Tag Archives: automation

2026, Business, Helpful Articles, Helpful Guides

Tech Stack Optimisation: How Smart Automation Streamlines Business Operations

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Every business today, whether it’s a solo freelancer or a big corporation, relies on a bunch of digital tools to get things done. This collection of software, apps, and platforms is your ‘tech stack,’ and chances are it’s just grown organically over time. While each tool might be great on its own, a disconnected stack can waste time, require manual data entry, and cause frustrating errors. This is where smart automation steps in, turning your collection of tools into a smooth, efficient machine that works for you, not against you.

Optimising your tech stack isn’t about buying more software; it’s about making the software you already have work better together. It means finding bottlenecks and repetitive tasks and letting technology handle them, freeing up your team to focus on what really matters.

What Is a Tech Stack and Why Should You Care?

Think of your tech stack as your business’s digital toolkit. It includes everything from the email client you use to communicate, to the project management board that organises your tasks, and the accounting software that tracks your finances. For many businesses, they haven’t intentionally designed this stack. You add a new tool when a new need pops up, which often creates a patchwork of systems that don’t talk to each other.

A tech stack that isn’t optimised creates hidden costs. For example, your sales team might close a deal in one system, then someone has to manually copy that customer’s details into a separate invoicing system, and then again into your marketing email list. Each manual step creates a chance for human error and drains productivity. A well-optimised stack connects these processes, saves time, and keeps data consistent across your entire operation.

Automating the Repetitive to Free Up Your Team

The most immediate benefit of automation is getting back the hours spent on tedious, repetitive work. Administrative tasks are often the biggest culprits, eating up valuable time that could be better spent on growth, strategy, or customer service. Consider the complex, rule-heavy processes in finance and HR, like managing the monthly payroll. Calculating wages, national insurance contributions, and pension deductions for every employee is time-consuming and high-stakes.

This is where dedicated payroll software can transform operations, ensuring accuracy and compliance while freeing up valuable time. Instead of manual calculations and data entry, the system handles it automatically. This principle applies across the business. From auto-responding to customer queries to generating weekly reports, automation tools can take over predictable tasks, letting your team focus on more creative and strategic work.

Connecting Your Tools: The Power of Integration

True tech stack optimisation happens when your different software platforms start talking to each other. This is called integration, and it’s the key to creating seamless workflows. Modern automation platforms like Zapier or Make let you create ‘if this, then that’ rules that connect applications that would otherwise be separate. The possibilities are huge and can be tailored to your specific business needs.

For instance, you could set up an automation where a new positive review on a public platform automatically triggers a post on your social media channels. Or, a completed task in your project management tool could automatically send a notification to your team’s communication channel. Building these bridges between your apps helps you streamline processes and removes the need for manual intervention, reducing delays and ensuring nothing gets missed.

Making Smarter Decisions with Data Automation

Beyond just completing tasks, automation is incredibly powerful for gathering and presenting data. Your business generates a huge amount of data every day across sales, marketing, operations, and customer support. Manually pulling this information into a useful format is a big challenge. Automated dashboards can pull data from all your tools into one central place, giving you a real-time view of how your business is performing.

You can track key performance indicators (KPIs) without spending hours building spreadsheets. This instant access to accurate information means faster, more informed decision-making. You can spot trends as they emerge, identify potential problems before they get bigger, and allocate resources more effectively. Many businesses use these insights to optimize IT budgets, ensuring that money spent on technology delivers a clear return.

Automating routine tasks and integrating your digital tools helps you build a more resilient, efficient, and intelligent business. It lets you do more with less, reduces the risk of costly errors, and gives your team space to focus on the work that truly drives growth. The best way to begin is to identify just one recurring frustration in your workflow and explore how automation could solve it.

AI, AI & Machine Learning, Gadget Man Projects, LLM

I Built Two AI Personalities That Sit on My Desk and Talk to Each Other

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I’ve been experimenting with local AI for quite a while now, but this particular project has started to become something rather different.

I now have two AI personalities, called George and Lewis, running on two separate computers in my office.

They read the news.

They talk to each other about it.

They remember what they’ve discussed before.

They have different interests and personalities.

They can wander off topic.

And, as the day wears on, they actually start getting tired.

This may have got slightly out of hand.

Two Minds, One Desk

The idea started simply enough.

I already had Ollama running local large language models on a couple of machines on my network. Rather than asking one AI a question and getting an answer back, I wondered what would happen if I let two of them talk to each other.

So I wrote a Python program that acts as the producer sitting between them.

One machine runs George. The other runs Lewis.

George says something, the program sends that to Lewis, Lewis generates a reply, and the reply is passed back to George.

Neither conversation is written in advance.

