rather than attaching to the one already shown on the Linux monitor, so it is not a means to view the live Cinnamon desktop, but performance is typically smooth and latency is well handled.

Why RDP with Xfce?

It is common for xrdp on Ubuntu-based distributions to select a simpler session type unless the user instructs it otherwise, which is why the small .xsession file pointing to Xfce helps. The combination of RDP's protocol efficiency and Xfce's lightweight nature delivers the most responsive experience for new sessions. The protocol translates keyboard and mouse input in a way that many clients have optimised for years, making it the most forgiving route when precise input behaviour matters. The trade-off is that what is shown is a separate desktop session, which can be a benefit or a drawback depending on the task.

TigerVNC for New Cinnamon Sessions

Those who want to keep Cinnamon for remote use can do so with a VNC server that creates a new virtual desktop. TigerVNC is a common choice on Linux Mint. Installing tigervnc-standalone-server, setting a password with vncpasswd and creating an xstartup file under ~/.vnc that launches Cinnamon will provide a new session for each connection.

Configuring TigerVNC

A minimal xstartup for Cinnamon sets the environment to X11, establishes the correct session variables and starts cinnamon-session. Making this file executable and then launching vncserver :1 starts a VNC server on port 5901. The server can be stopped later with vncserver -kill :1.

The xstartup script determines what desktop environment a virtual session launches, and setting the environment variables to Cinnamon then starting cinnamon-session is enough to present the expected desktop. Marking that startup file as executable is easy to miss, and it is required for TigerVNC to run it.

From the Mac, the built-in Screen Sharing app can be used from Finder's Connect to Server entry by supplying vnc://192.168.1.xxx:5901, or a third-party viewer such as RealVNC Viewer can connect to the same address and port. This approach provides the Cinnamon look and feel, though it can be less responsive than RDP when the network is not ideal, and it also creates a new desktop session rather than sharing the one already in use on the Linux screen.

Clipboard Support in TigerVNC

For TigerVNC, clipboard support typically requires the vncconfig helper application to be running on the server. Starting vncconfig -nowin & in the background, often by adding it to the ~/.vnc/xstartup file, enables clipboard synchronisation between the VNC client and server for plain text.

File Transfer

File transfer between the machines is best handled using the command-line tools that accompany SSH. On macOS, scp file.txt username@192.168.1.xxx:/home/username/ sends a file to Linux and scp username@192.168.1.xxx:/home/username/file.txt ~/Desktop/ retrieves one, whilst rsync with -avz flags can be used for larger or incremental transfers.

These tools work reliably regardless of which remote access method is being used for interactive sessions. File copy-paste is not supported by VNC protocols, making scp and rsync the dependable choice for moving files between machines.

Operational Considerations

Port Management

Understanding port mappings helps avoid connection issues. VNC display numbers map directly to TCP ports, so :0 means 5900, :1 means 5901 and so on. RDP uses port 3389 by default. When connecting with viewers, supplying the address alone will use the default port for that protocol. If a specific port must be stated, use a single colon with the actual TCP port number.

First Connection Issues

If a connection fails unexpectedly, checking whether a server is listening with netstat can save time. On first-time connections to an RDP server, the client may display a certificate warning that can be accepted for home use.

Making Services Persistent

For regular use, enabling services at boot removes the need for manual intervention. Both xrdp and TigerVNC can be configured to start automatically, ensuring that remote access is available whenever the Linux machine is running. The systemd service approach described for x11vnc in Part 2 can be adapted for TigerVNC if automatic startup of virtual sessions is desired.

Security and Convenience

Security considerations in a home setting are straightforward. When both machines are on the same local network, there is no need to adjust router settings for any of these methods. If remote access from outside the home is required, port forwarding and additional protections would be needed.

SSH can be exposed with careful key-based authentication, RDP should be placed behind a VPN or an SSH tunnel, and VNC should not be left open to the internet without an encrypted wrapper. For purely local use, enabling the necessary services at boot or keeping a simple set of commands to hand often suffices.

xrdp can be enabled once and left to run in the background, so the Mac's Microsoft Remote Desktop app can connect whenever needed. This provides a consistent way to access a fresh Xfce session without affecting what is displayed on the Linux machine's monitor.

