Back

How does AI generate images?

How AI Generates Images: From noise to art

28 July 2026

What happens when you type a prompt?

AI image generation might feel like magic, but it’s actually built on surprisingly logical maths. At its core, modern systems such as diffusion models work by answering a simple question billions of times: what should this pixel probably look like?
Instead of starting with a blank canvas, AI begins with pure noise — a chaotic static image. During training, the model studies millions of real images, then gradually corrupts them with noise. Its job is to learn how to reverse that process step by step, rebuilding clarity from chaos.

From text to pixels: how prompts are understood

When you type something like “a cat wearing a top hat”, the AI doesn’t “see” words the way humans do. Instead, a text embedding model converts your prompt into a mathematical representation in a high-dimensional space. This acts like a guiding map for the image generation process.
The diffusion model then uses this guidance while cleaning up the noise. At each stage, it asks what looks incorrect and adjusts the image slightly. Over hundreds or thousands of steps, vague shapes become defined objects — whiskers, textures, lighting, and even those oddly convincing (or slightly unsettling) human features.

Why AI images look different every time

Under the hood, neural networks analyse patterns at multiple levels. Early layers detect simple shapes like edges and colour blobs, while deeper layers interpret complex structures such as eyes, fabrics, or objects.
Importantly, AI doesn’t retrieve images from a database or copy existing artwork. It generates new outputs by sampling from learned probability distributions. That’s why even identical prompts produce different results.
A controlled amount of randomness is added at the start of the process. Too little, and outputs become repetitive. Too much, and things get surreal very quickly — sometimes brilliantly so.

The real “magic” behind AI image generation

AI isn’t imagining in the human sense. It’s performing highly sophisticated statistical reconstruction, refining noise into coherent images through repeated calculation. The real breakthrough is that this process can produce visuals that feel creative, original, and often astonishingly realistic.

Want to learn more? Watch our Lesson Hacker video HERE to see exactly how AI turns text into images step by step.

 

For more Lesson Hacker videos, check out the CraignDave YouTube playlist HERE.

Visit our website to explore more cutting-edge tech news in the computer science world!

Related posts

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Four revision techniques that actually work

Stop wasting time on ineffective revision. Learn four proven techniques that improve recall, confidence and exam performance.

8 July 2026

Back

Can AI remember?

From Goldfish Memory to Game-Changing Intelligence

27 July 2026

Why does AI forget everything so quickly?

Ever felt like you’re chatting with an AI that starts off brilliantly… then completely forgets everything a few messages later?

You’re not imagining it. Most modern AI systems rely on something called a context window—a limited amount of text they can “see” at once. Once that limit is reached, older parts of the conversation simply drop off.

In simple terms, AI doesn’t “remember” in the way humans do. Each time you send a message, the system rereads what fits within its token limit. Anything beyond that? Gone. It’s like revising for an exam on a whiteboard that keeps erasing your first notes as you add new ones.

The real challenge: scaling memory and attention

The issue isn’t just memory—it’s also performance. The attention mechanism that powers AI becomes increasingly expensive as conversations grow longer. Double the input, and the computational cost can skyrocket. That’s why today’s AI works well for short tasks, but struggles with long, complex interactions.

A smarter future: short-term vs long-term AI memory

Exciting new developments are aiming to fix this. Emerging approaches—like Google’s “Titans” architecture—introduce the idea of splitting AI memory into two parts:

  • Short-term memory for immediate context (like current conversations).
  • Long-term memory for storing important information over time.

This long-term memory works more like a structured system, allowing AI to retain key details without cramming everything into a limited space.

The “surprise” factor: how AI decides what to remember

One of the most fascinating ideas is the use of a surprise metric. Instead of remembering everything, AI prioritises unusual or important information—just like humans do.

Think of it as a bouncer for memory:

  • Predictable, repetitive information gets filtered out.
  • Unexpected or meaningful details get stored long-term.

This makes AI far more efficient and helps it retain what actually matters.

What this means in the real world

If these advancements continue, we could see:

  • Coding assistants that remember your entire project history
  • Study tools that retain knowledge across the academic year
  • Games with characters that build genuine long-term relationships
  • Customer support AI that actually remembers you (no more repeating yourself!)

In short, AI could become more personal, consistent, and genuinely useful over time.

But what about privacy?

With great memory comes great responsibility. Systems that store long-term information will need clear controls—what gets remembered, what gets forgotten, and why. Without this, there’s a real risk of storing incorrect or sensitive data.

We’re moving towards AI that doesn’t just respond—but remembers. Less goldfish, more elephant (a very fast one). And that shift could completely transform how we interact with technology.

Want to see this explained in action? Watch our Lesson Hacker video on our YouTube channel.

For more Lesson Hacker videos, check out the CraignDave YouTube playlist HERE.

Visit our website to explore more cutting-edge tech news in the computer science world!

 

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Four revision techniques that actually work

Stop wasting time on ineffective revision. Learn four proven techniques that improve recall, confidence and exam performance.

8 July 2026

Back

How do drones keep balanced?

The science behind hovering magic

23 July 2026

Ever watched a drone hover perfectly in mid-air and wondered how it doesn’t just tip over like a table on uneven ground? The truth is fascinating — drones don’t balance by being still. They balance by panicking hundreds of times a second… and doing it with incredible precision.

Tiny Sensors, Big Job

A quadcopter isn’t stable on its own. Left alone, it would immediately tip, accelerate sideways, and fall — obeying gravity in the most dramatic way. The secret lies inside: an Inertial Measurement Unit (IMU). This tiny device, packed with accelerometers and gyroscopes, measures linear acceleration and rotation across three axes. Essentially, the IMU is constantly asking, “Which way is down, and how fast are we messing this up?”

The Control Loop: Chaos with a Plan

The IMU feeds its measurements into a flight controller, usually using a PID controller — Proportional, Integral, Derivative.

This may sound technical, but it’s simply a way to answer three questions:

  • How wrong are we now?
  • How wrong have we been?
  • How fast is it getting worse?

If the drone tilts even slightly forward, the controller speeds up the rear motors and slows the front ones. This tiny adjustment pushes it level again. And it doesn’t stop there — this process happens hundreds or thousands of times per second, far faster than any human pilot could react.

