The funny thing – funny in the sense that it makes you laugh, but badly – is that I'm not alone. Every day, somewhere in the world, someone orders a mini PC, a Raspberry Pi, a managed Mikrotik switch, with the stated goal of taking back control of their digital life. They order it on Alibaba, pay with PayPal, wait for the courier. And they see nothing strange in any of this, because the contradiction is so structural it has become invisible. This article is an attempt to make it visible again. Without easy solutions, because I don't have any. And when have I ever…

The Promise of the Homelab

When, in 2019, I started self-hosting pretty much everything – Nextcloud (always on a Raspberry Pi, first RPi3 then RPi4), Jellyfin, Navidrome, FreshRSS, and about twenty-five other services on Proxmox LXC, each with its own isolated Docker daemon – I did it with a precise motivation: I wanted to know where my data lived, who could read it, and have the ability to switch it off myself if I ever felt like it. Not when a company decides to shut down a service, not when someone else changes the licence terms. Me. This came after a long period of reflection on myself, the work I was doing and still do, and the technological society I live in. It is an ideological choice before it is a technical one. Technology as a tool for autonomy rather than control; infrastructure as something you own instead of something that owns you. I hope no one is alarmed if I say that some of these reflections began with reading Theodore Kaczynski's Manifesto, before eventually landing, of course, on more authoritative sources.

Yes, I'm mad, but not quite that mad…

When you pay a subscription to a cloud service, the transaction does not end the moment you authorise the electronic payment. Shoshana Zuboff, in The Age of Surveillance Capitalism, calls this mechanism behavioral surplus: the behavioural data extracted beyond what is needed to provide the service, then resold as predictive raw material.

Under the regime of surveillance capitalism, however, the first text does not stand alone; it trails a shadow close behind. The first text, full of promise, actually functions as the supply operation for the second text: the shadow text. Everything that we contribute to the first text, no matter how trivial or fleeting, becomes a target for surplus extraction. That surplus fills the pages of the second text. This one is hidden from our view: “read only” for surveillance capitalists. In this text our experience is dragooned as raw material to be accumulated and analyzed as means to others' market ends. The shadow text is a burgeoning accumulation of behavioral surplus and its analyses, and it says more about us than we can know about ourselves. Worse still, it becomes increasingly difficult, and perhaps impossible, to refrain from contributing to the shadow text. It automatically feeds on our experience as we engage in the normal and necessary routines of social participation.

You are not the customer of the system – you are its product. Your habits, your schedules, your preferences, your hesitations before clicking on something: all of this is collected, modelled, sold. The transaction is not monthly: it is continuous, invisible, and never ends as long as you use the service. With hardware, in principle, the transaction is one-time: you buy, you pay, it ends, it is yours. The disk is in your room, not on a server subject to government requests, security breaches, or business decisions that are nothing to do with you but impact your access to those services. This distinction – between a tool you use and a system that uses you – is the real stake of the homelab. It is not about saving money, it is not about performance. It is about who controls what.

The problem is that building this infrastructure requires hardware, time, knowledge, and resources. The hardware comes from somewhere; the time, the knowledge, and the energy resources come from a privilege not granted to everyone.

The Market I Hadn't Seen

Search for “mini PC homelab” on any marketplace. What you find is a productive ecosystem that has exploded over the past five years in a way I honestly did not expect.

MINISFORUM, Beelink, Trigkey, Geekom, GMKtec. Zimaboard, with its single-board aesthetic designed explicitly for those who want home racks. Raspberry Pi and the galaxy of clones – Orange Pi, Rock Pi, Banana Pi. Managed Mikrotik switches at accessible prices. 1U rack cases to mount under the desk. M.2 NVMe SSDs with TBW figures calculated for small-server workloads. Silent power supplies designed to run 24/7. A market built from scratch, that exists precisely because there is a community of people who want to run servers at home. r/homelab and r/selfhosted on Reddit have approximately 2.8 and 1.7 million members respectively – numbers publicly verifiable, and growing. YouTube is full of dedicated channels. There is an entire attention economy built around “escaping” the attention economy.

But it is worth asking: who built this market, and why. MINISFORUM and Beelink do not exist out of ideological sympathy for the homelab movement. They exist because they identified a profitable segment and served it with industrial precision. Kate Crawford, in Atlas of AI, documents how technology supply chains follow niche demand with the same efficiency with which they follow mass demand: factories in Guangdong optimise production lines not for a worldview, but for a margin. The fact that the resulting product also satisfies an ideological need is, from the manufacturer's point of view, irrelevant.

The Victorian environmental disaster at the dawn of the global information society shows how the relations between technology and its materials, environments, and labor practices are interwoven. Just as Victorians precipitated ecological disaster for their early cables, so do contemporary mining and global supply chains further imperil the delicate ecological balance of our era.

The mechanism had been described with theoretical precision back in 1999 by Luc Boltanski and Ève Chiapello in The New Spirit of Capitalism. Their thesis: capitalism is never defeated by criticism – it is incorporated. When a critique becomes widespread enough, the system absorbs it and transforms it into a market segment. The artistic critique of the 1960s – autonomy, authenticity, rejection of standardisation – became the marketing of the creative economy. The critique of digital centralisation – sovereignty, privacy, control – has become an online catalogue to browse through.

Resistance has become a market segment. Every time someone buys a HUNSN to stop paying subscriptions to services they don't control, a factory in Guangdong sells a HUNSN. Capitalism has not been defeated – it has shifted (at least for a small slice of the population: the nerds, the hackers) the extraction point from subscriptions to hardware.

The Accumulation Syndrome

But there is a further level – more ridiculous and more personal – that homelab communities never discuss openly, yet anyone who has a homelab recognises immediately. The Raspberry Pi 4 bought “for a project.” The old ThinkPad kept because “you never know.” The 4TB disk salvaged from a decommissioned NAS – and “it might come in handy.” The second-hand switch picked up on eBay for eighteen euros because it was cheap and might be useful. The cables, the cables, the cables.

r/homelab has a term for this: just in case hardware. It is the hardware of the imaginary future, of projects that only exist in your head, of configurations that one day – one day – you will finally test. In the meantime it occupies a shelf, draws current in standby, and generates a diffuse sense of possibility that is indistinguishable from the most classic consumerism. The underlying psychological mechanism has a precise name: compensatory consumption – consumption as a response to a perceived loss of autonomy or control. You buy hardware because buying hardware gives you the feeling of recovering agency over something. The aesthetic is different from traditional consumerism – no luxury logos, no recognisable status symbols – but the mechanism is identical.

That said, there is a partially honest answer to all of this: the second-hand and refurbished market. The ThinkPad X230 on eBay, the Dell R720 server decommissioned from a datacentre, the disk from someone who upgraded their NAS. My ZFS NAS, to give one example, is a recycled old tower with four 1TB disks in RAIDZ – hardware that would otherwise have ended up in landfill, with a life cycle extended by years, without generating new production demand. It is closer to the ethics of repair than to compulsive buying. But it has its own internal contradiction: it requires even more technical competence than buying new – knowing how to assess wear, diagnose an unknown component, manage ten-year-old drivers. The barrier to entry rises further. And the refurbished market is itself now an organised commercial sector, with its own margins, its own platforms, its own pricing logic. It is not a clean way out. It is a less dirty way out.

And then there is the energy question, which is usually ignored in homelab discussions and is instead the most uncomfortable of all – uncomfortable enough to deserve a more in-depth treatment later on. For now, suffice it to say: every machine on your shelf that “draws current in standby” is a line item in the energy bill that the homelab movement rarely accounts for.

Not for Everyone. And It Should Not Be This Way.

There is a second level of the paradox that is even more uncomfortable than the first. Building a homelab costs money – relatively little, but it costs. It requires physical space. It requires a decent connection. And it requires time. A lot of time. Not installation time – that is measurable, finite. The learning time that precedes everything else. To reach the point where you can build a functional infrastructure with Proxmox, LXC containers, centralised authentication, reverse proxy, automated backups – you need to have already spent years understanding how Linux works, how to reason about networks and permissions, how to read a log. I started with a Red Hat in 1997, and it took me almost thirty years to get where I am. I should know this. Yet it always escapes me. And that time did not fall from the sky. It is time I was able to dedicate because I had a certain kind of job, a certain stability, a certain amount of mental energy left at the end of the day. It is middle-class-with-a-stable-position time, not the time of someone working three warehouse shifts a week. Passion is not enough.

Johan Söderberg documents this in Hacking Capitalism: the FOSS movement was born as resistance to capitalism, but reproduces within itself hierarchies of skill and merit that make it structurally exclusive. Freedom is technically available to anyone, but effective access requires resources distributed in anything but a democratic manner. Söderberg goes further than simply observing the exclusivity: the voluntary open source work produces use value – functioning software, documentation, community support – that capital then extracts as exchange value without remunerating those who produced it. Red Hat builds a billion-dollar company on a kernel written largely by volunteers. It is not just that not everyone can get in: it is that those who get in often work for someone without knowing it. The homelab inherits this problem and amplifies it.

The narrative of orthodox historical materialism corresponds with some very popular ideas in the computer underground. It is widely held that the infinite reproducibility of information made possible by computers (forces of production) has rendered intellectual property (relations of production, superstructure) obsolete. The storyline of post-industrial ideology is endorsed but with a different ending. Rather than culminating in global markets, technocracy and liberalism, as Daniel Bell and the futurists would have it; hackers are looking forward to a digital gift economy and high-tech anarchism. In a second turn of events, hackers have jumped on the distorted remains of Marxism presented in information-age literature, and, while missing out on the vocabulary, ended up promoting an upgraded Karl Kautsky-version of historical materialism.