I know what news story they are going to start with, but I don’t know what either of them is going to say.

That is where things started becoming interesting.

Meet George and Lewis

I deliberately didn’t want two identical AI assistants politely agreeing with each other.

George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.
George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.

George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.

Lewis is a little more mischievous. He tends to challenge George's conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.
Lewis is a little more mischievous. He tends to challenge George’s conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.

Lewis is a little more mischievous. He tends to challenge George’s conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.

They aren’t supposed to argue simply for the sake of it, but neither are they encouraged to agree just to be polite.

That distinction makes an enormous difference.

A conversation can start with an announcement about a new electric car and end up somewhere around boxed computer software, Commodore machines and the questionable wisdom of connecting a toaster to Wi-Fi.

In other words, rather like an actual conversation.

There’s a Newsreader Too

Before George and Lewis start discussing anything, the software collects stories from RSS news feeds.

The curator or newsreader who selects the stories that Lewis and George discuss, her name is Kokoro
The curator or newsreader who selects the stories that Lewis and George discuss, her name is Kokoro

A selected headline and its summary are displayed on screen and then read aloud using Kokoro, a local text-to-speech system.

I’ve given the newsreader a British female voice, while George and Lewis have their own separate British male voices.

So the sequence sounds a little like an extremely small and slightly eccentric radio station.

The newsreader introduces the story, pauses, and then George reacts to it.

Lewis responds.

And off they go.

They Know What I’m Interested In

Rather than simply choosing every story at random, the software now has an interest profile.

It knows I’m particularly interested in subjects including:

AI and local language models, gadgets, computers, retro computing, classic cars, web development, drones, home networking, cybersecurity, broadcasting, space, photography, video, graphic novels, comics, Blender and 3D graphics, music technology, gaming and science.

Stories are scored according to how closely they match those subjects.

That doesn’t mean the system completely ignores everything else. I’ve deliberately left some randomness in there, because otherwise it would rapidly become an automated echo chamber.

Sometimes it should find something none of us expected to be interesting.

George and Lewis also have their own individual preferences, so occasionally one of them effectively gets a story that is much more “his sort of thing” than the other’s.

Then I Gave Them a Memory

This was probably the point where it stopped feeling like a normal chatbot experiment.

The system now uses an SQLite database to remember what has happened.

It stores the news stories they’ve discussed, previous conversations, individual things George and Lewis have said, and condensed memories of earlier discussions.

This serves several purposes.

Firstly, it prevents them repeatedly discussing the same news story just because it appears in a feed again with slightly different wording.

Secondly, when a genuinely new development appears in a story they’ve previously discussed, they can remember the earlier conversation.

So instead of starting again from scratch, George might effectively say:

“We said this was going to happen.”

And Lewis might point out that George actually said something rather less definite at the time.

That’s when they start becoming recurring characters rather than disposable chatbot sessions.

Conversations Can Drift

Humans rarely stay perfectly on subject.

We might start talking about a new iPhone and somehow arrive at cassette recorders ten minutes later.

George and Lewis can now do the same thing.

Early in a conversation they stay reasonably close to the news story. As things progress, related subjects and older memories can begin appearing in their context.

Crucially, they’re not instructed to suddenly announce:

“According to our previous conversation…”

Instead, an old memory is simply made available as something they might naturally be reminded of.

Sometimes they use it.

Sometimes they don’t.

That makes callbacks much less mechanical.

They Also Know When They’ve Run Out of Things to Say

An earlier version simply ran for a fixed number of exchanges.

That worked, but it didn’t sound natural.

Eventually you get:

“That’s a good point.”

“Indeed.”

“Absolutely.”

Which is conversational purgatory.

The new system lets them decide whether there is genuinely anything new worth adding.

After a minimum amount of conversation, the next speaker can privately decide to continue or stop.

The Python program also checks new responses against previous remarks and can reject something that is effectively just a repetition.

So some conversations last a while.

Others end after only a handful of comments.

And Then I Made Them Tired

This is probably my favourite unnecessary feature.

The system periodically checks the actual UK time and alters George and Lewis’s behaviour throughout the day. It uses the Europe/London timezone and can fall back to the computer’s clock if the online time check is unavailable.

During the day they’re fully awake.

As evening arrives, they gradually become less enthusiastic about pursuing every conversational tangent.

By around 11pm they’re noticeably tired and much more willing to call an end to a discussion.

After midnight, there’s a very good chance they simply won’t want to start another conversation at all.

They may yawn, decide they’ve had enough or effectively go to bed.

At six in the morning they’re bleary-eyed and coffee becomes a perfectly reasonable thought.

By seven, they’re waking up again.

The important distinction is that this isn’t just the AI being told to say that it’s tired.