Torque Cancellation: Why Multiple Rotors Matter

Drones have multiple rotors to cancel out torque. Each spinning propeller wants to twist the drone in the opposite direction (thanks, Newton!). By pairing clockwise and counter-clockwise rotors, rotational forces cancel each other out. Want to yaw left? Speed up one diagonal pair and slow the other. Want to rise? Speed up all the motors equally.

Advanced Awareness: More Than Just Balance

GPS, barometers, magnetometers, and even vision systems give drones higher-level awareness — altitude, heading, and position relative to the ground. But these sensors are slower. The real magic of staying upright happens in the IMU and control loop: a relentless feedback cycle of overcorrecting tiny mistakes instantly.

Drones don’t hover by being steady — they hover by constantly falling and correcting faster than physics can react. What looks like effortless floating is actually hundreds of tiny “NOPE!” corrections every second. The result? A perfectly balanced drone and a glimpse into the brilliant engineering of modern flight.

Watch the full video HERE to see this balancing act in action.

Explore more tech insights and computer science fun at CraignDave.org

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Four revision techniques that actually work

Stop wasting time on ineffective revision. Learn four proven techniques that improve recall, confidence and exam performance.

8 July 2026

Back

Is fetch, decode, execute still a thing?

22 July 2026

Yes, it is—but it’s a lot more complicated than you remember.

If you remember your early computer science lessons, you might recall the simple mantra: fetch an instruction, decode it, execute it, repeat. That basic loop has been at the heart of CPUs since the 1970s—and surprisingly, it’s still there today. But modern processors have bulked up, gotten faster, and learned a few tricks along the way.

The classic rhythm of CPUs

At its core, every CPU still follows the same three-step process: fetch an instruction from memory, decode it into an operation the processor understands, and execute it. Software assumes this rhythm, and physics hasn’t suggested a better alternative yet. Strip away the cores, branding, and flashy diagrams, and the familiar fetch–decode–execute cycle remains.

Pipelining: the relay race of instructions

What’s different now is speed and efficiency. Modern CPUs don’t sit politely and handle one instruction at a time. Instead, they fetch multiple instructions ahead, decode them in parallel, and try to execute as many as possible simultaneously. This is called pipelining. Picture a relay race where runners start early, pass the baton multiple times at once, and occasionally bump into each other—chaotic but effective.

Out-of-order execution and speculation

Things get even more “clever” (and a little chaotic) with out-of-order execution. CPUs don’t always follow the order your program wrote; they execute instructions as resources are available, as long as the final result is correct. Add branch prediction and speculative execution, and your processor is effectively guessing what comes next, executing instructions in advance, and discarding the ones it didn’t need. If done right, this adds speed. If done wrong, well… let’s just say it occasionally makes headlines.

The same old cycle, just supercharged

Underneath all the complexity, the fundamentals haven’t disappeared. Instructions are still fetched, decoded, and executed. Even GPUs, AI accelerators, and multi-core monsters rely on this cycle—they’ve just widened, deepened, parallelised, and turbocharged it. Today’s CPU is like an over-caffeinated octopus juggling thousands of tasks at once—but from the software’s perspective, it still looks like a simple three-step dance.

So yes, fetch, decode, execute is absolutely still a thing. It’s just gone to the gym, had some coffee, and learned a few new moves along the way.

Want to see this explained visually? Check out the full video on our CraignDave YouTube channel.


Explore more computer science insights, tutorials, and resources at CraignDave.

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Four revision techniques that actually work

Stop wasting time on ineffective revision. Learn four proven techniques that improve recall, confidence and exam performance.

8 July 2026

Back

Why is RAM Suddenly So Expensive?

21 July 2026

It feels like just yesterday that RAM upgrades were practically free — and now a 32 GB stick can cost almost as much as a short city break. If you’ve been scratching your head over soaring memory prices, the usual explanation you’ll hear is “AI.” It sounds suspiciously vague… but in this case, it’s annoyingly accurate — just not in the way most people assume.

The Memory Family: RAM, VRAM, and HBM

To understand what’s happening, you need to know a bit about memory. RAM, VRAM, and the ultra-fancy AI memory called HBM are essentially cousins. All are forms of DRAM, manufactured using very similar processes, in the same factories, and by a surprisingly small number of companies — namely Samsung, SK Hynix, and Micron. That’s it. Three firms producing the vast majority of the world’s memory.
Think of it less like a bustling free market and more like three bakers deciding what bread exists in town.

How AI Data Centres Are Hoovering Memory

Enter AI data centres. Training and running large models isn’t just about GPUs packed with VRAM — they also need enormous amounts of system RAM to shuffle data, stage batches, and keep everything moving smoothly. A single AI server can easily house terabytes of DDR5 RAM, and a single data centre can contain thousands of such servers. That’s not “a bit more demand” — that’s hoovering the warehouse.

Why Memory Prices Are Climbing

Here’s the catch: memory fabs can’t just whip up more chips overnight. Building or expanding a fabrication plant takes years and costs billions. Memory is famously a boom-and-bust industry, and manufacturers have been burned before by ramping up production right before demand collapsed.
So, instead of expanding, they’re making the rational move: prioritising the most profitable memory. Right now, that’s HBM for AI accelerators, which sell for far higher margins than boring old consumer DDR5.
The result? Less capacity goes to consumer RAM, more goes to high-margin AI memory, and prices climb. Your PC isn’t suddenly demanding AI — it’s just that data centres can outbid you without even noticing you were in the room.

The Takeaway

Your next RAM upgrade isn’t expensive because it’s better. It’s expensive because someone else, somewhere, is willing to pay vastly more. Understanding the forces behind memory pricing helps make sense of those shocking sticker prices — and shows just how intertwined AI and everyday computing have become.

Watch the full video to see the story behind the RAM price hike in action.

Explore more computer science insights and resources at CraignDave.org

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Four revision techniques that actually work

Stop wasting time on ineffective revision. Learn four proven techniques that improve recall, confidence and exam performance.

8 July 2026

Back

Am I really Human?

