This Linux distro treats your PC like source code—and it almost works


Most operating systems work in the same basic fashion—you install apps from the binaries or an app store, change configuration files as needed, and perform piecewise updates as needed. NixOS changes that approach entirely.

What in the world is NixOS?

An OS configured by listing components

The NixOS configuration documentation.

Operating systems, Linux or otherwise, usually come with a set of pre-defined packages and configuration settings that determine what the OS looks like, how it acts, and what programs it can run.

NixOS relies on a different approach. Rather than modifying a pre-existing OS to suit your liking, you define exactly what you want using a description in a text file. That description includes all of the packages, services, account, and settings you want.

Once you’re done, you run nixos-rebuild switch, which reads flake.nix (or configuration.nix) and builds an operating system from that description.

In many ways, it reminds me a lot of getting a recipe on the internet and then creating a corresponding shopping list. You figure out what you need, then figure out how you need to put those things together. When you’re done, you have something to eat. Or, in this case, you have an operating system.

Underpinning the entire thing is a package manager called Nix, which manually drops each package into its own folder. Critically, the folder is named based on the hash not just the program, which makes installing different—and potentially conflicting—versions of software relatively easy. Your installer won’t try to mix them, and any dependency issues that might normally arise on a typical Linux installation are largely sidestepped.

Of course, this setup also introduces some problems. Most Linux software is designed to install using the typical Linux file system hierarchy, which means that something you install using something other than the Nix installer is going to have a problem unless you patch it.

It is like building an OS with a Docker compose file

Every time you build a “new” OS using Nix, the old version is left—inactive—alongside the new one. If something goes wrong, you just simply roll back to the last functioning version rather than messing with a recovery environment or manually troubleshooting the problem.

The text file-based configuration approach also makes it very easy to ensure that you can create, install, and run multiple identical copies of the operating system on different computers. As a home user, that won’t be super useful, but if you’re in a professional setting where you need to configure 100 PCs to be identical, it can be a great way to save time. It may also appeal to homelabbers. If you create a basic template, you can use it to roll out dozens of identical VMs or containers.

Who should use NixOS?

It is a niche OS

NixOS will be pretty jarring if it is your first foray into Linux, but developers, system admins, and homelabbers will all appreciate the ability to write one setup script and then reuse it dozens of times in the future.

That does come with some tradeoffs. Even minor tweaks should use Nix to pass along changes, whether that is a config tweak or installing a new app, so you’ll be rebuilding frequently. I’m used to invoking apt like a bad habit, and I found it was a pretty stark adjustment.

Additionally, Nix does take some time to learn, just like any other new system. However, you don’t get the same kind of transfer from apt or dnf to Nix that you get from apt to dnf—it is quite different. Additionally, Flakes, which is the direction the community is heading in, are still relatively new as Linux features go (many are now decades old).

And, as I quickly discovered, old NixOS builds tend to eat up space rather quickly. Over the course of a week, mine grew to about 20GB, and I’d expect that over a year it’d wind up over 100GB. Luckily, there is a succinct command to clean up the vestiges of your old system: nix-collect-garbage. I’d recommend running it relatively frequently, but only once you know your current version is stable.


You don’t need to dual boot to start with NixOS

I’d recommend just creating a bootable USB drive without installing it on the drive to see if you like NixOS. Once it is installed, mess with the configuration files however you’d like. You can also clone the whole configuration to a git repository, which makes rebuilding a new machine (or your current machine, if something goes horribly wrong) much faster.

For my own daily use, the build-rebuild system is a bit limiting. If I needed better stability, I think I’d be inclined to go with a more conventional immutable atomic distro like Kinoite instead.

However, for schools, government agencies, or even homelab applications, NixOS is an interesting approach to Linux that is worth checking out.



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India debates sovereign AI after the US forced Anthropic to kill Fable 5, with proposals for a $5B fund and calls to embrace open-source models.

When the US government ordered Anthropic to shut down Fable 5 and Mythos 5 on 12 June, the export control directive was aimed at restricting foreign nationals from accessing America’s most capable AI. In India, Anthropic’s second-largest market, it landed as a warning shot about what happens when your AI infrastructure runs on someone else’s politics.

The suspension cut off Indian developers and enterprises from Claude’s most advanced models overnight. India’s Claude run-rate revenue had doubled since October 2025, and Tata Consultancy Services had announced a partnership just one day earlier, on 11 June, to train 50,000 employees on Claude and build a dedicated Anthropic business unit. That deal is now in limbo.

