FLOLAC'14 talk on using Haskell in commercial settings.

1. 2014-07-01 FLOLAC’14
Commercial Uses of Functional Programming
Functional Programming as a means — not an end

2. 01

3. 01
✤ ICFP 2014: September 1~3

4. 01
✤ ICFP 2014: September 1~3
✤ CUFP 2014: September 4~6

5. 01
✤ ICFP 2014: September 1~3
✤ CUFP 2014: September 4~6
✤ 6-Hour Workday: July 1

6. 01
✤ ICFP 2014: September 1~3
✤ CUFP 2014: September 4~6
✤ 6-Hour Workday: July 1

7. 01

8. 01

9. 01
Worse is Better

10. 01
Worse is Better

11. 01
Worse is Better
Less is More!

12. Less is more!
λ
Closure ⊂ Value

13. Less is more!
λ
Closure ⊂ Value
f . g =

14. Less is more!
λ
Closure ⊂ Value
f . g =
x -> f (g x)

15. Less is more!
λ
Closure ⊂ Value
f . g =
x -> f (g x)
((+1) . (*2)) 3

16. Less is more!
λ
Closure ⊂ Value
function compose (f, g) {
return function (x) {
return f( g(x) )
}
}
f . g =
x -> f (g x)
((+1) . (*2)) 3

17. ANSI C Ada
Fortran Cobol

18. ANSI C Ada
Fortran Cobol

19. ANSI C Ada
Fortran Cobol

20. ANSI C Ada
Fortran Cobol

21. ANSI C Ada
Fortran Cobol

22. ANSI C Ada
Fortran Cobol

23. ANSI C Ada
Fortran Cobol

24. ANSI C Ada
Fortran Cobol

25. ANSI C Ada
Fortran Cobol

26. ANSI C Ada
Fortran Cobol

27. ANSI C Ada
Fortran Cobol

28. ANSI C Ada
Fortran Cobol

29. ANSI C Ada
Fortran Cobol

30. Generators: List Fusion

31. Generators: List Fusion
fibs = 1:1:[ x+y
| x <- fibs
| y <- tail fibs ]
take 6 (map (*10) fibs)
[10,10,20,30,50,80]

32. Generators: List Fusion
fibs = 1:1:[ x+y
| x <- fibs
| y <- tail fibs ]
take 6 (map (*10) fibs)
[10,10,20,30,50,80]
take 6 (map (*5) (map (*2) fibs))
[10,10,20,30,50,80]

33. Generators: List Fusion
fibs = 1:1:[ x+y
| x <- fibs
| y <- tail fibs ]
take 6 (map (*10) fibs)
[10,10,20,30,50,80]
take 6 (map (*5) (map (*2) fibs))
[10,10,20,30,50,80]
take 6 (map ((*5) . (*2)) fibs)

34. QuickCheck: PropertyTesting

35. QuickCheck: PropertyTesting
import Test.QuickCheck
prop :: [Int] -> [Int] -> Bool
prop xs ys =
reverse (xs ++ ys) ==
reverse xs ++ reverse ys
quickCheck prop -- Failed!

36. QuickCheck: PropertyTesting
import Test.QuickCheck
prop :: [Int] -> [Int] -> Bool
prop xs ys =
reverse (xs ++ ys) ==
reverse xs ++ reverse ys
quickCheck prop -- Failed!
reverse xs ++ reverse ys

37. QuickCheck: PropertyTesting
import Test.QuickCheck
prop :: [Int] -> [Int] -> Bool
prop xs ys =
reverse (xs ++ ys) ==
reverse xs ++ reverse ys
quickCheck prop -- Failed!
reverse xs ++ reverse ys
quickCheck prop -- Failed!

38. QuickCheck: PropertyTesting
import Test.QuickCheck
prop :: [Int] -> [Int] -> Bool
prop xs ys =
reverse (xs ++ ys) ==
reverse xs ++ reverse ys
quickCheck prop -- Failed!
reverse ys ++ reverse xs
quickCheck prop -- Passed!