How CAPTCHAs & AI test your humanity

20 July 2026

The tiny checkbox with a big job

Every time you tick the familiar “I am not a robot” box online, it might feel like a small insult to your dignity. Surely robots can click boxes — they can drive cars and even compose music. So why does a simple checkbox seem to challenge our very existence?
The truth is, that little square isn’t testing whether you can click — it’s observing how you click. Humans are delightfully messy: we overshoot targets, pause, wiggle the mouse, and sometimes hesitate for a moment of existential dread before finally hitting the box. Bots, on the other hand, move in perfectly straight lines at superhuman speed, trying to complete the task with robotic efficiency. Invisible scripts analyse cursor paths, timings, micro-hesitations, and device behaviours. If the interaction looks too perfect, the system thinks: “Robot vibes,” and you’re sent to the dreaded image test.

Enter CAPTCHA hell

CAPTCHAs — those grids of blurry images — are actually computer vision training disguised as security. You’re asked to select traffic lights, bicycles, or buses that appear half-hidden or distorted. Humans excel at recognising patterns in chaos, while early AI struggled. The system even compares your selections with those of other humans, effectively crowd-sourcing a reality check. Meanwhile, you debate whether that tandem bike counts as one or two.

Can AI beat CAPTCHAs?

Increasingly, yes. Modern neural networks can identify objects in CAPTCHAs far more accurately than humans, especially when fed enough examples. This is why newer systems have evolved beyond image puzzles, relying on behavioural biometrics, device fingerprints, risk scoring, and contextual signals like your IP reputation. Proving you’re human is no longer just about ticking a box — it’s about showing you’re a normal human doing normal human things on a normal device.

Humans are gloriously clumsy

Ironically, as AI gets better at mimicking us, websites may start testing behaviours that even humans find tricky. In the future, you might need to scroll in a slightly confused way or fail a simple maths problem to prove you’re human. Finally, our natural human quirks become a security feature — and one that bots can’t easily replicate.

Curious to see how this all works in practice and why your mouse movements matter more than you think? Watch the full video here.

For more fascinating computer science insights, teaching resources, and videos, visit CraignDave.

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Four revision techniques that actually work

Stop wasting time on ineffective revision. Learn four proven techniques that improve recall, confidence and exam performance.

8 July 2026

Back

Why are TVs so cheap now?

10 July 2026

The secret behind your £120 4K screen

Remember when buying a TV was a major event? A 32-inch telly back in the day was basically a family heirloom. You’d gather around it like it was the village fire, watching BBC One through a haze of static so thick it felt like the actors were performing behind frosted glass. And the price? A thousand pounds for a good one.
Fast forward to today, and a 43-inch 4K smart TV can cost less than a weekly meal deal. Witchcraft? Not quite. Mostly economics… and a little clever tech.

How modern TVs are made

The biggest change comes from how screens are manufactured. Old TVs relied on cathode ray tubes—massive, heavy boxes firing electrons at phosphor pixels, like some radioactive Nerf gun. Modern LCD and OLED panels, by contrast, are made with industrial precision.
Major manufacturers like BOE, AUO, LG Display, and Samsung Display produce enormous “mother glass” sheets the size of garage doors. These sheets are then sliced into individual panels like display lasagne. Larger sheets plus better yield rates dramatically lower the cost per screen.
Automation has also transformed the process. Making a TV panel today is closer to printing a newspaper than hand-crafting a delicate scientific artefact. Mass production crushes the price, plain and simple.

Why you’re part of the price equation

Here’s the spicy bit: your TV is cheap because you’re the product. Smart TVs collect data on what you watch, when you watch, and even how long you stick with Love Island before rage-quitting. This process, called Automatic Content Recognition (ACR), tracks content across apps and external devices. The data is anonymised but extremely valuable to advertisers.
In other words, manufacturers can subsidise the hardware cost because they’re making money on the software side. Think of it as a razor-and-blades model—except the razor is a 43” slab of glass, and the blades are your viewing habits.

The advertising ecosystem

Beyond data collection, your TV is prime real estate for ads and paid placements. Budget brands often earn more after you buy the TV than from the sale itself. It’s the same trick as cheap printers—but without the emotional abuse of running out of ink mid-document.

Market forces & competition

Finally, competition keeps prices low. There are countless TV manufacturers, and once you’ve nailed 4K LCD production, components like processors, LEDs, and Wi-Fi chips are cheap and commoditised. Seasonal spikes in TV purchases allow retailers to sell screens as loss-leaders, tempting you to buy a toaster, soundbar, or lifetime supply of discount stuffing along the way.

So yes, TVs are cheap because manufacturing is efficient, components are inexpensive, and companies quietly gather data about your viewing habits. That £120 miracle screen? Enjoy it… and maybe take a few minutes to disable tracking, or at least make sure your TV knows you have excellent taste.

Watch our Lesson Hacker video to find out more.

Discover more computer science insights and fun explanations at CraignDave.org.

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Four revision techniques that actually work

Stop wasting time on ineffective revision. Learn four proven techniques that improve recall, confidence and exam performance.

8 July 2026

Back

The Year 7 dip: why enthusiasm fades – and how to rebuild it

7 July 2026

Every secondary teacher knows the feeling. A Year 7 class arrives in September full of enthusiasm — hands up, eyes forward, eager to please. Fast forward a few months, and the same class can feel markedly different: quieter, less responsive, occasionally resistant.

Writing in TES, David Thomas, CEO of Axiom Maths, describes this familiar experience. His research suggests that, by the summer of Year 7, students are significantly less likely to enjoy Maths than they were in Year 6. What is striking is not just that this happens, but how quickly — and how unevenly.

Yet as many classroom teachers will recognise, this pattern is not confined to Maths. It can affect Computing too, albeit playing out on a slightly different timeline.

When the shine wears off

Thomas identifies the spring term, between Christmas and Easter as the critical point where engagement drops most sharply. With multiple lessons a week, Maths provides fertile ground for this decline to emerge quickly.

In Computing, Craig and Dave suggest the change is often delayed. With fewer curriculum hours, students tend to sustain their enthusiasm through Year 7. But the dip still comes.

Year sevens are generally keen all year, but it’s when they come back in Year 8 that you really notice the shift. They’re more comfortable in the school and more complacent about learning.

This is a useful reminder, the issue is not tied to a specific term, but to a broader transition. As students settle into secondary school, the initial novelty fades and deeper challenges begin to surface.

The balancing act teachers know too well

One of Thomas’s key findings centres on the tension between repetition and pace. Teachers must reconcile wildly different starting points, often reteaching content to establish a common foundation.

