Great PM is the person who tries setup the processes in the way that he/she will be as less required and as less involved in the project as possible.

# Category: Misc

### Akin’s Laws of Spacecraft Design

1. Engineering is done with numbers. Analysis without numbers is only an opinion.

2. To design a spacecraft right takes an infinite amount of effort. This is why it’s a good idea to design them to operate when some things are

wrong.

3. Design is an iterative process. The necessary number of iterations is one more than the number you have currently done. This is true at any point in time.

4. Your best design efforts will inevitably wind up being useless in the final design. Learn to live with the disappointment.

5. (Miller’s Law) Three points determine a curve.

6. (Mar’s Law) Everything is linear if plotted log-log with a fat magic marker.

7. At the start of any design effort, the person who most wants to be team leader is least likely to be capable of it.

8. In nature, the optimum is almost always in the middle somewhere. Distrust assertions that the optimum is at an extreme point.

9. Not having all the information you need is never a satisfactory excuse for not starting the analysis.

10. When in doubt, estimate. In an emergency, guess. But be sure to go back and clean up the mess when the real numbers come along.

11. Sometimes, the fastest way to get to the end is to throw everything out and start over.

12. There is never a single right solution. There are always multiple wrong ones, though.

13. Design is based on requirements. There’s no justification for designing something one bit “better” than the requirements dictate.

14. (Edison’s Law) “Better” is the enemy of “good”.

15. (Shea’s Law) The ability to improve a design occurs primarily at the interfaces. This is also the prime location for screwing it up.

16. The previous people who did a similar analysis did not have a direct pipeline to the wisdom of the ages. There is therefore no reason to

believe their analysis over yours. There is especially no reason to present their analysis *as* yours.

17. The fact that an analysis appears in print has no relationship to the likelihood of its being correct.

18. Past experience is excellent for providing a reality check. Too much reality can doom an otherwise worthwhile design, though.

19. The odds are greatly against you being immensely smarter than everyone else in the field. If your analysis says your terminal velocity

is twice the speed of light, you may have invented warp drive, but the chances are a lot better that you’ve screwed up.

20. A bad design with a good presentation is doomed eventually. A good design with a bad presentation is doomed immediately.

21. (Larrabee’s Law) Half of everything you hear in a classroom is crap. Education is figuring out which half is which.

22. When in doubt, document. (Documentation requirements will reach a maximum shortly after the termination of a program.)

23. The schedule you develop will seem like a complete work of fiction up until the time your customer fires you for not meeting it.

24. It’s called a “Work Breakdown Structure” because the Work remaining will grow until you have a Breakdown, unless you enforce

some Structure on it.

25. (Bowden’s Law) Following a testing failure, it’s always possible to refine the analysis to show that you really had negative margins all along.

26. (Montemerlo’s Law) Don’t do nuthin’ dumb.

27. (Varsi’s Law) Schedules only move in one direction.

28. (Ranger’s Law) There ain’t no such thing as a free launch.

29. (von Tiesenhausen’s Law of Program Management) To get an accurate estimate of final program requirements, multiply the initial time estimates by pi, and slide the decimal point on the cost estimates one place to the right.

30. (von Tiesenhausen’s Law of Engineering Design) If you want to have a maximum effect on the design of a new engineering system, learn to draw. Engineers always wind up designing the vehicle to look like the initial artist’s concept.

31. (Mo’s Law of Evolutionary Development) You can’t get to the moon by climbing successively taller trees.

32. (Atkin’s Law of Demonstrations) When the hardware is working perfectly, the really important visitors don’t show up.

33. (Patton’s Law of Program Planning) A good plan violently executed now is better than a perfect plan next week.

34. (Roosevelt’s Law of Task Planning) Do what you can, where you are, with what you have.

35. (de Saint-Exupery’s Law of Design) A designer knows that he has achieved perfection not when there is nothing left to add, but when there is nothing left to take away.

36. Any run-of-the-mill engineer can design something which is elegant. A good engineer designs systems to be efficient. A *great*

engineer designs them to be effective.

37. (Henshaw’s Law) One key to success in a mission is establishing clear lines of blame.

38. Capabilities drive requirements, regardless of what the systems engineering textbooks say.

39. Any exploration program which “just happens” to include a new launch vehicle is, *de facto*, a launch vehicle program.