This is not a quirk of the homelab movement: it is a recurring structure in every technological wave. Langdon Winner, in his influential essay Do Artifacts Have Politics?, argued that technological choices are never neutral – they incorporate power structures, distribute access in non-random ways. Amateur radio in the 1920s, the personal computer in the 1980s, the internet in the 1990s: every time the promise was democratising, every time the actual distribution followed the lines of pre-existing privilege. Not out of malice, but out of structure. The irony is this: those who would most need digital autonomy – those who cannot afford subscriptions, those who live under governments that surveil communications, those most exposed to data collection – are exactly those least likely to be able to build a homelab. Not for lack of interest or intelligence. For lack of time, money, and years of privileged exposure to technology.

Homelab communities do not usually talk about this. They talk about which mini PC to buy, how to optimise energy consumption, which distro to use as a base. The conversation about structural exclusivity exists, but at the margins – in Jacobin, in Logic Magazine, in EFF activism – while the centre of the discourse remains impermeable. It is not that no one speaks about it: it is that the peripheries speak about it, and the peripheries do not set the agenda. This entire conversation takes place in a room to which not everyone has a ticket. And those inside do not seem to find that particularly problematic.

A Technological Cosplay?

So is the whole thing a con? Is the homelab just anti-capitalist cosplay while you continue to fund the same supply chains? In part, yes.

The HUNSN 4K was designed in China, assembled in China, shipped by container on ships burning bunker fuel. Global maritime transport is responsible for approximately 2.5% of global CO₂ emissions – a share that the IMO (International Maritime Organization) has been trying to reduce for years with slow progress and targets continually postponed. Then: distributed through Alibaba, paid with a credit card. Every piece of technology hardware carries an extractive chain that begins in lithium mines in Bolivia and cobalt mines in the Democratic Republic of the Congo, passes through factories in Guangdong, and ends in electronic waste processing centres in Ghana. The hardware travels that supply chain exactly like any other consumer device. Furthermore, hardware has a lifecycle. In five years the HUNSN 4K will be too slow, or it will break, or something will come out with energy efficiency too much better to ignore. And I will buy again. The mini PC market for homelabs depends on the obsolescence of previous purchases – exactly like any other consumer market.

The critique of capitalism, when it is widespread enough, is not suppressed – it is incorporated. The system absorbs the values of resistance and transforms them into a market segment. Autonomy becomes a selling point. Decentralisation becomes a brand. The rebel who wanted to exit the system finds himself funding a new vertical of the same system, convinced he is making an ethical choice.

The Counter-Shot

But there is a structural difference that would be dishonest to ignore.

When you pay a subscription to a cloud service, the cost is not just the monthly fee. It is the continuous cession of data, behaviours, habits. It is the behavioral surplus Zuboff talks about: you are not using a service, you are being used as raw material to train models, build profiles, sell advertising. The transaction never ends, in ways you often cannot see and cannot escape from as long as you use the service.

With hardware, the transaction ends. The data stays on a physical disk in your room, not on a server subject to government requests, breaches, or business decisions that have nothing to do with you but impact your life. The software running on it – Proxmox, Debian, Nextcloud, Jellyfin – is open source; you can modify it. If something changes in a way you cannot accept, you can leave. This resilience has real value – but it is worth noting that it is asymmetric resilience: it works for those who have the skills to exercise it. For those who do not, the theoretical portability of their data from Nextcloud to something else requires exactly the same skills we have already identified as the barrier to entry. The freedom to leave is real. Access to that freedom, much less so.

And then there is the energy question, which I have deferred long enough. The major hyperscalers – AWS, Google, Azure – operate with a PUE (Power Usage Effectiveness) between 1.1 and 1.2. For every watt of useful computation they dissipate barely 0.1–0.2 watts in heat and infrastructure. They have enormous economies of scale, optimised industrial cooling, significant investments in renewable energy, and above all: their servers run at very high utilisation rates. Almost always busy.

A home homelab works in a radically different way. The machine runs 24/7 even when it is doing nothing – and for most of the time it is doing nothing. Navidrome serving three requests a day, FreshRSS fetching every hour, an LDAP container sitting listening without receiving connections. You are paying the energy cost of the infrastructure regardless of usage. The implicit PUE of a homelab, calculated honestly on the ratio between total consumption and actual workload, is much worse than that of a datacentre. IEA data (Data Centres and Data Transmission Networks, updated annually) shows that large cloud providers progressively improve energy efficiency thanks to economies of scale that no individual homelab can replicate. The flip side is that the same growth in demand that makes economies of scale possible negates the efficiency gains: Amazon's absolute emissions increased between 2023 and 2024 despite improved PUE. Efficiency improves. Total consumption grows anyway. This is Jevons' Paradox: energy efficiency, instead of reducing consumption, increases it, because it lowers the marginal cost of use and stimulates demand that grows faster than the efficiency gains.

Note: The comparison is not as linear as the numbers suggest. PUE measures the internal efficiency of a datacentre, not the energy cost of the network traffic that data generates every time it leaves it – traffic that a homelab eliminates almost completely for internal services. Nor does it measure proportion: AWS is efficient at delivering services to millions of users, but that scale says nothing about the real cost of storing fifty gigabytes of personal data on a server designed for loads a thousand times greater. A HUNSN N100 in idle consumes less than 8 watts. The honest energy comparison is not homelab vs hyperscaler in the abstract – it is homelab vs proportional share of hyperscaler for your specific workload, a calculation that nobody can make with publicly available data.

This does not automatically mean that the cloud is the ethically correct choice – the problem does not reduce to PUE, and surveillance has costs that are not measured in kilowatts. It means that anyone with SolarPunk values who chooses the homelab must reckon with a real contradiction: the choice of sovereignty may be, watt for watt, energetically more costly than the system one wants to escape. I have no clean answer, but ignoring the question would be dishonest. Söderberg acknowledges that the FOSS movement has produced concrete and undeniable gains – they simply are not enough, on their own, to subvert the dynamics of informational capitalism.

In short: this is not a critique of the homelab, but it is a critique of the homelab presented as a sufficient revolutionary act.

What Happens at Eleven PM – and Beyond

That night, with the HUNSN 4K on the table, I pressed on. I installed Proxmox. I configured the network. I started bringing up containers one by one. And at some point – three hours had passed, I had three terminals open and was debugging nslcd to centralise LDAP authentication across all the containers – I realised something: I was doing all of this simply because I enjoyed it. Not to resist something. Not to advance an ideological agenda. Because there was a problem to solve and solving it gave me satisfaction. Mihaly Csikszentmihalyi describes this state in Flow as total absorption in a task calibrated to one's own competencies: time expands, attention narrows, awareness of context vanishes. It is not motivation – it is something more immediate. Debugging an authentication problem at eleven at night on a system I could have chosen not to build is, neuropsychologically, indistinguishable from pleasure. Not the satisfaction of having finished: the process itself. Moreover, for an AuDHD person like me, going into hyperfocus allows you to lose your sense of time entirely, and to literally escape from a world you viscerally loathe.

Ah – you had not figured that out yet?

When I had finished and closed everything, the satisfaction was still there. Along with a mildly uncomfortable awareness: I could probably have used a hosted service, lived just as well, and not lost three hours of a weeknight. But in the meantime I had understood how PAM worked, I had read documentation I had never opened before, I had implemented it on my homelab, I had learned something I hadn't known I wanted to know.

And here the circle closes in a somewhat unsettling way. Söderberg speaks of voluntary open source work as the production of pure use value – the intrinsic pleasure of doing, understanding, building something that works. But it is exactly this use value that capital then extracts as exchange value: the competence I accumulate debugging LDAP at eleven at night is the same competence I bring to work the next day, that I put into articles like this one, that I share in communities where others use it to build their own homelabs. Technical pleasure is not neutral. It has a production chain. Not always visible, but real.

This is what the homelab is, at least for me: a way of learning that produces, as a side effect, an infrastructure I control. The ideology is there, but it comes second. First comes the pleasure of understanding how something works. Or rather: ideology and pleasure are interchangeable, and often run in parallel – but this does not resolve any of the contradictions I described above. It leaves them all standing, in fact makes them stranger. Am I resisting capitalism, or am I just cultivating an expensive hobby with a political aesthetic?

The Hacker Ethic

The word “hacker” has had bad press for decades. In 1990s news bulletins it was a synonym for a hooded cybercriminal; in the jargon of security companies it became a marketing term to prepend to anything. Neither has much to do with what the word historically means. Steven Levy, in Hackers: Heroes of the Computer Revolution, reconstructs the culture that formed around the MIT and Stanford labs in the 1960s: a community of programmers for whom code was an aesthetic object, access to information a moral principle, and technical competence the only legitimate hierarchy. The principles Levy identifies as the “hacker ethic” are precise: access to computers – and to anything that can teach you how the world works – should be unlimited and total. All information should be free. Decentralised systems are preferable to centralised ones. Hackers should be judged by what they produce, not by titles, age, race, or position. You can create art and beauty with a computer.

It is not a political manifesto in the traditional sense. It is something more visceral – a disposition toward the world, a way of standing before a system you do not yet understand: the correct response is to take it apart, understand how it works, and put it back together better than before.

Pekka Himanen, in The Hacker Ethic and the Spirit of the Information Age – with a preface by Linus Torvalds and an epilogue by Manuel Castells, which already says something about the project's ambition – performs a more explicit theoretical operation. He builds the hacker ethic in direct opposition to the Protestant work ethic described by Max Weber: where Weber saw work as duty, discipline as virtue, and leisure as absence of production, Himanen identifies in the hacker a figure who works out of passion, considers play an integral part of work, and rejects the sharp separation between productive time and free time. The hacker does not work for money – money is a side effect, when it comes. They work because the problem is interesting. Because the elegant solution has value in itself. Because understanding how something works is, in and of itself, sufficient.

