Pipi Data Centre Operational

Mike's Notes

Very good news for Pipi.

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Last Updated

25/02/2026

Pipi Data Centre Operational

By: Mike Peters
On a Sandy Beach: 25/02/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

The long-planned migration of Pipi 9 to its own data centre has been completed. It took 2 weeks to execute. The existing setup was split into an office network connected to the internet and an isolated data centre that is not connected to the internet.

Starting from zero

The initial data centre consists of a single 45U rack and some other shelving, with mainly older equipment. It will do for a start and can grow as more racks are added, equipment upgraded, and more servers are added, etc.

External hard drives used in the shift

Issues

  • Terabytes of data on backup hard drives to shift
  • Clean reinstalls of many operating systems
  • 14 machines to configure
  • Adobe CS4 does not like Windows 11
  • Making do with what is available now
  • Go slow, think twice and get there faster

Opportunities

  • Pipi can now run 24x7x365
  • All systems can be turned on using multiple servers
  • DevOps automation is now possible
  • The development cycle will speed up 10x
  • The road to Pipi 10 is now open

Whats next

  • Seat-of-the-pants experimenting to tune the setup
  • Stress test to build resiliance and reliability

Waimumu Field Days - Background to Ajabbi and Pipi

Mike's Notes

I recently attended the 3-day Southern Field Days held every two years at Waimumu, near Gore, Southland, New Zealand (NZ). It's an event for farmers and is very popular.

If you have never been, go next time. Farmers are clever people, and they feed us.

This year, I visited every company at the Field Days that has a software product for farmers to discuss the integration and scaling challenges they were facing and introduce Pipi.

Pipi could be used by developers to build SaaS for farm and forestry management.

I invited them to give me feedback by testing a demo workspace for agriculture later this year. I got quite a bit of interest and was given many business cards. I found it much easier to talk in person than over Zoom. I also learned that next time to show them a live demo on a tablet.

This is a copy of the personalised email text I sent afterwards to establish contact. To be followed up on in a few months with individual invites to have a closer look, test, give me feedback, etc.

This message is being updated as I learn what works.

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Last Updated

24/02/2026

Waimumu Field Days - Background to Ajabbi and Pipi

By: Mike Peters
On a Sandy Beach: 24/02/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Waimumu site



Tractors and diggers

Fencing @ Waimumu

"Hi xxx

I may have met you in person at the SI Field Days (or one of your people gave me your card) for a chat about an enterprise AI I am the architect of, which could be used for Agritec. It has open integration.

There will be nothing much to see publicly now, but here is some background information for you. I will get in touch in a few months as beta testing proceeds.

This is some general background information about Ajabbi.

Ajabbi is a community-driven bootstrap startup with a main product, Pipi, which has a closed core and open-source applications. Optimised to build self-managing no-code enterprise platforms for critical and socially useful infrastructure (Health, agriculture, transport, nature conservation, arts & culture, built infrastructure, utilities)

There will be several parts to Ajabbi

    1. Ajabbi.com for SaaS operations to handle usage-based income and cloud expenses. An % of income will support R&D. Net profit will go to the Ajabbi Foundation.
    2. Ajabbi Research for R&D on Pipi.
    3. Ajabbi Foundation will fund other open-source products, Pipi user groups, industry conferences, books, and science, among other areas.

Pipi

Started in 1997 as version 1. By version 4 (2005-2007), it was a SaaS platform supporting ecological restoration projects in NZ (govt valuation: NZ$ 3 M).

Now, version 9 is a multi-agent world-model AI (not an LLM) that shares similarities with a Godel Machine, able to learn, evolve, self-organise, and reproduce. Constrained by published ontologies and laws of physics.

Currently, the community is beta-testing an open-source multi-workspace UI and generating 20,000 pages of developer documentation. Expect to be in production late 2026.

Finding out more.

    • https://www.blog.ajabbi.com/ (daily engineering blog)

Contact me for a chat, slide talks, etc

The main 26 websites (developer, learn, handbook, design, pipiWiki, etc.) at ajabbi.com are currently hidden from search results and change frequently during testing.

Diving deeper with some reading

Any questions, feel free to ask"

Carlos Gershenson: On the Limits of the Scientific Study of Complex Systems

Mike's Notes

A talk recorded on Vimeo by Carlos Gershenson.

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Last Updated

23/02/2026

Carlos Gershenson: On the Limits of the Scientific Study of Complex Systems

By: Carlos Gershenson
Vimeo: 28/01/2026

Carlos Gershenson (Systems Science and Industrial Engineering, Binghamton University).

Binghamton Centre of Complex Systems (CoCo) Seminar: January 28, 2026

“On the Limits of the Scientific Study of Complex Systems”

Vimeo 58:17

gRPC Clearly Explained

Mike's Notes

Good big picture explanation of gRPC. Pipi will have a dedicated gRPC Engine (grpc).

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Last Updated

22/02/2026

gRPC Clearly Explained

By: Nikki Siapno
Level Up Coding System Design: 18/01/2026

Founder LUC | Eng Manager | ex-Canva | 400k+ audience | Helping you become a great engineer and leader.

What gRPC actually is, and the real reasons teams adopt it beyond “it’s faster”.

gRPC: What It Is and Why Teams Use It

Most teams reach for REST by habit.

It works, it’s everywhere, and every tool knows how to talk HTTP+JSON.

But once you have dozens of microservices calling each other thousands of times per request, REST can quietly become the bottleneck.

That’s where gRPC shows up with a very different model: “call a function on another service as if it were local,” and make it fast.

What gRPC (and RPC) really is

Before gRPC, there’s RPC.

Remote Procedure Call (RPC) is a model where you call a function that runs on another machine, but it feels local to the caller.