39. (alternate formulation) The three keys to keeping a new human space program affordable and on schedule:

1) No new launch vehicles.

2) No new launch vehicles.

3) Whatever you do, don’t develop any new launch vehicles.

40. (McBryan’s Law) You can’t make it better until you make it work.

41. There’s never enough time to do it right, but somehow, there’s always enough time to do it over.

42. Space is a completely unforgiving environment. If you screw up the engineering, somebody dies (and there’s no partial credit because *most *of the analysis was right…)

*I’ve been involved in spacecraft and space systems design and development for my entire career, including teaching the senior-level capstone

spacecraft design course, for ten years at MIT and now at the University of Maryland for more than two decades. These are some bits of wisdom that I have gleaned

during that time, some by picking up on the experience of others, but mostly by screwing up myself. I originally wrote these up and handed them out to my

senior design class, as a strong hint on how best to survive my design experience. Months later, I get a phone call from a friend in California complimenting me

on the Laws, which he saw on a “joke-of-the-day” listserve. Since then, I’m aware of half a dozen sites around the world that present various

editions of the Laws, and even one site which has converted them (without attribution, of course) to the Laws of Certified Public Accounting. (Don’t ask…) Anyone is welcome to link to

these, use them, post them, send me suggestions of additional laws, but I do maintain that *this* is the canonical set of Akin’s Laws…

- Source: Akin’s Laws

### Floating Point Arithmetic

Floating-point numbers are represented in computer hardware as base 2 (binary)

fractions. For example, the decimal fraction

```
0.125
```

has value 1/10 + 2/100 + 5/1000, and in the same way the binary fraction

```
0.001
```

has value 0/2 + 0/4 + 1/8. These two fractions have identical values, the only

real difference being that the first is written in base 10 fractional notation,

and the second in base 2.

Unfortunately, most decimal fractions cannot be represented exactly as binary

fractions. A consequence is that, in general, the decimal floating-point

numbers you enter are only approximated by the binary floating-point numbers

actually stored in the machine.

The problem is easier to understand at first in base 10. Consider the fraction

1/3. You can approximate that as a base 10 fraction:

```
0.3
```

or, better,

```
0.33
```

or, better,

```
0.333
```

and so on. No matter how many digits you’re willing to write down, the result

will never be exactly 1/3, but will be an increasingly better approximation of

1/3.

In the same way, no matter how many base 2 digits you’re willing to use, the

decimal value 0.1 cannot be represented exactly as a base 2 fraction. In base

2, 1/10 is the infinitely repeating fraction

```
0.0001100110011001100110011001100110011001100110011...
```

Stop at any finite number of bits, and you get an approximation. On most

machines today, floats are approximated using a binary fraction with

the numerator using the first 53 bits starting with the most significant bit and

with the denominator as a power of two. In the case of 1/10, the binary fraction

is `3602879701896397 / 2 ** 55`

which is close to but not exactly

equal to the true value of 1/10.

Many users are not aware of the approximation because of the way values are

displayed. Python only prints a decimal approximation to the true decimal

value of the binary approximation stored by the machine. On most machines, if

Python were to print the true decimal value of the binary approximation stored

for 0.1, it would have to display

```
>>> 0.1
0.1000000000000000055511151231257827021181583404541015625
```

That is more digits than most people find useful, so Python keeps the number

of digits manageable by displaying a rounded value instead

```
>>> 1 / 10
0.1
```

Just remember, even though the printed result looks like the exact value

of 1/10, the actual stored value is the nearest representable binary fraction.

Interestingly, there are many different decimal numbers that share the same

nearest approximate binary fraction. For example, the numbers `0.1`

and

`0.10000000000000001`

and

`0.1000000000000000055511151231257827021181583404541015625`

are all

approximated by `3602879701896397 / 2 ** 55`

. Since all of these decimal

values share the same approximation, any one of them could be displayed

while still preserving the invariant `eval(repr(x)) == x`

.

Historically, the Python prompt and built-in `repr()`

function would choose

the one with 17 significant digits, `0.10000000000000001`

. Starting with

Python 3.1, Python (on most systems) is now able to choose the shortest of

these and simply display `0.1`

.

