mo_42

Did the invention of the steam engine and all other heavy machines make us physically weaker? I guess so. People working on (literal) heavy stuff don't need the strength they used to.

But now they move around even more heavy stuff with machines.

I think something similar might happen to our brains. Maybe we won't be able to work ourselfs through every detail of a mathematical proof, of a software program, or a treatsie on philosophy. But we'll abe able to accomplish intellectual work that only really smart poeple could accomplish. I think this is what counts: outcome.

I spent 15 minutes looking at the diagram in the article and tried to figure out how I can conclude whether it's a bubble.

I couldn't conclude it.

It's just a diagram that shows the economic relationship between companies. If they'd all merge into MegaAI, these flows would just be departments supporting each other on different AI projects.

It's not suitable to prove a bubble.

I think a bubble is nothing else than a sudden change in expected value. Currently, the market expects that AI is very valuable because of actual practical value. In this expectation there's also future improvements which might or might not come.

We might see a crash if we realize that this technology reached its limits. Then, we'll all look poorer on paper in the reverse way that we look richer on paper now than 12 months ago. However, I don't think anything serious will happen if such a crash would occur. It's not that people take loans to bet on these stocks.

(Even the 2008 housing bubble could be seen as a temporary market correction when looking at the long-term real estate prices. Housing is much more expensive today than on the peak of that bubble.)

Back to the dashboard experiment: after you applied the Bonferroni correction you got... nothing.

I guess you got something: Users are not sensitive to these changes, or that any effect is too small to detect with your current sample size/test setup.

In a startup scenario, I'd quickly move on and possibly ship all developed options if good enough.

Also, running A/B tests might not be the most appropriate method in such a scenario. What about user-centric UX research methods?

I agree. Hamburger menus aren't any better than menu bars. It seems like an example where design has more importance than function.

My alternative to the menu bar would be a search bar that allowed me to search in a Google style everything related to that program: functions, features, shortcuts, and documentation.

File | Edit | View | etc. is not the right choice for every program.

Yes, this was also a concern when I started this approach. However, restricted time can also be a yard stick for accomplishing something in a short amount of time. But there are, of course, other factors that influence the success of such a method.

For some reason I tend to forget about task management apps. So whenever I got back to them, it was depressing to see all the unfinished tasks that I haven't touched.

My solution is extreme time boxing. Every Sunday, I sit down and time box the next week. Work and personal stuff goes into the same calendar. I've learned to keep enough slack to not get stressed out. I also know how much unplanned time I need to coordinate with co-workers.

It's kind of absurd that less freedom when to do things, gives me more happiness.

In the beginning I thought that this cannot work because sometimes you just need uninterrupted time to finish something. However, such uninterrupted long spans don't exist anyways for many people. There are stand-ups at work, you have appointments with some of your team members and at 5pm you need to pick up your kids.

I wouldn't say that I was a very chaotic person but after moving several times it felt like it takes longer to find some special tool than buy it new. So I created a little program to keep track of all my stuff [1]. It took quite a while to put everything in there but it helps me to check for a tool if a friend asks for something. Also, I like to be aware of what I own and what I should give away because I don't need it anymore.

[1] https://github.com/mo42/inven

I don't think one would put only the specification in Git. LLMs are not a reliable compiler.

Actual code is still the important part of a business. However, how this code is developed will drastically change (some people actually work already with Cursor etc.). Imagine: If you want a new feature, you update the spec., ask an LLM for the code and some tests, test the code personally and ship it.

I guess no one would hand over the control of committing and deployment to an AI. But for coding yes.

You implicitly assume that a good's price should be based on the amount of effort that was put in.

My impression is rather that the price of goods is based on how much people appreciate it. To me it better explains reality with the absurd prices for some stuff.

Lately, I've been thinking that LLMs will lift programming anyways to another level: the level of specification in natural language and some formal descriptions mixed in. LLMs will take care of transforming this into actual code. So not only users don't care about programming but also the developers. Switching the tech stack might become a matter of minutes.

I guess the standard answer would be a foundation. In some countries, there are minimum capital requirements for foundations but I don't think it's the case for the US. So some thousands of dollars should be enough to keep a website running forever and also hire web developers and accountants every now and then to maintain it.

The Programmer's Brain by Felienne Hermans

Humanity has bootstrapped itself out of a lot of BS over the centuries. There's a mechanism for discarding bad ideas. For example:

Badly-designed boats just don't return.

Ill-designed protection of cities means they'll be conquered.

Scientific ideas that do not corroborate, will be discarded.

etc.

Our current approach to AI doesn't have this mechanism. In the past, humanity just implemented ideas: a city was built according to some weird idea and lasted centuries. So the original idea would spread and be refined by further generations. I guess we need to bring such a mechanism into the loop.

An implementation of the game engine in the model itself is theoretically the most accurate solution for predicting the next frame.

I'm wondering when people will apply this to other areas like the real world. Would it learn the game engine of the universe (ie physics)?