39. Macros: Multi-Stage Programming

40. Macros: Multi-Stage Programming
import Text.InterpolatedString.Perl6
user = "world"
putStrLn [qq|
Hello, $user!
Your lucky number: { 6*7 }
|]

41. 2011 20142004

42. 2011 20142004
2005

43. 2011 20142004
2005 2012

45. ES/9000 R46
4CPU, 8GB RAM
50MTWD

46. RS/6000 H70
340MHz 1MTWD
ES/9000 R46
4CPU, 8GB RAM
50MTWD

47. InfoPrint/4000 ID5
1000 PPM 15MTWD
RS/6000 H70
340MHz 1MTWD
ES/9000 R46
4CPU, 8GB RAM
50MTWD

48. InfoPrint/4000 ID5
1000 PPM 15MTWD
RS/6000 H70
340MHz 1MTWD
ES/9000 R46
4CPU, 8GB RAM
50MTWD

49. InfoPrint/4000 ID5
1000 PPM 15MTWD
RS/6000 H70
340MHz 1MTWD
ES/9000 R46
4CPU, 8GB RAM
50MTWD
COBOL RPG
PL/1 REXX SQL

50. InfoPrint/4000 ID5
1000 PPM 15MTWD
RS/6000 H70
340MHz 1MTWD
ES/9000 R46
4CPU, 8GB RAM
50MTWD
COBOL RPG
PL/1 REXX SQL

51. Content Manager OnDemand
Feb 2004

52. Content Manager OnDemand
Feb 2004

53. Content Manager OnDemand
Encode::IBM
Parse::AFP
Feb 2004

54. Content Manager OnDemand
Encode::IBM
Parse::AFP
Feb 2004

55. Content Manager OnDemand
Encode::IBM
Parse::AFP
Feb 2004

56. September 2004

57. September 2004

58. September 2004

59.
September 2004

60.
September 2004
SAX-style iterators

61.
September 2004
SAX-style iterators
O(n) memory use

62.
September 2004
SAX-style iterators
O(n) memory use
300KB/sec (my laptop)

63.
September 2004
SAX-style iterators
O(n) memory use
300KB/sec (my laptop)
30KB/sec (production)

64.
September 2004
SAX-style iterators
O(n) memory use
300KB/sec (my laptop)
30KB/sec (production)
Input: 25GB/day
Throughput: 2.5GB/day

65.
September 2004
SAX-style iterators
O(n) memory use
300KB/sec (my laptop)
30KB/sec (production)
Input: 25GB/day
Throughput: 2.5GB/day
Deadline:
Feb 2005

66. October—November 2004

67. October—November 2004

68. October—November 2004

69. December 2004: Freenode

70. December 2004: Freenode
✤ “Yeah, CosmicRay recently ported GHC to AIX.”

71. December 2004: Freenode
✤ “Yeah, CosmicRay recently ported GHC to AIX.”
✤ “Defining a functor instance as liftM works I'm pretty sure, so… all
Monads are Functors (theoretically), instance Monad a => Functor a
where fmap = liftM would cover all monads.”

72. December 2004: Freenode
✤ “Yeah, CosmicRay recently ported GHC to AIX.”
✤ “Defining a functor instance as liftM works I'm pretty sure, so… all
Monads are Functors (theoretically), instance Monad a => Functor a
where fmap = liftM would cover all monads.”
✤ “Elaborate please? <- have only learned haskell for 5 days…”

73. December 2004: Freenode
✤ “Yeah, CosmicRay recently ported GHC to AIX.”
✤ “Defining a functor instance as liftM works I'm pretty sure, so… all
Monads are Functors (theoretically), instance Monad a => Functor a
where fmap = liftM would cover all monads.”
✤ “Elaborate please? <- have only learned haskell for 5 days…”
✤ “unsafeInterleaveIO is the best thing since sliced bread.”

74. December 2004: Freenode
✤ “Yeah, CosmicRay recently ported GHC to AIX.”
✤ “Defining a functor instance as liftM works I'm pretty sure, so… all
Monads are Functors (theoretically), instance Monad a => Functor a
where fmap = liftM would cover all monads.”
✤ “Elaborate please? <- have only learned haskell for 5 days…”
✤ “unsafeInterleaveIO is the best thing since sliced bread.”
✤ “What do I do with a compiler with exploding brains?”

