Great to see that they have not learned from this experience, and are repeating the mistake with Gemini.
2,928 karma · joined October 6, 2010
Great to see that they have not learned from this experience, and are repeating the mistake with Gemini.
The point of using an LLM is to find the thing that matches your preferences the best. As soon as the amount of money the LLM company makes plays into what's shown, the LLM is no longer aligned with the user, and no longer a good tool.
It's an incredibly frustrating "feature" to deal with, and causes lots of problems in proto3.
There also isn't much interest in improving healthcare from either side right now. The right has nothing. Their current platform is ignorant views about vaccines. The left has a stronger interest in Palestine and housing abundance right now, though all of that is dwarfed by trying to keep the rule of law going, and preventing us from falling out of a democracy. Healthcare is way way down the list for everyone sadly--even Bernie doesn't talk about it much anymore. The electorate has spoken, and they are not interested.
A very common example I see is Vec3 (just x, y, z). In proto2 you should be checking for the presence of x,y,z every time you use them, and when you do that in math equations, the incessant existence checks completely obscure the math. Really, you want to validate the presence of these fields during the parse. But in practice, what I see is either just assuming the fields exist in code and crashing on null, or admitting that protos are too clunky to use, and immediately converting every proto into a mirror internal type. It really feels like there's a major design gap here.
Don't get me started on the moronic design of proto3, where every time you see Vec3(0,0,0) you get to wonder whether it's the right value or mistakenly unset.
I'm now certain that significant whitespace is simply wrong.
On the practical end, your degree is still a big filter for how companies screen applicants. A lack of a CS degree makes it way more difficult to get in the door. I know there are counterexamples, but 99% of my coworkers had CS degrees. Unless the money is a huge burden, or the school you would go to really is crap, then yeah, the degree makes the rest of the journey much easier.
As advice, try to get evaluated out of the intro courses and skip to higher level stuff. Often if you can show a prof that you can program (before the school year begins) you can place right into a course that is at your level.
The AI arguments are also facile. A CS degree is still the best training to be ready to use AI effectively. You understand more about how it is built than everyone else, plus you know way more about every tool surrounding the AI, and would have an easier time making your own.
I know some people have success with alternative paths. But the main road is the main road for a reason here.
Edit: Please also look at the best schools for CS, and don't just discard them as options. My coworkers out of these schools got even more out of their classes and costudents than I did. Your son sounds talented, and the best schools can help talent go even further.
The general contractors know which subs are (currently) good and bad, so their recs are good, but you have to be friendly with them for them to share (instead of taking the project on themselves).
If I leave or am fired from your company, then you are not allowed to hire anybody who works in my field for at least one year.
Wow, these really do seem to be LLM written, and I would guess ChatGPT. This long README has a bunch of generic, redundant statements over and over again.
It may not be objective, but at least it's consistent, and it reflects something about the default human position.
For example, there are no good ways of measuring the amount of technical debt in a codebase. It's such a fuzzy question that only subjective measures work. But what if we show the AI one file at a time, ask "Rate, 1-10, the comprehensibility, complexity, and malleability of this code," and then average across the codebase. Then we get measure of tech debt, which we can compare over time to measure if it's rising or falling. The AI makes subjective measurements consistent.
This essay gives such a cool new idea, while only scratching the surface.
YC was founded more than 20 years ago, and I don't recall any lobbying on the issue of employee tax treatment. If they do care about this they are being super quiet about it.
The number one problem for startup employees is that the AMT tax makes it impossible to exercise options in a company that is doing well. YC is showing us that they will fight for changes to the tax code when it benefits their bottom line directly, but are silent when it comes to helping startup employees hold onto equity that they have earned.
Think carefully before joining a YC startup as an employee.
To be fair, they need jobs too! But giving them all the White House jobs does not seem fair or effective to me.
They decided to end all the research too.
It's not my fault he made such a dumb, impossible promise. It is true he broke it.
But ICE hid the evidence and prevented the courts from looking into it.