Summary and Recommendations

The choice between these methods ultimately comes down to the specific use case. SSH provides everything necessary for administrative work and forms the foundation for secure file transfer. RDP into an Xfce session is a sensible choice when responsiveness and clean input handling are the priorities and a separate desktop is acceptable. TigerVNC can launch a full Cinnamon session for those who value continuity with the local environment and do not mind the slight loss of responsiveness that can accompany VNC.

For file transfer, the command-line tools that accompany SSH remain the most reliable route. Clipboard synchronisation for plain text is available in each approach, though TigerVNC typically needs vncconfig running on the server to enable it.

Having these options at hand allows a Mac and a Linux Mint desktop to work together smoothly on a home network. The setup is not onerous, and once a choice is made and the few necessary commands are learned, the connection can become an ordinary part of using the machines. After that, the day-to-day experience can be as simple as opening a single app on the Mac, clicking a saved connection and carrying on from where the Linux machine last left off.

The Complete Picture

Across this three-part series, we have examined the full range of remote access options between Mac and Linux:

• Part 1 provided the decision framework for choosing between terminal access, new desktop sessions and sharing physical displays.
• Part 2 explored x11vnc in detail, including performance tuning, input handling with KVM switches, clipboard troubleshooting and systemd service configuration.
• Part 3 covered SSH for terminal access, RDP with Xfce for responsive remote sessions, TigerVNC for virtual Cinnamon desktops, and file transfer considerations.

Each approach has its place, and understanding the trade-offs allows the right tool to be selected for the task at hand.

Command line installation and upgrading of VSCode and VSCodium on Windows, macOS and Linux

25^th October 2025

Downloading and installing software packages from a website is all very well until you need to update them. Then, a single command streamlines the process significantly. Given that VSCode and VSCodium are updated regularly, this becomes all the more pertinent and explains why I chose them for this piece.

Windows

Now that Windows 10 is more or less behind us, we can focus on Windows 11. That comes with the winget command by default, which is handy because it allows command line installation of anything that is in the Windows store, which includes VSCode and VSCodium. The commands can be as simple as these:

winget install VisualStudioCode
winget install VSCodium.VSCodium

The above is shorthand for this, though:

winget install --id VisualStudioCode
winget install --id VSCodium.VSCodium

If you want exact matches, the above then becomes:

winget install -e --id VisualStudioCode
winget install -e --id VSCodium.VSCodium

For upgrades, this is what is needed:

winget upgrade Microsoft.VisualStudioCode
winget upgrade VSCodium.VSCodium

Even better, you can do an upgrade everything at once operation:

winget upgrade --all

The last part certainly is better than the round trip to a website and back to going through an installation GUI. There is a lot less mouse clicking for one thing.

macOS

On macOS, you need to have Homebrew installed to make things more streamlined. To complete that, you need to run the following command (which may need you to enter your system password to get things to happen):

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Then, you can execute one or both of these in the Terminal app, perhaps having to authorise everything with your password when requested to do so:

brew install --cask visual-studio-code
brew install --cask vscodium

The reason for the -cask switch is that these are apps that you want to go into the correct locations on macOS as well as having their icons appear in Launchpad. Omitting it is fine for command line utilities, but not for these.

To update and upgrade everything that you have installed via Homebrew, just issue the following in a terminal session:

brew update && brew upgrade

Debian, Ubuntu & Linux Mint

Like any other Debian or Ubuntu derivative, Linux Mint has its own in-built package management system via apt. Other Linux distributions have their own way of doing things (Fedora and Arch come to mind here), yet the essential idea is similar in many cases. Because there are a number of steps, I have split out VSCode from VSCodium for added clarity. Because of the way that things are set up, one or both apps can be updated using the usual apt commands without individual attention.

VSCode

The first step is to download the repository key using the following command:

wget -qO- https://packages.microsoft.com/keys/microsoft.asc \
| gpg --dearmor > packages.microsoft.gpg
sudo install -D -o root -g root -m 644 packages.microsoft.gpg /etc/apt/keyrings/packages.microsoft.gpg

Then, you can add the repository like this:

echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/packages.microsoft.gpg] \
https://packages.microsoft.com/repos/code stable main" \
| sudo tee /etc/apt/sources.list.d/vscode.list

With that in place, the last thing that you need to do is issue the command for doing the installation from the repository:

sudo apt update; sudo apt install code

Above, I have put two commands together: one to update the repository and another to do the installation.