Hacker activity is also joyful. It often has its roots in playful explorations. Torvalds has described, in messages on the Net, how Linux began to expand from small experiments with the computer he had just acquired. In the same messages, he has explained his motivation for developing Linux by simply stating that “it was/is fun working on it.” Tim Berners-Lee, the man behind the Web, also describes how this creation began with experiments in linking what he called “play programs.” Wozniak relates how many characteristics of the Apple computer “came from a game, and the fun features that were built in were only to do one pet project, which was to program … [a game called] Breakout and show it off at the club.”

Recognise something? I do. Those three hours debugging nslcd at eleven at night were not work in the Weberian sense – nobody was paying me, nobody had asked me to do it, there was no corporate objective to reach. They were hacking in the precise sense that Levy and Himanen describe: exploration motivated by curiosity, with the infrastructure as an object of study as much as of utility. The homelab is, culturally, a direct expression of the hacker ethic. It is no coincidence that homelab communities and open source communities overlap almost perfectly, that they use the same language, the same platforms, the same values. But here, as elsewhere in this article, the story gets complicated.

The hacker ethic promises a pure meritocracy: you are judged by what you can do, not by who you are. It is an attractive idea. It is also, in practice, a partial fiction. Technical meritocracy presupposes that everyone starts from the same point – that skills are accessible to anyone who really wants to acquire them, that the time to acquire them is distributed equally, that mentorship networks and learning resources are available regardless of context. The homelab as hacker practice inherits both things: the genuine nature of curiosity as a driver, and structural exclusivity as an undeclared side effect. The pleasure of taking a system apart to understand how it works is real and should not be devalued. But that pleasure is available, in practice, to those who already have the ticket.

Conclusions

The HUNSN 4K runs, alongside the other “little electronic contraptions,” on a rack next to my armchair – the one where, at the end of the day, I indulge my guilty pleasure of reading a book in the company of my cats. Proxmox, the Nextcloud server, the ZFS NAS, a small MINISFORUM box running Ollama with some local open-weight LLM models, a Raspberry Pi 5 running the Tor Relay, and a HUNSN RJ15 with pfSense controlling incoming and outgoing traffic. An infrastructure, in short, that allows me to have something resembling digital sovereignty within the limits of the possible. The contradictions I have described do not resolve. They are held together, with effort, as any intellectually complex position on a complex system must be held together.

The first: the market that made the accessible homelab possible is the same market the homelab is supposed to emancipate us from. If this explosion of affordable, efficient mini PCs had not happened – if capitalism had not decided to build exactly what we wanted – how many of us would have taken the same path? How much of our “ethical choice” depends on the existence of products designed and sold precisely for us?

The second: does incorporated resistance truly lose its force, or does it remain resistance even when someone profits from it? Boltanski and Chiapello describe the incorporation mechanism, but do not argue that critique loses all effectiveness in the process. Perhaps the homelab is simultaneously a product of the system and a real, if partial, form of withdrawal from it. The two things are not mutually exclusive.

The third: if digital autonomy requires decades of accumulated skills, enough free time to use them, and enough money to buy the hardware, are we building a democratic alternative? Or are we building an exclusive club with a rebel aesthetic, reproducing the same hierarchies of privilege it claims to want to fight?

The fourth: the energy question has no clean answer, and Jevons' Paradox makes it even more uncomfortable – because it works in both directions. The cloud improves efficiency and increases total consumption. A homelab consumes proportionally more, but does not fuel the demand that drives that total consumption upwards. Are we building digital sovereignty, or are we simply choosing where to position ourselves within a contradiction that cannot be resolved at the individual level?

I don't know. But at least I know where my data is.

Fun Fact

This article was written in Markdown using a Flatnotes instance running as a CT container on Proxmox, while listening to a symphonic metal playlist served by Navidrome – another CT container – pulling OGG files from a ZFS NAS over an NFS share. The cited books were in EPUB format on Calibre Web. In the background, Nextcloud on a Raspberry Pi 4 was syncing and backing up everything. Spelling mistakes were corrected by Qwen2.5, an LLM model served by Ollama on the MINISFORUM box, accessible locally via oterm and Open WebUI. And all of this, controlled from a laptop running Linux.

Coincidences? I don't think so.

Discuss...

#Homelab #SelfHosted #SurveillanceCapitalism #Privacy #OpenSource #HackerEthic #SolarPunk #DigitalSovereignty #FOSS #Linux

Same film, next scene.

Friday afternoon – because critical migrations always happen out of hours. I'm migrating a system from Red Hat 7 to Red Hat 9. Why? To support the new version of Charon-SSP, the Stromasys emulator that lets SPARC hardware run on x86. All of this to keep alive a virtual machine running Solaris 9, an operating system from 2002 that went end-of-life in 2014. It's a layered structure, each level propping up the one below. One of those classic houses of cards you can't quite understand how it stays balanced.

Welcome to the world of legacy systems. A world where “modernising” often means finding increasingly creative ways to change nothing at all, and where communities and old-school sysadmins are the ones guarding infrastructure that corporations abandoned long ago. Try asking Oracle for Solaris support: they'll laugh in your face.

The numbers

In January 2025, the UK government published a report that should have rattled a few chairs at Westminster. Twenty-eight percent of central government IT systems are classified as legacy – up from 26% in 2023. Estimated productivity losses? Forty-five billion pounds. In 2024, the NHS recorded 123 critical IT system crashes. One hundred and twenty-three.

But wait, because the numbers get even more interesting when you look at the banking sector. COBOL – a programming language dating back to 1959 – still processes 95% of global ATM transactions, 43% of the world's banking systems, and around 3 trillion dollars of commerce every day. Every day. It's estimated there are still 220 billion lines of COBOL code in production.

And Windows XP? The one Microsoft stopped supporting in 2014? Today, 1-2% of internet-connected devices still run it. Sounds small until you realise we're talking about millions of machines. And not your grandad's PC: we're talking about MRI scanners in hospitals, industrial control systems, bank ATMs. Critical devices that can't be updated because the software controlling them only runs on XP, and re-certifying the entire system would cost more than building a new one.

Remember WannaCry in 2017? The ransomware that paralysed 75,000 computers in 99 countries? The NHS was devastated. And do you know how many Windows XP machines the NHS had in 2019 – two years after the attack, five years after end-of-support? 2,300.

At this point in the story one might say “right, the problem is clear: legacy systems are dangerous and need replacing.” And that would be the easy narrative – the one that consultants selling “digital transformation” love, and vendors wanting to sell licences love. What if I told you that a Solaris 11 system, properly isolated in a VLAN, is significantly more stable and secure than a shiny new Ubuntu 24.04 LTS?

Reality, as always, is more complicated.

Problems upon problems

Here's the fundamental issue: we use the word “legacy” as if it meant one thing, when it actually covers at least three completely different situations.

Type 1: Unavoidable legacy Solaris 9 on SPARC hardware controlling industrial machinery. Windows XP on MRI scanners. Systems where hardware and software are inseparable, where an upgrade would require replacing equipment worth millions, where re-certification for medical or industrial use would take years and fortunes. These systems are legacy out of necessity, not negligence. There's no fault here. There's only the reality of a technological ecosystem where certain devices have 20-30 year lifespans and the software controlling them can't be changed without changing everything else.

Type 2: Avoidable legacy CentOS 7, for instance. End of support: 30 June 2024. Available alternatives: AlmaLinux, Rocky Linux, migration to RHEL. Cost of migration? Economically: it depends. In time, resources, learning: enormous. How many CentOS 7 systems are still in production today? Too many. Why? Because nobody wants to pay RHEL licences, because “we'll do it next quarter,” because “there are other important things to deal with,” because “if it ain't broke, don't fix it.” This is legacy by choice – or rather, by inertia. It's an organisational decision, not a technical one.

Type 3: Non-legacy perceived as legacy Take COBOL on modern IBM mainframes. Today's mainframes aren't the ones from the 1970s – they're immensely powerful machines, with dedicated processors, hardware security, 99.999% uptime. The COBOL running on them is the same as ever, but the underlying infrastructure is current. Is the code legacy, or the platform? And if the platform is modern, can we still call it legacy? The distinction is fundamental because it determines the strategy. A Type 1 system needs to be isolated and protected. A Type 2 system needs to be migrated. A Type 3 system needs to be left alone. Try explaining that to a CTO who just finished reading a Gartner report on “legacy modernisation.”

From a thread on TheLayoff:

“FWIW, there's a very good chance that your electronic footprint on any given day has passed through a piece of SPARC equipment running Solaris, and that will continue to happen for a good portion of your lifetime.”

Would you believe me if I told you I've seen original BSD systems with eleven years of uptime?

The real problem isn't the machines

Here we get to the heart of the matter. And the answer will surprise you: the real problem with legacy systems isn't technological. It's human.

Let's talk about the “COBOL Cowboys” – retired programmers called back on consulting contracts when something breaks. They're the last generation that knows how those systems actually work. When they leave, they take decades of undocumented knowledge with them. According to Deloitte, companies have seen a 23% decline in mainframe workforce over the last five years, with 63% of those positions left unfilled. It's not that there's no money to hire – it's that there's nobody to hire. Young developers don't want to learn COBOL. It's “unsexy.” It's “archaic.” It's “boomer stuff.”

From ComputerWeekly:

“The retirement of the generation of experts who possess in-depth knowledge of Cobol systems is leading to a severe knowledge shortage. They have knowledge not only of the Cobol programming language, but also of the specific systems they have worked on and built over the years” – Tijs van der Storm, CWI/University of Groningen

And so we find ourselves in a paradoxical situation: systems processing trillions of dollars a day, managed by people who might die of old age before anyone learns to replace them. Knowledge transfer never happened. Documentation – where it exists – is outdated, incomplete, written in a language nobody understands anymore. And every year that passes, the gap widens.