The timing has turned what was already a simmering debate about AI sovereignty into a full strategic reckoning. Proposals that sounded ambitious a week ago now sound urgent.

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Mohandas Pai, former Infosys CFO and one of India’s most prominent tech investors, has called for a ₹50,000 crore (roughly $5 billion) annual sovereign AI fund. He has also proposed a ₹2 lakh crore (approximately $21 billion) credit guarantee to finance cloud infrastructure, hardware procurement, and semiconductor development. The figures dwarf the government’s existing commitment.

India approved its IndiaAI Mission in March 2024 with a budget of ₹10,372 crore, approximately $1.25 billion. The programme has deployed around 38,000 GPUs so far. Pai’s proposal would quadruple annual spending and add a credit backstop an order of magnitude larger.

Sridhar Vembu, the founder of Zoho, has gone further. He argued that India should embrace smaller and open-source models, including Chinese ones, rather than depend on American frontier systems that can be switched off by executive order. “Technology is the ultimate weapon,” Vembu said. “Globalization is dead and Bharat must find her own way ahead.

The argument has teeth because the suspension demonstrated exactly the vulnerability Vembu is describing. Amazon’s CEO reportedly triggered the government crackdown by telling Treasury Secretary Scott Bessent that researchers had used Fable 5 to obtain information that could be used in cyberattacks. Anthropic called the action disproportionate, but compliance was immediate and global.

Policy expert Prasanto Roy put it bluntly: “American AI models are bound to American geopolitics.” For Indian enterprises that had built workflows around Claude, the lesson was that access to frontier AI is a privilege that can be revoked without notice, without consultation, and without regard for the commercial relationships it disrupts.

The Indian startup ecosystem is already adapting. Sarvam, a Bengaluru-based AI company, released 30-billion and 105-billion parameter open-source models at the India AI Impact Summit in 2026. Krutrim, founded by Ola’s Bhavish Aggarwal, has pivoted from building foundational models to providing cloud and AI infrastructure services, reporting ₹3 billion in revenue for fiscal year 2026.

Neither company is close to matching the capabilities of Fable 5 or Mythos 5. But the argument for sovereign AI was never about matching frontier performance immediately. It is about ensuring that the floor does not fall out when Washington makes a unilateral decision about who gets to use which models.

Aakrit Vaish, founder of the AI startup Activate, said the suspension “completely changes things” for the sovereign AI debate. Vijay Rayapati, CEO of Atomicwork, raised concerns about what the precedent means for Indian companies with multi-country teams that depend on American AI providers. If the US can shut off model access to enforce export controls, any country that relies on American AI is one policy decision away from disruption.

Not everyone agrees that India needs to build its own frontier models. Hemant Mohapatra, a partner at Lightspeed Venture Partners, argued that talent and compute access matter more than capital for building competitive AI. India has the engineering workforce, but the compute gap is significant, and closing it requires either massive domestic investment or continued access to foreign cloud infrastructure.

Anthropic opened a Bengaluru office as part of its India expansion, and the TCS partnership was designed to be a cornerstone of its enterprise strategy in the country. Whether those plans survive the suspension intact depends on how quickly Anthropic can restore access and whether Indian enterprises still trust a provider whose most capable models can vanish overnight.

The broader pattern is unmistakable. The US has spent four years tightening controls on AI technology, from chip export restrictions to model-level interventions. Each escalation pushes more countries toward the conclusion that dependence on American AI infrastructure carries political risk. India, with its 1.4 billion people and rapidly growing technology sector, is now asking whether it can afford that risk, and what it would cost to eliminate it.

The Opendoor layoffs in June 2026, which shut the company’s India office and affected roughly 250 employees, added another dimension. CEO Kaz Nejatian cited AI-native teams as the reason, suggesting that some US companies are using AI to reduce their reliance on Indian engineering talent at the same time that India is debating its reliance on American AI. The relationship is becoming less complementary and more competitive.

For now, the sovereign AI proposals remain proposals. Pai’s fund has no legislative vehicle, Vembu’s call for open-source adoption has no coordinated policy framework, and the IndiaAI Mission’s GPU deployment is still in early stages.

But the Anthropic suspension has done something that years of policy papers and conference speeches could not: it has given the sovereign AI movement a concrete, recent, and viscerally felt example of why dependence on foreign AI is a strategic liability. The debate is no longer theoretical.



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