drfunk

blame DOGE

"The camera knows where it is at all times. It knows this because it knows where it isn't. By subtracting where it is from where it isn't, or where it isn't from where it is (whichever is greater), it obtains a difference, or deviation."

sounds a lot like a tweet from the parody account @PeterMolydeux !

Rick-and-Morty-esque title

In my head I read "okara.ai" as "okarai", which sounds an awful lot like "o caralho" in Portuguese, and I had a good chuckle out of it...

Very cool, I thought about building exactly this a few months ago! Glad to see someone did :)

Very good summary / starting point to better understand what makes OLAP databases get far better performance than OLTP DBs on analytical tasks!

Started reading the thread, and stopped because I realized I was hyperventilating

Of course she's doubling down. Anything else would mean facing the extent of her responsibility in the death of her child, which would be absolutely crushing to her ego and self-image as a parent. She's protecting herself psychologically, that's not surprising.

Looks really interesting! I've been looking for resources on how to ask better questions, it'll surely come in handy. I wonder if there's a PDF / single-file version somewhere, for easier consumption on e-readers?

Wtf that's incredible

Ha! I went through half of the Turkish course but stopped around two years ago, thanks for the reminder, I resumed my progression! I'll be looking at the new courses too :) And thanks a lot for making all of this work available to all!!

One point for Cory Doctorow!

Worth noting that the article dates back from April

Use a journaling app, and write a few notes on what you did everyday. Each week, write a summary, grouped by topic: on project A, did 3 MRs, assisted pre-sales team on product X... At the end of the quarter, review the weekly summaries. Done.

I've been doing a version of this for the past 14+ years. Every manager I've had always told me I have the most detailed yearly review. It's been invaluable to me, as my work has usually been very fragmented on different topics / activities.

Nice project! I have a few qualms with the instructions (sometimes misleading or unclear) and the implementation. For instance some problems fail, because 0. is considered different from 0.0

Using np.testing.assert_allclose in your asserts would solve this I think (https://numpy.org/doc/stable/reference/generated/numpy.testi...).

Happy to contribute / elaborate if you think it's be useful! :)

Nice work, I really like it! Do you plan on adding a small explainer as a blog post? That would be neat!

Mostly good for talent pipeline imo. I try to look for curiosity and autonomy when interviewing prospective interns. That way I can let them explore cool topics that we don't have the bandwidth for. Bear in mind that (at least where I'm located) they are still students / in the middle of their training. They require more supervision than new employees. I consider this to be a plus, for instance if you want one of your team member to level up in coaching/management skills.

I find that most nice resources are paywalled unfortunately... (commenting to hopefully get pointers)

You can play with Guesstimate, a spreadsheet-like tool handling uncertainty.

Here's an example of estimating the duration of a task (shopping): https://www.getguesstimate.com/models/19512

3blue1brown's visualizations made a lot of things click for me, years after learning algebra and calculus. In my opinion they're the perfect companion to a more formal study with textbooks.

Playlist for linear algebra: https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQ...

Excalidraw is good when you need a virtual whiteboard. No login required, so I've used it a lot for remote job interviews.

https://excalidraw.com/