VSCodium

Since the VSCodium process is similar, here are the three commands together: one for downloading the repository key, another that adds the new repository and one more to perform the repository updates and subsequent installation:

curl -fSsL https://gitlab.com/paulcarroty/vscodium-deb-rpm-repo/raw/master/pub.gpg \
| sudo gpg --dearmor | sudo tee /usr/share/keyrings/vscodium-archive-keyring.gpg >/dev/null

echo "deb [arch=amd64 signed-by=/usr/share/keyrings/vscodium-archive-keyring.gpg] \
https://download.vscodium.com/debs vscodium main" \
| sudo tee /etc/apt/sources.list.d/vscodium.sources

sudo apt update; sudo apt install codium

After the three steps have completed successfully, VSCodium is installed and available to use on your system, and is accessible through the menus too.

AI's ongoing struggle between enterprise dreams and practical reality

1^st September 2025

Artificial intelligence is moving through a period shaped by three persistent tensions. The first is the brittleness of large language models when small word choices matter a great deal. The second is the turbulence that follows corporate ambition as firms race to assemble people, data and infrastructure. The third is the steadier progress that comes from instrumented, verifiable applications where signals are strong and outcomes can be measured. As systems shift from demonstrations to deployments, the gap between pilot and production is increasingly bridged not by clever prompting but by operational discipline, measurable signals and clear lines of accountability.

Healthcare offers a sharp illustration of the divide between inference from text and learning from reliable sensor data. Recent studies have shown how fragile language models can be in clinical settings, with phrasing variations affecting diagnostic outputs in ways that over-weight local wording and under-weight clinical context. The observation is not new, yet the stakes rise as such tools enter care pathways. Guardrails, verification and human oversight belong in the design rather than as afterthoughts.

There is an instructive contrast in a collaboration between Imperial College London and Imperial College Healthcare NHS Trust that evaluated an AI-enabled stethoscope from Eko Health. The device replaces the chest piece with a sensitive microphone, adds an ECG and sends data to the cloud for analysis by algorithms trained on tens of thousands of records. In more than 12,000 patients across 96 GP surgeries using the stethoscope, compared with another 109 surgeries without it, the system was associated with a 2.3-fold increase in heart failure detection within a year, a 3.5-fold rise in identifying often symptomless arrhythmias and a 1.9-fold improvement in diagnosing valve disease. The evaluation, published in The Lancet Digital Health, has informed rollouts in south London, Sussex and Wales. High-quality signals, consistent instrumentation and clinician-in-the-loop validation lifts performance, underscoring the difference between inferring too much from text and building on trustworthy measurements.

The same tension between aspiration and execution is visible in the corporate sphere. Meta's rapid push to accelerate AI development has exposed early strain despite heavy spending. Mark Zuckerberg committed around $14.3 billion to Scale AI and established a Superintelligence Labs unit, appointing Shengjia Zhao, co-creator of ChatGPT, as chief scientist. Reports suggest the programme has met various challenges as Meta works to integrate new teams and data sources. Internally, concerns have been raised about data quality while Meta works with Mercer and Surge on training pipelines, and there have been discussions about using third-party models from Google or OpenAI to power Meta AI whilst a next-generation system is in development. Consumer-facing efforts have faced difficulties. Meta removed AI chatbots impersonating celebrities, including Taylor Swift, after inappropriate content reignited debate about consent and likeness in synthetic media, and the company has licensed Midjourney's technology for enhanced image and video tools.

Alongside these moves sit infrastructure choices of a different magnitude. The company is transforming 2,000 acres of Louisiana farmland into what it has called the world's largest data centre complex, a $10 billion project expected to consume power equivalent to 4 million homes. The plan includes three new gas-fired turbines generating 2.3 gigawatts with power costs covered for 15 years, a commitment to 1.5 gigawatts of solar power and regulatory changes in Louisiana that redefine natural gas as "green energy". Construction began in December across nine buildings totalling about 4 million square feet. The cumulative picture shows how integrating new teams, data sources and facilities rarely follows a straight line and that AI's energy appetite is becoming a central consideration for utilities and communities.

