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That's just a function of their size. I guarantee you that Stripe and Airbnb's growth rates will drop to these types of growth rates if they hit the 100B valuation. The truth is when you are so large, the opportunities for growth become smaller and smaller. It's much easier to grow at high percentages when you are small. Buffet talks about this a lot with Berkshire; he has said he could grow Berkshire at a much higher rate if the company was smaller. Now he has to pass on amazing investment opportunities that are too small for him.

I failed the first time and passed the 2nd. I'm interviewing now so waiting to see if I get other offers.

To explain, one of the interviewers walked in and asked "have you heard of the rope data structure?"

I said "no" because I hadn't and he was like "great because otherwise I would give you another question". He then asked me to build 2 key functions for the data structure. I didn't need to study it beforehand, and if I did he would have gave me another question.

I think if they give you an uncommon data structure the expectation is that you have prior knowledge of it. This question was the favorite part of my interview loop (we chatted a bit about how google uses rope data structures)

I've only interviewed twice at Google for engineering so take this with a grain of salt.

Here are some things I would depriotize: ● Sorting: ○ Know how to sort. Don't do bubble-sort. ○ You should know the details of at least one nlog(n) sorting algorithm, preferably two (say, quicksort and merge sort). > I don't know anyone who has had to write a sort in an interview. Obviously you should know big O notation and also how they work, but practicing the implementation seems like a waste of time. In fact in my onsite, I used the .sort() method mentioned it was O(nlog(n)) and moved on to the rest of the problem. It was a minute part of the solution.

● Hash Tables: ○ Be prepared to explain how they work, and be able to implement one using only arrays in your favorite language, in about the space of one interview. > Again, while you should know how to use Hash Tables, I really don't think they would ask you to implement one.

● Trees and Graphs: * You should be familiar with at least one flavor of balanced binary tree, whether it's a red/black tree, a splay tree or an AVL tree, and you should know how it's implemented. > I actually still don't know how to build a balanced tree. It's never come up on any Big N interview I've had. The rest of this section is really important though, tree problems are extremely common in interviews.

○ If you get a chance, study up on fancier algorithms, such as Dijkstra and A (for graphs). > I dont know Dijkstra and I don't think it comes up that often. I have no idea what A is.

● Other data structures: ○ You should study up on as many other data structures and algorithms as possible. > I think if you know trees, tries, arrays, linked lists, and hash tables you will do fine. I only had one interview which had a different data structure (rope data structure) and the only reason he asked me the question was because I had never used it before. It was an intro question and if I said I knew it he wouldn't have given it to me. So I think if they give you another data structure, they are gonna assume you don't know it and will give you an intro question.

● Operating Systems, Systems Programming and Concurrency: ○ Know about processes, threads, and concurrency issues. Know about locks, mutexes, semaphores and monitors, and how they work. Know about deadlock and livelock and how to avoid them. > I don't know anything in this area (besides real basics on processes, threads, and locks). Never had a question here. I wouldn't study unless you are interviewing at team where this is relevant.

○ Know what resources a processes needs, a thread needs, how context switching works, and how it's initiated by the operating system and underlying hardware. ○ Know a little about scheduling. The world is rapidly moving towards multi-core, so know the fundamentals of "modern" concurrency constructs. > Again I don't know anything about OSes. I am not a CS major and never had a question in this area.do

● Data Structure Analysis and Discrete Math: ○ Some interviewers ask basic discrete math questions. This is more prevalent at Google than at other companies because we are surrounded by counting problems, probability problems, and other Discrete Math 101 situations. ○ Spend some time before the interview on the essentials of combinatorics and probability. You should be familiar with n-choose-k problems and their ilk – the more the better. > Also don't think this is that important. You should know how n choose k works (because that is needed for measuring complexity), but I definitely wouldn't spend my time on discrete math.

Just as one data point - I went through the onsite recently and I personally found that PDF to be unhelpful. While it does list everything you need to know, it also lists way too many things that you probably don't need to know. It's basically an exhaustive list of basically every resource and topic that could possibly show up in the algorithmic interview. In my view you just need to cover Cracking the Coding Interview and then do 50-100 Leetcode questions. If you have a strong grasp of intro algorithms that would work too, except for engineers like me who didn't major in CS and hence never took an algos course.

--- Here's the actual PDF contents -------

Algorithm Complexity: ○ Please review complex algorithms, including big O notation. For more information on algorithms, visit the links below and your friendly local algorithms textbook. ■ Online Resources: Topcoder - Data Science Tutorials, The Stony Brook Algorithm Repository ■ Book Recommendations: Review of Basic Algorithms: Introduction to the Design and Analysis of Algorithms by Anany Levitin, Algorithms by S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani, Algorithms For Interviews by Adnan Aziz and Amit Prakash, Algorithms Course Materials by Jeff Erickson, Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein

● Sorting: ○ Know how to sort. Don't do bubble-sort. ○ You should know the details of at least one nlog(n) sorting algorithm, preferably two (say, quicksort and merge sort). Merge sort can be highly useful in situations where quicksort is impractical, so take a look at it.