Note that this is in the very nature of binary floating-point: this is not a bug

in Python, and it is not a bug in your code either. You’ll see the same kind of

thing in all languages that support your hardware’s floating-point arithmetic

(although some languages may not *display* the difference by default, or in all

output modes).

For more pleasant output, you may wish to use string formatting to produce a limited number of significant digits:

```
>>> format(math.pi, '.12g') # give 12 significant digits
'3.14159265359'
>>> format(math.pi, '.2f') # give 2 digits after the point
'3.14'
>>> repr(math.pi)
'3.141592653589793'
```

It’s important to realize that this is, in a real sense, an illusion: you’re

simply rounding the *display* of the true machine value.

One illusion may beget another. For example, since 0.1 is not exactly 1/10,

summing three values of 0.1 may not yield exactly 0.3, either:

```
>>> .1 + .1 + .1 == .3
False
```

Also, since the 0.1 cannot get any closer to the exact value of 1/10 and

0.3 cannot get any closer to the exact value of 3/10, then pre-rounding with

`round()`

function cannot help:

```
>>> round(.1, 1) + round(.1, 1) + round(.1, 1) == round(.3, 1)
False
```

Though the numbers cannot be made closer to their intended exact values,

the `round()`

function can be useful for post-rounding so that results

with inexact values become comparable to one another:

```
>>> round(.1 + .1 + .1, 10) == round(.3, 10)
True
```

Binary floating-point arithmetic holds many surprises like this. The problem

with “0.1” is explained in precise detail below, in the “Representation Error”

section. See The Perils of Floating Point

for a more complete account of other common surprises.

As that says near the end, “there are no easy answers.” Still, don’t be unduly

wary of floating-point! The errors in Python float operations are inherited

from the floating-point hardware, and on most machines are on the order of no

more than 1 part in 2**53 per operation. That’s more than adequate for most

tasks, but you do need to keep in mind that it’s not decimal arithmetic and

that every float operation can suffer a new rounding error.

While pathological cases do exist, for most casual use of floating-point

arithmetic you’ll see the result you expect in the end if you simply round the

display of your final results to the number of decimal digits you expect.

`str()`

usually suffices, and for finer control see the `str.format()`

method’s format specifiers in Format String Syntax.

For use cases which require exact decimal representation, try using the

`decimal`

module which implements decimal arithmetic suitable for

accounting applications and high-precision applications.

Another form of exact arithmetic is supported by the `fractions`

module

which implements arithmetic based on rational numbers (so the numbers like

1/3 can be represented exactly).

If you are a heavy user of floating point operations you should take a look

at the Numerical Python package and many other packages for mathematical and

statistical operations supplied by the SciPy project. See <https://scipy.org>.

Python provides tools that may help on those rare occasions when you really

*do* want to know the exact value of a float. The

`float.as_integer_ratio()`

method expresses the value of a float as a

fraction:

```
>>> x = 3.14159
>>> x.as_integer_ratio()
(3537115888337719, 1125899906842624)
```

Since the ratio is exact, it can be used to losslessly recreate the

original value:

```
>>> x == 3537115888337719 / 1125899906842624
True
```

The `float.hex()`

method expresses a float in hexadecimal (base

16), again giving the exact value stored by your computer:

```
>>> x.hex()
'0x1.921f9f01b866ep+1'
```

This precise hexadecimal representation can be used to reconstruct

the float value exactly:

```
>>> x == float.fromhex('0x1.921f9f01b866ep+1')
True
```

Since the representation is exact, it is useful for reliably porting values

across different versions of Python (platform independence) and exchanging

data with other languages that support the same format (such as Java and C99).

Another helpful tool is the `math.fsum()`

function which helps mitigate

loss-of-precision during summation. It tracks “lost digits” as values are

added onto a running total. That can make a difference in overall accuracy

so that the errors do not accumulate to the point where they affect the

final total:

```
>>> sum([0.1] * 10) == 1.0
False
>>> math.fsum([0.1] * 10) == 1.0
True
```

### Composition over inheritance

Favor object composition over class inheritance.

The problem with object-oriented languages is they’ve got all this implicit environment that they carry around with them. You wanted a banana but what you got was a gorilla holding the banana and the entire jungle.