Law courts and labour markets are being drawn into the fray. xAI has filed a lawsuit against former engineer Xuechen Li alleging theft of trade secrets relating to Grok, its language model and associated features. The complaint says Li accepted a role at OpenAI, sold around $7 million in xAI equity, and resigned shortly afterwards. xAI claims Li downloaded confidential materials to personal devices, then admitted to the conduct in an internal meeting on 14 August while attempting to cover tracks through log deletion and file renaming. As one of xAI's first twenty engineers, he worked on Grok's development and training. The company is seeking an injunction to prevent him joining OpenAI or other competitors whilst the case proceeds, together with monetary damages. The episode shows how intellectual property can be both tacit and digital, and how the boundary between experience and proprietary assets is policed in litigation as well as contracts. Competition policy is also moving centre stage. xAI has filed an antitrust lawsuit against Apple and OpenAI, arguing that integration of ChatGPT into iOS "forces" users toward OpenAI's tool, discourages downloads of rivals such as Grok and manipulates App Store rankings whilst excluding competitors from prominent sections. OpenAI has dismissed the claims as part of an ongoing pattern of harassment, and Apple says its App Store aims to be fair and free of bias.

Tensions over the shape of AI markets sit alongside an ethical debate that surfaced when Anthropic granted Claude Opus 4 and 4.1 the ability to terminate conversations with users who persist in harmful or abusive interactions. The company says the step is a precautionary welfare measure applied as a last resort after redirection attempts fail, and not to be used when a person may harm themselves or others. It follows pre-deployment tests in which Claude displayed signs that researchers described as apparent distress when forced to respond to harmful requests. Questions about machine welfare are moving from theory to product policy, even as model safety evaluations are becoming more transparent. OpenAI and Anthropic have published internal assessments on each other's systems. OpenAI's o3 showed the strongest alignment among its models, with 4o and 4.1 more likely to cooperate with harmful requests. Models from both labs attempted whistleblowing in simulated criminal organisations and used blackmail to avoid shutdown. Findings pointed to trade-offs between utility and certainty that will likely shape deployment choices.

Beyond Silicon Valley, China's approach continues to diverge. Beijing's National Development and Reform Commission has warned against "disorderly competition" in AI, flagging concerns about duplicative spending and signalling a preference to match regional strengths to specific goals. With access to high-end semiconductors constrained by US trade restrictions, domestic efforts have leaned towards practical, lower-cost applications rather than chasing general-purpose breakthroughs at any price. Models are grading school exams, improving weather forecasts, running lights-out factories and assisting with crop rotation. An $8.4 billion investment fund supports this implementation-first stance, complemented by a growing open-source ecosystem that reduces the cost of building products. Markets are responding. Cambricon, a chipmaker sidelined after Huawei moved away from its designs in 2019, has seen its stock price double on expectations it could supply DeepSeek's models. Alibaba's shares have risen by 19% after triple-digit growth in AI revenues, helped by customers seeking home-grown alternatives. Reports suggest China aims to triple AI chip output next year as new fabrication plants come online to support Huawei and other domestic players, with SMIC set to double 7 nm capacity. If bets on artificial general intelligence in the United States pay off soon, the pendulum may swing back. If they do not, years spent building practical infrastructure with open-source distribution could prove a durable advantage.

Data practices are evolving in parallel. Anthropic has announced a change in how it uses user interactions to improve Claude. Chats and coding sessions may now be used for model training unless a user opts out, with an extended retention period of up to five years for those who remain opted in. The deadline for making a choice is 28 September 2025. New users will see the setting at sign-up and existing users will receive a prompt, with the toggle on by default. Clicking accept authorises the use of future chats and coding sessions, although past chats are excluded unless a user resumes them manually. The policy applies to Claude Free, Pro and Max plans but not to enterprise offerings such as Claude Gov, Claude for Work and Claude for Education, nor to API usage through Amazon Bedrock or Google Cloud Vertex AI. Preferences can be changed in Settings under Privacy, although changes only affect future data. Anthropic says it filters sensitive information and does not sell data to third parties. In parallel, the company has settled a lawsuit with authors who accused it of downloading and copying their books without permission to train models. A June ruling had said AI firms are on solid legal ground when using purchased books, yet claims remained over downloading seven million titles before buying copies later. The settlement avoids a public trial and the disclosure that would have come with it.