The software itself alters maximum conversation lengths, pauses and the probability of conversations ending according to the time of day.

The project relies on vast amounts of data
The project relies on vast amounts of data

Everything Is Running Locally

One aspect I particularly like is that George and Lewis aren’t remote characters sitting somewhere in a cloud service.

The language models are running locally on my own computers using Ollama.

The voices are generated locally with Kokoro.

The memory is stored locally in SQLite.

The Python application connects all of those pieces together.

That makes it a rather good example of what can now be built from consumer hardware and freely available AI tools.

Where This Is Going

There are plenty of possibilities.

I could broaden the news sources into motoring, space, drones, cybersecurity, classic computing and gaming.

They could become aware of local weather.

They could notice when one of my servers goes offline.

They could comment on things happening on the network.

Their opinions could gradually evolve.

They could develop running jokes.

They could even start remembering predictions they’ve made and later discover which one of them was right.

What began as “can I make two Ollama instances talk to each other?” is slowly turning into something closer to two persistent artificial characters occupying a corner of the office.

©2026 Matt Porter. A screenprint of one of the many conversations,
©2026 Matt Porter. A screenprint of one of the many conversations,

They’re not conscious.

They’re not alive.

They’re two language models, a Python program, a text-to-speech engine and an SQLite database.

But when one of them remembers something the other said yesterday, challenges him about it, wanders completely off topic and then decides it’s too late at night to continue arguing about smart kettles…

It can feel surprisingly convincing.

And I suspect George and Lewis are only just getting started.

Just like us, George and Lewis get tired and need a bit of down time.
Just like us, George and Lewis get tired and need a bit of down time.
Gadgets, Robotics

Robot Beggars, QR Codes and the Strangest Glimpse Yet of the AI Future

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There are some news stories that make you stop, read the headline again, and then wonder whether you have accidentally fallen asleep in front of an episode of Black Mirror.

This is one of them.

According to Oddity Central, humanoid robots have reportedly been spotted on the streets of several Chinese cities, apparently begging for money with signs asking passers-by to help pay their electricity bills.

Yes, you read that correctly.

Not a human asking for spare change. Not even one of those slightly unsettling robot dogs trotting around with a camera on its back. A humanoid robot, kneeling or crouching in the street, complete with a QR code for digital donations and messages such as “Please pay my electricity bill”.

It is funny, bleak, clever and faintly horrifying all at the same time.

Humanoid robots have reportedly been spotted begging on Chinese streets. Is it a stunt, social commentary, or a strangely perfect symbol of the AI age?
Humanoid robots have reportedly been spotted begging on Chinese streets. Is it a stunt, social commentary, or a strangely perfect symbol of the AI age?

The future has arrived, and it wants a top up

The reported scenes are almost too perfect as a piece of modern satire. A robot, presumably worth thousands of pounds, sitting on the pavement asking humans to help it recharge.

If Charles Dickens were alive today, he would probably be writing about a small Victorian automaton clutching a tin cup outside a data centre.

The reports suggest that these “robot beggars” have appeared in cities including Beijing, Chengdu and Fuzhou. Some appear to be posed with bowed heads, others with signs, bowls, QR codes and digital payment details.

Of course, the big question is whether this is real begging, performance art, marketing, or simply somebody with a very expensive sense of humour.

My money is on stunt or social commentary.

And in many ways, that makes it even more interesting.

The QR code is the clever bit

The most modern detail in the whole thing is not the robot. It is the QR code.

That tiny square turns the whole scene from a daft novelty into something strangely plausible. A robot begging for power while accepting digital payments feels like a perfect little snapshot of where technology is going.

It is absurd, but only just.

We already live in a world where buskers, cafés, market stalls and even charity collectors use contactless payments. In China, mobile payments are deeply embedded in daily life, so a begging robot with a QR code is not as far fetched as it might first appear.

The technology is not really the shocking part.

The shocking part is how quickly we accept it.

A decade ago, this would have looked like a comedy sketch. Today, people are debating whether the robot is genuine, whether it is an art installation, whether it is a marketing campaign, and whether even begging has now been automated.

That last point is obviously ridiculous.

But also, somehow, not ridiculous enough to dismiss completely.

Are robots really coming for every job?

The lazy version of the AI debate is that robots are coming for factory workers, call centre staff, writers, designers, drivers and anyone who has ever touched a spreadsheet.

But a begging robot flips the whole conversation on its head.

Nobody seriously expected “street beggar” to appear on the great AI replacement list. Yet here we are, staring at photos and videos of humanoid machines apparently asking humans for money.

It is probably not a new economic model. I doubt anyone has run the numbers and decided that placing a Unitree humanoid on a pavement is the fastest route to financial independence.