### Drop Support for Older IE

Internet Explorer

- Not secure
- Slow and buggy
- It takes a lot of time and resources to maintain websites for old IE. It does not worth it because only for few percents of users use old versions of IE.

Internet Explorer / Edge versions:

- IE8 was released in 2009 – not supported by Microsoft anymore
- IE9 was released in 2011 – not supported by Microsoft anymore
- IE10 was released in 2012 – not supported by Microsoft anymore
- IE11 was released in 2015
- Edge (aka IE12) was released in 2016

Microsoft drop support IE10 and below.

https://www.microsoft.com/en-ca/WindowsForBusiness/End-of-IE-support

### Support for older versions of Internet Explorer ended on January 12th, 2016

### What is end of support?

Beginning January 12, 2016, only the most current version of Internet Explorer available for a supported operating system will receive technical supports and security updates. Internet Explorer 11 is the last version of Internet Explorer, and will continue to receive security updates, compatibility fixes, and technical support on Windows 7, Windows 8.1, and Windows 10.

Internet Explorer 11 offers improved security, increased performance, better backward compatibility, and support for the web standards that power today’s websites and services. Microsoft encourages customers to upgrade and stay up-to-date on the latest browser for a faster, more secure browsing experience.

### What does this mean?

It means you should take action. After January 12, 2016, Microsoft will no longer provide security updates or technical support for older versions of Internet Explorer. Security updates patch vulnerabilities that may be exploited by malware, helping to keep users and their data safer. Regular security updates help protect computers from malicious attacks, so upgrading and staying current is important.

### Internet Live Statistics

### Google Search Statistics

Google now processes about **1.2 trillion searches per year worldwide**. Its about 100 billion searches per month or 3.5 billion searches per day or over 40,000 search queries every second.

### Curious facts

- In 1999, it took Google one month to crawl and build an index of about 50 million pages.

In 2012, the same task was accomplished in less than one minute. - 16% to 20% of queries that get asked every day have never been asked before.
- Every query has to travel on average 1,500 miles to a data center and back to return the answer to the user.
- A single Google query uses 1,000 computers in 0.2 seconds to retrieve an answer.

Links:

### Internet Users

There are over **3.4 billion internet users** on the world wide web today.

Around 40% of the world population has an internet connection today. In 1995, it was less than 1%.

The number of internet users has increased tenfold from 1999 to 2013.

The first billion was reached in 2005. The second billion in 2010. The third billion in 2014.

### The chart and table below show the number of global internet users per year since 1993:

Year | Internet Users** | Penetration (% of Pop) |
World Population |
Non-Users (Internetless) |
1Y User Change |
1Y User Change |
World Pop. Change |

2016* | 3,424,971,237 | 46.1 % | 7,432,663,275 | 4,007,692,038 | 7.5 % | 238,975,082 | 1.13 % |

2015* | 3,185,996,155 | 43.4 % | 7,349,472,099 | 4,163,475,944 | 7.8 % | 229,610,586 | 1.15 % |

2014 | 2,956,385,569 | 40.7 % | 7,265,785,946 | 4,309,400,377 | 8.4 % | 227,957,462 | 1.17 % |

2013 | 2,728,428,107 | 38 % | 7,181,715,139 | 4,453,287,032 | 9.4 % | 233,691,859 | 1.19 % |

2012 | 2,494,736,248 | 35.1 % | 7,097,500,453 | 4,602,764,205 | 11.8 % | 262,778,889 | 1.2 % |

2011 | 2,231,957,359 | 31.8 % | 7,013,427,052 | 4,781,469,693 | 10.3 % | 208,754,385 | 1.21 % |

2010 | 2,023,202,974 | 29.2 % | 6,929,725,043 | 4,906,522,069 | 14.5 % | 256,799,160 | 1.22 % |

2009 | 1,766,403,814 | 25.8 % | 6,846,479,521 | 5,080,075,707 | 12.1 % | 191,336,294 | 1.22 % |

2008 | 1,575,067,520 | 23.3 % | 6,763,732,879 | 5,188,665,359 | 14.7 % | 201,840,532 | 1.23 % |

2007 | 1,373,226,988 | 20.6 % | 6,681,607,320 | 5,308,380,332 | 18.1 % | 210,310,170 | 1.23 % |

