I did cover it and had a toggle for it in the visualisations but decided to cut it out because the article was long enough already. There's maybe still a reference to the "vrio system" lurking in it.
My understanding -- correct me if wrong -- is that this hugely complicates things because the search space expands so much. If you imagine three straight pieces in a row, with vrio they now form a fan of possible positions. Then with corners and so on you have a near infinite amount of wiggle room. To do it accurately you probably need to map out the physical space a given connector can move within, but for the visualisation I just loosened the "connectors must align" rule to be "connectors must be within 6mm of each other", which is wrong but fairly close.
The struggle with being self-taught is that you don’t know what you don’t know. This is probably even worse in areas like Unity, where the coding part is sort of a sideshow to the main event. Nowadays the problem is you lack the discernment to evaluate AI output.
I wrote The Conputer Science Book (https://a.co/d/01e62STx) to act as that basic building block and help orient self-taught developers.
What did come out from the blog post though:
- OP writes really well
- OP has learned to be very honest with themselves (and I hope not too self-critical now)
- OP seems really good at delivering things people like, even if they’re a bit cobbled together
All of which are very valuable and harder to learn than programming fundamentals tbh.
Super interesting company and I like the ambition. Sheffield does have a growing number of “deep tech” startups and university spin outs but not so many HN-style startups. This is probably a good thing long term but it does mean companies fly under the radar somewhat. Sadly salaries still lag too.
I’ve heard this a few times. Could you elaborate why? Surely at that point, less you are hired to a very senior role, you are going to get a very small equity % and a lot of the capitalisation growth has already been priced in? In exchange it is far less risky.
Do you just go for the market salary and treat the equity as a minor plus?
Does it not leave inaccessible vast swathes of philosophy where, almost by definition, science cannot contribute answers? Such as the nature of numbers and other metaphysical things.
I haven’t heard of the areas you mention in a sibling comment, so I’ll look them up. Do you find that you need to refocus attention to areas where scientific premises are useful as axioms so that progress can be made?
I’ve read the book under review and would recommend it.
Philosophy has always appealed to me but disappointed because it never seems to settle on answers. There’s a bit in the book where Kleene (I think?) is advising young academic philosophers to go into logic instead as at least there they’ll get answers.
Someone in the book describes philosophy’s truth value as less like scientific inquiry and more like poetry done in logical argument. That seemed like a potentially valuable way of looking at it.
I have been doing the Math for ML course and would recommend.
I have UK A level math but not Further Math, so up to basic calculus. But I forgot most of it and so Math Academy has me going through a lot of the Math Foundation units along the way.
I was initially put off by the monthly price, as it is quite steep. The clincher is that about a year before starting Math Academy I had gone through the Open University’s MST124/125 textbooks (covering the same stuff as Foundations). Except even after a year I’d already forgotten most of it.
Math Academy learning feels much more robust, since it includes spaced reviews and regular tests. I record things in Anki but it’s useful to have regular practice questions too. I also use ChatGPT to spell out things and find it works well at this level.
Some things I’d like Math Academy to have:
- ability to skip lessons (I don’t want to spend ages going over symbolic integration again)
- a reference page to track unlocked material, maybe with Anki integration
- fewer multiple choice questions and more in depth problems
- proof-based math. I’m told this is coming but the degree-level courses have missed their estimated due dates.
I will definitely finish Math for ML and then do linear algebra and multivariate calculus. You’d still need a good textbook to do them rigorously, but I think Math Academy sets you up well.
When I studied Russian we were taught (by native speakers) that щ was pronounced “shch”. But then when we went to Russia literally everyone pronounced it like a longer and softer ш. I still don’t really understand what happened there.
To sort of add to this, I’ve found that meditation has had far more profound effects than psychedelics ever did. Maybe they help to kick start things or show how much of yourself you don’t know.
My understanding -- correct me if wrong -- is that this hugely complicates things because the search space expands so much. If you imagine three straight pieces in a row, with vrio they now form a fan of possible positions. Then with corners and so on you have a near infinite amount of wiggle room. To do it accurately you probably need to map out the physical space a given connector can move within, but for the visualisation I just loosened the "connectors must align" rule to be "connectors must be within 6mm of each other", which is wrong but fairly close.