In principle, this is sound pedagogy. In practice, it can be deeply frustrating for students with some thinking, “We’ve already done this — why are we still here?” Others are completely bamboozled and trying to keep up.

In Computing, this disparity can feel even more pronounced. Pupils arrive with highly variable experiences — from those who have explored coding in depth to those who have barely used a keyboard. The result is a classroom where boredom and anxiety coexist.

The danger lies in assuming that this tension is unavoidable. While it may not be fully solvable, thoughtful planning, adaptive tasks, extension pathways, low-threshold/high-ceiling activities can mitigate its impact.

 

Confidence: the slow erosion

If the mismatch of difficulty is the spark, then loss of confidence is the slow-burning fire. Thomas’s research highlights a growing confidence gap — one that emerges before any clear attainment gap. Teachers see this every day, though often in subtle ways. In Year 7, when you pose a question to the class, it’s “me, me, me” — hands shooting up. By Year 8, some students just… stop. They don’t want to put themselves out there anymore.

What changes is not just academic confidence, but social awareness. Participation becomes risky. It is no longer about impressing the teacher, but about fitting in with peers.

This creates a difficult tension. The very behaviours that support learning — asking questions, making mistakes, persevering — are precisely those that can feel socially uncomfortable. Left unchecked, this quiet withdrawal becomes self-reinforcing. The more you think students don’t want to engage, the less you push it, and the less you push it, the less they do. It becomes a downward spiral.

Classroom climate: more than behaviour management

Thomas also identifies a decline in classroom climate — reduced collaboration, less effective listening, and increased disruption. This is not simply a behaviour issue, but a cultural one. Being “too cool for school” changes everything.

Once that shift takes hold, even well-planned lessons struggle to land. Group work becomes harder to sustain. Discussion loses its richness. Students who want to engage can feel inhibited by those around them. However, sustaining high expectations is essential, even when it feels difficult. The moment you lose heart, they lose heart.

The unequal recovery

Perhaps the most concerning aspect of Thomas’s findings is what happens next. Some students begin to re-engage — but this recovery is not evenly distributed. More advantaged pupils are more likely to regain enjoyment. Others remain disengaged, widening the gap in both attitude and, eventually, attainment.

Teachers recognise this pattern, even if they do not always name it explicitly. Some pupils find their way back through extracurricular activities or additional support. Others drift further away. For a long time, both teachers and students have responded to this by asking whether the curriculum is truly fit for purpose for all learners.

This tension sits at the heart of inclusive teaching. The goal is not universal passion, but universal opportunity.

Beyond “teenagers being teenagers”

A common explanation for declining motivation is adolescence itself. Hormones, identity, social pressures all play a role, but we should caution against using it as a catch-all explanation. Instead, perhaps there is a deeper mismatch between student needs and school structures.

As adolescents develop, they increasingly seek:

  • autonomy — choice and independence;
  • competence — a sense of progress and success;
  • belonging — connection and identity.

Yet, secondary schools can often drift in the opposite direction creating environments that are controlling, performance-focused which results in spoon-feeding and impersonal. Then we’re surprised when motivation drops.

This is not an indictment of individuals, but of systems. Teachers operate within demanding structures — large classes, heavy workloads, constant pressures. It is easy to fall into cycles of survival rather than reflection. But awareness is a starting point.

Rebuilding the spark

The motivation dip is not inevitable. Nor is it irreversible. If anything, the research and classroom reflections suggest that there is a crucial window — particularly towards the end of Year 7 where small, deliberate shifts can have lasting impact.

Some principles stand out:

  • Protect confidence early
    Notice the students who have gone quiet. Create opportunities where success feels visible and safe.
  • Guard the classroom culture
    Maintain high expectations for participation and collaboration, even when it is challenging.
  • Keep the subject ‘alive’
    Don’t strip out engaging activities simply because they are harder to manage.
  • Offer meaningful challenge
    As Thomas argues, the answer is not easier content, but richer experiences — problems that genuinely engage curiosity.

A profession worth backing

Ultimately, this is as much about teachers as it is about students. Sustaining engagement requires energy, reflection and, at times, resilience. Teaching is not a theoretical exercise. It is lived, complex, and often messy, but within that complexity lies opportunity. The patterns we see are not fixed; they are shaped by the environments we create. While no classroom is perfect, every classroom has the potential to shift the trajectory. The Year 7 dip may be predictable, but it is also preventable.

Want to know more? Check out our ‘At the chalk face’ episode on YouTube.

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

Will quantum computers break encryption?

6 July 2026

The Truth Behind the Hype

Quantum computers are all over the headlines, and one question keeps cyber-security experts — and curious Reddit users — awake at night: will they break encryption? Let’s unpack this complex topic in a way that’s informative, a little fun, and very relevant for computer science enthusiasts.

What makes quantum computers different?

A standard computer thinks in bits — ones and zeroes, on or off. Quantum computers, however, use qubits, which can exist as one, zero, and everything in between simultaneously. This phenomenon, called superposition, allows quantum computers to explore multiple possibilities at once. Imagine trying every combination of a password simultaneously — it’s like a student’s excuse for not doing homework, but way more powerful.

Shor’s Algorithm: Why experts worry

The panic around quantum computing often centres on Shor’s Algorithm. This quantum method can factor extremely large numbers at lightning speed. Most modern encryption, such as RSA, relies on the difficulty of breaking these numbers into prime factors — essentially trying to un-mix a smoothie. With Shor’s Algorithm, those numbers could be split back into their ingredients in seconds. If a sufficiently powerful quantum computer existed, today’s encryption could be compromised.

The reality check: we’re not there yet

Here’s the reassuring bit: current quantum computers have only a few hundred noisy qubits — prone to mistakes, like a Year 10 coding project. To break modern encryption, we’d need millions of perfectly stable qubits with near-perfect error correction. This is decades away, requires extreme cooling near absolute zero, and a level of engineering that makes the Large Hadron Collider look like a Lego set.

Post-quantum encryption: staying ahead of the curve

By the time quantum computers are powerful enough to threaten encryption, post-quantum encryption will likely be in place. These new cryptographic methods are designed to withstand quantum attacks, keeping your data safe. In short, quantum computing is exciting — but not yet a cyber apocalypse.

