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Comment · 10 years ago
Comment · 10 years ago
As others have mentioned this is indeed not true and is referred to as the Martingale betting system [0]. In the case of casino games Martingale still has negative EV. Your EV for any round n is (1-q^n)B-q^n(B^n-1) (chance of winning within n rounds times amount won minus chance of losing n rounds times amount lost) [1] which is B(1-(2q)^n) with B the initial betting amount and q the chance of losing. Since in any round your chance of losing is always slightly greater than 1/2, you will have a negative EV in any round you play, there is no number of rounds for which the EV becomes positive.
The standard deviation of this is fairly large though so that the chance of this happening is very small but nevertheless it isn't zero and what you lose is more than you can expect to win. As sleepychu mentioned, the amount you bet goes up exponentially ($1M after 20 rounds).
Aside: It is sometimes said that in order to do this "double when you lose" strategy, you need to e.g. "stick to red for the entire run". This is false. If I bet red-red-red-...-red-red I am just as likely to win/lose as when I bet red-black-odd-odd-even-...-red
[0] https://en.wikipedia.org/wiki/Martingale_%28betting_system%2...
Comment · 10 years ago
I understand this for products that have product-market fit. But what about products that aren't generating any revenue yet? Wouldn't the fact that someone is willing to pay to develop it further for them indicate demand for the product? Also couldn't this be viewed as doing things that don't scale by building a product that one company loves? How do you draw the line between this, and creating technical debt?
Comment · 10 years ago
Paul Graham wrote an essay on this topic which might help you. http://paulgraham.com/startupideas.html
Comment · 10 years ago
Michael has also talked about how to pitch investors in a similar, clear way. [0] It's part of the "how to start a startup" series.
Comment · 10 years ago
I experience the exact same thing. I get an idea for a side project, get really excited about it, think about all the great things it could do, work on it like crazy for a weekend, and then never touch it again. I got to a point where I got really frustrated with myself that I can't finish anything. So this past Saturday I picked a really small simple project I had and said to myself "No matter what, I'm doing a ShowHN before I go to bed." So (after some hesitation) I did [0]. It really helped knowing that the HN community doesn't tear down ideas or projects. And while this was not a big, badass, super cool project like some others, it does feel good to have finally put a project out there.
Based on this I would say, define a small simple project that you can finish before losing motivation or succumbing to fear of failure, and then just get it out there. You will receive valuable feedback and learn a lot. You have little to fear as the HN community seems to shine at not making you feel embarrassed about what you've made. At least this is the impression I got from watching previous ShowHNs.
Comment · 10 years ago
Thanks for the feedback! The ErgoDox is indeed one that's on my todo list =), along with the unicomp, ultimate hacking keyboard and a few others.
Comment · 10 years ago
Earlier this year I finally bought a decent keyboard (Kinesis Advantage). While I was searching for what I wanted, I put together a list of keyboards with some features and have just put it online. Hopefully it can help others in search of a good keyboard. I'm sure the list is incomplete (both in terms of keyboards and listed features). I'll be adding keyboards in the coming days. Feel free to give feedback and suggestions if there are certain things missing.
(e.g. I was thinking about adding filter for cherry mx key color)
Comment · 10 years ago
If you are interested in learning the basics of Machine Learning I really recommend Andrew Ng's course on Coursera[0]. It starts off very basic and requires almost no prior knowledge other than some linear algebra. It evolves to more advance topics like Neural Networks, Support Vector Machines, Recommender Systems, etc. It is amazing how much you can do after just a few weeks of lessons. It's a very practical course with real world examples and exercises each week. The focus is mostly on using and implementing machine learning algorithms and less so on how and why the math behind them works. (To me this is a negative, but I'm sure some people will appreciate that.)
Comment · 10 years ago
I saw my dad looking at charts one day and asked him what he was doing. He explained some stuff and the idea of owning part of a business really appealed to me. I could sit behind a pc, own part of a business, and make money doing it, what wasn't to like?
Since my dad wasn't the richest investor in the world, he wasn't the best so I started googling and quickly came across Warren Buffett. Not only is he the best (personal opinion), he also had a strategy that resonated with me. I had read basic books on technical traders, position traders, day traders and none of that made sense to me since they often neglects the underlying business. (I also didn't find traders close to the top of forbes' list.)
Once I found something that made sense, I pretty much watched every video online or tried to read every book he recommended. Luckily Warren Buffett loves teaching so there is plenty online you can find. The intelligent investor is definitely a must read. So is "security analysis", "common stocks uncommon profits" and a bunch of others. Here [1] you can find a list of "Buffett approved" books in case you run out.
[1] http://www.bookwormomaha.com/store/c-2-berkshire-hathaway-20...
Comment · 10 years ago
NPV = SUM_t Rt/(1+i)^t (wikipedia). This is similar to your formula. If Rt=constant for all t and t is infinite (hence in perpetuity), the formula simplifies to what I have. More info can also be found here: http://www.investopedia.com/walkthrough/corporate-finance/3/...
I'm not sure, I would like to know the answer to this. At least for modern Fortran.
2) What makes Fortran so much 'faster' at computation than C?
I don't know if the computation of Fortran is always faster than C (I thought they were comparable), but at least in development time, it was a huge improvement for me. I was forced to use Fortran 2003 at university for numerical simulations etc and expected it to be terrifying but found it very pleasant. Instead it was when programming C later on that I got slightly frustrated.
- It's easy to learn. No need to learn about pointers etc.
- It's very math friendly. The fact that you can easily extract and use sub matrices/array is probably my favorite feature. In C I needed to write loops, pass around array length or matrix sizes, etc. Multi dimensional arrays got annoying to work with and ended up writing matrices as column-major vectors.
- It has some nice built in functions such as matrix multiplications (no loops for this). It also has great math libraries like BLAS/LAPACK
- Offers OOP
I haven't made use yet of the advantage I hear most often i.e. for HPC.