75. December 2004: Freenode
✤ “Yeah, CosmicRay recently ported GHC to AIX.”
✤ “Defining a functor instance as liftM works I'm pretty sure, so… all
Monads are Functors (theoretically), instance Monad a => Functor a
where fmap = liftM would cover all monads.”
✤ “Elaborate please? <- have only learned haskell for 5 days…”
✤ “unsafeInterleaveIO is the best thing since sliced bread.”
✤ “What do I do with a compiler with exploding brains?”
✤ “Haskell has a solid niche as a PhD generator :)”

76. January 2005

77. January 2005
✤ OpenAFP.hs delivered —

78. Input: 25GB/day
Throughput: 500GB/day
DOM-Style selectors
O(1) Memory Use!
January 2005
✤ OpenAFP.hs delivered —

79. Input: 25GB/day
Throughput: 500GB/day
DOM-Style selectors
O(1) Memory Use!
January 2005
✤ OpenAFP.hs delivered —
✤ NTD$3k/hr * 3 months

80. Input: 25GB/day
Throughput: 500GB/day
DOM-Style selectors
O(1) Memory Use!
January 2005
✤ OpenAFP.hs delivered —
✤ NTD$3k/hr * 3 months
✤ “You don't need category theory, you need BC Pierce's
'Types and Programming Languages’”

81. Input: 25GB/day
Throughput: 500GB/day
DOM-Style selectors
O(1) Memory Use!
January 2005
✤ OpenAFP.hs delivered —
✤ NTD$3k/hr * 3 months
✤ “You don't need category theory, you need BC Pierce's
'Types and Programming Languages’”

82. Input: 25GB/day
Throughput: 500GB/day
DOM-Style selectors
O(1) Memory Use!
January 2005
✤ OpenAFP.hs delivered —
✤ NTD$3k/hr * 3 months
✤ “You don't need category theory, you need BC Pierce's
'Types and Programming Languages’”
✤ “My target language is huge. I need all the help I can get :)”

83. 2005: Pugs
Feb 1: Started on Freenode #haskell
Lambdas & Camels joined in #perl6
Specification revised, iterated, tested
Concurrency, Coroutines, Commit Bits!

84. 2006: Erdősing
YAPC: Worst is Best on targeting JS
OOPSLA: Reconciling the Irreconcilable
Succeeded by Moose & Rakudo
Perl 5 runtime became Modern Perl
Concluding talk: Optimizing for Fun
楽
⼟土

85. 2007: S-Team
New tech stack: Jifty, Moose, and GHC
OpenAFP Utilities: Report Generation,
Extraction, Management, Distribution,
Conversion…
Enhanced & eventually replaced IBM
OnDemand with PDF-based workflow

86. 2008: Socialtext
Midlife crisis — Becoming a PHB?
Hierarchical organization & culture —
can we change that with technology?
Decided to telecommute full time:
- ST (social workplace), or
- FB (social media)?

87. 2009: SocialCalc
Worked with Dan Bricklin & friends
Summarized into three book chapters:
- Architecture of Open Source Applications
- Performance of Open Source Applications
- 500 lines or less (in progress)
EtherCalc: Backend of g0v.today

88. 2010:Apple
Cloud Service Localization
Everything under NDA…
…with one single exception

89. May 2011

90. May 2011
✤ “Can you write me some code that takes a regular expression
and enumerates the domain it accepts?”

91. May 2011
✤ “Can you write me some code that takes a regular expression
and enumerates the domain it accepts?”
✤ “I think I can do this with the yices2 SMT solver and
the SBV Haskell library to turn regexes into
satisfiability problems, and so we can get all examples.

92. May 2011
✤ “Can you write me some code that takes a regular expression
and enumerates the domain it accepts?”
✤ “I think I can do this with the yices2 SMT solver and
the SBV Haskell library to turn regexes into
satisfiability problems, and so we can get all examples.
✤ But this is too much fun for an one-off script…
Would you mind if I do this pro bono so I can release
it into public domain? :-)”

93. SMT Constraint Solving

94. SMT Constraint Solving
✤ Genex: Regex as predicates on integer variables
/([AB]C)1/

95. SMT Constraint Solving
✤ Genex: Regex as predicates on integer variables
✤ Solve for [x, y, z, w]:
/([AB]C)1/
(x=‘A’ ∨ x=‘B’) ∧
(y=‘C’) ∧
(z=x) ∧ (w=y)

96. SMT Constraint Solving
✤ Genex: Regex as predicates on integer variables
✤ Solve for [x, y, z, w]:
✤ “ACAC”
“BCBC”
/([AB]C)1/
(x=‘A’ ∨ x=‘B’) ∧
(y=‘C’) ∧
(z=x) ∧ (w=y)