Until then, your passwords are safe — unless you’re still using “password123”. In that case, your cat on the keyboard is a bigger threat.

Want to learn more about quantum computing and cyber security? Watch the full explainer video HERE.

Explore more computer science insights, lesson plans, and resources at craigndave.org

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

How does that website know where I live?

5 July 2026

The spooky “Good evening, Manchester!” moment

You’ve opened a website, and it greets you with “Good evening, Manchester!” and your first thought is: Right. Which one of you told them? Don’t worry—it’s not psychic powers, and your phone isn’t secretly spying on you. Unless you’ve been oversharing every thought on social media, it’s probably just IP geolocation—the tech equivalent of guessing someone’s postcode from the accent they mumble through a kebab.

IP addresses: Your digital return address

Every device connecting to the internet gets an IP address, a numeric label that works a bit like a return address on a letter (less charming, more colons if it’s IPv6). Internet providers assign these addresses in big regional blocks, and companies like MaxMind and IP2Location gather this data into huge databases mapping IP ranges to countries, cities, and sometimes even neighbourhoods.
So when you visit a website, it checks your IP against one of these databases and voilà—“Ah, this one’s from Leeds.” Not exactly Sherlock-level deduction, but surprisingly effective.

Why it’s not always perfect

IP geolocation isn’t 100% precise. Your village might be resolved to the nearest city, or worse, a default entry could place you in a random field in Kansas! Bad data can cause chaos—but it’s usually harmless.
And if you’re worried about your exact house number, rest easy. Websites would need your ISP’s internal records to get that, which are tightly guarded. Only a court order could lift that veil.

When accuracy improves

IP geolocation can be more precise if you use apps or services that access GPS, Wi-Fi positioning, or mobile networks—but only if you explicitly share that data. Otherwise, a plain IP lookup just gives a rough “local-ish vibe.”

Next time a website welcomes you by your city, don’t panic. It’s not magic, it’s not sinister surveillance, and there’s no crystal ball involved. It’s just your internet address doing the talking.
Want to know more? Watch our Lesson Hacker video on our YouTube channel.

For more Lesson Hacker videos, check out the CraignDave YouTube playlist HERE.

Visit our website to explore more cutting-edge tech news in the computer science world!

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

Has Steam brought AAA gaming to Linux?

Exploring Valve’s Steam Deck, Steam Machine, and the Future of Linux Gaming

2 July 2026

Gaming on Linux has long been the underdog story of the tech world. Windows has dominated AAA game development for decades, thanks to its tight integration with DirectX, the go-to toolkit that makes characters pop, physics run smoothly, and graphics shine. But Valve, with its Steam Empire, may be changing all that.

Steam’s bold move: Linux gaming gets a boost

Valve’s Steam OS, built on Linux, powers devices like the Steam Deck and the Steam Machine. By cutting out Windows licensing fees, Valve can focus resources on performance, giving gamers more power for the things that matter most—the games themselves. Historically, Linux gaming was tricky, like showing up with Marmite sandwiches while everyone else had cheese and ham. Now, with Valve bringing AAA titles to Linux, the game is changing.

Proton: The unsung hero

The real magic lies in Proton, Valve’s translation layer built on Wine. Proton converts Windows’ DirectX calls into Vulkan, Linux’s preferred graphics API. It’s like putting on a pair of magic glasses: most games run smoothly, but sometimes the translation stumbles. Despite occasional quirks and minor performance overheads, Proton allows popular AAA games like Cyberpunk to run on Linux devices without major headaches.

The anti-cheat hurdle

While Proton bridges the DirectX gap, anti-cheat software remains a sticking point. Programs like EasyAntiCheat and BattlEye operate in Windows’ kernel space, a restricted area Linux doesn’t readily allow. This limits Linux’s ability to run competitive multiplayer titles securely, leaving some AAA games inaccessible unless Windows is installed.

The path ahead

Valve has made major strides: standardised distribution with Flatpak, Vulkan performance gains, and a vibrant modding community. Cloud gaming also promises to lessen OS limitations, opening the door for broader Linux adoption. Still, anti-cheat challenges and developer hesitation mean the journey isn’t over.
What do you think? Is Linux finally ready for AAA gaming, or are anti-cheat and DirectX hurdles too high for one company to overcome?

Join the conversation and let us know your thoughts!

Watch our Lesson Hacker video to find out more.

For more great Lesson Hacker videos, check out the CraignDave YouTube playlist HERE.
Visit our website to explore more cutting-edge tech news in the computer science world!

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

What is Cloudflare?

Understanding the Internet’s secret superhero

2 July 2026

The Web’s bouncer, bodyguard, and personal trainer

If you’ve ever wondered why some websites load instantly while others feel like they’re moving through treacle, the answer might just be Cloudflare. You’ve probably seen the name pop up online, but what does it actually do? Think of it as the internet’s combination of bouncer, bodyguard, and personal trainer — keeping websites fast, secure, and significantly less explode-y.

Speed matters: How Cloudflare makes the Web faster

At its core, Cloudflare runs a massive global network of data centres — over 300 locations and counting. When a website uses Cloudflare, your requests get routed to the nearest data centre, thanks to something called a Content Delivery Network (CDN).
Imagine it like a library, but instead of books, you get your favourite cat videos or blog pages delivered from a server just a few miles away instead of across the Atlantic. This caching system makes websites load much faster, improving user experience and keeping impatient visitors happy.

Security first: Defending against cyber chaos

Speed is only part of the story. Cloudflare is also a cybersecurity powerhouse, filtering billions of dodgy requests every day. Using DDoS protection, Web Application Firewalls, and advanced bot-detection algorithms, it can stop malicious traffic in its tracks.
Think of it like this: if a website gets bombarded the way a toddler hits a piano — aggressively, loudly, and with zero rhythm — Cloudflare absorbs the attack. Their systems can handle traffic surges in terabits per second, essentially catching a falling building and saying, “No worries, sorted.”

Why developers love Cloudflare

One of the reasons Cloudflare is so popular is its accessibility. A single developer with a £2.99 shared hosting plan can suddenly enjoy enterprise-level performance and protection. It democratises security and speed in a way that once required expensive servers and hefty budgets. For many websites, putting Cloudflare in front of their infrastructure isn’t just smart — it’s survival.