You call a method, pass arguments, and get a result back. The network exists, but it’s intentionally hidden so developers can think in terms of functions instead of sockets and packets.

gRPC is a modern, open-source RPC framework released by Google in 2015. It takes the RPC idea and standardizes how services define methods, exchange data, and communicate efficiently over the network.

Two building blocks drive almost everything you feel in practice:

  • HTTP/2 → The transport protocol. It keeps a long-lived connection and supports multiplexed streams.
  • Protocol Buffers (Protobuf) → The data format. It’s a compact binary serialization with a schema (a defined shape).

Together, these turn “call a service” into a strongly typed operation instead of a loosely structured document exchange.

How gRPC actually works

gRPC starts with a service definition. You describe your API in a .proto file by defining services (methods) and messages (request and response shapes).

This file is the contract. Both client and server are built from it.

From that contract, gRPC generates code:

  • Client stubs → Methods you call like local functions.
  • Server interfaces → Methods you implement with your business logic.

When a client calls a gRPC method, the flow is straightforward:

  1. Serialize → The request is encoded into Protobuf’s binary format.
  2. Send → The message travels over an existing HTTP/2 stream.
  3. Dispatch → The server routes it to the correct method.
  4. Execute → Your code runs.
  5. Respond → The result is serialized and streamed back.

Because gRPC uses HTTP/2, many calls share one connection and run in parallel. A slow response doesn’t block faster ones behind it, which keeps tail latency under control.

Streaming uses the same mechanism:

  • Server-streaming → One request, many responses over time.
  • Client-streaming → Many requests, one final response.
  • Bidirectional streaming → Both sides send messages independently.

Backpressure is built in. If one side slows down, gRPC slows the stream instead of piling up memory or threads.

The core idea is simple: define a strict contract, generate code from it, and move typed messages efficiently over a shared connection.

Where gRPC pays off

gRPC is a strong fit when you control both ends and you care about efficiency.

  • High call volume microservices → Lower per-call overhead adds up fast at scale.
  • Latency-sensitive graphs → Multiplexing + smaller payloads reduces tail latency pressure.
  • Polyglot stacks → One .proto contract generates stubs across languages, reducing “JSON drift.”
  • Service mesh environments → gRPC routes cleanly through modern proxies and is common in mesh control-plane protocols.

Tradeoffs you feel immediately

gRPC’s downsides are predictable, and they usually show up early on.

  • Browser calls are not native → You often need gRPC-Web or a REST/JSON gateway for front-end use.
  • Debugging is less “curl-friendly” → Binary payloads require tooling like grpcurl or GUI clients with schema access.
  • Contracts tighten coupling → Clients must update generated code as schemas evolve, so versioning discipline matters.
  • Infra must support HTTP/2 well → Some proxies and firewalls need explicit support or configuration.

How to Decide: Is gRPC a Fit for Your System?

Use gRPC when most of the following are true:

  • You control both client and server → Internal microservices inside the same organization.
  • You’re performance-sensitive → Many small calls per request, or very high QPS between services.
  • You’re polyglot → Multiple languages across teams and services.
  • You need streaming → Real-time updates, telemetry, chat, or continuous feeds.
  • You want strict contracts → You care about compile-time guarantees and explicit schemas.

Stick to REST (or layer a REST gateway in front of gRPC) when:

  • You expose public APIs to unknown clients.
  • You want easy browser and curl-based experimentation.
  • Your main pain is clarity and discoverability, not raw latency or throughput.

In practice, most teams don’t choose one or the other; they split the responsibility.

  • gRPC inside your network → Service-to-service, behind an API gateway or service mesh.
  • REST/JSON at the edge → For browsers, partners, and mobile apps that prefer HTTP+JSON.

Recap

gRPC is not “REST but faster.”

It’s a different model: remote procedure calls over HTTP/2, using Protobuf contracts, with first-class support for streaming and strong typing.

That makes it excellent for internal microservices, high-performance backends, and real-time systems, as long as you’re willing to invest in schemas, tooling, and a slightly steeper learning curve.

If you’re hitting the limits of REST inside your system (too many chatty JSON calls, tricky real-time updates, or a messy polyglot codebase) gRPC is worth a serious look.

The Heresy of Breakthrough Startups

Mike's Notes

Very cool and spot on. I like the example of Galileo.

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Last Updated

21/02/2026

The Heresy of Breakthrough Startups

By: Mike Marples
Pattern Breakers: 01/10/2025

Thunderlizard Hunter.

Breakthrough startups happen when founders refuse a hidden assumption, replace it with a better explanation, and make it impossible for early believers to unsee.

In Florence, one of Galileo’s telescopes is on display. It’s a simple leather tube with scratched glass and optics that look primitive today. Yet it was enough to overturn a conviction that had guided human thought for centuries: Earth was not the fixed center of the cosmos but just another planet in motion.

The Church felt threatened by Galileo’s new way of seeing the universe. In 1633, they put him on trial, forced him to recant his view, and kept him under house arrest for the rest of his life. They could silence his voice for a while, but they couldn’t erase his discovery. Centuries later, even the Church admitted the truth of what he had seen.

Galileo recants his discovery after the Inquisition.

Galileo’s greatest achievement was not the telescope itself. Others could make lenses. The breakthrough was Galileo’s willingness to see differently, to question what tradition declared unquestionable. The tool mattered less than the insight it led to.

This is similar to what we see in pattern-breaking startups. What begins as heresy often becomes the next foundation of knowledge.

Heresy and Pattern-Breaking Founders

Almost every breakthrough founder I’ve ever known was, in some sense, a heretic. True, they were smart and determined and persuasive. But it was more than that. They had seen a future that clashed with the present so decisively that most people dismissed it as impossible, stupid, or even crazy.

Here’s the hard truth: if what you’re doing doesn’t sound like heresy to someone, it’s probably too similar to what’s already been done. Incremental ideas can make money, but breakthroughs often seem like a business heresy. Without it, you’re just improving things inside the old frame instead of creating a new one.