● Hash Tables: ○ Be prepared to explain how they work, and be able to implement one using only arrays in your favorite language, in about the space of one interview.

● Trees and Graphs: ○ Study up on trees: tree construction, traversal, and manipulation algorithms. You should be familiar with binary trees, n-ary trees, and trie-trees at the very least. You should be familiar with at least one flavor of balanced binary tree, whether it's a red/black tree, a splay tree or an AVL tree, and you should know how it's implemented. ○ More generally, there are three basic ways to represent a graph in memory (objects and pointers, matrix, and adjacency list), and you should familiarize yourself with each representation and its pros and cons. ○ Tree traversal algorithms: BFS and DFS, and know the difference between inorder, postorder and preorder traversal (for trees). You should know their computational complexity, their tradeoffs, and how to implement them in real code.

○ If you get a chance, study up on fancier algorithms, such as Dijkstra and A (for graphs). ● Other data structures: ○ You should study up on as many other data structures and algorithms as possible. You should especially know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.

● Operating Systems, Systems Programming and Concurrency: ○ Know about processes, threads, and concurrency issues. Know about locks, mutexes, semaphores and monitors, and how they work. Know about deadlock and livelock and how to avoid them. ○ Know what resources a processes needs, a thread needs, how context switching works, and how it's initiated by the operating system and underlying hardware. ○ Know a little about scheduling. The world is rapidly moving towards multi-core, so know the fundamentals of "modern" concurrency constructs.

● Coding: ○ You should know at least one programming language really well, preferably C/C++, Java, Python, Go, or Javascript. (Or C# since it's similar to Java.) ○ You will be expected to write code in your interviews and you will be expected to know a fair amount of detail about your favorite programming language. ○ Book Recommendation: Programming Interviews Exposed; Secrets to landing your next job by John Monagan and Noah Suojanen (Wiley Computer Publishing)

● Recursion and Induction: ○ You should be able to solve a problem recursively, and know how to use and repurpose common recursive algorithms to solve new problems. ○ Conversely, you should be able to take a given algorithm and prove inductively that it will do what you claim it will do. ● Data Structure Analysis and Discrete Math: ○ Some interviewers ask basic discrete math questions. This is more prevalent at Google than at other companies because we are surrounded by counting problems, probability problems, and other Discrete Math 101 situations. ○ Spend some time before the interview on the essentials of combinatorics and probability. You should be familiar with n-choose-k problems and their ilk – the more the better.

● System Design: ○ You should be able to take a big problem, decompose it into its basic subproblems, and talk about the pros and cons of different approaches to solving those subproblems as they relate to the original goal. ○ Google solves a lot of big problems; here are some explanations of how we solved a few to get your wheels turning. ■ Online Resources: Research at Google: Distributed Systems and Parallel Computing ■ Google File System ■ Google Bigtable ■ Google MapReduce

● Development Practices and Open-Ended Discussion: ○ Sample topics include validating designs, testing whiteboard code, preventing bugs, code maintainability and readability, refactor/review sample code. ○ Sample topics: biggest challenges faced, best/worst designs seen, performance analysis and optimization, testing and ideas for improving existing products.

You could ask coding questions that are not algorithms? Realistic problems that are commonly found in web work.

That's not even how eminent domain works. You get the market value for the asset seized, not the delta in the market value for the asset remaining post seizure.

Yea except your math is wrong. You include the value of the beach in X which represents what Khosla owns even though he doesn't own the beach. Even Khosla wasn't arguing he owned the beach. So you magically inflated the X by a huge amount which makes the valuation off (even though this isn't even a real valuation).

Eminent domain would compensate Khosla on the market value of the road land value. It has nothing to do with the beach. The beach is owned by the public. No legal team on either side is arguing that. The legal case is about easements (i.e. access).

Why don't you share your comparable analysis or discount cash flow model to see what that road easement actually is worth since you seem believe pretty strongly in the valuation. I'm really curious to how you think a narrow strip of land that is for the easement is worth $30 million given recent land sales in similar areas.

No I replied to the correct comment. I am explaining why I would consider something like Impossible.

OP asked why I am interested in Impossible Meats. I answered why I did it personally. What does that have to do with you? Eat whatever you want.

Because I like the flavor and texture but I don't like the carbon emissions associated with beef production?

I am a person who would eat these (and am a meat eater). I care about reducing my carbon footprint, have concerns about our food supply, and also the treatment of animals to some degree. I still eat meat, but as these products improve (and they are improving very quickly), I will have them more and more often as a change from meat. My wife is also vegetarian so it also means we can share more food together.

There is definitely a use case for this, I've had impossible stuff with a lot of non vegetarians. In fact most vegetarians don't care about this product.