2006 | 1,162,916,818 | 17.6 % | 6,600,220,247 | 5,437,303,429 | 12.9 % | 132,815,529 | 1.24 % |

2005 | 1,030,101,289 | 15.8 % | 6,519,635,850 | 5,489,534,561 | 12.8 % | 116,773,518 | 1.24 % |

2004 | 913,327,771 | 14.2 % | 6,439,842,408 | 5,526,514,637 | 16.9 % | 131,891,788 | 1.24 % |

2003 | 781,435,983 | 12.3 % | 6,360,764,684 | 5,579,328,701 | 17.5 % | 116,370,969 | 1.25 % |

2002 | 665,065,014 | 10.6 % | 6,282,301,767 | 5,617,236,753 | 32.4 % | 162,772,769 | 1.26 % |

2001 | 502,292,245 | 8.1 % | 6,204,310,739 | 5,702,018,494 | 21.1 % | 87,497,288 | 1.27 % |

2000 | 414,794,957 | 6.8 % | 6,126,622,121 | 5,711,827,164 | 47.3 % | 133,257,305 | 1.28 % |

### Internet Users by Region

In 2014, nearly 75% (2.1 billion) of all internet users in the world (2.8 billion) live in the top 20 countries.

The remaining 25% (0.7 billion) is distributed among the other 178 countries, each representing less than 1% of total users.

China, the country with most users (642 million in 2014), represents nearly 22% of total, and has more users than the next three countries combined (United States, India, and Japan). Among the top 20 countries, India is the one with the lowest penetration: 19% and the highest yearly growth rate. At the opposite end of the range, United States, Germany, France, U.K., and Canada have the highest penetration: over 80% of population in these countries has an internet connection.

Links:

### Total number of Websites

There are over **1 billion websites** on the world wide web today.

By “Website” it means unique hostname (a name which can be resolved, using a name server, into an IP Address).

Around 75% of websites today are not active, but parked domains or similar.

Year (June) |
Websites | Change | Internet Users | Users per Website |
Websites launched |

2015 | 863,105,652 | -11% | 3,185,996,155* | 3.7 | |

2014 | 968,882,453 | 44% | 2,925,249,355 | 3.0 | |

2013 | 672,985,183 | -3% | 2,756,198,420 | 4.1 | |

2012 | 697,089,489 | 101% | 2,518,453,530 | 3.6 | |

2011 | 346,004,403 | 67% | 2,282,955,130 | 6.6 | |

2010 | 206,956,723 | -13% | 2,045,865,660 | 9.9 | |

2009 | 238,027,855 | 38% | 1,766,206,240 | 7.4 | |

2008 | 172,338,726 | 41% | 1,571,601,630 | 9.1 | Dropbox |

2007 | 121,892,559 | 43% | 1,373,327,790 | 11.3 | Tumblr |

2006 | 85,507,314 | 32% | 1,160,335,280 | 13.6 | Twttr |

2005 | 64,780,617 | 26% | 1,027,580,990 | 16 | YouTube, Reddit |

2004 | 51,611,646 | 26% | 910,060,180 | 18 | Thefacebook, Flickr |

2003 | 40,912,332 | 6% | 778,555,680 | 19 | WordPress, LinkedIn |

2002 | 38,760,373 | 32% | 662,663,600 | 17 | |

2001 | 29,254,370 | 71% | 500,609,240 | 17 | Wikipedia |

2000 | 17,087,182 | 438% | 413,425,190 | 24 | Baidu |

1999 | 3,177,453 | 32% | 280,866,670 | 88 | PayPal |

1998 | 2,410,067 | 116% | 188,023,930 | 78 | |

1997 | 1,117,255 | 334% | 120,758,310 | 108 | Yandex |

1996 | 257,601 | 996% | 77,433,860 | 301 | |

1995 | 23,500 | 758% | 44,838,900 | 1,908 | Altavista, Amazon, AuctionWeb |

1994 | 2,738 | 2006% | 25,454,590 | 9,297 | Yahoo |

1993 | 130 | 1200% | 14,161,570 | 108,935 | |

1992 | 10 | 900% | |||

Aug. 1991 | 1 | World Wide Web Project |

Links: