One thing I've learned from these large migration projects is that v1 always seems like total crap, while v2 appears to be the perfect dream. However, as you begin building v2, you start to realize that v1 was not actually that bad and had many great but unappreciated features. Additionally, you come to understand that many v1 features took a long time to develop, were battle-tested, and would require significant effort to rebuild in v2 with minimal benefits.
So, what I've learned is not to completely discard v1. Instead, it's better to refactor or rebuild only the parts that pose issues, even though it may not be as sexy or exciting as starting v2 from scratch.
In practice, I would begin by cloning v1 and deploying it to a development environment to start tweaking it. I would also ensure to implement numerous automated tests to safeguard against any potential issues caused by refactoring. Of course, if you can keep using the same database that's even better as you can test refactored features with real customer data and even run both builds in parallel to spot any differences.
I think I'm closer to the target audience as I usually learn either by "ear" or by watching someone play the song. Actually, what I prefer is looking/finding the chords first, and then I fill up the melody and everything in-between. So, an app like this is very helpful. My only feedback is I find the UI piano at the bottom of the screen hard to read without black keys
That's how Andrej Karpathy's video lectures are shaping up for me... I'm learning a lot about not just neural networks, but Python and Pytorch.
It's more or less magical when you find just the right conjugate match between tutorial material and your own learning style. I can see how Beej's Guide might do the same for someone new to both socket programming and C.
Ocarina of Time does a fantastic job going from the bright world of young Link to the more serious and darker world of adult Link. This adds a ton of depth to the game. And let's not forget the clever game mechanic of swapping between young and adult Link to solve puzzles.
Is this really a problem that can be fixed by an app? It seems more like a human behavior issue to me. There is a filter, and people are trying to get through it. It's adversarial, and automating the process makes it easier to game, which is why those solutions have not been adopted.
I think it could, in theory. I've had the idea of approaching it more like a sorting problem. Scrape social media of the candidates and the company, use NLP to provide a 'cultural fit' metric that circumvents the interview process...
Problem with culture fit is that no one is willing to be completely honest about what they want, or what their culture is. No candidate is going to ever say "I'm a mediocre engineer really just looking for something easy where I can coast. Below market comp is OK." And no company is ever going to say "Kind of bureaucratic for a company this size, so want somebody who has a high BS tolerance, and will put up with that in exchange for good pay and reasonably low productivity standards."
Also god help you if your culture fit algorithm has correlations with race / gender / nationality. And there's pretty much a 100% chance it will.
I think the problem is how the vast majority of inbound job requests are spam or irrelevant, while at the same time there are a few gems out there that can't seem to reach the right candidates.
Sure, but how do you address this routing issue? Let recruiters target candidates in a different and more granular way than Linkedin, I guess? But then there is the issue that the paying customers are going to be on the recruiting end (like they are on Linkedin), not the employees, and the recruiters want to spam. I do think you could find a better way to solve this problem, though. Unfortunately I also think that you would run into this problem (https://www.fortressofdoors.com/so-you-want-to-compete-with-...) except replace Steam with Linkedin.
this has been done a hundred times since it's the low hanging fruit that every engineer has gone through. The biggest pain at this moment in time as someone who's searching is that I have to hand build a table in notion of:
1. hiring freezes from twitter/blind
2. specific insurance providers from corporate info/ glassdoor
3. salary ranges from levels.fyi/glassdoor
4. specific roles and tech stack from google jobs/glassdoor
5. finding refering relationships through linkdin/blind
6. general company reviews, work life balance, from glassdoor/blind
7. then the actual application through the company website
It's a huge pain in the ass to hand-populate and research. Every platform has a piece of the puzzle but it's not in once place.
Personally I think it's because we're "looking" at it from the wrong angle. A bit like in programming when you're stuck and you need to take a step back and approach the problem differently. IMHO the fundamental construct is probably something way more abstract, i.e. "information", with some laws that we aren't even aware of yet that will probably challenge the principle of locality.
This. It’s bad philosophy. Non local, non physical, these are the concepts to get comfortable with before attempting to understand the quantum in a coherent way, unless understanding the nature of physicality and locality isn’t your goal. Looking at the quantum world as a bunch of tiny objects will only lead to an infinite regression, where new even smaller objects are continuously discovered.
So, what I've learned is not to completely discard v1. Instead, it's better to refactor or rebuild only the parts that pose issues, even though it may not be as sexy or exciting as starting v2 from scratch.
In practice, I would begin by cloning v1 and deploying it to a development environment to start tweaking it. I would also ensure to implement numerous automated tests to safeguard against any potential issues caused by refactoring. Of course, if you can keep using the same database that's even better as you can test refactored features with real customer data and even run both builds in parallel to spot any differences.