These machines are still expensive, and they are not exactly discreet. You would need a lot of generous pedestrians to cover the cost of the robot, let alone its maintenance, transport and charging.

But as a symbol, it is brilliant.

It says: if a robot can be made to mimic labour, service, companionship, entertainment and now even desperation, where exactly do we draw the line?

The unsettling human reaction

What fascinates me most is not the robot itself, but how humans react to it.

Do people laugh?

Do they feel sorry for it?

Do they scan the QR code?

Do they take photos and walk away?

We are very good at projecting feelings onto machines. Give a robot a face, a posture and a slightly pathetic sign, and suddenly we start treating it as something more than plastic, metal, servos and software.

This is why robot dogs feel different from wheeled drones. It is why humanoid robots attract so much attention. They borrow just enough from us to make our brains do the rest.

A robot kneeling on a pavement does not need to be sentient to make people uncomfortable. It only needs to look like it is asking.

That is where the story becomes less about robotics and more about us.

Art, marketing or warning?

There is every chance these robot beggars are not what they appear to be. The Oddity Central story itself notes that people online have questioned the authenticity of the trend, with some suggesting that the robots may be art installations designed to make people think about the changing relationship between humans and machines.

If that is the case, then it worked.

A humanoid robot asking for electricity money is a wonderfully simple idea. It compresses dozens of modern anxieties into one image:

AI replacing people.

Machines becoming more lifelike.

Humans becoming more detached.

The gig economy becoming stranger.

Digital payments replacing cash.

Technology needing constant feeding.

And perhaps most importantly, our endless ability to turn almost anything into content.

Because whatever the original intention, the robots have done what all successful modern spectacles do: they went viral.

The Gadget Man view

I do not think this means we are about to see robot beggars on every high street.

At least, not yet.

But I do think it shows how quickly humanoid robots are moving from laboratory curiosities into public imagination. Whether they are used for research, marketing, entertainment, public service or bizarre street theatre, they are becoming more visible.

And visibility matters.

Once people see robots in public spaces, they stop being abstract. They become part of the mental furniture of everyday life. The first time you see one, you take a photo. The tenth time, you step around it on your way to buy a sandwich.

That is how the future usually arrives. Not with one enormous leap, but with a series of odd little moments that make us say, “Well, that’s new.”

A robot begging for electricity money may not be the future of poverty, employment or AI.

But it might be one of the strangest warning signs yet that the AI revolution is not going to stay neatly tucked away inside laptops, smartphones and cloud servers.

Sooner or later, it will be sitting on the pavement, holding up a sign, and asking us to scan a QR code.

And knowing us, somebody probably will

AI, AI & Machine Learning, Technology

Half of Workers Fear AI Will Take Their Jobs, and I Can Understand Why

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Artificial Intelligence is everywhere at the moment.

It is in our phones, our laptops, our search engines, our photo apps, our cars, our customer service systems and, increasingly, our workplaces. For those of us who love technology, AI is fascinating. I use it, I write about it, I test it, and I can see enormous potential in what it can do.

But there is another side to this story, and it is one we cannot afford to ignore.

A new mass survey by GMB Union has found that almost half of workers are worried AI will take their job. The survey, which questioned 5,294 workers across a range of sectors in May and June 2026, found that 48 per cent are concerned that the introduction of Artificial Intelligence in their workplace could lead to them losing their job.

That is not a small number. That is not a fringe concern. That is nearly one in two workers looking at the rapid rise of AI and wondering whether the machine is coming for them next.

The same survey found that 58 per cent of workers believe AI will take jobs away in their workplace. Almost a third said their employer has already introduced AI, and around a quarter of those said AI is now doing tasks they would usually do themselves.

Perhaps most worrying of all, nearly half said AI is being used to track the activity of them or their colleagues during working time.

AI as a tool, or AI as a workplace watchdog?

That, for me, is where the conversation changes.

There is a world of difference between using AI as a helpful tool and using it as a digital overseer. One can make work easier, safer and more productive. The other risks turning workplaces into something cold, monitored and deeply uncomfortable.

This week, there have also been reports of around 1,000 jobs at Asda’s George brand being affected as the supermarket expands its use of AI and automation. Nestlé is also planning hundreds of job cuts at UK sites, with concerns that many roles could be replaced by AI and robotics.

Robert Battell, a Nestlé worker, is due to speak at GMB’s annual congress in Blackpool about what this means for workers on the ground. His words are stark. He describes the heartbreak of seeing colleagues and friends lose their jobs and be replaced by robots.

And that is the human bit we must not lose sight of.