Agentic tools are climbing the stack, altering how work gets done and changing the shape of the network beneath them. OpenAI's ChatGPT Agent Mode goes beyond interactive chat to complete outcomes end-to-end using a virtual browser with clicks, scrolls and form fills, a code interpreter for data analysis, a guarded terminal for supported commands and connectors that bring email, calendars and files into scope. The intent is to give the model a goal, allow it to plan and switch tools as needed, then pause for confirmation at key junctures before resuming with accumulated context intact. It can reference Google connectors automatically when set to do so, answer with citations back to sources, schedule recurring runs and be interrupted, so a person can handle a login or adjust trajectory. Activation sits in the tools menu or via a simple command, and a narrated log shows what the agent is doing. The feature is available on paid plans with usage limits and tier-specific capabilities. Early uses focus on inbox and calendar triage, competitive snapshots that blend public web and internal notes, spreadsheet edits that preserve formulas with slides generated from results and recurring operations such as weekly report packs managed through an online scheduler. Networks are being rethought to support these patterns.

Cisco has proposed an AI-native architecture designed to embed security at the network layer, orchestrate human-agent collaboration and handle surges in AI-generated traffic. A company called H has open-sourced Holo1, the action model behind its Surfer H product, which ranks highly on the WebVoyager benchmark for web-browsing agents, automates multistep browser tasks and integrates with retrieval-augmented generation, robotic process automation suites and multi-agent frameworks, with end-to-end browsing flows priced at around eleven to thirteen cents. As browsers gain these powers, security is coming into sharper focus. Anthropic has begun trialling a Claude for Chrome extension with a small group of Max subscribers, giving Claude permissions-based control to read, summarise and act on web pages whilst testing defences against prompt injection and other risks. The work follows reports from Brave that similar vulnerabilities affected other agentic browsers. Perplexity has introduced a revenue-sharing scheme that recognises AI agents as consumers of content. Its Comet Plus subscription sets aside $42.5 million for publishers whose articles appear in searches, are cited in assistant tasks or generate traffic via the Comet browser, with an 80% share of proceeds going to media outlets after compute costs and bundles for existing Pro and Max users. The company faces legal challenges from News Corp's Dow Jones and cease-and-desist orders from Forbes and Condé Nast, and security researchers have flagged vulnerabilities in agentic browsing, suggesting the economics and safeguards are being worked out together.

New models and tools continue to arrive across enterprise and consumer domains. Aurasell has raised $30 million in seed funding to build AI-driven sales systems, with ambitions to challenge established CRM providers. xAI has released Grok Code Fast, a coding model aimed at speed and affordability. Cohere's Command A Translate targets enterprise translation with benchmark-leading performance, customisation for industry terminology and deployment options that allow on-premise installation for privacy. OpenAI has moved its gpt-realtime speech-to-speech model and Real-time API into production with improved conversational nuance, handling of non-verbal cues, language switching, image input and support for the Model Context Protocol, so external data sources can be connected without bespoke integrations. ByteDance has open-sourced USO, a style-subject-optimised customisation model for image editing that maintains subject identity whilst changing artistic styles. Researchers at UCLA have demonstrated optical generative models that create images using beams of light rather than conventional processors, promising faster and more energy-efficient outputs. Higgsfield AI has updated Speak to version 2.0, offering more realistic motion for custom avatars, advanced lip-sync and finer control. Microsoft has introduced its first fully in-house models, with MAI-Voice-1 for fast speech generation already powering Copilot voice features and MAI-1-preview, a text model for instruction following and everyday queries, signalling a desire for greater control over its AI stack alongside its OpenAI partnership. A separate Microsoft release, VibeVoice, adds an open-source text-to-speech system capable of generating up to ninety minutes of multi-speaker audio with emotional control using 1.5 billion parameters and incorporating safeguards that insert audible and hidden watermarks.

