As a maintainer of a different library, I think there’s something here. A revised version of this tool that also gets fed the docs and asked to find inaccuracies could be great. Even if false positives and false negatives are let’s say 20% each, it would still be better than before as final decisions are made by a human.
Yeah, I don't think the 12" MacBook was anywhere near the most controversial, because it was always clear that it was a niche product. I had one and loved it. Those who it wasn't right for didn't buy it.
The 2016-2019 Pros on the other hand replaced a machine that was universally loved. there were entire podcasts dedicated to complaining about them for four years.
Not sure which version was more popular, but I bought a red/blue switch on launch day. And anecdotally I'd say I've seen more of those than the grey one over the years.
CO2 Monitor to let me know when I need to let some new air into my office. Mine is a TFA Dostmann 31.5009, but any model with an NDIR sensor should be fine, there are many choices around 70-150 EUR.
Shouldn't be too difficult. The main thing you'd need is a dictionary that's as close as possible to covering the entire language (ie a digital-first one rather than a digital representation of a paper dictionary), and at least one but ideally several frequency lists. Ideally in the JSON format that Yomichan uses, as that's what the script for generating the data currently expects, but if not then the data can always be massaged. Then you just need to replace the specifics in the app (hardcoded frequency list names etc) with the stuff for your language.
When language learners learn vocab, there are two main processes:
1. Use a premade list of the top x words. Pro: they are guaranteed to be common words. Con: Once you make it past 2000 or so, they might not show up depending on what sort of niche things you read/watch/talk about
2. Look up random words as you come across them, and learn those. Pro: These are words you really saw. Con: You don't know how common that word is, maybe this is the only time in your life that you'll see it
I made a web app that lets you note down words that you come across and might want to learn, and then generates a learning order of those words based on a variety of frequency lists, as well as linking offsite for sample sentences etc. It allowed me to pass the JLPT N1 with just 6k known words (people usually need 8-10k)
According to many commenters in the linked thread, it's fixed in MacOS 13.2.1. Not great that it happened at all, but good to see that it was a software issue.
T3 stack (Next.js, tRPC, Prisma with MySQL, NextAuth, either Tailwind or a component library depending on the project), deployed to Vercel. Maybe swap Prisma for Kysely if cold starts become an issue.
Disclaimer: I maintain an init tool for this stack so I'm somewhat biased
At age 31 I was was working as a part-time university lecturer in a non-stem field plus constantly struggling to find any additional freelance work, couldn't really see a realistic path forward in that career. Had also just become a father, so being broke felt even worse than before. I spent about 18 months learning web dev (just free resources online), and successfully made the career change. Required a lot of sacrifices in that time period though as well as a bit of luck, and I don't think my path is representative of the average person who tries this.
I got a bit frustrated at YouTube dev videos being aimed primarily at entry-level developers, so I tried to record some more mid-level content. There is definitely room for improvement on the production values, but hopefully the content itself makes up for it. Any feedback appreciated :)