This is the real legacy problem. Not the systems. The people.

When modernisation fails (spoiler: often)

There's a story that people in the UK know well, but that strangely never comes up when “digital transformation” is being discussed. It's called the National Programme for IT, or NPfIT.

Launched in 2002, it was the largest public sector IT project in British history. The goal? Modernise the entire NHS IT infrastructure. Initial budget: 6 billion pounds. Planned completion: 2010.

In 2011, after nine years of delays, exploding costs, vendors abandoning the project, and a system that simply didn't work, the UK government announced the dismantling of NPfIT. Final estimated cost: over 10 billion pounds. For a system that was never completed.

What went wrong? Practically everything. Top-down decisions made by politicians who didn't understand technology. Rigid contracts with vendors who didn't understand the NHS. Resistance from medical staff who hadn't been consulted. Continuously shifting requirements. Impossible integrations with existing systems.

From TechMonitor:

“A lack of digital and procurement capability within government has led to wasted expenditure and lack of progress on major digital transformation programmes.”

The lesson? “Modernising” is not automatically better than “maintaining.” Sometimes, the legacy system that works is preferable to the modern system that never will. But this lesson, apparently, we haven't learned. Because the dominant narrative remains the same: legacy = bad, modern = good. And consultants keep selling the shiny new thing.

Strategies that actually work

TL;DR: There is no single solution. There's a matrix of options ranging from virtualisation to isolation, from refactoring to API wrapping. The choice depends on the type of legacy, the budget, and the acceptable level of risk.

The Gartner 7Rs (yes, they have a name for everything):

1. Retire – Switch it off. Only works if nobody's actually using it.
2. Retain – Keep it as is. Sometimes the best choice.
3. Relocate – Move it to new infrastructure without changes.
4. Rehost – “Lift and shift” to cloud. Changes the hardware, not the software.
5. Replatform – Minimal changes to run on a modern platform.
6. Refactor – Rewrite parts of the code while maintaining functionality.
7. Rearchitect – Completely redesign. The riskiest and most expensive.

Virtualisation and emulation For systems on proprietary architectures (SPARC, VAX, Alpha, PA-RISC), solutions like Stromasys Charon emulate the original hardware on x86-64 platforms. The operating system and software don't change – only the iron underneath does. For legacy x86 systems (Windows XP, Server 2003, old Linux), standard virtualisation (Proxmox, VMware, KVM) allows you to “freeze” the environment and keep it running indefinitely. I've seen Proxmox setups running Windows 3.11. I'm not joking.

Network isolation If a system can't be patched, it can at least be isolated. Dedicated VLANs, restrictive firewalls, air-gap where possible. It doesn't fix the problem, but it limits the impact in case of compromise.

API wrapping Put a modern REST layer in front of a legacy system. The mainframe keeps doing what it knows how to do; the outside world talks to the API. This is the strategy many banks use to expose COBOL functionality to mobile applications.

The public sector: a special case

Those who work in the public sector know that the dynamics differ from the private sector in ways that make the legacy problem even more complex.

Multi-year budgets. You can't decide in January to modernise a system and have the money by March. Funding cycles are long, rigid, subject to political priorities that change with every election.

Procurement. Buying software in the public sector is a bureaucratic nightmare. Tenders, compliance requirements, impact assessments, GDPR, accessibility. A purchase that takes a week in the private sector takes months here.

Compliance. Systems handling health, education, or tax data are subject to stringent regulatory requirements. You can't simply “migrate to the cloud” – you have to demonstrate that the cloud complies with an endless list of standards.

Service continuity (which in my view is the core problem). If a private company's system goes down for a day, they lose money. If a system managing national exams, or medical prescriptions, or pension payments goes down, the consequences fall on real people with no alternatives. The risk of downtime during a migration is often simply unacceptable.

And then there's the political dimension. Every government wants to announce its own “digital revolution.” Nobody wants to inherit the previous government's problems. And so projects get started, abandoned, restarted, re-abandoned, in an endless cycle of waste.

NPfIT wasn't an exception. It was the rule.

The uncomfortable question

At this point, the question nobody wants to ask is this: what if some legacy systems were simply… better? Not better in an absolute sense, but better for their specific purpose?

Let me tell you something. I worked for years in environments dealing with large-scale Oracle infrastructure – the company that sells “cloud transformation” and “modern infrastructure” to half the world. And among other things, you know what got managed day to day? Old ZFS storage. Stuff that, on paper, should have been “modernised” years ago. Those machines had been running since before Docker existed, before Kubernetes, before “cloud native” became a term. And they worked. Quietly. Without drama. Nobody was in any hurry to replace them. Why would they be? In pursuit of what advantage, exactly?

The COBOL processing bank transactions has been optimised for sixty years. Every bug has been found and fixed. Every edge case has been handled. Every possible scenario has been tested in production billions of times. It's code that has achieved a kind of perfection through Darwinian evolution. Rewriting it in Python would mean starting from scratch. New bugs. New untested scenarios. Years of instability before reaching the same level of reliability.

And in the meantime? In the meantime, the legacy system keeps working. There's a reason banks aren't in a rush to abandon mainframes. It's not ignorance. It's not laziness. It's that they've done the maths and understood that the risk of the new outweighs the cost of the old. And the old administrators have retired. But this is an uncomfortable truth. It doesn't sell well in PowerPoint presentations. It doesn't generate consulting contracts. It doesn't make tech headlines.

And so we keep talking about “modernisation” as if it were automatically a good thing. As if “new” meant “better.” As if technology had a moral direction.

So what?

Legacy doesn't mean old – it means abandoned. The problem is never technical – it's always organisational. And “modernising” is not automatically better than “maintaining.”

If there's one lesson, it's this: be suspicious of anyone with simple answers to complex problems.

Every time I hear some manager say “we need to automate everything with AI,” I think about the software pachyderms holding up half of critical infrastructure. I think about the time it would take to train a model on COBOL written in 1987 with no documentation. I think about how long it would take to migrate a Java 1.7 system running on Solaris 9. I think about the hours spent reverse-engineering platforms still running Lotus Notes. I think about the costs. I think about the risks. And then I think that those same managers don't have the budget to hire juniors willing – and why should they be, when the IT world is moving in a completely different direction – to learn systems that have been decommissioned for at least thirty years. And I laugh. Bitterly, but I laugh. Then I take a few drops of CBD to calm myself down.

Before talking about artificial intelligence – and those who know me know I'm not against AI at all – perhaps we should make sure that human intelligence doesn't retire, taking years of undocumented knowledge with it. But that, evidently, is a less sexy priority to put on the slides.

Sources and further reading

UK government reports – NAO: “The sustainability of government IT” (January 2025) https://www.nao.org.uk/reports/local-government-financial-sustainability-2025/ – NHS Digital: Infrastructure assessment reports https://www.bma.org.uk/advice-and-support/nhs-delivery-and-workforce/the-future/building-the-future-healthcare-infras

COBOL and mainframes – Reuters: “Banks scramble to fix old systems” (Commonwealth Bank Australia cost analysis) https://www.reuters.com/article/technology/banks-scramble-to-fix-old-systems-as-it-cowboys-ride-into-sunset-idUSKBN17C0CN/ – IBM: “COBOL Modernization” https://www.ibm.com/think/topics/cobol-modernization

Legacy virtualisation – Stromasys: “What are legacy systems” https://www.stromasys.com/resources/what-are-legacy-systems-challenges-benefits/ – Proxmox Forums: discussions on legacy system virtualisation https://forum.proxmox.com/tags/legacy/

Sector analysis – Gartner: 7Rs of Application Modernization https://www.techtarget.com/searchCloudComputing/tip/Use-the-7-Rs-to-develop-an-app-modernization-strategy – Deloitte: Mainframe workforce decline study https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2023/future-mainframe-technology-latest-trends.html – WSJ: How AI Can Rev Up Mainframe Modernization https://deloitte.wsj.com/cio/how-ai-can-rev-up-mainframe-modernization-2e3c1c4a

Case studies: failures – Computer Weekly: “What went wrong with the National Programme for IT” https://www.computerweekly.com/opinion/Six-reasons-why-the-NHS-National-Programme-for-IT-failed – NAO: Post-implementation review NPfIT https://www.nao.org.uk/reports/review-of-the-final-benefits-statement-for-programmes-previously-managed-under-the-national-programme-for-it-in-the-nhs/

Security – WannaCry incident reports https://any.run/malware-trends/wannacry/ – NHS Windows XP audit findings (2019) https://www.verdict.co.uk/windows-xp-nhs/

Discuss...

#LegacySystems #Sysadmin #COBOL #Solaris #Linux #PublicSector #DigitalTransformation #Mainframe #OpenSource #Infrastructure

It took me years to understand how naive I'd been.

Years in which I continued to follow the project from afar. Years in which I read stories of deployments in Africa, Asia, prisons, refugee camps. Years in which I understood that the internet isn't everywhere, it's a privilege, not a given. And even where it exists, it's not necessarily accessible, affordable, or free from censorship.

Years later, when I set up my Proxmox server, one of the first containers I decided to install was Kiwix. Not because I needed it—my connection works fine, thanks for asking—but because I wanted to be part of that project, so to speak. Because I had understood that Kiwix wasn't just software. It's a philosophy. It's practical proof that another web is possible: decentralized, offline, in users' hands.