A slightly worrying reality

Here’s the catch: with a huge portion of the internet relying on Cloudflare, a single company becomes a gatekeeper for global traffic. While outages are rare, when they happen, they can make the internet look like it’s having a midlife crisis. It’s a reminder that the internet, originally built as a decentralised network, now leans heavily on a handful of very important players.
Cloudflare is brilliant — genuinely — but it also highlights the delicate balance between speed, security, and centralisation in today’s online world.

Curious to see Cloudflare in action and understand how it really works? Watch the full Lesson Hacker video here.

For more insights, tutorials, and fun tech explanations, visit our CraignDave website.

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

The social media debate: What it really means for schools

20 June 2026

There is a growing sense that something significant is about to change in how young people access the online world. Political rhetoric has intensified, pressure from parents has mounted, and governments—both in the UK and abroad—are increasingly signalling that intervention is inevitable. For secondary school teachers, this raises an important question: what will all of this actually mean in the classroom?

At first glance, the issue appears straightforward. Social media is linked to harm; therefore, restrict access. But as with so many aspects of education and technology, the reality is far more complex.

Good intentions, complicated realities

It is difficult to argue with the motivations behind potential reforms. Concerns around online challenges, grooming, abuse, and the wider impact on young people’s mental health are real and well documented. Some medical bodies have even drawn comparisons between social media and smoking, citing links to sleep deprivation, anxiety, and increased exposure to harmful content.

Politically, the tone is clear. There is a sense that action is no longer optional. The idea of a “game changer” has been publicly floated, suggesting that incremental change may no longer satisfy public demand for stronger safeguards.

However, international examples show how difficult implementation can be. In Australia, where restrictions have already been attempted, young people have continued to access social media through workarounds. Criticism has also emerged over which platforms were included or excluded from bans, highlighting a deeper issue: regulation is only as strong as its definitions—and those definitions are increasingly unstable.

The definition problem

At the heart of the debate lies a deceptively simple question: what is social media?

A decade ago, the answer might have seemed obvious. Platforms like Facebook, Instagram, or Snapchat. Today, that clarity has disappeared. The boundaries between “social” and “educational” technology are blurring at speed.

Consider the following:

  • A large language model providing conversational feedback to a student.
  • An AI tutoring platform offering personalised guidance.
  • A shared Word or Google document used collaboratively.
  • An educational YouTube video with comments enabled.
  • A revision platform encouraging regular engagement.

All of these involve interaction. All enable communication, either with other users or with AI systems that simulate human response. At what point does that interaction become “social”?

This is not simply a philosophical question; it has practical consequences. If legislation attempts to restrict “social media”, it risks sweeping up tools that are now integral to teaching and learning. Increasingly, the same features that make platforms engaging—interactivity, collaboration, responsiveness—are precisely the features that make them pedagogically valuable.

The uncomfortable truth is that there is no clear line anymore. The category of “social media” has expanded to the point where it overlaps with almost all digital tools students use.

The teacher reality

For teachers, the debate is not really about banning phones. Schools have already grappled with that issue in various forms. The emerging conversation is about banning or restricting software—and that presents a very different set of challenges.

In practice, stricter controls could bring a range of unintended consequences:

  • Safeguarding complexities: Age verification systems may be introduced, potentially requiring students to provide personal data or biometric information.
  • Consent fatigue: Schools could face increasing administrative burdens as they manage permissions, accounts, and compliance requirements.
  • Onboarding friction: Even simple tasks, like signing up to a revision platform, may become more cumbersome, reducing lesson efficiency.
  • Digital overload: Teachers may spend more time troubleshooting access issues than delivering learning.

Anyone who has tried to get a full class logged into an online platform understands how fragile that process can be. Add layers of verification, restrictions, or blocked functionalities, and the risk is clear: many teachers may simply abandon these tools altogether.

This raises a critical question. At what point does protection begin to undermine the very educational experiences it is meant to support?

The risk of unintended consequences

There is also a broader concern that outright bans could push behaviour underground rather than eliminate it. If students are determined to access social platforms—as evidence suggests they are—they will find alternative routes.

These may be less visible, less regulated, and ultimately more dangerous.

For example, a shared document or file link can quickly become a hidden communication space. Without oversight, such environments could replicate the very risks that legislation is trying to address, but without the safeguards that established platforms at least attempt to provide.

This is the paradox at the centre of the debate: restricting access may reduce exposure in theory, but in practice it may simply relocate it to harder-to-monitor spaces.

Possible directions for change

While no single solution has emerged, several approaches are being explored. These range from technological controls to cultural shifts in behaviour.

Some proposals include platform-level changes such as removing auto-play or endless scrolling features, both of which are designed to increase user engagement. Others involve structural interventions, like age verification at the device or app store level, or even curfews that limit overnight usage.

There is also increasing interest in reframing the issue as a public health concern. Encouraging conversations about screen time in medical settings, collecting data for research, and educating families about usage patterns all point towards a more holistic approach.

At the same time, there are calls to place greater responsibility on technology companies themselves—requiring them to design safer systems rather than relying solely on user behaviour or external regulation.

Where does this leave schools?

For now, uncertainty remains. Yet one thing feels certain: the direction of travel is towards increased regulation, not less.

In this context, schools may need to hold onto a few key principles:

  • Digital literacy remains essential. Even if access is restricted, understanding online environments will still be crucial for young people.
  • Education cannot rely solely on prohibition. Students will encounter these technologies eventually, and they need the skills to navigate them safely.
  • Balance will be critical. The challenge is not just to protect students, but to do so in a way that preserves the benefits of digital learning.

Ultimately, the debate is not just about social media, it’s about how we prepare young people to live in a digital world that is constantly evolving.

A conversation worth having

Perhaps the most important takeaway is that there are no easy answers. The question is not whether young people should be protected online, there is universal agreement on that point. The question is how to do so effectively, without creating new problems in the process.

For teachers, this is not an abstract policy discussion. It is a daily reality, lived out in classrooms where technology is both a powerful tool and a potential source of risk.

As the conversation continues, one question remains open, and vital:

Where should we draw the line between learning tools and social platforms?

It is a question that policymakers, educators, parents, and students will need to answer together.

Want to know more? Check out our ‘At the chalk face’ episode on YouTube.

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

Why Do So Many Teachers Leave Teaching?

