> 5% would already be well within the margin of difference for separate identical clean installations of windows on the same hardware.

what is the source of this non-determinism?

Did you evaluate other projects, like openspec, before deciding on spec-kit?

Feels like it can be solved wirh even more AI: adverserial models reviewing and testing work performed by main model.

Actually I am curikus to try somwthing like that myself. Is there an existing orchestrating engine (or single agent) which can spawn multiple subagents and keep passing their feedback/output between each other until all of them agree that assignment overall is complete?

Ultra-optimized HW-specific engines is what Mojo lang seems to be targeting, but I rarely hear about it here.

> Mojo lang seems to be targeting, but I rarely hear about it here

Momentum over at Mojo lang seems very very slow.

According to their roadmap, they're still busy on Phase 1 ("High performance CPU + GPU coding"), and haven't touched Phase 2 ("Systems application programming") and Phase 3 ("Dynamic object-oriented programming").

So perhaps there isn't much to talk about?

They've got a lot of work yet to do to be a general purpose language, but for GPU programming they have already demonstrated that they can outperform CUDA on Nvidia GPUs.

That's pretty compelling.

1670 on Netflix was hilarious

I hadn’t come across this before. Looks really good, thanks for sharing

old sysadmin trick: create large file on a disk and in a dire situation when DB runs out of space delete it.

Genuine kubernetes scaling strategy: add a do-nothing container that runs with a lower priority than your real workloads, that requests half a machine’s worth of mcpu.

When you deploy a new container, and all your nodes are fully allocated, that low priority container will get evicted, and your container will immediately get scheduled in its place. Then k8s will try to find somewhere to put that half-machine container. If it finds somewhere it fits, it’ll schedule it. If not, it’ll trigger your cluster auto scale to add a new node where that task can run, making sure the next container you want to deploy has some readily available capacity to drop on to.

Basically the same sysadmin strategy, automated.

Or on Amazon elastic filesystems... create giant files just to ensure you're in the right performance class for the files you do need (that was the official way of doing it for a while!).

The classic Speed Up Loop

https://thedailywtf.com/articles/The-Speedup-Loop

Zero it first.

My layman question is why plastic cant be painted? Case temperatures are not that high and there are no plastic parts rubbbing.

This is answered in the first paragraph of the article. Painting requires re-calculating the weight, strength and aerodynamics. Paint does not weigh zero, it changes the flexibility of the plastic, and the texture which changes flow.

But the article didn't give any ballpark numbers, so the interesting bit is missing, and we still know basically nothing.

It can very well be like the snake oil which makes you feel better maybe for the three seconds after you bought it. Or those gold plated audio jacks which are 0.0001% improvement in quality.

Recent example I looked at: https://github.com/nilskch/zed-jj-lsp, which downloads jj-lsp if not found in the system. I have seen other extensions doing similar for convenience, but can't remember names to give concrete links.

There was a story how similar initiative for a courts decisions scraping was shut off.

Weren't there reports that quality decreased when using non-CC harnesses too? Nothing in blog post can explain that.