Simply a matter of fundamental rights

There's a moment in 2004 when Emmanuel Engelhart—a French computer engineer working between Germany and Switzerland—becomes a Wikipedia editor and asks himself an apparently simple question: “What about those without internet access?” It wasn't a rhetorical question. At the time, as today, billions of people lived (and live) in areas where connectivity is a luxury, where broadband is science fiction, where even a single megabyte of data costs more than a meal.

Engelhart's answer was radical: if people can't reach Wikipedia, then Wikipedia must reach people. Even without the internet.

You know that thing about “if the mountain won't come to Muhammad...”? Exactly that.

And so, in 2007, together with Renaud Gaudin—a Malian information management expert—Engelhart launched Kiwix. Open source software that allowed downloading the entire Wikipedia (and much more) to consult it completely offline.

In a 2014 interview, Engelhart stated:

The contents of Wikipedia should be available for everyone! Even without Internet access. This is why I have launched the Kiwix project. Our users are all over the world: sailors on the oceans, poor students thirsty for knowledge, globetrotters almost living in planes, world's citizens suffering from censorship or free minded prisoners. For all these people, Kiwix provides a simple and practical solution to ponder about the world.

And:

Water is a common good. You understand why you have to care about water. Wikipedia is the same; it's a common good. We have to care about Wikipedia.

Digital Sovereignty

Why talk about Kiwix today? Because it's not just a technical solution to a connectivity problem. Kiwix represents something deeper: digital sovereignty in its purest form.

While projects like Mastodon, Matrix, Lemmy, and Pixelfed create distributed networks—many nodes communicating with each other in federation—Kiwix goes beyond, or perhaps beneath, depending on your perspective. It's so radically independent that it doesn't even need a network. It's local. Completely. A single Kiwix installation is an autonomous island that communicates with nothing and no one.

No federation, no peer-to-peer, no cloud.

You have Wikipedia on your Raspberry Pi? It's yours—or rather, it's yours thanks to the contribution of all Wikipedians. It works without internet, without external dependencies. You can copy it to a USB stick and give it to someone else. You can take it to the middle of the ocean, the desert, Antarctica. You can share it on a local computer network. And it will work. Always. The data is on your hardware, under your physical control.

The birth of the project

Kiwix's 2007 launch didn't happen with grand announcements or marketing campaigns. It was open source software, released under GPL license, developed by two enthusiasts. That's it.

The technological heart of the project was (and is) the ZIM format—”Zeno IMproved”—an open source archive format optimized for wiki-style content. Highly compressed, easily indexable, designed to be searchable even without connection. All of Wikipedia's content is converted to static HTML, compressed into ZIM, and made available for download.

To give you an idea of scale: the entire English Wikipedia—6.4 million articles, images included—takes up about 97 GB in ZIM format. Seems like a lot? The sum of all human knowledge now fits on a microSD card that costs 15 euros. On a 1TB portable hard drive you can put Wikipedia in ten different languages, the entire Project Gutenberg library, all TED talks, complete Stack Exchange, and you'll still have space left over.

Between 2007 and 2011, the team also released three CD/DVD versions with article selections. Today they seem like archaeological artifacts, but at the time they were the solution for bringing Wikipedia to African schools where the internet simply didn't exist.

The XULRunner problem and the rebirth

Like every serious open source project, Kiwix had its “winter.” Between 2014 and 2020, the software disappeared from many Linux distribution repositories. The reason? XULRunner, the Mozilla framework Kiwix was based on, was deprecated and removed from package databases.

For six years, Kiwix was technically “dead” for many Linux users. But the community didn't give up. The team worked to completely rethink the software's architecture, rewrite it from scratch, and modernize it. When it reemerged in 2020, it was stronger than before: progressive WebApp, browser extensions, native mobile support, Raspberry Pi integration.

It's the usual open source story: an obstacle that would seem fatal becomes an opportunity to improve and grow. How many proprietary companies would have simply shut down? But in open source, software doesn't die as long as the code is available and someone believes in it.

Where Kiwix saves lives (not hyperbole)

Numbers are important, but it's the stories that make us truly understand a project's impact.

Kenya: the Thika Alumni Trust

In 2015, seven friends who had studied together in the '60s at a high school in Thika return for a visit. The principal asks for help: they need 50 computers to create a lab. The problem? The internet connection is 100 kbps. Useless.

The solution was to create completely offline digital learning environments using Kiwix. Today, that project has transformed education in 61 schools throughout Kenya, reaching over 70,000 children. They've installed 164 microservers running Kiwix—probably one of the largest networks in the world.

The results? In primary schools where the Trust operates, national exam results improved from 8 to 12%. In special needs units, where absenteeism reached 50%, attendance now exceeds 90%.

Mary Mungai, principal of a school with special needs units, says: “All our children have benefited tremendously from the digital libraries. We have children who refused to attend classes but now do so faithfully, some who couldn't read or write but now do very well on computers.”

Ghana: the Kiwix4Schools Project

In 2019, four Ghanaian students from Ashesi University launched Kiwix4Schools with a simple goal: bring digital education to rural schools. They installed Kiwix on 15 Raspberry Pi devices, reaching 2,000 students in 15 schools.

The impact was immediate. Teachers reported students staying after school to explore content. Children who had never touched a computer were navigating Wikipedia articles. Science class changed completely when students could look up experiments, see diagrams, understand concepts beyond what the single available textbook offered.

India: Internet blackouts and censorship

In 2019-2020, the Indian government imposed internet blackouts in Kashmir—the longest in a democracy's history. For months, millions of people were cut off from the digital world. Hospitals, schools, businesses paralyzed.

But those who had Kiwix continued accessing medical information, educational content, technical documentation. It wasn't a complete solution, but it was a lifeline. It demonstrated that offline access isn't just for poor countries—it's a resilience tool even in developed nations with unstable political situations.

The ZIM format: open everything

The genius of Kiwix lies in the ZIM format. It's not just a compression format—it's an open standard specifically designed for offline content distribution. Any developer can create ZIM files, any software can read them. There's no vendor lock-in, no proprietary license.

But ZIM isn't just for Wikipedia. Today ZIM archives exist for:

• Project Gutenberg (50,000+ public domain books)
• Stack Exchange (all sites, all Q&As)
• TED Talks (thousands of videos with subtitles)
• Khan Academy
• Ubuntu documentation
• Arch Wiki
• WikiMed (medical encyclopedia, used by 100,000 doctors and students)

The format is completely open, documented, and anyone can create ZIM archives of their content. It's the open source spirit in its purest form.

Everything works

In 2018, Kiwix formalized collaboration with the Wikimedia Foundation, receiving $275,000 to improve offline access. In 2023, came a $250,000 grant from the Wikimedia Endowment.

Stephane Coillet-Matillon, Kiwix CEO, in December 2018 declared:

Our hope is that one day everyone will have access to the internet, and eliminate the need for other offline methods of access to information. But we know that there are still serious gaps in internet access globally that require solutions today. Kiwix is a tool to start fixing things right now.

Today, in 2025:

• Over 10 million users in more than 220 countries
• More than 10,000 websites crawled regularly
• Available on all platforms: Android, iOS, Windows, macOS, Linux
• Browser extensions for Firefox, Chrome, Edge
• Partnership with Orange Foundation to reach 500,000 children in West Africa

You can explore the entire catalog at library.kiwix.org.

The philosophy behind the code

Here we arrive at the heart of the matter. Why is Kiwix important? Not just because it works, not just because it's helped millions of people. But because it represents a way of thinking about technology.

Kiwix is:

• Open Source: all code on GitHub, GPL license. Anyone can study it, modify it, improve it.
• Completely local: doesn't depend on central servers, cloud, or connections. Each installation is autonomous.
• Privacy-first: no tracking, no telemetry, no data sent to third parties. Impossible—it's offline.
• Community-driven: developed by volunteers, funded by donations.
• Accessible: designed to work even on old or limited hardware.

It's the antithesis of the Big Tech model. There's no company controlling access, no centralized database of who reads what, no algorithms deciding which information to show you. It's technology as it should be: serving the user, before corporations transformed it into a machine for extracting data and selling advertising.

A “dangerous” precedent

There's an interesting paradox. Kiwix exists because the internet isn't accessible to everyone. But its success demonstrates that maybe we don't even need it to be—at least not the way we conceive it now.

Think about it: if I can have Wikipedia, Stack Exchange, Project Gutenberg, Khan Academy on a 128GB SD card, why should I depend on an always-on internet connection? If I can sync updates once a month when I pass by the library with WiFi, why should I pay 50 euros a month for a home connection?

Kiwix demonstrates that the “always connected, always online, always tracked” model isn't the only possible one. That an alternative exists where knowledge is local, accessible, controllable. The monopoly isn't inevitable.

And this, for Big Tech, is dangerous. Because if people realize they can access information without going through Google, without being tracked, without seeing ads... well, the entire business model collapses. It's also no secret that the entire streaming model—everything, no one excluded: Spotify, YouTube, Netflix, etc.—is ecologically unsustainable. Downloading once and playing a thousand times (locally) is less wasteful than downloading zero times and playing a thousand times (remotely). If it can be done for Wikipedia, TED Talks, and Project Gutenberg, it can be done for everything else.

But the biggest challenge remains the same: making Kiwix known. Because the software exists, works, is free. But how many people know they can have Wikipedia in their pocket without the internet? How many African schools know they can have a complete digital library for the cost of a Raspberry Pi?

Conclusions: what I learned

Innovation often doesn't come from Silicon Valley. It comes from a young French engineer working in Germany asking a simple question. It comes from developers scattered around the world contributing in their free time. It comes from the community, not corporations.

Open source works. Kiwix is almost twenty years old, has overcome technical crises that would have killed a proprietary project, has continued to grow with ridiculous budgets. Why? Because the community believes in it. Because the code is open. Because the mission is clear.

