FourSigma

I am pretty sure Apple containers on MacOS Tahoe are Kata containers

That is a great idea. Have you looked into Docker's Sandbox feature?

I've been exploring this space. There are some use cases where I'd love to run an isolated Claude agent asynchronously. I think running Docker in rootless mode might solve some of the OP's concerns—I believe Podman does this implicitly. Also, there are tools like Kaniko that does not need Docker to create container images. You can also try changing the underlying container runtime to something like gVisor if you want more security.

Does anybody have experience using microVMs (Firecracker, Kata Containers, etc.) for this use case? Would love to hear your thoughts.

Nice! I use the Helm Component Chart [1] from the creators of DevSpace [2] for exact same reasons on personal projects. At a previous company, all of our dev, staging, production services used the Component Chart and it really helped with maintainability and significantly reduced the amount of YAML we had to write.

[1] https://www.devspace.sh/component-chart/docs/introduction

[2] https://www.devspace.sh/

Thank you! Great details. Definitely want convenient.

Could someone please explain the difference between K0s[1] and K3s? They seem to both target the same minimalist K8s segment.

[1]https://k0sproject.io/

Could someone please rundown the pros and cons of using third party cost management software (ex. Ternary)? Are the tools in this space specifically designed for enterprise level cloud cost management?

There seem to be a lot of options out there. Would love a critical overview and recommendations for SaaS in this space.

This is something that boggles my mind. Why would not lead with compensation range so we don't waste each others time? Moreover, it some state it is law to reveal the comp range in the job post.

Would like to recommend reading the book `Range by David Epstein`. It has some interesting thoughts about being a generalist vs specialist in our complex world.

Any idea what the Hetzner equivalent in the US would be?

What is the difference between Firecracker vs LXC/LXD?

Take a look at Regolith Linux. Has some sane configurationsfor i3 on top of Ubuntu.

https://regolith-linux.org/

This is some cool stuff. Contact information? Would love to keep in touch.

I'm interested but do you have any remote software engineering positions?

This comment should be way higher up.

This is excellent advice!

I haven’t seen PGPlot in ages! I didn’t realize it is still being used!

Awesome! Thank you for the insight!

I am currently going through this book. Any advice on how to extract the most value out of it?

Ditto! Would love some clarification.

My June/July/Aug booklist:

   - How to Read Literature Like a Professor by Thomas Foster
   - The Power of Habit by Charles Duhigg
   - The Story of the Human Body by Daniel Lieberman 
   - The Virgin Suicides by Jeffrey Eugenides
   - Founders at Work by Jessica Livingston
   - Coders at Work by Peter Seibel 
   - Drive  by Daniel Pink
   - American Gods by Neil Gaiman

Food classification API that has only two responses: Hotdog or Not Hotdog.

Can anyone comment on the benefits of using the Goodreads API vs Amazon?

I am curious about your path back to math as well.

YES! My favorite book! Make sure to get the unabridged version in particular the Robin Buss translation. Love introducing people to this great work of literature.

https://www.amazon.com/Count-Monte-Cristo-Penguin-Classics-e...

I would 100% lean towards to a more computational skill set while having some exposure to a wet-lab experimentation. I think having a data science skill set in biology is becoming extremely valuable. Moreover, if you choose to leave academia you have a set of skills that are in high demand across a range of industries.

I would definitely be interested. One the biggest problems is that the barrier for entry to do any type of laboratory science is really high (lab space, equipment, expensive kits and reagents, etc) compared to software. I think bioinformatics is a good place to start.

I am extremely happy that the my initial postdoc route was a total bust. It was so hard to leave academia and take a risk in the startup space directly after my PhD. I ended up in data engineering/science at a small EdTech startup. Looking beyond the bump in salary (relative to a postdoc), I find my work extremely rewarding, love my colleagues, and really look forward to going into work everyday and developing my skill set. This was probably the best career decision that I made in life so far. This non-traditional career path is hard for my family, friends, and professors to fully digest and absorb. My dad still wonders why I got a degree in virology/immunology and decided to basically go into software engineering. My friends who are still in academia think I made a terrible mistake going into a startup scene completely unrelated to my graduate education. However, I feel like I am getting great training and setting up for a wonderful career. I seriously doubt I would have felt the same way if I ended up as a postdoc in some research lab.

Do you have any notes/slides from that class? That class sounds very practical and interesting!

These guys seem to make a decent living.

https://data.cityofchicago.org/Administration-Finance/Curren...