A compiler from SQL to type-checked C++ to speed-up an ML pipeline.

For technical writing, journalism, etc., one can follow the simple rule no adjectives.

For prose, replace all common adjectives by more specific or descriptive one, or even remove them too and describe properties. For example: The F-35 passed by my house. When I heard its sound, the jet had already disappeared at the horizon. (This describes super-sonic speed without an adjective or an overly precise speed number.)

Edit: past perfect based on comment

I came across this blog as well because I got interested in writing a compiler that converts SQL into source code of a type-checked language.

Are there any blog series about the details of query planning, optimization etc?

One reason I can think of is the amount of capital required. So basically when Microsoft or Apple started out, you didn't need a lot of capital to build a minimum viable product. There were not many tech products.

Nowadays, it's it's obviously different. To come up with something innovative, you need to develop something for a longer time so that it's better than everything that's already out there.

To be correct, he maybe didn't coin the term. At least I didn't find it on quick search in his book Cybernetics. Seems like the term ultrastable actually comes from Ashby [1].

[1] https://users.sussex.ac.uk/~ezequiel/AS/lectures/AdaptiveSys...

I'd like to point out that Norbert Wiener was the first to discover the concept of antifragility (under a different term though).

It's also worth checking out more of his works as he initiated the field of cybernetics.

There's JavaScript The Good Parts by Douglas Crockford.

I've read the book and watched a similar series on YouTube.

As a computer scientist who got in touch with quite some theoretical computer science, I find Wolfram's approach appealing. I suppose this approach resonates quite well in CS departments as our minds already know about things like fractals, cellular automata, hypergraphs, etc.

What's not so present in CS (at least where I studied) is philosophy of science. Falsifiability and how theories are created and tested is less grounded in my mind than the topics already mentioned. Though, in physics, this is really important.

Last time I checked, his approach was not able to make real predictions about our world. So it's not yet a real theory. Of course, this doesn't mean people should stop working on this. It also took humans a long time to develop the mathematics to describe gravity correctly.

Will the future of the internet be entirely dynamically generated AI blogs in response to user queries?

I still enjoy commenting on HN and writing some thoughts on my blog. I'm pretty sure that there are many other people too.

At some point everything that is not cryptographically singed by someone I know and trust needs to be considered AI generated.

Maybe AI-generated content might have better quality than generated by humans. But then it's likely that I'm under the influence of some bigger corporation that just needs some eyeballs.

My best plan at the moment is to work on my skill set, get a few certifications, and create an impressive project or two to showcase my new skills.

I suppose certificates aren't worth a lot in most software engineering roles. This might be different in your area but you should definitely reconsider it. I value actual experience in ML or some working projects on GitHub more than any related $BIG_CORP certificate.

An easy way to make use of your time is contributing to some existing and relevant project. It doesn't need to be React, Pandas, or Tensorflow. But usually you have worked with some smaller package in your jobs and encountered some idiosyncrasies. It's much easier to contribute to existing projects than spin up something meaningful yourself.

Except for products where the price is itself a feature. For example a Luis Vuitton hand bag. They show round prices like 2k or 3k.

Very nice. Printed it will be a gift for (your) children. I wish I had gotten something like this.

I recently embarked on a journey to make myself a "dumbphone". I started with an iPhone 12 mini, removed every app possible and kept Phone, Contacts, Messages, Camera, Photos, and Safari with a 15 minute daily limit. My main motivations for going with iPhone were

I did the same with Android. I even removed the browser. All the installed apps fit on a single screen and three of them are basically authenticator apps.

I graduated in CS and in economics. I don't see much of a difference in work performance compared to my co-workers. I think it's because positions nowadays are very much created for predefined combinations of personality and education and I'm an outlier.

I think the main benefit of studying two fields is intellectual satisfaction. It's a source of happiness for live because I understand more about the world on a deeper level than many others (at least I believe that).

True, thanks for sharing. Here's the one: https://imgur.com/a/SsdVhCT

I had a similar experience during the pandemic. I got hold of an old frame for a road bike that I somehow liked. Even the paint was pretty bad so I went all the way of stripping the old paint, cold-setting the rear dropouts (thanks to Sheldon Brown's website) so that it will take the modern wheels, painting the frame, and assembling everything with new parts.

It took me two years because I had to learn a lot how different components fit together and all sorts of specific spacings.

Now, I have a very unique and beautiful bike (people on the street tell me). But above all, I know every detail of that bicycle and how to fix it.

The reason why IT people love this stuff (also woodworking, gardening, etc.) so much is that there's routine. Most of the bikes are very similar. If you've rebuilt one, you have the competency to build another one.

In software, every project is a new challenge. It's more like building a new technical object all the time. I think software development could benefit from rewriting stuff every now and then. Many of my former projects would benefit a lot if I had a couple of week to take apart all the functions and assemble them in a better way that consideres everything I've learned so far.