Behind the buzzwords are real people

Behind every phrase like “efficiency savings”, “automation”, “streamlining” or “digital transformation”, there are real people. People with mortgages, rent, children, caring responsibilities, bills, routines and lives built around the work they do.

I am not anti-AI. Far from it. I think AI could be one of the most important technological developments of our lifetime. Used properly, it can help people work smarter. It can take away dull, repetitive tasks. It can help with accessibility, creativity, admin, logistics, research, design, customer support and countless other areas.

But the key phrase there is “used properly”.

Technology should serve people, not quietly replace them with no safety net.

This is our Industrial Revolution moment

The Industrial Revolution changed the world of work forever. Machines altered entire industries, and society had to adapt. AI feels like another of those moments.

It is not just another piece of software. It is a shift in how work itself is organised, measured and valued.

That means we need a serious conversation about rules, protections and responsibilities.

If AI removes a task, what happens to the person who used to do it? Are they retrained? Redeployed? Supported? Or simply shown the door?

If AI is being used to monitor staff, who decides what is fair? How much tracking is too much? What happens when an algorithm gets it wrong?

And if companies are saving money by replacing people with automation, what responsibility do they have to the communities and workers who helped build those businesses in the first place?

AI is not the enemy

AI is not the enemy. Badly used AI is the problem.

There is a version of the future where AI helps doctors, teachers, engineers, designers, drivers, warehouse staff, office workers and small businesses do more with less stress.

There is another version where it becomes a blunt cost-cutting tool, used to squeeze every last drop of productivity out of people before replacing them altogether.

We still have a choice about which version we build.

The technology is moving quickly. The question now is whether the laws, workplace protections and business ethics can move quickly enough to keep up.

Because if half of workers are already worried AI will take their job, then this is no longer some distant debate about the future.

It is happening now.

2026, AI, AI & Machine Learning, Gadget Man, Work

AI and the Future of Work: Are We Excited, Terrified, or Just Trying to Keep Up?

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There are moments in technology when you can almost hear the gears of history turning.

I remember when having a computer in the office made you “the computer person”. I remember dial-up modems, fax machines, early websites, clunky email systems, and the strange magic of watching a machine do something that previously required a drawer full of paper, a telephone call, and usually someone called Janet who knew where everything was filed.

Artificial intelligence feels like one of those moments, except this time the machine is not just helping us type the letter. It is writing the letter, summarising the meeting, drawing the logo, coding the website, generating the video, and quietly eyeing up half the tasks we thought were safely ours.

AI and the Future of Work: Are We Excited, Terrified, or Just Trying to Keep Up?
AI and the Future of Work: Are We Excited, Terrified, or Just Trying to Keep Up?

A new report from The Policy Institute at King’s College London, AI and the Future of Work, gives a fascinating snapshot of how the British public, workers, students and employers are feeling about all this.

And the overall picture is not simple optimism. It is more like standing in front of a very clever robot vacuum cleaner that has suddenly learned accountancy.

 

We are wary, but we know it is coming

One of the most interesting findings is that the public are more negative than positive about AI, yet many people still expect to use it.

Almost half of the public say they would rather avoid AI-based technologies, 41% say they are afraid of AI, and only 24% think AI is positive for humanity. Yet 43% agree they will use AI in the future.

That feels very human to me.

It is the same feeling we had when smartphones began taking over our pockets. We complained about them, worried about them, said they were ruining attention spans, then used them to check the weather, order a takeaway, find a route, take photos of the dog and pay for parking.

AI may be following the same path, only with rather larger consequences.

Parents are looking at this very differently

The part of the report that really lands is the section about parents.

Half of parents with children under 30 say they are worried about how AI will affect their children’s career prospects. Yet only around three in ten parents of 11 to 29-year-olds have actually had a conversation with their child about how AI might affect their future career, and a similar number have encouraged them to learn how to use AI tools.

That gap matters.

Because whether we like AI or not, pretending it is not happening is not a strategy. The best advice we can give young people is probably not “avoid AI”, but “understand it, question it, and learn how to use it better than the next person”.

When I was younger, knowing your way around a computer gave you an edge. Then knowing the web gave you an edge. Then knowing social media, search engines, ecommerce, video, and automation gave you an edge.

Now the edge may come from knowing how to work alongside AI without becoming completely dependent on it.

The fear is not just science fiction

The report also shows that concern about jobs is widespread.

Seven in ten people are worried about the economic impact of job losses caused by AI, and majorities of the general public, young people, university students and workers believe AI will eliminate far more jobs than it creates.

That is not a small worry. That is not people muttering about robots in the pub. That is a mainstream concern.

There is also a particularly sharp anxiety around entry-level roles. The report notes that many people believe AI could eliminate half of all entry-level white-collar jobs within five years.

