HN user
Yeah, based on pilot interviews I've watched where they discuss the ejection decision process, this situation was a pretty clear-cut case of "definitely eject immediately". What really impressed me was how quickly he was able to realize there was a mismatch between the HUD data and the ambient sound in the cockpit, consult a second set of instruments, realize what was going on, and make the decision.
I recently watched what appeared to be a USAF ejection seat training video. One of the most interesting things was that most ejection fatalities were attributable to pilots taking too long to make the decision to pull the handles. Essentially, people often thought they had more time to try to save the aircraft than they actually did. Another interesting thing was how surviving pilots described their thought process prior to ejecting. Very by-the-book rather than seat-of-the-pants.
The shuttle SRBs floated vertically after splashdown, so I don't know why someone would be dumb for thinking the Falcon 9 booster might do the same.
I was hanging out with a buddy while he experimented with this. The quoted fare dropped from over $30 at around 2am (when all bars are legally required to close in my state) to around $20 about 30-40 minutes later.
If a driver does 15 shifts per month (which would be a pretty lazy schedule for most full-time cabbies I've talked to), you're looking at roughly $1500 per month just to use the car. A quick google search indicates that the MSRPs for top-of-the-line Toyota Camrys and Chevy Malibus (just a couple of cars I thought might be well-suited to the task) are around $30,000, which, at 3% over 5 years, would require a monthly payment of about $540. But let's round that up to $600 to cover taxes and anything else that might be rolled into the loan.
As for insurance, I think $200/mo is probably a conservative guess. I pay way less than that as a 20-something male driving a "high risk" vehicle.
That brings us to $800/mo, leaving $700 to cover maintenance. I'm sure maintenance costs would be quite a bit above average given how much the car is driven, but I can't imagine they'd come anywhere near $700/mo, especially in the first five years (the term of the loan). After the car is paid for, you can either continue to drive it if it's cheaper to do that, or you can sell it for a few thousand bucks and start over. Plus, you don't have to buy your own car, since you've already got it, which would save you a few hundred every month.
Not exactly an in-depth look at the issue, but it seem likely that you're better off owning your own vehicle.
Unrelated, but my worry (for the drivers, anyway, both full-time and casual) is that given the low barriers to entry, a lot of people who own vehicles already will see how they can make some decent money on a casual basis with just slightly higher maintenance costs, and before long, there will be enough UberX drivers that the amount of time spent waiting to get a fare will bring the hourly wages down considerably. Maybe they'll limit the number of new drivers after a point or something, I have no idea, but I'd be concerned about that if I were a driver.
Not exactly relevant, but I was chatting with one UberX driver a few weekends ago, and she told me that the company sends each driver a monthly spreadsheet of metrics for that driver, allowing them to track their own performance. I thought that was interesting.
Slightly OT, but spending a bit of time on Gallup's website recently has shown me much I've misjudged the popularity of a lot of attitudes.
The risk of shifting from the 9-to-5 mindset to "getting things done" is that people will end up (and some already are) working 70/80 hours a week and die young of heart attack.
This is the scenario that worries me- a massive race to the bottom in order to get "ahead". Most jobs aren't that important, and don't merit that much of the employee's time. At an e-commerce place I used to work, I overheard a c-level exec tell a guy who was putting in lots of overtime, "Remember, we're not curing cancer here." I think it's great that the exec was encouraging the guy to not place too much importance on his job, but it's too bad that it was necessary in the first place.
I like this advice. I worked with someone who built an aerospace company around an ultra-reliable actuator design he developed with a friend. They had put hardware on dozens of missions, including some inter-planetary missions, before they were acquired a few years later.
A lot of the advice he gave me centered around understanding how supplier contracts are granted / won, and the importance of building relationships and a reputation in the industry. The biggest thing that stuck with me, though, was that it was even possible for a startup company to put hardware in space, let alone mission-critical stuff.
So, I second the "just start" suggestion. Maybe I'll take the same advice myself some day.
This happens to me often. It's also the reason I love the fact that StackOverflow automatically searches for the text in the question title box as you type it in. There have been a few times where I will start to write out a good, descriptive title for my problem, and realize that I've been searching for the wrong things all along.
Since people have been hopeful about finding water there for a while, are there any good estimates for how much exploration mission costs would be reduced by if water were found?
Thanks for the detailed reply! I'm going to experiment with some of the things you mentioned.
Which conversion step do you think it would be easiest to improve upon? I'm curious, because increasing the rate of free trial signups by 30%, from 2% to 2.6% (which seems like a modest increase), would result in the same number of subscribers as if you were to manage to increase trail-to-paid conversions by the same 30%, from 70% to 91% (which seems like a much more difficult feat).
