jbstack

There's definitely appeal in key-driven window managers in general. Projects like i3 and Niri are popular. But you can get that with any Linux distro (albeit not many have it set up that way by default). You don't generally choose a distro just for whatever DE/WM it happens to start with.

As someone who uses Signal and WhatsApp, I would be extremely happy if those companies blocked access in my jurisdiction if such laws are implemented. In fact, if they agreed to insert backdoor access I would never trust them again, even if they subsequently reversed it.

We have to be willing to suffer personal inconveniences to stand up to these types of policies.

With all the current concerns about rogue and mis-aligned AI, and about how we've made essentially zero progress on making sure that AI is safe and trustworthy, NOW you want to put backdoors in encryption algorithms? We should be doing everything we possibly can to make our systems more secure, not less.

This type of policy was terrible when they first thought of it. Now it's outright negligent and dangerous.

I do something similar. I have a base NixOS image in Incus, with whatever tools apply to every project (e.g. Git, OpenCode) already installed. When I work on a project, I spin up a VM instance, use nix shell to add any project-specific tools, then share only the project folder from the host to the guest. This way, the worst the agent can do is destroy my project folder, and I can always restore that from another clone of the repo.

I know a lot of people are using containers for sandboxing, but given how capable the latest models have shown themselves to be for breaking out of sandboxes, I prefer the extra isolation of VMs for this.

I do all this locally - it's an interesting point to able to turn the laptop off but keep the agents running. I might consider running some of these on my homelab server just for that.

> Permits

Obtained by your AI agent, granted by another AI agent.

> Materials, trucks, equipment etc.

Ordered by your AI agent, delivered by a self-driving truck or drone, loaded/unloaded by robots to/from the warehouse/site.

Most of this stuff can already be done today in some form, so its just about improving the autonomy and integrations along the supply chain.

> Xephyr

Given that X11 is becoming more and more obsolete over time, what's the Wayland option?

There's a lot of middle ground between doom-scrolling type content and a 10 hour block of non-stop immersion. It doesn't have to be one or the other.

Many factories in China now already have 90% of their work being done by robots. Is it really so hard to imagine that we reach a point where they can be prompted?

You're assuming a handover happens in a single definable moment. More realistically it will be one small decision making power at a time, so that at any given moment it never really feels like control is being given up. Boiled frog.

> in Germany people are reluctant to give their IBAN to others

Are they worried people might send them money without their permission?

Doesn't matter if you aren't asking the type of questions where hallucinations are relevant e.g. you're seeking pure reasoning rather than factual information.

> I thought the supreme court said the tariffs are illegal without congressional approval

The ruling wasn't as generalised as that. What it said was that the president doesn't have the authority to impose tariffs under the provisions of the International Emergency Economic Powers Act, which Trump had cited as the basis for some tariffs. That made those tariffs effectively illegal, but the ruling said nothing about tariffs which had other legislative provisions as their legal basis. The tariffs that you see today cite some other rule as their basis rather than the IEEPA.

You want both. The harness and the sandbox do different things.

The harness says "You have access to tool X, Y, and Z, but not A, B, C".

The sandbox says "If you try to use X to access a forbidden resource, I'll prevent you from reaching it".

> Moreover I am not sure it is even good advice?

I think it is. He isn't saying to learn how to train a LLM so that you can go on to train LLMs. He's saying to learn it so that you gain a deep understanding of how LLMs work. Ordinary startups can still benefit from things like training or fine tuning highly specialised smaller models, knowing how to select and configure an appropriate model for the task at hand, knowing what software to use and why, understanding what's going on behind the scenes instead of treating everything like a black box, having a higher level of intuition about LLMs generally, etc.

Most computer science courses do in fact teach things which are lower level than coding, such as how transistors work.

I think this is the only sensible way to work with agents, if you care about code quality and reliability but still want the benefits of AI. There seem to be three camps that people more or less fall into: (a) AI is terrible/bad/evil and should never be used, (b) you should one-shot everything and be happy if it seems to "work" when you try it, (c) the middle ground, where the AI writes code which you carefully review.

I definitely prefer (c). But I get why (b) can feel necessary. If your competition is using (b) there can be pressure to do the same just to keep up.

You've selectively quoted the article. The full quote (emphasis added):

"When Moore’s Law slowed in the mid-2000s (specifically, single-threaded performance stagnated), we suddenly had to think about parallelization, architecture, memory locality, etc."

Your link is talking about transistor count. The article is talking about single-threaded performance. Today's CPUs are faster in large part because they have more and more cores.

In my case, it's not so much that I find it childish, it's that I find it incredibly distracting which means I can't focus as easily on the content. Instead of "oh, now I understand how that equation works" I catch myself thinking "why is he repeating the same word over and over - that's so annoying!". Then my brain starts obsessing over "when is he going to do it again?", even though I don't want it to. It's a bit like trying to deliberately ignore something. It becomes all you can think about.

I don't think you can make blanket statements. Consider:

1. Teacher 1 speaks at 100 words per minute.

2. Teacher 2 speaks at 50 words per minute, for the exact same content as Teacher 1.

You're not really losing anything by playing Teacher 1 at normal speed vs Teacher 2 at 2x speed. For every learner, there will be an optimal teaching speed for a given topic. This may mean listening to some audio at normal speed, some at higher speeds, and some even at slower speeds. I always customise my playback speed to the specific content I'm trying to learn.

Repeating a sentence twice in a row during an explanation, is not spaced repetition at all. Spaced repetition means testing recall at increasing intervals, usually starting 1 day apart.

"Paris is the capital of France. Paris is the capital of France" -> not spaced repetition.