Technology is political. Deciding that knowledge must be accessible offline is a political choice. Deciding to use open source licenses is a political choice. Deciding not to track users is a political choice.

Kiwix shows us an alternative. That we don't have to choose between functionality and ethics. That another web is possible.

And now, if you'll excuse me, I'm going to add a Python ZIM library to my Kiwix container, because I'm studying it—or rather, “I have to study it”—for a bunch of small projects I have in mind. AI server included.

#Kiwix #SmallWeb #DigitalSovereignty #OpenSource #Wikipedia #Offline #Privacy #Education #Africa

Discuss...

This is the story – part documented reality, part urban legend – of how a folk song became the unwitting mother of the greatest revolution in music distribution since vinyl. A story that has always fascinated me because it contains all the contradictions of our digital age: innovation and destruction, democratization and loss of quality, openness and control. And yes, it's also because I've always had a soft spot for stories that intertwine in unexpected ways. Perhaps because I too, during my years in radio, saw first-hand what it means to work with audio, manipulate it, compress it, broadcast it. Perhaps because, like many of us who lived through the transition from analog to digital, I still carry the memory of those first MP3 collections downloaded via a 56k modem (crimes do become time-barred after 20 years, right?). But above all, this story fascinates me because it reminds us that behind every technological innovation there's always a human element: a voice, an aesthetic choice, an obsession. And in the case of MP3, that human element was precisely Suzanne Vega's voice singing about coffee and rain on a November morning.

Late 1980s: the race for compression

To understand how “Tom's Diner” ended up in the laboratories of the #Fraunhofer Institute, we need to step back and understand what was happening in the world of digital audio in the late 1980s. The CD had arrived in 1981, bringing the promise of perfect audio quality, crystalline, immune to scratches and the wear of time. But there was a massive problem: digital audio files were enormous. A three-minute song, encoded in PCM (Pulse-Code Modulation) format at 44.1 kHz and 16 bits, occupied around 30-35 megabytes. An entire album? Over 600 megabytes.

To put this in perspective: in the 1980s, the portable listening revolution was the Sony Walkman, which played analog cassettes. With the arrival of CDs, Sony launched the Discman, but these portable CD players were bulky, drained batteries, and skipped at the slightest movement. The idea of carrying an entire record collection was still science fiction.

In an era when a 40MB hard drive was considered gigantic, these numbers were simply impractical. You couldn't think of transmitting music via the internet – which was still an academic and military network – nor of efficiently archiving it on home computers. A radical solution was needed: audio had to be compressed while maintaining acceptable quality. This is where the small city of Erlangen, in Bavaria, enters the scene. Not exactly Silicon Valley, but a German town with a long tradition of scientific excellence. Here was the headquarters of the Fraunhofer Institute for Integrated Circuits, a research centre that would forever change the way we listen to music. The team was led by a man named Dieter Seitzer, who had worked for years on psychoacoustics – that branch of science studying how humans perceive sounds. Seitzer had a vision: to find a way to transmit high-quality music through ISDN telephone lines. It seemed like science fiction, but his doctoral student, a young engineer named Karlheinz Brandenburg, was convinced it was possible. The underlying idea was elegant in its simplicity: the human ear isn't perfect. There are frequencies we don't hear, sounds that get “masked” by louder ones, sonic details that our brain simply discards. Why waste disk space for information we can't perceive anyway?

The goal, therefore, was to create an algorithm that eliminated everything the human ear couldn't distinguish, reducing an audio file to a tenth of its original size without the average listener noticing the difference. But the competition was fierce. In 1989, when the Moving Picture Experts Group (MPEG) – the international standardisation organisation – issued a call for audio codec proposals, 14 candidates arrived from around the world. Among them were AT&T Bell Labs in the United States, Thomson in France, Philips in the Netherlands, and naturally the Erlangen team with their algorithm called ASPEC (Adaptive Spectral Perceptual Entropy Coding). It was a race where whoever demonstrated the most efficient algorithm won: maximum compression, minimum perceptible quality loss. And to prove it, tests were needed. Many tests. Obsessive, maniacal tests, repeated hundreds, thousands of times. In other words, a reference song was needed. A song that would put the algorithm to the most ruthless test possible.

Why that voice?

Several versions exist of how Brandenburg discovered “Tom's Diner”. In one interview, he tells of hearing it on the radio while walking down a corridor. In another, he says he read about this song in a hi-fi magazine that used it to test high-quality speakers. The stories change, overlap, contradict each other. Brandenburg himself has given different versions over the years. But one thing is certain: when he heard that voice, he immediately knew he had found his ultimate test.

“I was ready to fine-tune my compression algorithm,” Brandenburg recalls in a 2009 interview, “and somewhere down the corridor a radio was playing Tom's Diner. I was electrified. I knew it would be nearly impossible to compress this warm a cappella voice.”

And it's precisely in that phrase – “nearly impossible” – that you understand the challenge. The human voice is the most difficult instrument to compress. Evolutionarily, our ears are optimised to recognise voices. We evolved to hear nuances, emotions, the micro-tonal variations that distinguish one person from another, that tell us if someone is happy or sad, sincere or lying. Voice is the primary interface of human communication, and our brain has developed sophisticated mechanisms to analyse it. For this reason, any artifact, any distortion introduced by compression, immediately jumps out when dealing with voice. If MP3 could faithfully reproduce Suzanne Vega's voice, then it could handle anything.

But why “Tom's Diner” specifically? What made this song so special?

First: it's an a cappella recording. There are no instruments to mask or distract. There's no powerful bass covering the low frequencies, no electric guitars filling the mid-range. It's just voice. Naked, exposed, with nowhere to hide. Second: it's an exceptionally high-quality recording. It was recorded at A&M Records studio with professional equipment, meaning it captures all the nuances, all the breaths, all the details of Vega's performance. There's no background noise that might mask compression artifacts. Third: Suzanne Vega's voice has a particular timbre – warm, intimate, with that touch of huskiness that makes it instantly recognisable. It has an interesting dynamic range, with more whispered passages and more assertive ones. It is, in essence, an acoustically “complex” voice.

Brandenburg began working obsessively on that song. He listened to it hundreds of times a day, modifying the algorithm, listening again, modifying again. It was an exhausting, maniacal process. Every time he made a change to the code, he had to listen again to verify whether the result was acceptable or not. The problem was that where instrumental music still sounded acceptable, the voice became a disaster.

Brandenburg had to keep refining, optimising, adjusting the algorithm until that voice sounded good, until he managed to capture that warmth, that intimacy, that human quality that made “Tom's Diner” so special. To be fair, “Tom's Diner” wasn't the only song used in testing. Brandenburg and his team also used other tracks: “Mountains O' Things” by Tracy Chapman, “In All Languages” by Ornette Coleman, “Diamonds on the Soles of Her Shoes” by Paul Simon. James Johnston, from the AT&T team working on a competing algorithm, also used some of these tracks. But “Tom's Diner” became the symbol, the ultimate test, the benchmark. If the algorithm could reproduce that voice, it could reproduce anything.

1992: the MPEG Audio Layer-3 Standard is born

The hard work paid off. In 1992, after years of comparative testing conducted by independent institutes, the MPEG committee approved the MPEG-1 Audio Layer-3 standard. Brandenburg's team had won the competition. Their algorithm had proven superior to the others, capable of compressing audio by a factor of 10-12 while maintaining quality that most listeners judged “indistinguishable” from the original. But no one, at that moment, could imagine what was about to happen. MPEG-1 included three audio encoding layers: Layer-1, Layer-2, and Layer-3. Layer-3 was the most complex and most efficient, but also the most computationally demanding. In the early 1990s, home computers were still too slow to encode audio in Layer-3 in real time. It was cutting-edge technology, but without immediate practical applications. Layer-2, simpler and less efficient, was adopted for Digital Audio Broadcasting (DAB) in Europe. It seemed that Layer-3 – what would later become MP3 – was destined for a marginal role, a technical curiosity for audiophiles with powerful computers.

Brandenburg himself had already developed a successor called Advanced Audio Coding (AAC), which was even more efficient than MP3. It seemed Layer-3 was destined for oblivion before it even took off. And then 1995 arrived. Two things changed everything: the World Wide Web and Windows 95. The Web was exploding. Suddenly, millions of people had internet access and wanted to share things: images, texts, and naturally, music. But connections were incredibly slow – 28.8k modems, if you were lucky, that took hours to download files of just a few megabytes. A format was needed that allowed music sharing in reasonable sizes. Windows 95 brought increasingly powerful computers into millions of homes, with processors capable of decoding compressed audio in real time. And, crucially, Windows used three-character file extensions to identify file types. On 14 July 1995, with a simple internal email at the Fraunhofer Institute, Layer-3 got its definitive name: .mp3

Date: Fri, 14 Jul 1995 12:29:49 +0200
Subject: File extension for Layer 3: .mp3
Hello, In light of the overwhelming consensus of the survey participants, 
the file extension for ISO MPEG Audio Layer 3 is .mp3

Three letters that would change the history of music.

But MP3 still needed a catalyst to take off. That catalyst arrived in the form of software. Brandenburg and his team, perhaps sensing the possibilities, perhaps just to experiment, developed a software player for Windows. They released it for free. Other developers began creating MP3 encoders, some legal with Fraunhofer licenses, others less so. The format spread virally, completely beyond its creators' control. And when #Napster arrived in 1999 – the peer-to-peer file sharing service – MP3 became the standard format for large-scale music piracy. The record industry, caught completely off guard, cried scandal. Metallica protested (anyone who remembers that period raise your hand...). But it was too late. The genie was out of the bottle.

