davispeck

This feels like shifting the trust boundary from the OS (containers) into your runtime (WASIX + shims).

Curious how this holds up under hostile workloads, especially with native modules and libuv in the mix.

This feels less like "agents" and more like a controlled generate → execute → fix loop.

Works great when you have a clear verification signal (tests passing), but what drives convergence when that signal isn’t well-defined?

This looks like moving context from prompts into files and workflows.

Makes sense for consistency, but also shifts the problem:

how do you keep those artifacts in sync with the actual codebase over time?

That's fair.

I probably should have phrased it better.

What I meant is that LLMs made code generation much faster, so now the validation step became much more visible in the workflow.

Before that, writing the code itself usually took more time.

Yes, I am!

I've been working in the tech field for a while, but I'm new to HN. I'd never explored the platform in depth before, and recently decided to start participating and interacting with people here.

The discussions and knowledge shared here have already been very valuable to my own learning. So I hope to contribute to the community in the same way... but I felt I needed to be more active in the community before that.

If anyone is curious or still has questions, I can also share my LinkedIn or GitHub.

Kernel anti-cheats are a fascinating example of security trade-offs.

They solve a real problem (cheats running at higher privilege levels), but at the same time they introduce a massive trusted component into the OS. You're basically asking users to install something that behaves very much like a rootkit, just with a defensive purpose.

The bottleneck seems to have shifted.

Before LLMs the slow part was writing code. Now the slow part is validating whether the generated code is actually correct.

I always like seeing implementations that start from trial division and gradually introduce optimizations like wheel factorization.

It makes the trade-offs much clearer than jumping straight to a complex sieve.

The interactive explanations here are still some of the best examples of how visualization can make ML concepts intuitive.

I wish more technical articles took this approach instead of starting with equations.

This is a really interesting direction.

Separating the compositor and window manager feels like one of those ideas that seems obvious in hindsight, but the protocol/state-machine design here shows why it took real work to make it practical.

Lowering the barrier for writing Wayland window managers without forcing everyone to build a full compositor seems like a big win.