"Paris is the capital of France. [one day later]. What is the capital of France? [three days later] What is the capital of France?" -> spaced repetition.

And in fact, it's this plus practice where the real value lies in learning and memorising. The only purpose of an explanation from a teacher is to help you understand the concept. Generally once you understand it once, you don't need to understand it again - unless you forget, and that's where the practice / spaced repetition comes in. That's why I prefer the teaching part to be as efficient as possible, so I can focus my limited time on the parts that will make me internalise it.

I disagree with this. Unlike training models (which requires huge compute), harness development is available to anyone with an editor and ideas. That means that solo devs and small startups can still make meaningful progress.

Also, having only a "standard implementation" makes no sense for a harness. A standard implementation would need to try to be as good as possible at all things. But you'd often want a specialised harness designed for exactly your use case.

I've not tried it myself, but I consistently hear good things about Math Academy. It uses a knowledge graph so that it knows whether you've satisfied prerequisites for a topic, and it adapts itself to your level. I've never taken the plunge because it's very expensive at $49 a month, but it seems to be the one that serious maths learners use.

I did the trial of Brilliant the other day. You can get 7 days free, then another 7 days added if you cancel the first trial before the end. After that you can continue to get 2 free lessons per day with ads. I think it's about $20 a month for unlimited lessons. The maths problems are more visual (which makes them quite intuitive) and it has a nicer, more polished UI than Khan Academy. It's easier to stay in the mood for doing more lessons. The main downsides compared to Khan are that the explanations are quite minimal (short descriptions, no videos), and it doesn't reach as advanced a level as Khan. I'm looking at it as more a way to do a refresher on the maths I already learned at school rather than a serious learning tool. If it were $5 a month I'd probably consider subbing for a couple of months and trying to blitz through all the material before switching back to Khan to fill in the gaps.

There's a difference between pacing (good), and repetitive speech (bad). Between pacing and repetitive speech.

Side note, but much as I like Khan Academy overall, I've always struggled with Sal's slow and repetitive speaking style in the videos.

"Now you add the five ... [pause] ... add the five ... add the five to the seven ... to the seven..."

Running them at 1.5 - 2x helps a bit, but it's still annoying to the point that I find it very distracting.

I find it difficult to understand people who are wildly skeptical about LLMs leading to AGI (assuming we can even agree on what that means). Consider:

- They can already reason better than many humans and are still improving all the time

- Harnesses are improving all the time

- We're already exploring things like long term memory, long term goals, and other things that humans have which LLMs traditionally lack

- An AI agent can read and reason about every piece of AI research ever published, including looking for insights that humans may have missed. A team of humans could never do this even if they dedicated their whole lives to it.

- They can design and execute experiments on a mass scale to determine what does and doesn't work

- Large AI labs have more than sufficient resources and motivation to throw at the problem, and are in fact doing this.

> one thing is pretty clear: the act of familiarizing yourself with a language no longer matters

I completely disagree with this statement as a premise to the article.

Sure, if you have zero care about whether the agent's work is reliable or not, then there's no need to learn a language. But if you're a developer who cares about their work and isn't just outputting 100% vibe code, then at a minimum you should be familiar enough with the language that you can review the agent's output ,follow along with the code, and make an informed decision about whether to commit.

Interestingly, I've found that my approach to learning languages has fundamentally changed. Before, I'd study a book on the one or two languages which were important to me at that time. The goal was to become proficient. Now I choose to read about a variety of languages simply because they demonstrate some interesting paradigm that is new to me (e.g. Haskell -> functional, Elixir -> concurrency). I read a programming book like I'd read an engaging narrative non-fiction book: cover to cover relatively quickly, then I'm done. This gives me that "familiarity" which is useful for agentic coding, without bothering to learn every obscure bit of syntax or library call. My priority has become breadth (get an overview of many languages and paradigms) rather than depth (learn a language or two really well).

Actually the main thing I want is to be able to use my phone however I like, and install whatever I want, without Google telling me whether I have their permission to do so or not.

> My partner got the Moto G Stylus 2025 after we swore off Samsung last year.

I've been wanting to get away from Samsung for years, but keep coming back because of the s-pen. I've yet to find another manufacturer that makes a decent phone with a proper stylus. As in, a stylus which the device recognises as being something different to your finger. Most styluses use the same technology as the cheap ones you can buy on Amazon and use on any phone: all they do is imitate the finger. As far as I know, the Moto stylus phones are like that, right? That makes them very awkward to use for many things e.g.:

S-pen -> scroll down a PDF with my finger -> highlight with the pen

Regular stylus -> scroll down a PDF with my finger -> select highlight option in app -> highlight with stylus, being very careful not to touch the screen anywhere with my finger -> select browse option in app

With the latter, you have to specifically tell the app every time you are switching to/from finger and stylus. With the former, the app knows the difference.

If you give me a high end phone with a proper stylus and GrapheneOS, I'd probably never look back.

> Did you read it all the way to the end? It's coherent ...

I prefer my articles to be coherent without having to wade through pages worth of irrelevant material.

Except there was nothing "cool" about them. Vibe-coding ugly looking GUIs with slop-code backends is not interesting or impressive in 2026. Literally anyone can do it, and I'm pretty sure 90% of HN readers are aware that it's possible. Plus it did nothing to further the claimed purpose of the article, which was to explain why you shouldn't make TUIs.

I found most of the article completely unreadable. I was actually beginning to wonder if the HN post had linked to the wrong article, as it had nothing to do with TUIs until the very end. It was just a long list of "look at this slop code I made" and "look, here's another one!".