17 June 2026

If you ask almost any secondary teacher in England why colleagues are leaving the profession, you’ll likely hear a familiar answer: “It’s not the teaching — it’s everything around it.”

That simple line captures a much deeper and more complex story. One that blends policy, workload, school culture, and personal experience. To really understand it, we need to go beyond statistics and look at the lived reality of teachers themselves.

Two such stories, that of Craig and Dave, former teachers turned education resource providers offer a powerful lens through which to explore the issue.

The big picture: what the data tells us

Across Department for Education research and national surveys, five consistent drivers explain why teachers leave:

  • Excessive workload.
  • Stress and poor wellbeing.
  • Pupil behaviour challenges.
  • Pay and financial considerations.
  • Leadership, accountability, and working conditions.

At the top of that list, by a considerable margin, is workload. Around 90% of teachers considering leaving cite it as a factor, and it’s not difficult to see why.

The job is no longer just teaching. It is planning, marking, data entry, behaviour logging, meetings, emails, evidence-gathering, and more. Individually, each demand is reasonable. Together, they become overwhelming.

But statistics only take us so far. What does this actually feel like?

Craig’s story: when the job becomes too much

Craig didn’t go into teaching expecting to leave. Quite the opposite, he loved it. He progressed quickly, becoming head of department and taking on additional responsibilities. On paper, it looked like success.

But beneath the surface, something was changing.

“I didn’t notice it happening… it chipped away at me.”

The workload wasn’t just heavy, it was relentless. Leadership expectations, accountability measures, and administrative tasks accumulated to the point where prioritising became impossible. At one stage, Craig found himself asking his line manager a simple question:

“What do you want me to do first?”

The answer? Everything.

This is exactly what the research highlights: workload isn’t just about long hours, it’s about competing, often unrealistic demands. Teachers are expected to plan meticulously, mark extensively, track data, attend meetings, respond to initiatives, provide evidence and react to constant accountability pressures all at once.

Eventually, the impact became personal. Craig describes a gradual slide into stress, anxiety, and ultimately medically diagnosed depression. Crucially, it wasn’t the classroom that caused it.

“The bit I loved, the teaching, I was doing less and less of.”

This aligns closely with national findings: many teachers report that they still love teaching itself but cannot sustain the conditions around it.

At his lowest point, Craig found himself walking into lessons unprepared. Not through laziness, but exhaustion.

“What did we do last lesson? … That wasn’t for them, it was for me.”

For a conscientious teacher, that moment is deeply uncomfortable and it creates a vicious cycle. Overwork leads to underperformance, which leads to guilt, which worsens wellbeing.

Eventually, Craig had to step away from full-time teaching, but his story doesn’t end there.

When stripped back to just teaching — no meetings, no excessive admin, no leadership burden — he rediscovered what he loved.

“I fell back in love with the profession.”

That contrast is telling.

Dave’s story: When leadership and culture don’t align

Dave’s journey out of teaching took a different path, but points to another key factor in teacher attrition: leadership and working culture.

As an experienced assistant headteacher, Dave was at a career crossroads, considering promotion to deputy head. But a series of interactions made him question whether he wanted to continue.

One moment, in particular, stood out: a meeting about attendance.

Dave wanted to discuss strategies, student stories, and impact. His headteacher wanted a number — nothing more.

“What’s the percentage attendance in Year 8?”

Repeatedly, the conversation was reduced to data rather than professional dialogue.

“A number’s arbitrary… what matters is how we improve it.”

This clash reflects a broader issue identified in research: high-stakes accountability systems can shift focus away from meaningful teaching and leadership toward metrics, compliance, and evidence.

For Dave, it wasn’t just disagreement, it was a signal.

“I knew… I couldn’t work for this man anymore.”

Leadership style and professional trust are critical. When teachers feel reduced to data managers rather than educators, dissatisfaction grows.

Research supports this: lack of autonomy, rigid systems, and poor leadership culture are major contributors to teachers leaving the profession. Teachers want to feel trusted, valued, and able to exercise professional judgement.

When that’s missing, even senior leaders walk away.

It’s not just one thing, it’s the accumulation

What both stories make clear, and what the evidence strongly supports, is that teachers rarely leave for a single reason.

It’s not just workload.
It’s not just stress.
It’s not just leadership.

It’s the accumulation.

Consider a typical week:

  • Teaching multiple classes across different year groups.
  • Planning lessons and resources.
  • Marking hundreds of books or assessments.
  • Logging behaviour incidents and following up.
  • Entering and analysing data.
  • Attending meetings and CPD sessions.
  • Communicating with parents.
  • Preparing for inspections or internal reviews.

Now layer on emotional demands: supporting students, managing behaviour, dealing with safeguarding concerns.

Then add accountability pressures and limited recovery time.

The result? A job that routinely stretches into 50–60+ hours per week, spilling into evenings and weekends.

As Craig described, the consequence is not just tiredness, it’s a gradual erosion of capacity, motivation, and wellbeing.

Why teachers stay — until they can’t

One of the most striking points from both Craig and Dave’s stories is that they didn’t leave because they didn’t care. They left because they cared too much to continue as they were.

Craig and Dave both felt “trapped”. Aware that teaching offered:

A stable salary.

A strong pension.

Job security.

Familiarity.

It also required a unique skillset, and how easy was this to transition into other sectors?

These are powerful anchors. For many teachers, they delay the decision to leave, but when the job begins to affect health, relationships, and self-worth, those anchors can start to feel like weights.

So… why do so many teachers leave?

Because the job, as it is currently experienced by many, has drifted away from its core purpose.

Teaching should be about:

Inspiring students.

Explaining ideas.

Building relationships.

Making a difference.

Instead, too often it becomes:

Managing systems.

Producing evidence.

Meeting targets.

Surviving workload.

One thing very clear. Teachers don’t leave because they stop loving teaching. They leave because the conditions make it unsustainable.

A final thought

Both Craig and Dave still work in education. They still visit schools, support teachers, and engage with students. They didn’t leave education — they left the full-time classroom as it had become.

Perhaps that’s the most important reflection of all. If we want to retain great teachers, the question isn’t “Why are they leaving?” It’s “What has changed that made staying so difficult?”

Want to know more? Check out our ‘At the chalk face’ episode on YouTube.

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

When AI plays the music: The Velvet Sundown hoax that fooled the internet

5 March 2026

What happens when an AI band goes viral?

