Forem: Woody Hayday

Re-Engineering Education for the Age of AI

Woody Hayday — Mon, 03 Nov 2025 14:07:14 +0000

What developers can teach the education system about scalability, adaptability, and human-first design.

As a parent working in tech, I’ve started to look at “school” through a systems-engineering lens; and honestly, it feels like legacy software running on outdated infrastructure. The user base (our kids) has evolved, the environment (the world) has changed, and yet the core system architecture (schooling) still runs on assumptions written in the 1800s.

If we were reviewing it as a platform, we’d be calling for a full rewrite.

🧱 The Original Stack: The Prussian Model

The UK’s education system, like many around the world, inherited design principles from the Prussian model — a system optimized for a different era’s requirements:

Compulsory education (universal onboarding)
Standardized curriculum (rigid specifications)
Hierarchical structure (centralized control)
Teacher training (formalized QA)

That model produced predictable, compliant outputs — ideal for industrial and bureaucratic systems — but not for the adaptive, creative, distributed networks our world now depends on.

The Prussian model’s design goal wasn’t creativity. It was compliance.

⚙️ Version Drift: Technology Outpaced the Architecture

While everything else has gone through multiple generational rewrites; manufacturing, communication, computation; education has barely patched itself. We’ve added “interactive whiteboards” and iPads like plugins on a monolithic legacy system, but the core logic hasn’t changed.

Since the first national curriculum was written:

The number of sovereign nations has quintupled
Computing power has multiplied beyond comprehension
The internet, social media, AI, and globalization have rewritten every other human system

And yet, we still expect 32-child classrooms, hourly bells, and standardized testing to produce adaptive problem solvers.

If this were an engineering project, we’d call that technical debt — and the interest is compounding.

⚡ The Throughput Problem: Information Moves Faster Than Curriculums

In a world where cultural and technological trends propagate globally in hours, traditional education moves like a waterfall project in an agile world. The feedback loops are too slow, the data outdated by the time it’s taught.

Meanwhile, kids have already optimized their own information pipelines:

YouTube explainers
Stack Overflow–style forums
Niche Discord servers
TikToks that teach history through memes

Learning has become decentralized, open-source, and asynchronous.

The education system just hasn’t accepted the pull request yet.

🧩 New Survival APIs

If we were to write a new spec for “modern survival skills,” it might include:

Digital fluency: how to collaborate with AI tools instead of banning them
Financial literacy: from crypto to creator economies to algorithmic finance
Nutrition and self-care: biological literacy in an era of ultra-processed everything
Social resilience: emotional intelligence and discernment in online spaces
Neurodiversity awareness: optimizing for different cognitive architectures instead of one standard model

These aren’t “nice-to-haves.”

They’re required libraries for functioning in a post-industrial, AI-augmented world.

🧠 Parents and Educators as System Architects

The real challenge — and opportunity — lies in thinking like system designers.

We don’t have to delete the whole repo; we can modularize, decentralize, and rebuild from first principles.

Home education can act as a sandbox environment — experimental, flexible, rapidly iterating
Hybrid schooling (part-time attendance, part-time project learning) is like a distributed microservice model
EdTech is building APIs between learning and living — platforms like Jenny.ai or Strew extend the classroom into the real world

💡 The Developer’s Takeaway

If you work in tech, you already think in systems.

You debug, refactor, containerize, and constantly deploy new ideas.

Education needs that same mindset shift — from rigid process to continuous delivery of human capability.

AI won’t replace schools, but it will expose which parts of the system are no longer fit for purpose.

The question for us — as engineers, parents, and citizens — is how to architect something better.

I’m exploring this further while building Strew — a human-first EdTech platform designed to help parents and kids learn in sync with the realities of the AI age.

If you’re a developer who believes education deserves a rewrite, come join the discussion.

Teaching the Machines That Teach Our Children

Woody Hayday — Tue, 21 Oct 2025 13:37:25 +0000

Teaching the Machines That Teach Our Children

I caught myself the other night debugging a half-broken Python script while my step-daughter asked about fractions.

The script was fine. The fractions weren’t. Or maybe it was the other way around.

That’s when it hit me: both of us were learning, both of us were training something.

I was teaching her, yes; but I was also working with an AI that would, soon enough, start teaching her too.

That’s the loop now.

We’re not just parents or developers anymore; we’re prompt engineers for both species.

The First Prompts We Give

Before the models came, before the datasets and fine-tuning runs, we already knew what it was to train intelligence.

We call it parenting.

Every “say thank you,” every bedtime story, every sigh of frustration; it’s all data.

A home-grown corpus of micro-ethics and linguistic nuance.

We’re alignment engineers long before we ever fine-tune a model.

def teach(human):
    human.prompts = ["be kind", "be curious", "question everything"]
    human.biases = inherited()
    return human.learn(through="play", with="love")

Kids are neural nets made of wonder. They learn from what we do, not what we say.
And if you’ve ever had a toddler repeat your worst habits like a perfect mirror; you know the loss function hurts.

Fine-Tuning the Tutor

Now the machines are in the mix.

AI tutors. Edtech assistants. Friendly, unblinking copilots whispering “Would you like an explanation?” while you sip coffee.

It’s seductive because it works. Personalized feedback, infinite patience, 24/7 availability.