Take Twitch. For years, the assumption was: “Nobody wants to watch other people play video games.” Gaming was seen as something you did, not something you spectated. Sports were for watching, games were for playing.

Twitch refused the conventional assumption and introduced a heretical idea: anyone could livestream their gameplay. Within a few years, it became clear that millions of people not only wanted to watch, but also to chat, cheer, and build community. What seemed like a niche hobby proved to be a new form of entertainment that could compete with cable television and even sports. Twitch showed that, for lots of people, watching games can be as compelling as playing them.

Galileo’s telescope opened the way to a new explanation of the cosmos. Twitch’s breakout success revealed a hidden truth about human behavior. Both were heresies because they attacked the foundation of what people “knew” to be true.

But here’s the deeper truth for founders: heresy itself is not the central issue. Most heresies are wrong. What matters is the creation of better explanations — ideas that solve more problems than the ones they replace. In Pattern Breakers, we call these better explanations insights. Heresy is simply what better explanations look like to those still committed to the old way of seeing.

The Messy Truth

Most heretical ideas don’t start as breakthroughs. They more often start as annoyances.

Before starting Stripe, the Collisons were just trying to accept payments for their previous startup. The process was so painful—merchant accounts, paperwork, weeks of waiting—that they built something for themselves. Brian Chesky wasn’t trying to revolutionize hospitality. He was broke and needed rent money, so he created a Wordpress site that rented out air mattresses during a design conference.

These weren’t empire builders executing a master plan, at least not at first. They were people frustrated enough with current circumstances to build their own solution.

It’s also tempting to view heretical startup ideas through the lens of contrarianism. But that is also overly determined. Contrarianism disagrees with the crowd; heresy replaces the crowd’s assumption with a better explanation. Beware contrarianism for its own sake. Many startup ideas are imitation dressed up as originality, like “Uber-for-X” derivatives that imitate a model of success without finding a new way to fix a real frustration. True heresy solves a deep problem in a way that departs from the consensus through original thinking, not just by being in opposition to existing ideas.

These messy truths are worth remembering, because we often turn breakthroughs into myths after they succeed. We create frameworks and principles to explain them. Yet in the beginning, they usually start with someone who simply says, ‘This doesn’t make sense. There must be a better way than the one everyone assumes.’

What Makes Some Ideas Matter

Not every frustration turns into a breakthrough. I’ve noticed that the ones that do often share three traits:

First, they challenge an assumption so deep, people don’t even realize they believe it. “Payments require cumbersome paperwork and banking compliance.” “You need to own music.” “Strangers won’t sleep in each other’s homes.” These aren’t opinions people argue about. They feel like facts about how the world works. This is where the refusal begins.

Second, they replace the old way with something so much better that the old way becomes unacceptable. The change is not marginal, but fundamental. After Stripe, merchant accounts seemed outdated. After Spotify, buying MP3s seemed unnecessary. This is the new way of seeing, even though at first it looks heretical.

Third, a fundamental change must occur to make the breakthrough possible. Stripe emerged because APIs had become widespread. Streaming music worked only when broadband was broadly available. Ridesharing became real when smartphones shipped with embedded GPS chips. The inflection is what allows a founder to see a potential new truth and imagine a different future.

Progress is not inevitable. It requires more than the availability of new enabling technology. A creative new explanation must unlock its potential. Technologies by themselves do nothing. Broadband, APIs, and GPS remained idle until someone explained how they could be applied to create new value.

Timing depends equally on people being prepared to adopt new habits. Uber and Lyft succeeded not only because smartphones existed, but because customers had become willing to share rides with strangers.

Even Galileo, who had truth on his side, had to face the fact that most of the world was not ready. In startups, timing often decides whether an idea remains a curiosity or grows into a transformative company. If you are too early, the technology may not support your idea, or society may not be ready to accept it. If you are too late, others might beat you to the opportunity.

Living Ahead of Others

Founders who uncover heresies aren’t necessarily “smarter” in the usual sense. They’re often just living in a different time — a few years ahead of the rest of us.

Take Daniel Ek at Spotify. In the mid-2000s, music executives saw piracy as theft, a threat to be shut down. They looked at The Pirate Bay and saw a crime scene.

Ek saw differently. Growing up in Sweden, where piracy was everywhere, he noticed something deeper: people weren’t rejecting music’s value. They were rejecting the friction of ownership. So when Daniel Ek looked at CDs, iTunes, and per-track purchases, he didn’t just see inconvenience. He saw an obsolete worldview, as outdated as buying a CD player. What people wanted was access. Instant, limitless, effortless. Piracy was ugly and illegal, but it hinted at a different future.

This is why so many smart people missed Spotify. They were still seeing the present, where music was a product to sell and would always be that way because it was the only approach the record labels would accept. Ek was seeing the future, where streaming was a better explanation for customers and the music industry.

Proof by Demos

Just explaining your heresy isn’t enough. You need tangible proof that grabs people. Galileo didn’t just argue; he invited others to look through his telescope.

Galileo demos his telescope for the Doge of Venice

Great startup products do this as well. They don’t debate. They show.

  • Stripe: Before, founders faxed forms and waited weeks for approval from a system organized around banking and compliance. Patrick Collison typed eight lines of Ruby code and charged a credit card in a room full of founders. What once took weeks collapsed into seconds.
  • Spotify: Daniel Ek took requests to play any tune at crowded parties. He typed whatever song people wanted, hit play, and music streamed instantly.
  • Tesla: In 2015, Tesla pushed a software update. Drivers double-clicked the cruise control stalk. The car steered itself. The definition of what a car even was seemed to instantly change.
  • Figma: Designers opened a shared file and saw multiple cursors moving at once. Collaboration wasn’t around a file attachment anymore…it was live.