The Irony: A Lossy Technology to Democratise Music

There's a profound irony in all this. MP3 is a “lossy” technology – with loss of information. Every time you compress an audio file to MP3, data is lost. Permanently. It's not reversible. An MP3, technically speaking, is a degraded version of the original. Yet this “imperfect” technology democratised access to music in a way no one could have predicted. It made it possible to have an entire record collection in your pocket. It allowed millions of people to discover artists they would never have listened to otherwise. It gave independent artists the ability to distribute their music without needing record labels. Brandenburg himself always had mixed feelings about MP3's success. On one hand, he was proud that his technology had had such an enormous impact. On the other, he was frustrated that many people used low bitrates – 128 kbps or less – that produced obvious sonic artifacts.

MP3 at 320 kbps sounded excellent, practically indistinguishable from the original for most listeners. But for reasons of space and download speed, many settled for lower quality. And then there was the piracy question. Brandenburg had never imagined his technology would be used primarily to violate copyright on an industrial scale. The Fraunhofer team had worked for years on copy protection systems, DRM, digital watermarking. But none of these technologies were ever effectively implemented in the MP3 ecosystem that developed in the wild (but beautiful) west of the internet at the end of the '90s. In a 1994 interview, Ricky Adar – an Indo-British entrepreneur – said to Brandenburg: “Do you know that you will destroy the music industry?”

Brandenburg, at the time, thought it was an exaggeration. It wasn't. MP3 didn't destroy the music industry in the literal sense – music still exists, artists continue to create, people continue to listen. But it radically transformed it. The business model based on selling physical albums collapsed. Record labels lost their power, only to reorganise and regain it in subsequent years. Distribution became democratised. And all this thanks to a mathematical formula that eliminated frequencies the human ear struggles to perceive.

How MP3 compression actually works

Behind the “magic” of MP3 lies solid mathematics. The algorithm is based on four fundamental pillars:

MDCT Transform The audio signal is broken down into 576 samples per frame, transformed from the time domain to the frequency domain. Basically, instead of having a waveform, we get a spectrum.

Psychoacoustics The algorithm calculates which frequencies are “masked” by louder ones. Example: if there's a very powerful drum at 100 Hz, our ear won't hear a weak sound at 110 Hz. Why waste bits encoding it? The psychoacoustic model divides the spectrum into 32 critical bands that correspond to the frequency resolution of the human ear.

Quantisation The “important” frequencies (those we hear) are encoded with more bits. Those masked or barely audible are coarsely quantised or eliminated entirely. A sound at 15 kHz, almost at the limit of audibility, might be represented with 2-3 bits instead of 16.

Huffman Coding The already compressed data is further compressed with entropy coding. More frequent patterns get shorter codes.

Numerical result: PCM Audio: 44100 samples/sec × 16 bits × 2 channels = 1411.2 kbps MP3 at 128 kbps: compression ratio 11:1 MP3 at 320 kbps: compression ratio 4.4:1

Suzanne Vega discovers she's the mother of MP3s

For years, Suzanne Vega had no idea of the role her song had played in MP3 development. It was the year 2000. Vega, by then an established artist with a consolidated career, was taking her daughter to nursery school. A father approached and congratulated her on being “the mother of the MP3”. Vega had no idea what he was talking about. The man explained he had read an article – hyperbolically titled “Ich Bin Ein Paradigm Shifter: The MP3 Format is a Product of Suzanne Vega's Voice and This Man's Ears” – that recounted how Brandenburg had used “Tom's Diner” to develop the compression algorithm. Vega was astonished. Her song, that small intimate track she had written in the 1980s while attending Barnard College, had become a fundamental piece in the history of digital technology.

In 2007, Vega was invited to the Fraunhofer Institute in Erlangen. Brandenburg and his team played her how “Tom's Diner” sounded in the early versions of the algorithm, before it was refined. It was, in Brandenburg's own words, “horrible”. The voice was distorted, full of artifacts, almost unrecognisable. They then showed her how they had worked for months, iteration after iteration, to capture that vocal quality that made the track special. They explained the psychoacoustics, the listening tests, the obsession with detail. Vega, who had always been attentive to the quality of her recordings, appreciated the irony: a song recorded with maniacal care had helped develop a compression technology that, in a sense, sacrificed part of that quality for practical reasons.

And there's another irony in this story. In 2012, Vega was invited to the Thomas Edison National Historical Park in New Jersey. There, she sang “Tom's Diner” – the song that had become the symbol of the digital revolution – recording it onto an Edison cylinder, one of the oldest and most analog recording technologies in existence. It was a symbolic gesture: bringing the song back to its analog roots, recording it with technology that predated even vinyl by decades. And naturally, someone took that Edison cylinder recording and converted it to MP3, closing the circle in a way that only modern technology could allow. The Museum of Portable Sound made that MP3 file available – an analog wax recording of the track that defined digital audio compression – as a gift for enthusiasts. An act that symbolically connects the Edison era to the Spotify era.

From Walkman to Spotify, via iPod

Before the iPod: for twenty years, from 1979, the Sony Walkman had dominated portable listening. First with cassettes, then with the Discman for CDs. But you always had a physical limit: one cassette, one CD at a time. Pre-iPod MP3 players – like the MPMan F10 of 1998 – promised to solve this problem, but with only 32MB of storage (about 8 songs at 128kbps) they were little more than technological curiosities.

1999: Napster arrives. Shawn Fanning, a nineteen-year-old student, creates software that allows MP3 files to be shared directly between users, without central servers. Within months, millions of people are downloading music for free. The record industry panics. Lawsuits follow, court battles. Napster is shut down in 2001, but it's too late. The model has been established: music can circulate freely online.

2001: Apple launches the iPod. “1000 songs in your pocket” is the slogan. The definitive MP3 player, elegant, with an intuitive interface. The iPod wasn't the first MP3 player – there were already dozens on the market – but it was the one that made the idea mainstream. Suddenly, having your entire music collection in your pocket wasn't a nerd's dream anymore, it was a consumer reality.

2003: Apple launches iTunes. Finally, a legal way to buy digital music. 99 cents per song, reasonable quality, no invasive DRM. It doesn't solve the piracy problem, but it offers a valid alternative. Within a few years, iTunes becomes the world's largest music retailer.

2008: Spotify launches in Sweden. A new model: streaming, not downloading. Unlimited access to millions of tracks for a monthly fee (or free with ads). The MP3 as a file to own slowly begins to become obsolete. Why have files on your hard drive when you can have instant access to everything?

2017: MP3 patents expire. The Fraunhofer Institute officially announces the “death” of MP3 and focuses on more modern codecs like AAC and Opus. But it's a purely technical death: MP3 continues to be used everywhere, a legacy format that will probably never completely die.

Throughout all these years, Fraunhofer earned hundreds of millions of euros in royalties from MP3 patents. That money was reinvested in research, creating new generations of ever more efficient audio codecs: AAC (used by Apple), MPEG-H (for immersive audio), EVS (for 5G calls). Brandenburg, who in 2000 received the prestigious “Deutscher Zukunftspreis” (the German innovation prize), never stopped. Today he leads Brandenburg Labs, a startup working on advanced audio technologies like immersive audio for headphones, trying to create sonic experiences indistinguishable from reality. The original Fraunhofer team – Brandenburg, Bernhard Grill, Jürgen Herre, Harald Popp, Ernst Eberlein – has been awarded prizes and recognition worldwide. They've entered the Internet Hall of Fame. The CE Hall of Fame. The German Research Hall of Fame. But perhaps the most significant recognition is the simplest: go to any corner of the world, ask someone of any age what an “MP3” is, and they'll know. A format that defined an entire era of digital culture.

FLAC, OGG, vinyl, and the return of quality

And here we arrive at one of the most interesting parts of this story. Because not everyone embraced MP3. Not everyone embraced streaming. Not everyone settled for convenience at the expense of freedom and control. In the 2000s, while MP3 dominated and Fraunhofer profited from patents, there was already a counterculture growing silently.

#OGG Vorbis – released in 2000 by the Xiph.Org Foundation – was the open source community's response to the MP3 monopoly. While Fraunhofer and Thomson required licenses and royalties for MP3 encoders, OGG was completely free, without patents, without restrictions. Not only that: at the same bitrate, OGG often offered quality superior to MP3. It was technically better and philosophically consistent with free software ethics. For those who believed in open source, for those who rejected the idea of paying royalties on an audio format, for those who wanted full control over their tools, OGG became the format of choice. It wasn't just a technical matter: it was a matter of principle. The same spirit that had animated the free software movement in the 1980s – the GPL, the Free Software Foundation, all of Stallman's work – now extended to the world of audio codecs.

And then there were those who completely rejected lossy compression. #FLAC – Free Lossless Audio Codec, released in 2001 – offered compression without data loss. Larger files, sure, but bit-for-bit identical to the original. For the most uncompromising audiophiles, FLAC was the only acceptable choice. But it wasn't just about digital formats. Just as digital seemed to have won, vinyl records began making a comeback. Sales, which had collapsed in the '90s and 2000s, started growing again. In 2020, for the first time in decades, vinyl sales surpassed CD sales.

Nostalgia, certainly. The charm of the physical object, the large cover, the ritual of putting the record on the turntable, certainly. But there's also a “visceral” element: owning a vinyl, or a CD, means owning something real, tangible. Something that can't be deleted from a server, revoked by a streaming service, lost in a hard drive crash.

I myself, for years, have decided to stay out of streaming services. I buy, physically, CDs (almost always used), rip them to OGG, tag them properly, and put them on my FreeBSD NAS with ZFS. And then my #Navidrome server, calling them via NFS, does the rest. I've chosen to maintain control over my data, to privilege a free and open source format over proprietary convenience. It's a choice that requires time (and a few scattered curses...), hard drives to manage, docker compose files to update, backups to make, players to configure. But it's also a choice that gives me a sense of ownership, of control that streaming cannot provide.