97. SMT Constraint Solving
✤ Genex: Regex as predicates on integer variables
✤ Solve for [x, y, z, w]:
✤ “ACAC”
“BCBC”
✤ Supports b 1 ^ $
/([AB]C)1/
(x=‘A’ ∨ x=‘B’) ∧
(y=‘C’) ∧
(z=x) ∧ (w=y)

98. A Regular Crossword
(ND|ET|IN)[^X]*
(DI|NS|TH|OM)*
.*(IN|SE|HI)
[CHMNOR]*I[CHMNOR]*
C*MC(CCC|MM)*
([^EMC]|EM)*
[CEIMU]*OH[AEMOR]*
[AM]*CM(RC)*R?
N.*X.X.X.*E
.*XHCR.*X.*
(RR|HHH)*.?
(...?)1*
.*XEXM*
[CR]*
[^C]*MMM[^C]*
(E|CR|MN)*
([^X]|XCC)*
.*OXR.*
.*PRR.*DDC.*
R*D*M*
.*(.)(.)(.)(.)4321.*
(RX|[^R])*
.*SE.*UE.*
.*LR.*RL.*
(S|MM|HHH)*
.*G.*V.*H.*
[^C]*[^R]*III.*
(HHX|[^HX])*
.(C|HH)*
.*DD.*CCM.*
P+(..)1.*
(O|RHH|MM)*
([^MC]|MM|CC)*
.*(.)C1X1.*
F.*[AO].*[AO].*
[^M]*M[^M]*
.*H.*H.*
.*
.*

99. A Regular Crossword
✤ Source: MIT Mystery Hunt 2013
(ND|ET|IN)[^X]*
(DI|NS|TH|OM)*
.*(IN|SE|HI)
[CHMNOR]*I[CHMNOR]*
C*MC(CCC|MM)*
([^EMC]|EM)*
[CEIMU]*OH[AEMOR]*
[AM]*CM(RC)*R?
N.*X.X.X.*E
.*XHCR.*X.*
(RR|HHH)*.?
(...?)1*
.*XEXM*
[CR]*
[^C]*MMM[^C]*
(E|CR|MN)*
([^X]|XCC)*
.*OXR.*
.*PRR.*DDC.*
R*D*M*
.*(.)(.)(.)(.)4321.*
(RX|[^R])*
.*SE.*UE.*
.*LR.*RL.*
(S|MM|HHH)*
.*G.*V.*H.*
[^C]*[^R]*III.*
(HHX|[^HX])*
.(C|HH)*
.*DD.*CCM.*
P+(..)1.*
(O|RHH|MM)*
([^MC]|MM|CC)*
.*(.)C1X1.*
F.*[AO].*[AO].*
[^M]*M[^M]*
.*H.*H.*
.*
.*

100. A Regular Crossword
✤ Source: MIT Mystery Hunt 2013
✤ Solved in ~40 lines of Haskell with Genex!(ND|ET|IN)[^X]*
(DI|NS|TH|OM)*
.*(IN|SE|HI)
[CHMNOR]*I[CHMNOR]*
C*MC(CCC|MM)*
([^EMC]|EM)*
[CEIMU]*OH[AEMOR]*
[AM]*CM(RC)*R?
N.*X.X.X.*E
.*XHCR.*X.*
(RR|HHH)*.?
(...?)1*
.*XEXM*
[CR]*
[^C]*MMM[^C]*
(E|CR|MN)*
([^X]|XCC)*
.*OXR.*
.*PRR.*DDC.*
R*D*M*
.*(.)(.)(.)(.)4321.*
(RX|[^R])*
.*SE.*UE.*
.*LR.*RL.*
(S|MM|HHH)*
.*G.*V.*H.*
[^C]*[^R]*III.*
(HHX|[^HX])*
.(C|HH)*
.*DD.*CCM.*
P+(..)1.*
(O|RHH|MM)*
([^MC]|MM|CC)*
.*(.)C1X1.*
F.*[AO].*[AO].*
[^M]*M[^M]*
.*H.*H.*
.*
.*