This is where I think the real danger lies. Not necessarily in AI replacing every professional overnight, but in it quietly removing the first rung of the ladder.

Most of us learned by doing the boring stuff first. We answered support calls, updated spreadsheets, wrote simple copy, fixed small bugs, processed orders, filed things, checked things, tested things, and gradually became useful.

If AI takes away the junior work, where exactly do the next generation learn?

You cannot become experienced without first being inexperienced.

Employers are more optimistic, but even they are worried

Employers are generally more positive about AI than the wider public, but they are not blindly cheerful.

According to the report, 63% of employers are worried about the economic impact of job losses caused by AI, even while many are excited about new jobs opening up.

That is the strange contradiction at the heart of this whole debate.

AI is both an opportunity and a threat. It can help small businesses move faster, reduce admin, improve customer service, generate ideas, speed up research and make previously expensive tasks accessible to people working from a spare room.

But it can also concentrate power.

One of the starkest findings is that 65% of the public think the economic benefits of AI will mainly go to wealthy investors and large companies, while just 7% think the benefits will be shared fairly across society.

That is probably the bit we should be talking about more.

The question is not simply “will AI be clever?” It clearly will be. The question is “who benefits?”

My view from the Gadget Man shed

I use AI. I find it fascinating, useful, occasionally infuriating, sometimes astonishing and often a little unsettling.

It can be like having an enthusiastic assistant who has read everything, forgotten where it read it, and sometimes confidently hands you a screwdriver when you asked for a banana.

But used properly, it is powerful.

For people like me who create websites, write content, tinker with servers, make videos, build odd little systems and generally chase ideas down rabbit holes, AI can be a genuine productivity boost.

It can help you get from “I wonder if this is possible?” to “here is a working prototype” much faster than before.

But I do not think we should confuse productivity with progress.

If AI helps a small business survive, brilliant. If it helps a student learn, excellent. If it helps someone with a disability communicate, create, work or live more independently, fantastic.

If it simply allows large companies to employ fewer people while making a handful of shareholders wealthier, then we have built something clever but not necessarily something good.

The future is not automatic

Technology does not arrive with a moral compass fitted as standard. We decide how it is used.

That means schools, parents, businesses and government all have some catching up to do.

Young people need to understand AI not as magic, but as a tool. Workers need training, not vague reassurance. Employers need to think about responsibility as well as efficiency. And the rest of us need to keep asking awkward questions.

AI is coming into the workplace whether we welcome it with open arms or hide behind the photocopier.

The important thing now is not to panic, but not to sleepwalk either.

We have been here before with big technological shifts, but this one feels faster, wider and stranger.

The machine is no longer just on the desk.

It is in the conversation.

Source: King’s College London, The Policy Institute, “AI and the Future of Work”, May 2026.

2026, AI, AI & Machine Learning, Gadget Man, Security, Self Aware AI, Technology

Anthropic’s Project Glasswing Could Change Cybersecurity Forever

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There are moments in tech when you read an announcement and immediately realise that something important has shifted.

That was very much my reaction when I came across Project Glasswing, a newly announced initiative from Anthropic that is aimed squarely at one of the biggest looming problems in modern computing: what happens when AI becomes exceptionally good at finding software vulnerabilities. Source

According to Anthropic, Project Glasswing brings together a heavyweight list of partners including Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA and Palo Alto Networks, all with the goal of securing critical software for what Anthropic calls the AI era. It is also extending access to more than 40 additional organisations that build or maintain important software infrastructure. Source

Now, that alone would be interesting enough, but the real headline here is the model sitting behind it all.

Anthropic says its unreleased model, Claude Mythos Preview, has already demonstrated the ability to find and exploit software vulnerabilities at a level beyond all but the most skilled human experts. That is a huge claim, and if it holds up in practice, it means we may have crossed into a very different phase of cybersecurity. Source

In plain English, this is not just about a chatbot helping someone write a bit of code more quickly. This is about AI being able to inspect complex software, spot weaknesses that humans and automated tools have missed for years, and in some cases work out how those weaknesses could be exploited. Anthropic says the model has already found thousands of high-severity vulnerabilities, including flaws affecting major operating systems and web browsers. Source

Some of the examples are rather startling. Anthropic says Mythos Preview uncovered a 27-year-old vulnerability in OpenBSD, a 16-year-old flaw in FFmpeg, and even chained together several Linux kernel vulnerabilities in a way that could escalate ordinary user access into full control of a machine. The company says those issues have now been responsibly disclosed and patched. Source

That, to me, is the bit that really lands.