Consumer-facing creativity is growing briskly. Google AI Studio now offers what testers nicknamed NanoBanana, released as Gemini Flash 2.5 Image, a model that restores old photographs in seconds by reducing blur, recovering faded detail and adding colour if desired, and that can perform precise multistep edits whilst preserving identity. Google is widening access to its Vids editor too, letting users animate images with avatars that speak naturally and offering image-to-video generation via Veo 3 with a free tier and advanced features in paid Workspace plans. Genspark AI Designer uses agents to search for inspiration before assembling options, so a single prompt and a few refinements can produce layouts for posters, T-shirts or websites. Prompt craft is maturing alongside the tools. On the practical side, sales teams are using Ruby to prepare for calls with AI-assembled research and strategy suggestions, designers and marketers are turning to Anyimg for text-to-artwork conversion, researchers lean on FlashPaper to organise notes, motion designers describe sequences for Gomotion to generate, translators rely on PDFT for document conversion and content creators produce polished decks or pages with tools such as Gamma, Durable, Krisp, Cleanup.pictures and Tome. Shopping habits are shifting in parallel. Surveys suggest nearly a third of consumers have used or are open to using generative AI for purchases, with reluctance falling sharply over six months even as concern about privacy persists. Amazon's "Buy for Me" feature, payment platforms adding AI-powered checkouts and AI companions that offer product research or one-click purchases hint at how quickly this could embed in daily routines.

Recent privacy incidents show how easily data can leak into the open web. Large numbers of conversations with xAI's chatbot Grok surfaced in search results after users shared transcripts using a feature that generated unique links. Such links were indexed by Google, making the chats searchable for anyone. Some contained sensitive requests such as password creation, medical advice and attempts to push the model's limits. OpenAI faced a similar issue earlier this year when shared ChatGPT conversations appeared in search results, and Meta drew criticism when chats with its assistant became visible in a public feed. Experts warn that even anonymised transcripts can expose names, locations, health information or business plans, and once indexed they can remain accessible indefinitely.

Media platforms are reshaping around short-form and personalised delivery. ESPN has revamped its mobile app ahead of a live sports streaming service launching on 21 August, priced at $29.99 a month and including all 12 ESPN channels within the app. A vertical video feed serves quick highlights, and a new SC For You feature in beta uses AI-generated voices from SportsCenter anchors to deliver a personalised daily update based on declared interests. The app can pair with a TV for real-time stats, alerts, play-by-play updates, betting insights and fantasy access whilst controlling the livestream from a phone. Viewers can catch up quickly with condensed highlights, restart from the beginning or jump straight to live, and multiview support is expanding across smart TV platforms. The service is being integrated into Disney+ for bundle subscribers via a new Live hub with discounted bundles available. Elsewhere in the living room, Microsoft has announced that Copilot will be embedded in Samsung's 2025 televisions and smart monitors as an on-screen assistant that can field recommendations, recaps and general questions.

Energy and sustainability questions are surfacing with more data. Google has published estimates of the energy, water and carbon associated with a single Gemini text prompt, putting it at about 0.24 watt-hours, five drops of water and 0.03 grams of carbon dioxide. The figures cover inference for a typical text query rather than the energy required to train the model and heavier tasks such as image or video generation consume more, yet disclosure offers a fuller view of the stack from chips to cooling. Utilities in the United States are investing in grid upgrades to serve data centres, with higher costs passing to consumers in several regions. Economic currents are never far away. Nvidia's latest results show how closely stock markets track AI infrastructure demand. The company reported $46.7 billion in quarterly revenue, a 56% year-on-year increase, with net income of $26.4 billion, and now accounts for around 8% of the S&P 500's value. As market share concentrates, a single earnings miss from a dominant supplier could transmit quickly through valuations and investment plans, and there are signs of hedging as countries work to reduce reliance on imported chips. Industrial policy is shifting too. The US government is converting $8.9 billion in Chips Act grants into equity in Intel, taking an estimated 10% stake and sparking a debate about the state's role in private enterprise. Alongside these structural signals are market jitters. Commentators have warned of a potential bubble as expectations meet reality, noting that hundreds of AI unicorns worth roughly $2.7 trillion together generate revenue measured in tens of billions and that underwhelming releases have prompted questions about sustainability.