Imagine stumbling across a dreamy indie band on Spotify with 850,000 monthly listeners. They’ve got a verified profile, ethereal lyrics, and moody cover art — everything you’d expect from the next big thing in alternative music. Except… they’re not real.

Welcome to the curious case of The Velvet Sundown — an AI-generated band that tricked listeners, baffled journalists, and highlighted some big questions at the intersection of technology, music, and copyright law.

The fake band with real fans

On the surface, The Velvet Sundown seemed like a typical four-piece: Gabe, Lennie, Milo, and Rio. But internet sleuths noticed something odd — no live gigs, no social media, no interviews. Even the “press photo” looked suspiciously AI-generated.

Eventually, a supposed spokesperson admitted the entire band (and he himself) were fakes — creations built around music generated using an AI tool called Suno. Think ChatGPT for sound: you describe a vibe, it creates a song. Vocals, lyrics, melody — all fully synthetic.

Streaming algorithms, blurred realities

What’s worrying is how The Velvet Sundown thrived on Spotify’s algorithm, gaining thousands of listens through curated playlists and auto-play suggestions. Spotify hasn’t taken them down, and CEO Daniel Ek has confirmed there’s no intention to ban AI-generated music — unless it impersonates a real artist. But when even tech-savvy users can’t tell the difference, where’s the line?

Meanwhile, real musicians are furious. Artists like Elton John and Dua Lipa have pushed for stronger copyright protections in the UK, arguing that AI music models often rely on scraped human-made content. But government action? Still “under consultation”.

Does it matter who makes the music?

If you’re listening to lo-fi study beats or ambient playlists, do you care if the artist has a pulse? As AI becomes more convincing, it’s a real question — especially for young people growing up in a digital world where authenticity is often optional.

As Professor Gina Neff from Cambridge points out, we’re living in an age where deepfakes, AI influencers, and virtual personas make it increasingly hard to separate the real from the artificial. Music is just one part of that bigger picture.

Want the full story and a few laughs along the way?  Watch the full video to explore AI in music and more fascinating computer science concepts.


For more Lesson Hacker videos, check out the Craig’n’Dave YouTube playlist HERE.

Be sure to visit our website for more insights into the world of technology and the best teaching resources for computer science and business studies.

Stay informed, stay curious!

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026

Back

Does anyone still use low-level code?

14 January 2026

In an age where everyone seems obsessed with the latest AI chatbot or shiny new high-level programming language, you might wonder: Does anyone still use low-level code? 

The short answer: Yes. 

The long answer: YEEEEEEEEEEEEEES.

While most of the tech world is busy creating chatbots that sound like they’ve just devoured Freud and downed a Red Bull, somewhere in a dimly lit corner, a humble C developer is quietly making sure your toaster doesn’t launch into orbit.

The hidden power of low-level programming

Low-level programming is far from dead. In fact, it’s the invisible force quietly running the technology you use every day. Your car, your washing machine, the plane you’re not on because you spent your money on a new GPU — all of these rely on software written in C, C++, Rust, or even intimidating assembly language. (If you’ve ever seen assembly code, you’ll know it looks like someone tried to type while fending off a raccoon.)

You might be thinking, “Isn’t AI coding now? What’s the point?” Well, here’s the catch — someone still has to build the very systems that AI runs on. Think frameworks, compilers, virtual machines, and device drivers. AI agents don’t know how to manage memory in C, nor do they understand that using eval() like confetti is a bad idea.

Why learning low-level code matters

Learning low-level programming is like learning to fix an engine while everyone else is just learning to drive Teslas. Sure, a Tesla can drive itself… until it doesn’t. Then guess who they call? Not the AI coder — they call you.

If you’re fascinated by game engines, hardware drivers, or compilers, keep going. You’re not outdated — you’re underappreciated. When automation takes over many roles, your skills will remain invaluable because someone has to debug those GPIO pins robots can’t touch.

Stay low. Stay powerful. 

Curious to learn more about the importance of low-level programming?

Watch the full Lesson Hacker video to explore endianness and more fascinating computer science concepts. 

For more Lesson Hacker videos, check out the Craig’n’Dave YouTube playlist HERE.

Be sure to visit our website for more insights into the world of technology and the best teaching resources for computer science and business studies.

Stay informed, stay curious!

Related posts

How does AI generate images?

AI image generation turns random noise into detailed visuals using powerful maths, diffusion models, and learned patterns from millions of images.
What looks like creativity is actually a step-by-step process of refining chaos into coherent, realistic (or surreal) artwork guided by your prompt.

28 July 2026

Can AI remember?

Why does AI feel like it forgets everything mid-conversation? Discover how new breakthroughs in long-term memory could transform AI from goldfish to genius.

27 July 2026

What is E-Ink?

From e-readers to colour screens, discover how e-ink works, why it uses so little power, and why adding colour isn’t as easy as it seems.

24 July 2026

How do drones keep balanced?

Drones don’t hover by staying still — they balance by panicking hundreds of times per second and correcting instantly. Inside, tiny sensors and smart controllers keep them upright in a constant dance with physics.

23 July 2026

Is fetch, decode, execute still a thing?

Fetch, decode, execute: the classic CPU cycle is still alive and well, just turbocharged with pipelines, out-of-order execution, and branch prediction. Modern processors may be chaotic, but underneath it all, the old three-step dance still runs the show.

22 July 2026

Key in a lock with a house keyring.

Time2Code for Python has moved to GoCodeIt and Dodona

Following the closure of Trinket, the Time2Code Python course is now hosted on the GoCodeIt and Dodona platforms. As part […]

21 July 2026

Why is RAM Suddenly So Expensive?

Why has RAM gone from bargain-bin prices to costing like a city break? It’s not your PC suddenly getting fancy — AI data centres are hoovering up memory, and a few big companies are calling the shots.

Am I really Human?

Ever wondered why ticking “I am not a robot” feels like a test of your very humanity? It turns out your human clumsiness is exactly what keeps bots at bay.

20 July 2026

Why are TVs so cheap now?

Ever wondered why you can grab a 43” 4K smart TV for just £120? It’s a mix of industrial-scale manufacturing, razor-sharp competition, and the surprising role your viewing habits play in keeping prices low.

10 July 2026