Because for years we have tended to think of cybersecurity in terms of patching known issues, following best practice, keeping software up to date and hoping the really serious flaws are found by the good people before the bad people. But if AI systems are now reaching the point where they can autonomously discover dangerous bugs in code that has survived decades of scrutiny, then the pace of both defence and attack could increase dramatically. Source

Anthropic is clearly trying to frame Glasswing as a defensive first move. The company says it is committing up to $100 million in usage credits for Mythos Preview and $4 million in direct donations to open-source security organisations. The idea seems to be to put these capabilities into the hands of defenders, infrastructure operators and maintainers before similar systems become more widely available. Source

And that is probably the most sensible angle here.

Because whether we like it or not, the genie is not going back in the bottle. If one frontier AI lab can build a model that is frighteningly good at vulnerability discovery, others will too. Eventually, those capabilities will spread further. The question is not really whether AI will reshape cybersecurity. It is whether defenders can get enough of a head start to stop things getting seriously messy. That is an inference from Anthropic’s announcement and the examples it gives, rather than a direct claim from the company, but it feels like the unavoidable conclusion. Source

For those of us who run websites, servers, ecommerce platforms, mail systems or anything else connected to the wider internet, this should be a bit of a wake-up call. The old approach of leaving systems half-maintained, delaying updates, or assuming that obscure software will somehow stay below the radar looks even more risky in a world where AI can inspect code at speed and scale.

Project Glasswing may turn out to be remembered as one of those early milestone moments, the point where the cybersecurity industry publicly acknowledged that AI is no longer just a helpful assistant for defenders. It is becoming a serious force multiplier, and one that could work for either side.

That makes this announcement both exciting and slightly chilling.

And, in true Gadget Man fashion, it is exactly the kind of development that reminds us technology is never just about shiny new tools. It is also about consequences, responsibility and how quickly the world has to adapt when the rules suddenly change.

Source

Anthropic, Project Glasswing: Securing critical software for the AI era

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How to Stop Sweating the Small Stuff in Business

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If you find yourself sweating over every email and panicking over pie charts, you’re not alone. But it’s time to learn the art of chilling out for the sake of your business and your sanity. Let’s break down how to keep your cool in the office and focus on what truly matters, without the stress-fest.

  1. Delegate, Don’t Dictate

You might think that you are the most competent person in the office and the best person to run your business, and you might be right, but that doesn’t mean you can or should, do everything alone. You need to delegate those tasks that someone else can do to the people who can do them best, whether it be an IT support team or a freelance writer. Don’t be a dictator and trust your employees to have your back.

  1. Automate the Irritating Bits

If a task is repetitive, then chances are it can be repeated by a machine, AI or a piece of well-written software, so let it be done that way, free up your time, and stop worrying about those irritating little things like email sorting and invoicing. The tech will do all the sweating for you!

  1. Dream Big, Plan Smart

Setting unachievable goals is like setting yourself up for a series of very public faceplants. Instead, dream big but plan realistically. Break your goals into manageable chunks. Celebrate the small victories on your way to the big win. It’ll keep morale high and the dread of failure low. And when you do hit those targets, it’ll feel like winning the business lottery.

  1. Remember Not Everything is Urgent

In the fast-paced world of business, everything can seem urgent. Spoiler alert: it’s not. Learn to prioritise tasks using the good old Eisenhower Box – divide tasks into urgent, not urgent, important, and not important. You’ll soon see that the “urgent email” from marketing about the colour of the newsletter banner can probably wait until after you’ve secured your next big client.

  1. Take a Breather. Literally

Sometimes, the best way to stop sweating is to just… stop. Take regular breaks. Step away from your desk. Stretch, take a walk, meditate – do whatever it takes to clear your mind. Remember, a clear mind leads to better decision-making, creativity, and fewer coffee-spill incidents.

  1. It’s Okay to Say No

In business, the power of saying no is criminally underrated. You don’t have to accept every project, attend every meeting, or jump on every opportunity that flies through your open office window. Sometimes, saying no is the best way to avoid unnecessary stress and keep your focus sharp on what truly matters.

  1. Keep Learning and Adapting

Nobody gets everything right the first time, or even the second time. But every misstep is a chance to learn. Stay flexible and be willing to adapt your strategies based on what works and what doesn’t. Keeping this mindset helps you view challenges as learning opportunities rather than stress-inducing disasters.

How to Stop Sweating the Small Stuff in Business

  1. Maintain a Humour-Filled Environment

Never underestimate the power of a good laugh. Maintaining a sense of humour can transform the office vibe and turn even the most stressful day into an episode of a sitcom. When you laugh, stress levels drop, and suddenly, those big scary problems don’t seem so intimidating.