101. A Regular Crossword
✤ Source: MIT Mystery Hunt 2013
✤ Solved in ~40 lines of Haskell with Genex!(ND|ET|IN)[^X]*
(DI|NS|TH|OM)*
.*(IN|SE|HI)
[CHMNOR]*I[CHMNOR]*
C*MC(CCC|MM)*
([^EMC]|EM)*
[CEIMU]*OH[AEMOR]*
[AM]*CM(RC)*R?
N.*X.X.X.*E
.*XHCR.*X.*
(RR|HHH)*.?
(...?)1*
.*XEXM*
[CR]*
[^C]*MMM[^C]*
(E|CR|MN)*
([^X]|XCC)*
.*OXR.*
.*PRR.*DDC.*
R*D*M*
.*(.)(.)(.)(.)4321.*
(RX|[^R])*
.*SE.*UE.*
.*LR.*RL.*
(S|MM|HHH)*
.*G.*V.*H.*
[^C]*[^R]*III.*
(HHX|[^HX])*
.(C|HH)*
.*DD.*CCM.*
P+(..)1.*
(O|RHH|MM)*
([^MC]|MM|CC)*
.*(.)C1X1.*
F.*[AO].*[AO].*
[^M]*M[^M]*
.*H.*H.*
.*
.*
N H P E H A S
D I O M O M T H
F O X N X A X P H
M M O M M M M R H H
M C X N M M C R X E M
C M C C C C M M M M M M
H R X R C M I I I H X L S
O R E O R E O R E O R E
V C X C C H H M X C C
R R R R H H H R R U
N C X D X E X L E
R R D D M M M M
G C C H H C C

102. SmallCheck: Enumerated Series

103. SmallCheck: Enumerated Series
✤ Type-Level Literals: Numbers and Symbols
xs :: [Matching "a?b?c?"]
xs = list 100 series
[,a,b,ab,c,ac,bc,abc]

104. SmallCheck: Enumerated Series
✤ Type-Level Literals: Numbers and Symbols
✤ Check properties for all strings matching a regex
xs :: [Matching "a?b?c?"]
xs = list 100 series
[,a,b,ab,c,ac,bc,abc]

105. QuickCheck: Random Sampling

106. QuickCheck: Random Sampling
✤ Regular Expressions and XML Schemata:
generate (matching rfc2822)
"9%az4@rar1do04jkd1.agzy.org"

107. QuickCheck: Random Sampling
✤ Regular Expressions and XML Schemata:
generate (matching rfc2822)
"9%az4@rar1do04jkd1.agzy.org"
putStr . showXmlTree =<<
generate . matchingRNG =<<
loadRNG “book.rng”
<?xml version="1.0" encoding="UTF-8"?><book>
<author>o09a-_6w@tnj.qom.today</author>
<author>3qd9g02xlu@swbr.kmly.eu</author>
<author>z6ckxi8@jtm.cbopios.asia</author>
</book>

108. 2012: LiveScript
Feb 1: @gkz forked @satyr’s fork of .coffee
“Like a smaller language within Perl 6,
struggling to get out…”
May 1: ST joined PeopleFluent at Bedford
June 1: CoffeeRedux by @michaelficarra
Launched JS2LS: @clkao, @gkz, @mpgutta
Coco

109. 2013: MoeDict
Feb 1: Revised MoE Dictionary in HTML5
- Every word linked to its definition
- Fair Use and CC0 as legal frameworks
moedict.tw: Android, iOS, Firefox OS
Holo, Hakka, Cross-Strait Language DB
萌典

110. May 2014: CUFP Program Committee

111. May 2014: CUFP Program Committee
✤ “I enjoyed @simonmar's slides The Haxl Project at
Facebook and the related /r/haskell discussions.

112. May 2014: CUFP Program Committee
✤ “I enjoyed @simonmar's slides The Haxl Project at
Facebook and the related /r/haskell discussions.
✤ It’s a reasonably easy-to-understand Commercial
Use of Functional Programming that I plan to
talk about in my FLOLAC’14 talk.

113. May 2014: CUFP Program Committee
✤ “I enjoyed @simonmar's slides The Haxl Project at
Facebook and the related /r/haskell discussions.
✤ It’s a reasonably easy-to-understand Commercial
Use of Functional Programming that I plan to
talk about in my FLOLAC’14 talk.
✤ Would it make sense to invite @simonmar to
the CUFP keynote?”