There's an irony in all this: the technology that “Tom's Diner” helped create – MP3, lossy compression, the idea that “good enough” is sufficient – triggered two types of resistance. Those who rejected it for quality reasons (audiophiles with FLAC), and those who rejected it for freedom reasons (the open source community with OGG). And often, these two souls overlapped.

But this choice is only possible because hard drives have become enormous, internet connections fast, storage cheap. The same technologies that made MP3 obsolete have made it possible to collect OGG or FLAC without thinking twice. In a sense, MP3 created the conditions for its own obsolescence – and for the birth of freer and often better alternatives.

Some Lessons to Take Away

This story has taught us several things. It taught us that convenience often beats perfection. It taught us that technologies developed for one purpose (professional transmission via ISDN) can end up being used in completely different ways (mass file sharing). It taught us that established industries can be disrupted by technologies that initially seem marginal or niche. But perhaps the most important lesson is this: technology is always, at its core, a human matter. MP3 isn't just a mathematical algorithm. It's Suzanne Vega's voice singing about coffee and rain.

I am sitting in the morning At the diner on the corner I am waiting at the counter For the man to pour the coffee

It's Brandenburg's obsession with capturing that warm vocal tonality. We are living, in other words, the consequences of those thousands of repeated listens to “Tom's Diner”, of that obsession with detail, of that search for perfect compression.

And if Suzanne Vega hadn't written that song? If Brandenburg had chosen another track for his tests? Probably MP3 would have been developed anyway. The technology was in the air, the problem of audio compression had to be solved. But perhaps it would have taken longer. Perhaps the algorithm would have been slightly different. Perhaps history would have taken a different turn.

I like to think that technological progress is inevitable, deterministic, that it follows an unstoppable internal logic. But stories like this remind us how random it is, how much it depends on individual choices, on coincidences.

And now, if you'll excuse me, I'm going to update the latest release of Navidrome on my Proxmox server. With Docker, obviously.

#MP3 #DigitalAudio #SuzanneVega #TomsDiner #Fraunhofer #MusicHistory #AudioCompression #OpenSource #FLAC #TechHistory

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The '60s and '70s were characterized by excessive optimism: people thought strong AI was just around the corner. Two “AI winters” followed – periods when funding disappeared and research slowed – because promises weren't materializing. But some continued working in the shadows. Geoffrey Hinton, Yann LeCun, Yoshua Bengio – those we now call the “godfathers of deep learning” – continued their studies on neural networks when no one believed in them anymore.

The real breakthrough came with three converging factors: computational power (GPUs), enormous amounts of data, and better algorithms. In 2012, AlexNet won the ImageNet Challenge by an overwhelming margin, demonstrating that deep learning really worked. From there, an unstoppable acceleration.

Once upon a time in the Carboniferous...

Before ChatGPT exploded, my only knowledge of AI came from science fiction books. Philip K. Dick and his reflections on what it means to be human. Cyberpunk in general, with its technological dystopias. Gibson's Sprawl trilogy, where AIs live in cyberspace like digital deities. Those pages were my only window to a future that seemed incredibly distant.

When I hosted the podcast Caccia al Fotone (a nice thing, but now belonging to the Carboniferous period...), I delved deeper into the subject. I read several papers published on arXiv and dedicated two episodes to AI development. In 2019, during the pandemic period, I devoured “Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell – a book that also helped me write a “thing” (those who know, know; those who don't, never mind...) on the evolution of computer systems and surveillance capitalism.

I thought I had a clear picture. I thought I was prepared.

Mea culpa

Then ChatGPT arrived.

November 2022. First approach: total amazement. I couldn't believe my eyes. I kept asking questions, and despite all the initial hallucinations I encountered, I continued to have that “wow effect” typical of a child finding the most beautiful shell on the seashore (forgive me Newton for stealing that phrase, but it's always too beautiful).

And here's my mea culpa: I set aside all my protective filters that I generally have regarding privacy, open source, control over my data. I let myself go for hours of conversations on the most diverse topics. Until one night – one of many sleepless nights – I found myself discussing with that LLM about depression, various mental disorders, and how one or more abuses can influence a person's life.

When I realized what was happening, I stopped abruptly. I deleted the conversation, canceled my OpenAI subscription and didn't touch any LLM for more than a month. I was entrusting my most intimate thoughts to a proprietary system controlled by a corporation. I was betraying every principle I believed in.

But I work in IT. This is a huge revolution. I couldn't afford to fall behind, nor could I simply reject it on principle. I had to find an alternative. I began to study seriously.

Local, always local

I encountered the first models I could test locally. I discovered Hugging Face, and it was like finding an oasis in the desert. I began studying transformers, the datasets developed by the community. And I was astounded.

Transformers are the architecture that revolutionized AI. Presented in the 2017 paper “Attention Is All You Need”, they replaced old recurrent neural networks (RNNs) with a more elegant and efficient mechanism: the attention mechanism.

In simple words: instead of processing text word by word in sequence, a transformer looks at all words simultaneously and calculates which ones are most relevant to the context. When you read “The bank of the river was green,” the attention mechanism understands that “bank” refers to the river and not the financial institution, because it evaluates the weight of each word relative to the others.

This architecture made models like BERT, GPT, and all modern LLMs possible. It's scalable, parallelizable, and extremely powerful.

Hugging Face and the Open Source revolution

Hugging Face is much more than a platform: it has become the Library of Alexandria of the artificial intelligence era. Founded in 2016, it now hosts over 500,000 pre-trained models, 250,000 datasets, and thousands of demo applications.

Their transformers library has democratized access to AI. With a few lines of Python you can download and use models that would cost millions of dollars to train from scratch. Hugging Face isn't the only platform doing this – there are also Ollama, LM Studio, GPT4All – but it's certainly the most extensive and collaborative.

Here, praise must be given to the developers: this community of people scattered around the world is doing extraordinary work. They release open source models, share knowledge, meticulously document everything. They're building a real alternative to Big Tech's monopoly on AI.

History repeating

Watching this explosion of open models, global collaboration, shared code, I had a powerful déjà-vu. This is incredibly similar to the open source revolution that happened 30 years ago.

In the '90s, Linux and the free software movement challenged Microsoft's dominance and proprietary systems. Many said it was impossible, that free software would never work. Today Linux powers 96% of the world's servers, all Android smartphones, and much of the Internet infrastructure.

Now the same thing is happening with AI. Llama, Mistral, Falcon, Mixtral – “open weight/open source” models that compete with (and often surpass) their proprietary counterparts. History repeats itself, and this time I know which side to be on.

Another server in my homeLab

I resumed studying Python, a study I had left on standby years ago. I began experimenting with training local LLM models. I added old scripts to provide my writing style (yes, it seems incredible but every coder has their own style, and it says a lot about their personality). I used Llama 3 to improve my Bash coding.

And when I was ready, I decided to make an important purchase: I bought a small server – to add to my homelab: Proxmox, pfSense, Nextcloud, WireGuard etc... – that I would transform into an OpenWebUI system.

OpenWebUI is a self-hosted web interface for local language models. Like ChatGPT, but running entirely on local hardware, without sending a single byte to someone else's servers.

For the nerds reading: the simplest way to install is obviously through Docker. Here's a basic example:

docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

Once installed, just connect OpenWebUI to Ollama (the runtime for local models), download your preferred models, and you're operational.

GPU usage is fundamental: a medium-sized LLM requires a lot of RAM and computing power. A dedicated GPU (like an NVIDIA GTX of various types) makes an enormous difference. For those using AMD, there's ROCm. With 16GB of RAM and an 8GB GPU, you can comfortably run 7B parameter models quantized to 4-bit.

My favorite combo? AMD, Debian, Docker, OpenWebUI, Ollama and Mistral.

A revolution. and a choice to make

We're facing a revolution that we cannot avoid. AI is here, it's powerful, and it's evolving rapidly. There are two roads ahead of us.

The first: avoid it now, close our eyes, hope it passes or that someone else deals with it. And then, in twenty years, find ourselves chasing an evolved AI, probably impossible to understand, completely in the hands of those who controlled it from the beginning. This is the path of least resistance, but also of maximum risk. It means ceding control, understanding, and ultimately power to whoever gets there first.

The second: study it, analyze it, use it and understand it today to be able to handle it better tomorrow. Actively participate in its evolution. Contribute to the open source community, ensure that this technology remains accessible, understandable, in the hands of many instead of a few. This path requires effort, time, sometimes admitting we were wrong (as I did). But it's the only path that leads to actual agency over our technological future.

The choice seems obvious when stated this way, but it's not easy in practice. It requires overcoming fear, investing time, challenging our assumptions. It means getting our hands dirty with code, running models locally, understanding how these systems actually work instead of treating them as black boxes.

I made my choice that night when I deleted my ChatGPT conversation history. I chose not to be a passive consumer of AI technology controlled by corporations. I chose to understand, to build, to contribute to the alternative that's being constructed by thousands of developers around the world.

The technology is already here. The question is: will it be controlled by a few companies optimizing for profit and control, or will it be a tool accessible to everyone, understandable, modifiable, improvable by the community?

As I've learned on this journey, choosing to understand – even when it's difficult, even when it means admitting you were wrong – is always better than passively submitting.

AI is not magic. It's mathematics, code, hardware, and above all: it's made by people. And if it's made by people, it can be understood, modified and shaped by people. For the better, not for the worse.

The revolution is happening. The only question is: are you participating, or are you watching?

#AI #OpenSource #LocalLLM #Privacy #ChatGPT #HuggingFace #Ollama #SelfHosted #MachineLearning #DigitalSovereignty

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