  1. Develop a Strong Support Network

Build a network of peers and mentors whom you can turn to for advice, or simply to vent. Having a supportive community means you have a sounding board for your frustrations and a cheering squad for your successes. Plus, they can offer invaluable advice on how not to sweat the small stuff, because chances are, they’ve been there.

  1. Focus on Your Health

Often, the stress of not sweating the small stuff starts with how you’re feeling physically and mentally. If you’re running on empty, every little issue can seem like a mountain. Invest in your health with regular exercise, a good diet, and sufficient sleep — essentials that keep your mind sharp and your tolerance for minor annoyances high. A healthy entrepreneur is more capable of distinguishing between minor issues and real problems.

  1. Embrace Positive Distractions

Incorporate positive distractions into your daily routine to break the monotony and stress of work. This could be anything from a midday yoga session to a creative hobby that you engage in during your break. Positive distractions can refresh your mind and enhance your creativity, making you less likely to be bogged down by small annoyances.

  1. Regular Team Reviews

Hold regular sessions with your team to review workflows, processes, and ongoing projects. These meetings can help identify potential stress points and inefficiencies that may cause minor issues to become major headaches. By proactively addressing these in a collaborative environment, you encourage a team-focused approach to problem-solving, which can significantly reduce stress levels.

  1. Implement a Feedback System

Encourage a culture of feedback where employees feel comfortable sharing their concerns and suggestions. A feedback system helps in early detection of minor issues before they escalate into significant problems. It also empowers your team to contribute to solutions, which can enhance their engagement and reduce the burden on your shoulders.

  1. Master the Art of Compartmentalization

Learn to compartmentalize various aspects of your business to prevent overlap that can lead to confusion and stress. Keep client communications separate from internal project discussions, and financial planning distinct from marketing strategies. This clear segmentation helps in managing each segment more effectively without the stress of juggling multiple issues at once.

  1. Set Clear Boundaries

When you run a business, it’s really important to set clear boundaries between your working life and your personal life. This will enable you to get some respite from the hustle of the office and actually be able to decompress once in a while. So, whether it’s blocking out weekends for travel or making sure you stop work at 6 P.M no matter what, boundaries matter.

Sweating the small stuff doesn’t just ruin your suit; it hampers your ability to lead with clarity and purpose. So, chill out, strategize, and watch as you and your business start to bloom!

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I Automated Malicious IP Blocking with the Help of AI

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Introduction: Navigating the complexities of cybersecurity requires a blend of alertness and innovation. In my latest project, I embraced this ethos by deploying AI, particularly ChatGPT-4, to craft a Python script that automates the blocking of malicious IP addresses. (Moreover, the ‘tags‘ you see in this post are the product of a WordPress plugin, also primarily developed with insights from ChatGPT-4 too!)

The Challenge: With brute force attacks becoming increasingly common, developing a swift and automated response mechanism was crucial. My aim was to create a system capable of promptly identifying and neutralizing threats from malicious IP addresses.

AI as a Collaborative Partner: ChatGPT-4’s advanced language understanding capabilities played a pivotal role in this project, assisting both in scripting and in WordPress plugin development.

  1. Script Development: ChatGPT-4 provided step-by-step guidance in crafting a Python script to parse alert emails and extract IP addresses. Its ability to generate code snippets and troubleshoot issues was invaluable.
  2. Integrating Security Tools: The script was integrated seamlessly with cPHulk and CSF, using Python’s subprocess module. This integration, suggested by ChatGPT-4, was critical for the effective blocking of suspicious IPs.
  3. Handling Sensitive Data Securely: In dealing with login credentials, ChatGPT-4 recommended secure practices, such as using environment variables, which were crucial for maintaining the script’s integrity.
  4. Error Handling and Efficient Logging: Robust error handling and comprehensive logging, as advised by ChatGPT-4, ensured the system’s reliability and maintainability.
  5. Automating with Cron Jobs: The script’s automation was achieved through a cron job setup, a process in which ChatGPT-4 played an advisory role.
  6. WordPress Plugin for Tag Generation: Beyond the script, ChatGPT-4’s assistance extended to developing a WordPress plugin for generating tags for my blog posts. This plugin analyzes the content and contextually generates relevant tags, showcasing the AI’s versatility.

The Outcome: The result is a robust, AI-enhanced cybersecurity mechanism, complemented by an AI-driven tagging system for my blog. This project exemplifies the synergy between human expertise and AI in solving complex technical challenges.

Conclusion: This venture into AI-assisted development has not only fortified my server’s security but also revolutionised how I manage content on my blog. It stands as a testament to the power and potential of AI in cybersecurity and beyond.

Future Prospects: This successful collaboration with AI opens new horizons for future projects. I am excited to delve deeper into the integration of AI in various aspects of problem-solving, especially in technology and cybersecurity.