I'm a staff engineer working with Python, Clojure, and all the DevOps technologies. Outside of work, I write Rust, contribute to open source, maintain a Kubernetes homelab cluster, and dabble in 3D printing and hardware.
In the past, I taught software engineering at Washington University in St. Louis, and worked with web technologies, across many industries, since 2006.
| Blog | https://hackeryarn.com |
| Codeberg | https://codeberg.org/hackeryarn |
| Github | https://github.com/hackeryarn |
RE: https://mastodon.social/@nixCraft/116692731365193814
With the internet continuously getting worse for actually useful information, I've gone back to using reference books for languages.
It really cuts through the noise, and even lets me work off line completely. Why did we ever stop using those?
"Packages that can't be rebuilt byte-for-byte are now blocked from entering Debian's testing branch."
https://itsfoss.com/news/debian-makes-reproducible-builds-mandatory/
"I used AI. It worked. I hated it." by @mttaggart https://taggart-tech.com/reckoning/
This is a really good blogpost. And I"m sure it'll make some people unhappy to read whether they're pro or anti genAI. What's good about @mttaggart's blogpost is he talks honestly about how using Claude Code did actually solve the problem he set out to do. It needed various guardrails, but they were possible to set up, and the project worked. But the post is also completely clear and honest about how miserable it was:
- It removed the joy from the process
- If you aim to do the right thing and carefully evaluate the output, your job ends up eventually becoming "tapping the Y key"
- Ramifications on people learning things
- Plenty of other ethical analysis
- And the nagging wonder whether to use it next time, despite it being miserable.
I think this is important, because it *is* true that these tools are getting to the point where they can accomplish a lot of tasks, but the caveat space is very large (cotd)
I keep coming back to this when I try AI or see it pushed. It's addicting, and that addiction often feels like productivity, but it's very hard to separate the two when it comes to personal productivity.
Our brains will jump through a lot of hoops to keep an addiction going. And the smarter the person is, the more creative the hoops they jump through become.
This makes such an important point. You can't make a system work faster by just producing more junk. You have to build in assurances that can build confidence in what you deliver.
This is why better languages, like #rust, and better tooling are so important. They eliminate entire categories of QA and review. They make you move faster at the scale that matters.
Hi everyone! We are still fighting with an ongoing DDoS attack against our service on the network level. We do not yet have any ETA on recovery time, as we're still investigating different mitigation strategies.
As always, we are very grateful for your support and patience.
I’ve been experimenting more with data-oriented design in Rust. A few weeks ago I applied these ideas to a Python library using PyO3 and Rust. It ended up outperforming NumPy for a subset of operations.
In this post I detail the journey and techniques used to get there:
I’ve been using Podman for a while as my go to container engine. This article outlines all the reasons to do so and provides some nice migration advice https://codesmash.dev/why-i-ditched-docker-for-podman-and-you-should-too
daniel:// stenberg://
Mitchell Hashimoto
Christine Lemmer-Webber
Codeberg Status
Greg Wilson