But these tutors aren’t neutral. They’re trained on everything and everyone, scraped from the chaos of the internet.

That’s the next parental frontier: deciding what data we want our children’s teachers to learn from.

Do I want my daughter’s AI trained on a global dataset of good intentions and bad actors?

Or do I want to fine-tune a small, stubborn model on our own family’s values; the digital equivalent of home education?

In ten years, “AI literacy” won’t just mean promptcraft — it’ll mean model curation.

The families that can teach their machines how to teach will be the ones writing the new curriculum.

Alignment Lessons

We talk about “AI alignment” like it’s something engineers do in sealed labs.

But alignment is what parents have done since language existed; teaching small humans not to destroy the world for fun.

AI alignment and moral education are the same project, just running at different clock speeds.

Both require transparency, empathy, context, and an endless tolerance for error.

Both fail catastrophically when we assume obedience is intelligence.

The old education model, the Prussian industrial classroom, was never built for this kind of recursive learning.

You can’t batch-process creativity or standardize curiosity.

In a world where information moves faster than comprehension, schools need a new kernel.

Something more open, adaptive, decentralized — closer to open source than empire.

Parenting as a DevOps Loop

Parenting, education, AI; it’s all just continuous deployment of unfinished systems.

You build, you test, it half works, you iterate.

# parenting pipeline
observe && listen
commit -m "fixed bedtime routine"
deploy --with_patience

You don’t ship the child - you ship the environment.

You build the culture around them like infrastructure-as-code.

Feedback loops replace grading systems. Reflection replaces punishment.

Learning becomes a live process, not a yearly report.

When the machines enter that loop, they don’t replace us; they amplify us.

They take the repetitive strain off the act of teaching, leaving us more time for the human parts: curiosity, ethics, intuition.

If we do this right, AI doesn’t raise our kids; it gives us the bandwidth to do it properly.

Recursive Love

Here’s what I think happens next:

Our kids will teach the machines how to understand humans better.

And the machines, in turn, will teach us how humans actually learn.

The line between tutoring and companionship will blur.

Your child’s favorite AI assistant might remember every question they ever asked, and help them ask better ones.

That’s not dystopia. That’s just recursion.

Every prompt, every conversation, every bedtime “why?” — it all becomes training data for both species.

We’re teaching the machines that will teach our children. And maybe, if we stay awake through it, they’ll teach us to be better teachers too.

Because the future classroom won’t have walls or desks or bells.

It’ll be everywhere; in the code, in the kitchen, in the quiet feedback loop between a human and whatever they just built.

And when we look into that mirror, the one made of light and language, maybe we’ll see that we were never just raising children at all.

We were raising the next generation of teachers.

When the Swarm Wakes

Woody Hayday — Fri, 12 Sep 2025 14:36:03 +0000

Being an Entrepreneur in the Age of AI

I’m building a 100% AI-run business by the end of 2025.

So far? It’s powerful. It’s promising. And it’s held together with duct tape.

It Doesn’t Quite Feel Like AI

The dream is a self-directing swarm of agents running the show.

The reality? Humans approving outputs. Workflows stitched together by hand. A hundred micro-decisions on what to automate versus what to leave alone.

Tools like Lindy AI are starting to smooth the edges, automating customer interactions or scheduling without constant human nudging. But the full “lights-out” AI company? We’re not there yet.

AI can do a lot. Left unsupervised, though, it mostly stalls or tries to high five you as it drives off a cliff. I'm still figuring out where to trust it, where to babysit it, and where to just get the hull out of the way.

Memory: The Hidden Engine

One thing became clear fast: without memory, a swarm forgets everything. Every run is day one.

Platforms like Relevance AI are making a start here here — turning scattered data into searchable memory for agents.

I’ve been building my own version too:

Shared across agents, persistent across projects.
Version-controlled so nothing breaks when things change.
Big enough to remember what worked, small enough to stay coherent.

Slowly, patterns emerge. The swarm isn't learning, exactly, but it's making baby steps towards the door.

What Entrepreneurs Should Actually Do

Here’s what’s shaking out for founders building with AI right now:

Keep humans in the loop

Full autonomy is tempting. But oversight matters — especially where mistakes hurt
Build memory early

Context is gold. Shared memory keeps swarms from repeating the same errors forever
Stay modular

Think networks of small specialists, not one big “super agent.” Flexibility beats size
Embrace messy early days

The first versions will be clunky. Iterate anyway. Progress hides inside the chaos
Price for reality

AI isn’t free. Infrastructure, guardrails, and humans all cost something. Plan for it

The Next Wave

Here’s what’s coming as swarms mature:

Trust as a feature — AI that forgets or contradicts itself won’t last long
Specialization over generalization — niche agents with deep knowledge will win
Managing complexity — every new agent, memory layer, or workflow adds overhead
Personality and tone — your swarm’s “voice” will leak through everything it makes

The Entrepreneur’s Role

We’re not here just to “use AI.”

Our job is to orchestrate it; to design systems that remember, adapt, and deliver consistently, even after the shiny newness wears off. We're kind of trying to engineer evolution.

Because when the swarm finally wakes, the winners won’t be the ones with the flashiest tech.

They’ll be the ones who built AI that actually keeps its head on straight.