atomicnature

To the author: What are your thoughts on tiger Beetle? I think it provides most of the primitives that you mention in a highly optimized package (except hierarchical relationships).

Doing a Job by Admiral Rickover: https://govleaders.org/rickover.htm

This one-pager guide never fails to set me straight whenever I feel like I've lost my way.

If you have more time - I recommend reading everything Rickover has written, it'll serve you throughout life.

The central thing to me is - "quality of efforts" more than "quality of results". For anything sufficiently complicated or "difficult" - one needs to persevere. There's nothing magically special in any place, like a university, except perhaps a few understanding people around to egg you on (that can make a big difference in the right conditions). But end of the day, it is the individual's effort that matters. Effort can make up for many, many deficiencies. More quality effort held steady over a long-enough time should produce at least tolerable results, if not exceptional results.

Good catch.

Equating data structures to types is an over simplification that misses the core point.

I think the original call here is to simply think harder about the problem and avoid picking structures that'll burn you later.

For example, take Unix pipes, see how far they've traveled, how many domains, how many use cases. It's a brilliant way to visualize system building while respecting the constraints of minds and machines.

And it took Ken and others quite a while to realize something like pipes could make sense in Unix. It was not an insight easily obtained but required a bit of hustle and followup and obsession with finding the right building blocks for a system.

Looking at the history of challenging engineering programs, I'd say, "math helps, but is in itself insufficient". I think serious engineering takes way more than good math chops. And having a mathematical inclination or turn of mind doesn't automatically make someone an effective engineer.

PS: My point is, Engineering should be seen a serious discipline on its own, which draws from math, science, psychology, management and a dozen other fields. No single aspect (either math or leetcode) should be emphasized as a panacea to Engineering mastery. There's no shortcut.

Phind is down too.

How come? Basics such as lack of nourishment is not a solved problem in many parts of the world. And of course thats the bare minimum of material existence. Human potential offers way more, right?

OK - a big problem for starters - how to get people off of their screens and focus on something else - like life in general?

Given the way digital infrastructure exists as is today, this is a freakishly difficult problem.

In other words - how'd you make digital systems all around you "attention-enhancing" rather than "attention-diluting"

I think one of Lamport's arguments was that at specification level one shouldn't go for an imperative approach. Imperative is good for "how to" instructions (programs). Declarative is good for "what is" or "what should be" instructions (specifications). So in that sense TLA+ was about helping you build state machines via simple declarative syntax. Of course this was Lamport's argument, in practice, your approach may work just as well. Will definitely be interested to see how your approach evolves.

Nice - didn't know of this before. Looks awesome.

More details here: https://github.com/NodeOS/NodeOS

Start with your prototype first. Have something to show, something to get feedback on, and potential allies and customers.

Make "launch my startup" not an event, but a process spanning a few months at least. Ease into it.

I used to hate it when I had too much time on my hands. Once I switched to doing more constructive things, I no longer have the energy to sustain the hate :)

No, it is not AI generated, but took about a week to produce.

If the content irritates you so much, please ignore it.

Essay: Rickover's "Doing a job" - https://govleaders.org/rickover.htm

Books:

- Rickover's "The Never-Ending Challenge of Engineering"

- Taichi Ohno's "WorkPlace Management"

Back then, it was just people talking. Now it is players, playing.

It is a definition used within org behavioral research in general (accepted).

Culture = What Is (problem solving capabilities) + What the group desires

Also, nothing autocratic about it, since the group is self-selected. Each group/biz has to define some central themes to function properly.

(I'm the author)

Learn mathematics. Learn about orgs. Finances. Quantification keeps you grounded.

Human organization (theory & practice)

Love Zotero. I've found it to be the best document manager of the available options. Good for research organization, casual reading, highlighting, annotations, and so on.

Just want to thank the devs for sharing it with the world :)

Sanity prevails, at least in some HN comments; this is why I come to HN :)

That's a unique suggestion. Any chance you could share your favorite resources around it? Also, since you seem to have accumulated experience/expertise, would be super happy to read about it from you as well. Thanks for the advise.

Great resources, especially Brockman's blog makes the experiences so much acceptable, knowing that even the top people had to struggle to get going in ML

Pooling experiences, to learn more efficiently

These are super helpful, thanks

This is so cool, thanks for sharing.

Yes, I was leaning more towards the "personal project" idea as well, something around document understanding. I subscribe to the "learning by doing/immersion" philosophy as well (upto a large extent).

The problem with projects is one's understanding tends to go more and more specialised, and collaborating/connecting with other ML engineers requires a broader knowledge base sometimes.

Also, for giving advice and useful inputs to others (on their projects), I feel a balanced knowledge base is useful.

Hence the question.

Love the suggestions, especially the one about implementing papers.

Do you have any starters on how one selects papers in the early days, to implement?

Also - any great papers you recommend beginners expose themselves to?

The moat is you'll be at a level higher than the person who doesn't put in an hour a day.

Also, if it puts you at a few levels higher, and you keep learning more, combined with an already strong knowledge base... eventually combined experiences lead to something quite valuable and useful.

An AI teaching how a human can learn to build AI, interesting :)