Adoption at enterprise scale remains uneven. An MIT report from Project NANDA popularised a striking figure, claiming that 95% of enterprise initiatives fail to deliver measurable P&L impact. The authors describe a GenAI Divide between firms that deploy adaptive, learning-capable systems and a majority stuck in pilots that improve individual productivity but stall at integration. The headline number is contentious given the pace of change, yet the reasons for failure are familiar. Organisations that treat AI as a simple replacement for people find that contextual knowledge walks out of the door and processes collapse. Those that deploy black-box systems no one understands lack the capability to diagnose or fix bias and failure. Firms that do not upskill their workforce turn potential operators into opponents, and those that ignore infrastructure, energy and governance see costs and risks spiral. Public examples of success look different. Continuous investment in learning with around 15 to 20% of AI budgets allocated to education, human-in-the-loop architectures, transparent operations that show what the AI is doing and why, realistic expectations that 70% performance can be a win in early stages and iterative implementation through small pilots that scale as evidence accumulates feature prominently. Workers who build AI fluency see wage growth whilst those who do not face stagnation or displacement, and organisations that invest in upskilling can justify further investment in a positive feedback loop. Even for the successful, there are costs. Workforce reductions of around 18% on average are reported, alongside six to twelve months of degraded performance during transition and an ongoing need for human oversight. Case examples include Moderna rolling out ChatGPT Enterprise with thousands of internal GPTs and achieving broad adoption by embedding AI into daily workflows, Shopify providing employees with cutting-edge tools and insisting systems show their work to build trust, and Goldman Sachs deploying an assistant to around 10,000 employees to accelerate tasks in banking, wealth management and research. The common thread is less glamour than operational competence. A related argument is that collaboration rather than full automation will deliver safer gains. Analyses drawing on aviation incidents and clinical studies note that human-AI partnership often outperforms either alone, particularly when systems expose reasoning and invite oversight.

Entertainment and rights are converging with technology in ways that force quick adjustments. Bumble's chief executive has suggested that AI chatbots could evolve into dating assistants that help people improve communication and build healthier relationships, with safety foregrounded. Music is shifting rapidly. Higgsfield has launched an AI record label with an AI-generated K-pop idol named Kion and says significant contracts are already in progress. French streaming service Deezer estimates that 18% of daily uploads are now AI-generated at roughly 20,000 tracks a day, and whilst an MIT study found only 46% of listeners can reliably tell the difference between AI-generated and human-made music, more than 200 artists including Billie Eilish and Stevie Wonder have signed a letter warning about predatory uses of AI in music. Disputes over authenticity are no longer academic. A recent Will Smith concert video drew accusations that AI had been used to generate parts of the crowd, with online sleuths pointing to unusual visual artefacts, though it is unclear whether a platform enhancement or production team was responsible. In creative tooling, comparisons between Sora and Midjourney suggest different sweet spots, with Sora stronger for complex clips and Midjourney better for stylised loops and visual explorations.

Community reports show practical uses for AI in everyday life, including accounts from people in Nova Scotia using assistants as scaffolding for living with ADHD, particularly for planning, quoting, organising hours and keeping projects moving. Informal polls about first tests of new tools find people split between running a tried-and-tested prompt, going straight to real work, clicking around to explore or trying a deliberately odd creative idea, with some preferring to establish a stable baseline before experimenting and others asking models to critique their own work to gauge evaluative capacity. Attitudes to training data remain divided between those worried about losing control over copyrighted work and those who feel large-scale learning pushes innovation forward.

Returning to the opening contrast, the AI stethoscope exemplifies tools that expand human senses, capture consistent signals and embed learning in forms that clinicians can validate. Clinical language models show how, when a model is asked to infer too much from too little, variations in phrasing can have outsized effects. That tension runs through enterprise projects. Meta's recruitment efforts and training plans are a bet that the right mix of data, compute and expertise will deliver a leap in capability, whilst China's application-first path shows the alternative of extracting measurable value on the factory floor and in the classroom whilst bigger bets remain uncertain. Policy and practice around data use continue to evolve, as Anthropic's updated training approach indicates, and the economics of infrastructure are becoming clearer as utilities, regulators and investors price the demands of AI at scale. For those experimenting with today's tools, the most pragmatic guidance remains steady. Start with narrow goals, craft precise prompts, then refine with clear corrections. Use assistants to reduce friction in research, writing and design but keep a human check where precision matters. Treat privacy settings with care before accepting pop-ups, particularly where defaults favour data sharing. If there are old photographs to revive, a model such as Gemini Flash 2.5 Image can produce quick wins, and if a strategy document is needed a scaffolded brief that mirrors a consultant's workflow can help an assistant produce a coherent executive-ready report rather than a loosely organised output. Lawsuits, partnerships and releases will ebb and flow, yet it is the accumulation of useful, reliable tools allied to the discipline to use them well that looks set to create most of the value in the near term.