These demos didn’t argue about the future. They dragged you into it. Suddenly, you were living in the founders’ reality, where payments happened instantly, where any song ever recorded played immediately, where electric cars could accelerate faster than Ferraris and drive themselves. And once you experienced that reality - even for just a second - the old one looked broken.

A pitch is an argument about the future. A great demo transcends argument by making people feel it.

Keeping Yourself Honest

There is no recipe for inventing heresies. Genuine breakthroughs cannot be summoned on demand, because the discovery of breakthrough insights is inherently unpredictable.

By the same token, founders rarely lack ideas. What they often lack is the ability to tell whether those ideas are breakthrough insights or merely variations on existing assumptions. In this uncertainty, I find the below questions useful for stress testing an idea’s potential.

  • Refusal: What entrenched assumption am I rejecting? If you cannot name it clearly, you may only be tinkering inside the old frame.
  • Heresy: What replacement truth explains more than the old view? If it does not solve more problems than it creates, it is not strong enough.
  • Inflection: Why now? What has changed in knowledge, technology, or human behavior to make it feasible today? If nothing fundamental has shifted, the idea may remain inert.
  • Demo: How can I show it in seconds rather than slides? A genuine breakthrough is experienced, not just argued.

These questions cannot predict which ideas will succeed. They do not generate vision, nor do they guarantee progress. Their value lies in the power of self-criticism: they help founders avoid self-deception, highlight where an idea is weak, and keep energy from being wasted on rationalizations that cannot stand.

Breakthroughs still depend on imagination, persistence, and error-correction. But with honesty, a founder can focus scarce attention on the few ideas that might truly overturn assumptions, rather than being distracted by the many that never could.

The Weight of Heresy

Every heresy carries a cost. Galileo spent his last years under house arrest. Darwin delayed publishing his work for fear of the extreme reaction he would surely provoke. Founders, too, face skepticism when they challenge accepted assumptions. But the most difficult resistance is not social. It is conceptual.

Old explanations persist because people cannot yet imagine an alternative. To Galileo’s contemporaries, the idea of a moving Earth was not only offensive, it was unthinkable. They defended the old framework because it was the only one available to them.

This is the true burden of heresy: it requires persistence at a time when others literally cannot see what you see. Institutions and customers resist not out of malice but because they lack the conceptual tools to recognize a better truth. The task of the founder is not simply to endure opposition, but to supply the new framework that makes the old one untenable.

Most contrarian ideas fail because they never progress to this stage. They create discussion, but they do not deliver a superior explanation. When a stronger explanation does appear, one that is broad in scope, resilient to criticism, and difficult to dismiss, resistance begins to weaken. What once seemed absurd becomes accepted. History later describes the change as inevitable, even though it never was.

The weight of heresy is not measured by the volume of objections, but by the time required for the better explanation to take hold. Success belongs to those who can carry it until reality and imagination catch up.

See what others don’t; Solve what others can’t

Breakthrough founders usually do not predict the future. They discover better explanations that reveal it. And once you truly see it, you cannot unsee it. The old way no longer just looks worse.

It looks like history.

The American Dream needs a factory reset

Mike's Notes

An interesting solution to the housing problem. Shows what is possible if there were the will.

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Last Updated

19/02/2026

The American Dream needs a factory reset

By: Stephen McBride
Rational Optimist Society: 18/01/2026

Rational Optimist Society - Founder.

Housing’s “iPhone moment”

In today’s diary:

  • Expensive homes = fewer new families
  • Where most housing innovators fail
  • How Tesla’s “tent hack” solves housing
  • Factories that update like an iPhone
  • Build your home like The Sims—in 30 days

Dear Rational Optimist,

I have a friend back in Ireland named Zach.

Zach is a mechanic with his own business. He grinds and does everything right. Yet every night he walks past the main house and goes to sleep in a log cabin in his girlfriend’s parents’ backyard.

Now his girlfriend is pregnant with their first child. They’re about to bring a new life into the world, and they don’t have a place to put the crib.

I have another friend in New York City, the founder of a promising, high-growth startup. He’s crushing it but lives in a Manhattan one-bedroom apartment with his wife and daughter. They want another baby but can’t afford the extra bedroom.

Studies show rising housing costs explain roughly half of the fertility decline in America between 2000 and 2020.

That’s millions of kids who were never born because the rent was too high.

Having kids is a vote of confidence in the future. It’s the ultimate act of optimism. We’re pricing people out of that optimism.

We know the solution: Build. More. Housing. Yet we’re building homes today slower than we did in 1971.

Over the last decade venture capitalists incinerated billions of dollars betting on startups that promised to fix housing. They all failed.

But I think I’ve finally found…

The Henry Ford of housing.

Before we meet this company, let’s visit the graveyard of failed housing startups. There are many headstones.

A few years ago housing disruptor Katerra raised $2 billion to build “gigafactories” for homes. It wanted to mass-produce homes on an assembly line like iPhones, ship them nationwide, and snap them together on-site.

We build cars in factories, so why not houses? It sounded inevitable.

Katerra went bust in 2021. It stepped on two booby traps which killed almost every would-be housing disruptor.

Booby trap No. 1: shipping air.

When you build finished rooms in a factory and ship them, you’re shipping floors, walls, and ceilings. You’re essentially paying to ship empty boxes of air.

This eats up every penny saved by the factory efficiency.

Booby trap No. 2: cash incinerator.

Giant factories cost a lot of money to build. To make the math work they must run at near-full capacity.

But housing is boom-and-bust. When the market dips (it always does), the factory doesn’t stop costing money and turns into a cash incinerator. Katerra built cathedrals of manufacturing requiring perfect economic weather to survive. When it started raining, it drowned.

Then there’s the deadliest booby trap of all.