Anyway, I'm sure you know the various scenarios, so my question is: how do you determine where to focus (or how to distribute) your efforts? Of course, the free trial signup rate becomes a limiting factor as your trail-to-paid rate approaches 1, but at this point, where do you see the best opportunity for improvement?
(apart from the numbers adding up to 130%...)
One issue is that the area of the figure should correspond to the number it represents. For example, the area for 3% should be 75% of the size of the area for 4%, but it clearly isn't in this illustration. If they don't match up, what is the point of having a visual at all? To show that 3% is smaller than 4%?
Even if some of the others match up (I can't tell for sure), their shape is so different that it really doesn't give us any better understanding of how 34% compares to 89%. And again, what is the point of a visualization that doesn't aid in understanding?
Thanks for posting that. I had got about 80% of the way towards a similar model myself, but to see one laid out and formalized like that was very helpful.
Assuming one has created something that they are attempting to bootstrap, how does one go about finding these communities? Can they be found just by searching, or is it more difficult than that?
Same here. I was really excited when I first found out about it, but then it didn't come out for another few years. I'm sure I'll be ordering a copy soon, though.
I really enjoyed that book as well. Around that time, I came a across a documentary about both his run as well as the old Cannonball and US Express races. It was still being made at the time, but it's been released since.
http://www.32hours7minutes.com/
I haven't seen it yet, but it looks quite good.
This relates to something I noticed recently. Out of the blue, I started receiving "join my network" requests from people whose names sounded vaguely familiar. I couldn't remember ever meeting them, and their profiles didn't give me any clues as to where I might know them from. I searched the inbox of the email account associated with my LinkedIn profile, and it turns out that they were all people I had contacted about items for sale on craigslist.
So, I'm guessing that my address was somehow added to their address books, which they then imported, and that's how LinkedIn identified out "connection". What that doesn't explain, though, is why these invitations were sent out. I'm confident that none of these people would have knowingly sent an invitation to me, so I'm guessing that LinkedIn is obtaining their "consent" without making it clear what is going on.
If anybody understands the process behind this, I'd be glad to know. The sad part is that once I realized who these people were and that they almost certainly didn't intend to send invitations to me, I didn't bother to investigate any further, because that's the kind of thing I've come to expect.
That might be true for certain classes or certain programs, but it didn't matter a bit for 90% of what I did.
Life experience doesn't help you project free cash flow, consider the complexities of pricing strategy, understand accounting rules, or really, do anything else we did over the course of those two years.
Maybe it helps with management classes, but the only management class I took was a complete throwaway.
Edit- Got hung up on the life experience bit and didn't address work experience, but in many cases, much of the same applies. There was nothing conceptually difficult about any of the classes I took. At times, my work experience was relevant, but the only benefit was needing less time than some of my classmates to internalize some of the ideas.
Now, I realize that I can't just jump in to a position where someone with 10 years of experience in X + an MBA would be, but that was never my expectation. My expectation was to get good foundational knowledge in a variety of areas. And I got that, just not to the extent I expected. I was just disappointed in how easy a lot of it was.
I would agree with this completely. I was in an MBA program from the ages of 23-25. A year later, the company I was working for started to run out of money. I had a lot of time on my hands throughout the day, so I started going through programming tutorials thinking it might be good mental exercise.
I started really getting into it, and now I've spent a ton of time over the intervening year learning and practicing. I think back and wonder where I might be now if I had started at 23 instead of going back to school.
It's not that what I learned wasn't worthwhile; it's that it took an amount of time and money disproportionate to its value.
On top of that, while I only (ha) paid about $58k for mine, having that amount of debt really hinders one's flexibility.
Clothing choices is one of the least consequential places for there to be a lack of individualism, and I hardly think it's a good proxy for judging how a group values freedom and individualism in areas that matter. I know a lot of people (myself included) whose dress is influenced by prevailing trends, but whose ideas, values, priorities, etc are not (or are to a much lesser degree).
This is what impresses me so much about this photo. I think of nuclear bomb fissile materials as being one of the most precise things ever humans have ever made, yet the the fireball is so incredibly irregular.
It reminds me of when I first learned that irregularity in the early universe is what enabled material to condense (probably not the best word) and eventually form gas clouds.
Edit: I was wondering if there was some connection between the two, but thanks to DanBC's comment, it seems like the actual cause is a lot less mysterious.
This is great; I've been looking forward to this book for a while. I'd recommend the author's blog, Quant Pythonista (http://blog.wesmckinney.com/), where he posts details of his various quantitative Python projects. It's great for a beginner such as myself to see real-world applications outside of my own projects.
I would be concerned about the differences in canning materials used then vs. now (for beverages, specifically), since I don't believe aluminum cans were common at the time of the study.