114. Fighting Spam with Pure Functions

115. Fighting Spam with Pure Functions
✤ Nov 2012: Simon Marlow joins Facebook

116. Fighting Spam with Pure Functions
✤ Nov 2012: Simon Marlow joins Facebook
✤ Jan 2013: FXL — Subset of ML, interpreted in C++

117. Fighting Spam with Pure Functions
✤ Nov 2012: Simon Marlow joins Facebook
✤ Jan 2013: FXL — Subset of ML, interpreted in C++
If (Average(Map(Reputation,
PreviousSharedUrls(User, 5))) < 0)
Then [WarnUser, LogRequest] Else []

118. Fighting Spam with Pure Functions
✤ Nov 2012: Simon Marlow joins Facebook
✤ Jan 2013: FXL — Subset of ML, interpreted in C++
✤ Aug 2013: Haxl compiler implemented in GHC

119. Fighting Spam with Pure Functions
✤ Nov 2012: Simon Marlow joins Facebook
✤ Jan 2013: FXL — Subset of ML, interpreted in C++
✤ Aug 2013: Haxl compiler implemented in GHC
✤ Jun 2014: Deployed and Open Sourced

120. Fighting Spam with Pure Functions
✤ Nov 2012: Simon Marlow joins Facebook
✤ Jan 2013: FXL — Subset of ML, interpreted in C++
✤ Aug 2013: Haxl compiler implemented in GHC
✤ Jun 2014: Deployed and Open Sourced
✤ Sep 2014: There is no fork @ ICFP

121. FXL on Haxl
If (Average(Map(Reputation,
PreviousSharedUrls(User, 5))) < 0)
Then [WarnUser, LogRequest] Else []

122. FXL on Haxl
If (Average(Map(Reputation,
PreviousSharedUrls(User, 5))) < 0)
Then [WarnUser, LogRequest] Else []

123. FXL on Haxl
If (Average(Map(Reputation,
PreviousSharedUrls(User, 5))) < 0)
Then [WarnUser, LogRequest] Else []
Marlow et al, The Haxl Project at Facebook

124. FXL on Haxl
If (Average(Map(Reputation,
PreviousSharedUrls(User, 5))) < 0)
Then [WarnUser, LogRequest] Else []
Marlow et al, The Haxl Project at Facebook

125. Concurrency & Caching

126. Concurrency & Caching
✤ With the Applicative Do macro:
fun :: Haxl String
fun = [ado|
x <- fetch “A”
y <- fetch “B”
z <- fetch y
x ++ y ++ z
|]

127. Concurrency & Caching
✤ With the Applicative Do macro:
fun :: Haxl String
fun = [ado|
x <- fetch “A”
y <- fetch “B”
z <- fetch y
x ++ y ++ z
|]
fetch
“A”
fetch
“B”

128. Concurrency & Caching
✤ With the Applicative Do macro:
fun :: Haxl String
fun = [ado|
x <- fetch “A”
y <- fetch “B”
z <- fetch y
x ++ y ++ z
|]
fetch
“A”
fetch
“B”
H A

129. Concurrency & Caching
✤ With the Applicative Do macro:
fun :: Haxl String
fun = [ado|
x <- fetch “A”
y <- fetch “B”
z <- fetch y
x ++ y ++ z
|]
fetch
“A”
fetch
“B”
H A
fetch
“A”
H
cached

130. Concurrency & Caching
✤ With the Applicative Do macro:
fun :: Haxl String
fun = [ado|
x <- fetch “A”
y <- fetch “B”
z <- fetch y
x ++ y ++ z
|]
fetch
“A”
fetch
“B”
H A++
fetch
“A”
H
cached
++
HAH

131. Recap

132. Recap
✤ Generators: List Fusion

133. Recap
✤ Generators: List Fusion
✤ QuickCheck: Property Testing

134. Recap
✤ Generators: List Fusion
✤ QuickCheck: Property Testing
✤ Macros: Multi-Stage Programming

135. main :: IO ()
Functional Programming as a means —

136. main :: IO a
Functional Programming as a means —

137. main :: IO a
Functional Programming as a means —
newtype IO a =
IO ( State# RealWorld
-> (# State# RealWorld, a #))

138. main :: IO a
Functional Programming as a means —
newtype IO a =
IO ( State# RealWorld
-> (# State# RealWorld, a #))
main :: State# RealWorld
-> (# State# RealWorld, a #)

139. main :: IO a
Functional Programming as a means —
newtype IO a =
IO ( State# RealWorld
-> (# State# RealWorld, a #))
main :: State# RealWorld
-> (# State# RealWorld, a #)
to observe the world,

140. main :: IO a
Functional Programming as a means —
newtype IO a =
IO ( State# RealWorld
-> (# State# RealWorld, a #))
main :: State# RealWorld
-> (# State# RealWorld, a #)
to observe the world,
to change it, and to create a new value.