The US government tried to build houses in factories…

1969 was a great year for the optimists. America put a man on the moon and Concorde flew its first supersonic voyage.

It was also the year of Operation Breakthrough, the US government’s experiment to industrialize housing. The goal was to fund mass-producible building systems and construct 25,000 modern homes.

It was a total disaster. They built fewer than 3,000 units before shutting down.

Uncle Sam failed to account for local governments.

Factories work when they build the exact same thing, over and over. You can’t do that with homes because there are 26,000 towns and cities with different building codes.

It’s like trying to mass-produce a car, but every town has its own rules about where the brake pedal and steering wheel should go.

We’ve been trying to build houses like cars for a century. But houses aren’t cars. They’re legal projects, financial products, and custom assemblies rolled into one.

Malcom’s box

Malcom McLean was a North Carolina truck driver with an idea.

Create steel containers that could neatly and quickly stack on ships, trains, and trucks. The shipping container was born!

On a spring morning in 1956, McLean’s refitted oil tanker left Newark carrying 58 identical steel boxes. That was the day global trade got rewired.

The cost of shipping fell by more than 90% over the following years. Those steel boxes became the universal language of trade, easily swappable across ships, trains, and trucks. Every global brand you know—Nike, Walmart, Apple—owes its business model to McLean’s steel box enabling global trade.

What McLean did for shipping, Cuby Technologies is doing for housing.

“What’s in the box?”

If you’re a Dune superfan like me, you know the scene.

"What's in the box" Dune quote image

Ask that question to Cuby co-founder Aleks Gampel and he won’t respond “pain.” He’ll say, “Everything you need to build a new home in just 30 days.”

Cuby doesn’t build houses. It builds the factories that build houses.

It took an entire automotive-grade production line—robotics, CNC machines, welding stations—and packed it into approximately 122 shipping containers.

Cuby’s product is the Mobile Micro-Factory (MMFTM). It’s a standardized, portable factory that turns homebuilding into a predictable manufacturing process.

When Tesla hit “production hell” in Fremont, it couldn’t get permission to build a new facility fast enough. So Elon put up a massive tent in the parking lot. Because it was a “temporary structure,” he bypassed the zoning nightmare and saved the company.

Cuby takes Tesla’s tent hack to the next level:


Cuby lean-tos image
Source: Cuby

If you build a factory, you need permits and years of approvals. Cuby figured out how to snap 122 shipping containers together and be classified as one giant “machine.”

This hack allows Cuby to stand up an MMF, capable of pumping out 200 homes per year, in just 30 days.

MMFs are compact enough to slot into a mall parking lot. You inflate a massive, pressurized dome. Inside the dome the shipping containers open up to become a fully functioning housing factory:


Cuby housing factory interior image
Source: Cuby

Cuby’s other co-founder, Aleh Kandrashou, walked me (virtually) through its test facility in Eastern Europe to see how an MMF works.

Cuby broke the construction process down into 35 different departments. Walk past one container and inside is a dedicated welding robot fusing steel foundations. Move to the next container, and it’s a specialized paint booth coating the exterior panels.

The containers snap together to form a conveyor belt that takes raw materials—steel coils, glass, resin—and spits out a complete “kit of parts” to build a home:


Cuby conveyor belt image
Source: Cuby

Every stud, pipe, wire, and floorboard needed for a specific house is flat-packed.

Cuby = affordable homes.

Cuby’s target cost is $100 to $110 per square foot. That’s far cheaper than traditional builders that spend $150 to $300+ per square foot depending on location.

Aleks stressed to me Cuby is relentlessly focused on costs: “Tesla launched with the expensive Roadster to fund the cheap Model 3. You can’t do that in housing. If you are a Roadster on day 1, you die.”

“If Jesus came back today the only job he’d recognize is a…”

Carpenter. That was Aleh’s humorous way to describe the lack of innovation in housing.

It’s not for lack of trying. As I mentioned, startups have been trying to disrupt housing for a century.

Cuby has “last-mover advantage.” It designed the MMF specifically to disarm the three booby traps that killed its predecessors.

Shipping air.

Katerra built big whole rooms and shipped them to the site. Cuby ships the factory to near where the house will be built.

Cash incinerator.

A Cuby factory costs 10% as much as a normal factory. It only needs to build 70 homes a year to make money.

Best of all, it’s mobile. If the housing market in Phoenix cools, you can pack the 122 containers and move them to Dallas, where demand is hot.

Cuby doesn’t build homes. It builds the factory that builds the home, which is another safety buffer. It enters into joint ventures with local developers that put up the $10 million to build the factory. Cuby doesn’t deploy the machine until the demand is guaranteed.

Regulatory camouflage.

Cuby’s factories produce a kit of parts that follows International Building Code specifications. With small tweaks they are compliant with America’s 26,000 jurisdictions.

Aleh told me its first US test home in Michigan had zero permitting issues. The home was built under 60 working days at 30% to 40% below local contractor quotes!

To a building inspector, a Cuby home looks like a normal house, just built with unusually high precision:


Cuby home example image
Source: Cuby

Cuby is basically…

A software company wrapped in steel

As an ROS member you know all about the physical innovation famine.

For 50 years progress was trapped in a narrow cone of software, apps and the web. That’s why your phone is a supercomputer, but your house is still built like it’s 1925.

Now that cone is widening into the physical world. Cuby manually mapped out the 10,000 steps required to build a house from scratch. It filmed every process, wrote code for every action, and built it into a system called “FactoryOS.”

This is Cuby’s secret sauce. It’s LEGO instructions on steroids.

FactoryOS spits out 3D instructions for every single screw in the house. It’s built on Unreal Engine, the same video game engine used for Fortnite. These digital guides allow even an idiot like me who struggles to assemble an IKEA desk to build a house.

The software also acts as a relentless quality control manager. For example, it won’t let a worker move to the next step until the AI visually confirms the last step is perfect.

There’s a reason I call Cuby “the Henry Ford for homes.”

Before Ford pioneered the assembly line, building cars relied heavily on highly skilled craftsmen. Ford’s innovations simplified the process and drastically reduced build time. Cuby’s software does the same for homes.

Its digitally guided microtask system atomizes assembly. Four workers (in two shifts) can go from foundation through finishes in roughly 45–60 days. Cuby plans to drive this under 30 days.

We need to talk about toilet paper

Cuby clocked 1 million engineering hours designing its Mobile Micro-Factories, kit of parts, and software. That obsession shows up in strange places, like the bathroom.

When Cuby ships MMF extension units to a site (which are like self-contained command centers equipped with Starlink, workstations, lockers, hot showers, and every tool the crew needs), it packs the exact number of toilet paper rolls needed for four workers for the specific duration of the build.

That precision planning defines Cuby. If a worker finishes a task but a specific wrench isn’t back in its slot, the AI recognizes it. The software won’t let him finish his working day until he finds it. No delays due to missing tools:


Cuby workspace image
Source: Cuby

To avoid “shipping air,” the software calculates the volume of empty space in a container down to the cubic inch. It will delay ordering small parts (like door hinges) until they can perfectly fill the gaps in a shipment of larger materials. Tetris for supply chains.

But my favorite feature is how the factories improve themselves.

We all know about Tesla’s over-the-air updates. Back in 2017 when Hurricane Irma was hurtling toward Florida, Tesla remotely unlocked extra battery range for owners fleeing the storm. With the flick of a switch, the car got better.

Cuby does the same for factories. If a crew in Nevada finds a faster way to install a window, that process update is pushed to every Cuby factory worldwide instantly. Factories now update like your iPhone.

Ultimately the only thing that matters is: Can Cuby build homes faster and cheaper?

Yes. Labor accounts for roughly 70% of the cost of building a home, depending on location. Cuby’s FactoryOS aims to slash that by over 80%.

Today a traditional construction crew burns about 450 minutes of human sweat to finish a single square foot of a house. Cuby does the exact same work in 50 minutes.

A traditional builder needs over 15,000 hours of labor to go from foundation to move-in ready for a standard 2,000-square-foot family home. Cuby crosses the same finish line in just 1,659 hours. It’s building the same house with one-ninth the human effort.

This allows Cuby to pump out more affordable homes while not compromising on quality. Its houses come with steel framing and triple-pane windows, typically luxuries in the US.

On the desert outskirts of Las Vegas…

Cuby’s first US Mobile Micro-Factory is going up. It’s scheduled to pump out homes this fall for a local developer building 3,300 units. I can’t wait to visit.

But one factory won’t solve the housing crisis.

That’s why Cuby stood up a “papa factory” in China. This is the machine that builds the machines. Its job is to mass-produce the 122-container Mobile Micro-Factories.

Next year the papa factory will pump out four MMFs. The year after, 8 to 12. The exponential curve is starting now.

Talk to Aleh for five minutes and you realize he’s a serial inventor. He walked me through a dozen patented technologies, from the “magnetic skin” that lets you swap a home’s exterior like a phone case, to the pressurized factory dome that inflates like a tennis bubble.

And Cuby’s ultimate invention is, to quote Aleks, “a universal manufacturing engine that scales to whatever the world needs next. We’re already working on military barracks, data centers, and contractor garages.”

Housing is arguably the most broken industry in the world, with tough competition from healthcare and education. It’s a gigantic market that affects us all.

High housing costs mean fewer kids. It also warps politics as people feel locked out. Just look at NYC voting in a communist mayor!

If Cuby wins, the payoff is civilization-scale. I asked Aleks and Aleh for their vision:

“A 25-year-old schoolteacher in North Carolina no longer spends her weekends touring open houses she can’t afford. She opens an app to design her house like she’s playing The Sims.

You can drag and drop rooms, see the exact cost update in real-time, push a button to see available plots and finally click order. The MMF gets to work, and she moves in one month later.”

Aleks ended with: “We want to build more homes than anyone else on earth.”

If Cuby succeeds it has a shot at rebuilding the American Dream.

What future would you build if you could have a cheap, custom house by next month? Let me know in the comments below. And remember to click “like” and “restack” to help us spread rational optimism.

—Stephen McBride

Mountains of Evidence

Mike's Notes

Another excellent article from After Babel. I agree 100% with no social media for kids. It's causing a mental health epidemic.

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Last Updated

19/02/2026

Mountains of Evidence

By: Jon Haidt and Zach Rausch
After Babel: 15/01/2026

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Two new projects catalogue research on social media’s many harms to adolescents. Some of the strongest evidence comes from Meta.

Much of the confusion in the debate over whether social media1 is harming young people can be cleared away by distinguishing two different questions, only one of which needs an urgent answer:

The historical trends question: Was the spread of social media in the early 2010s (as smartphones were widely adopted) a major contributing cause of the big increases in adolescent depression, anxiety, and self-harm that began in the U.S. and many other Western countries soon afterward?

The product safety question: Is social media safe today for children and adolescents? When used in the ordinary way (which is now five hours a day), does this consumer product expose young people to unreasonable levels of risk and harm?

Social scientists are actively debating the historical trends question — we raised it in Chapter 1 of The Anxious Generation — but that’s not the one that matters to parents and legislators. They face decisions today and they need an answer to the product safety question. They want to know if social media is a reasonably safe consumer product, or if they should keep their kids (or all kids) away from it until they reach a certain age (as Australia is doing).

Social scientists have been debating this question intensively since 2017. That’s when Jean Twenge suggested an answer to both questions in her provocative article in The Atlantic: “Have Smartphones Destroyed a Generation?” In it, she showed a historical correlation: adolescent behavior changed and their mental health collapsed just at the point in time when they traded in their flip phones for smartphones with always-available social media. She also showed a correlation relevant to the product safety question: The kids who spend the most time on screens (especially for social media) are the ones with the worst mental health. She concluded that “it’s not an exaggeration to describe iGen [Gen Z] as being on the brink of the worst mental-health crisis in decades. Much of this deterioration can be traced to their phones.”

Twenge’s work was met with strong criticism from some social scientists whose main objection was that correlation does not prove causation (for both the historical correlation, and the product safety correlation). The fact that heavy users of social media are more depressed than light users doesn’t prove that social media caused the depression. Perhaps depressed people are more lonely, so they rely on Instagram more for social contact? Or perhaps there’s some third variable (such as neglectful parenting) that causes both?

Since 2017, that argument has been made by nearly all researchers who are dismissive about the harms of social media. Mark Zuckerberg used the argument himself in his 2024 testimony before the U.S. Senate. Under questioning by Senator Jon Osoff, he granted that the use of social media correlates with poor mental health but asserted that “there’s a difference between correlation and causation.”

In the last few years, however, a flood of new research has altered the landscape of the debate, in two ways. First, there is now a lot more work revealing a wide range of direct harms caused by social media that extends beyond mental health (e.g., cyberbullying, sextortion, and exposure to algorithmically amplified content promoting suicide, eating-disorders, and self-harm). These direct harms are not correlations; they are harms reported by millions of young people each year. Second, recent research — including experiments conducted by Meta itself — provides increasingly strong causal evidence linking heavy social media use to depression, anxiety, and other internalizing disorders. (We refer to these as indirect harms because they appear over time rather than right away).

[IMG]

Source: Shutterstock

Together, these findings allow us to answer the product safety question clearly: No, social media is not safe for children and adolescents. The evidence is abundant, varied, and damning. We have gathered it and organized it in two related projects which we invite you to read:

  • A review paper, in press as part of the World Happiness Report 2026, in which we treat the product safety question as a mock civil-court case and organize the available research into seven lines of evidence. The first three lines reveal widespread direct harm to adolescents around the world. Lines four through seven reveal compelling evidence that social media substantially increases the risk of anxiety and depression, and that reducing social media use leads to improvements in mental health. Taken together, these lines of evidence provide a firm answer to the product safety question.
  • MetasInternalResearch.org, a new website that catalogues 31 internal studies carried out by Meta Inc. The studies were leaked by whistleblowers or made public through litigation — despite Meta’s intentions to keep them hidden. The most incriminating among them: an experiment designed to establish causality, where Meta’s researchers concluded that social media causes harm to mental health.

In the rest of this post we present the Tables of Contents from these two projects, so that you can jump into the projects wherever you like and see for yourself the many kinds of research demonstrating harm to adolescents. After that, we return to the historical trends question to suggest an answer. We show that the scale of harm we found while answering the product safety question is so vast, affecting tens of millions of adolescents across many Western nations, that it suggests (though does not prove) that the global spread of social media in the early 2010s probably was a major contributor to the international decline of youth mental health in the following years. We suggested this in Chapter 1 of The Anxious Generation. The two mountains of evidence we present here make that suggestion even more plausible today.

The Review Paper: Seven Lines of Evidence

The World Happiness Report (WHR) is a UN-backed annual ranking that has become the global reference point for national well-being research. It draws on Gallup World Poll data from more than 150 countries. We were invited to write a chapter for the upcoming WHR on the 2026 theme: the association between social media and well-being. Following their 2024 report, which documented a widespread decline of well being among young people, this year they ask whether social media’s global spread in the 2010s was a major contributor to that decline. Our chapter, “Social Media is Harming Young People at a Scale Large Enough to Cause Changes at the Population Level,” offers an answer to the product safety question — no — and to the historical trends question — yes.

The editors graciously allowed us to post our peer-reviewed chapter online before the March 19 publication date so that discussion and debate on this topic can begin immediately.

We structured the chapter as if we were filing a legal brief offering 15 exhibits organized into seven separate lines of evidence. The first three lines are the equivalent of testimony from witnesses in a trial. If the people who had the clearest view of an event say that Person A punched Person B, that would count as evidence of Person A’s guilt. The evidence is not definitive — the witnesses could be mistaken or lying — but it is legitimate and relevant evidence. Here’s the structure of that part of the chapter:

After establishing that the most knowledgeable witnesses perceive harm from social media, we move on to the four major lines of academic research. While most researchers agree that correlational studies find statistically significant associations between social media use and measures of anxiety and depression, and that social media reduction experiments find some benefits for mental health, the debate centers on whether the effects are large enough to matter.2 We show that the experimental effects and risk elevations are larger than is often implied — in fact, they are as large as many public health effects that our society takes very seriously (such as the impact of child maltreatment on the prospective risk of depression.)3

Furthermore, we take a magnifying glass to some widely cited studies that claim to show only trivial associations or effects between social media use and harm to adolescents (e.g., Hancock et al. (2022) and Ferguson (2024). We show that these studies actually reveal much larger associations when the most theoretically central relationships are examined — for example, when you focus the analysis on heavy social media use (rather than blending together all digital tech) linked specifically to depression or anxiety (rather than blending together all well-being outcomes) for adolescent girls (rather than blending in boys and adults).

Meta’s Internal Research: Seven More Lines of Evidence

Throughout 2025, a variety of lawsuits against social media companies were progressing through the courts. In the briefs posted online by various state Attorneys General, we found references to dozens of studies that Meta had conducted. Some of this information had been available to the general public since 2021, when whistleblower Frances Haugen brought out thousands of screenshots of presentations and emails from her time working at Meta. Others were newly found by litigators in the process of discovery.4

The descriptions of these studies are scattered across multiple legal briefs, most of which are hundreds of pages long, so it has been difficult to keep track of them — until now. We have collected all publicly available information about the studies in one central repository, MetasInternalResearch.org. Indexed in this way, the scattered reports form a mountain of evidence that social media is not safe for children. The evidence was collected and hidden by Meta itself.

We found information on 31 studies related to the product safety question that Meta conducted between 2018 and 2024. Meta has long hired PhD researchers, particularly psychologists, to conduct internal research projects. (In January 2020, Jon met with members of this team and shared his concerns about what Instagram was doing to girls.) Meta’s researchers have access to vast troves of data on billions of users, including what exactly users saw and what emotions or behaviors they showed afterward. (This is known as “user-behavioral log data.”) Academic researchers never get access to rich data like this; they must devise their own surveys, which obtain a few crude proxy variables (such as “how many hours a day do you spend on social media?” and “How anxious were you yesterday?”). So we should pay attention to what Meta’s researchers found and how they interpreted their findings.

In one example, recently unsealed court documents from lawsuits brought by U.S. school districts against Meta and other platforms reveal that Meta conducted its own randomized control trial (considered to be the best way to study causal impact) in 2019 with the marketing research firm Nielsen. The project — code-named Project Mercury — asked a group of users to deactivate their Facebook and Instagram accounts for one month. According to the filings, Meta described the design of their study as being “of much higher quality” than the existing literature and that this study was “one of our first causal approaches to understand the impact that Facebook has on people’s lives… Everyone involved in the project has a PhD.” In pilot tests of the study, researchers found that “people who stopped using Facebook for a week reported lower feelings of depression, anxiety, loneliness, and social comparison.” One Meta researcher also stated that “the Nielsen study does show causal impact on social comparison.”

In other words, Meta’s own research on the effects of social media reduction confirms those from academic researchers that we report in Line 6 of our review paper. Both sets of researchers find evidence of causation, not mere correlation.

We were impressed by the great variety of methods that Meta’s researchers used. In fact, the 31 studies we located fit neatly into seven lines that are similar to the seven lines we used in our review paper. The findings from Meta researchers are highly consistent with the findings from academic researchers, which gives us even more confidence in our conclusions about the product safety question.

Here’s the Table of Contents. Once again, after the introductory material, we present three lines of testimony:

We then move on to lines 4, 5, and 6, which correspond exactly to lines 4, 5, and 6 in the review paper: correlational, longitudinal, and experimental studies, although line 7 is unique. (It involves reviews of academic literature conducted by Meta’s researchers.)

Returning to the Historical Trends Question

The product safety question is distinct from the historical trends question. A consumer product (e.g., a toy or food) can be unsafe for children without it producing an immediate or easily detectable increase in national rates of a particular illness.5

But social media is an unusual consumer product because of its vast user base and the enormous amount of time it takes from most users. It’s as if a new candy bar, intentionally designed to be addictive, was introduced in 2012 and, within a few years, 90% of the world’s children were consuming ten of these candy bars each day, which reduced their consumption of all other foods. Might there be increases in national rates of adolescent obesity and diabetes?

In our WHR review paper, we estimate the scale of direct harms (e.g., cyberbullying, sextortion, and exposure to disturbing content) and indirect harms (e.g., elevated risks of depression, anxiety, and eating disorders). We then show that these estimates are likely underestimates because they don’t account for network effects inherent to social media, nor the heightened impact of heavy use during the sensitive developmental period of puberty. All told, the number of affected children and adolescents likely reaches into the hundreds of millions, globally.

Once we consider the vast scale at which social media operates — used by the large majority of young people, for many hours each day, over many years, and across nearly all Western nations — it becomes clear that social media companies are harming young people on an industrial scale. It becomes far more plausible that this consumer product caused national levels of adolescent depression and anxiety to rise, especially for girls.

Conclusion: What Now?

Academic debates over media effects often take decades to resolve. We expect that this one will continue for many years. But parents and policymakers cannot wait for resolution; they must make decisions now, based on the available evidence. The evidence we have collected shows clearly that social media is not safe for adolescents.

We believe that the evidence of direct and indirect harm that we have collected in these two complementary projects is now sufficient to justify the sort of action that the Australian government took in 2025 when it raised the age for opening or maintaining a social media account to 16. Just as the recent international trend of removing smartphones from schools is beginning to produce educational benefits, the research we reviewed suggests that removing social media from childhood and early adolescence is likely to produce a great variety of benefits, including lower rates of depression and many fewer victims of direct harms such as sexual harassment and sextortion.

Countries around the world ran a giant uncontrolled experiment on their own children in the 2010s by giving them smartphones and social media accounts at young ages. The evidence is in: the experiment has harmed them. It is time to call it off.

  1. By “social media” we mean platforms that include user profiles, user-generated content, networking, interactivity, and (in most cases) algorithmically curated content. Platforms such as Instagram, Snapchat, TikTok, Facebook, YouTube, Reddit, and X all share these features. This means that ordinary use includes interacting with adult strangers.
  2. For examples of studies showing substantial risk elevations, see Kelly et al. (2019), Riehm (2019), Twenge et al. (2022), and Grund (2025). For examples of meaningful experimental effects, see Burnell et al. (2025).
  3. Burnell et al. (2025) report an average effect of roughly g = 0.22 (about one-fifth of a standard deviation) for “well-being” outcomes in sustained social-media-reduction studies. Grummitt et al. (2024) estimate that the increased risk of depression and anxiety attributable to childhood maltreatment corresponds to effects of d = 0.22 and d = 0.25, respectively. See section “Indirect Harms to Millions” for more details.
  4. We note that this is our only source of this information because Meta lobbies against legislation that requires them to share data with researchers, such as the Platform Accountability and Transparency Act.
  5. The trend of any particular harm may of course have several major influences, some of which may counteract each other. This can add considerable complexity to the historical trends question.