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Premature Optimization 2.0 - Intercon 2016

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PREMATURE OPTIMIZATION The Root of ALL Evil @akitaonrails
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Page 1: Premature Optimization 2.0 - Intercon 2016

PREMATUREOPTIMIZATION

The Root of ALL Evil

@akitaonrails

Page 2: Premature Optimization 2.0 - Intercon 2016

PREMATUREOPTIMIZATION

The Root of ALL Evil

@akitaonrails

2.0

Page 3: Premature Optimization 2.0 - Intercon 2016

@akitaonrails

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Page 5: Premature Optimization 2.0 - Intercon 2016
Page 6: Premature Optimization 2.0 - Intercon 2016
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Uber para segurança particular

Uber para advogados

Uber para maconha

Uber para massagem

Uber para álcool

Uber para lavanderia

Uber para passeio de cachorro

Page 12: Premature Optimization 2.0 - Intercon 2016
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Airbnb para barcos

Airbnb para cachorros

Airbnb para equipamentos

Airbnb para acampamentos

Airbnb para banheiros (!!)

Airbnb para impressão 3D

Airbnb para cozinhas

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Page 15: Premature Optimization 2.0 - Intercon 2016

Tinder para sapatos

Tinder para cachorros

Tinder para notícias

Tinder para networking

Tinder para empregos

Tinder para saias

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http://bit.ly/1iUMuuF

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elastic.co

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“The Social Network" (2010)

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“The Social Network" (2010)

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“The Social Network" (2010)

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“The Social Network" (2010)

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“The Social Network" (2010)

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“The Social Network" (2010)

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Page 37: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Kong 217 117 100 17 802

2 Samus 211 110 101 9 842

3 Wario 197 102 95 7 824

4 Luigi 186 95 91 4 841

5 Zelda 160 81 79 2 847

6 Pikachu 209 105 104 1 851

7 Yoshi 223 112 111 1 803

8 Mario 203 101 102 -1 820

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Page 38: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Kong 217 117 100 17 802

2 Samus 211 110 101 9 842

3 Wario 197 102 95 7 824

4 Luigi 186 95 91 4 841

5 Zelda 160 81 79 2 847

6 Pikachu 209 105 104 1 851

7 Yoshi 223 112 111 1 803

8 Mario 203 101 102 -1 820

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Page 39: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Kong 217 117 100 17 802

2 Samus 211 110 101 9 842

3 Wario 197 102 95 7 824

4 Luigi 186 95 91 4 841

5 Zelda 160 81 79 2 847

6 Pikachu 209 105 104 1 851

7 Yoshi 223 112 111 1 803

8 Mario 203 101 102 -1 820

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Page 40: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Samus 221 120 101 19 896

2 Kong 217 117 100 17 802

3 Luigi 186 95 91 4 841

4 Zelda 160 81 79 2 847

5 Pikachu 209 105 104 1 851

6 Yoshi 223 112 111 1 803

7 Mario 203 101 102 -1 820

8 Wario 207 102 105 -3 760

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Samus (2nd) wins from Wario (3rd) 10 times

Page 41: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Samus 221 120 101 19 896

2 Kong 217 117 100 17 802

3 Luigi 186 95 91 4 841

4 Zelda 160 81 79 2 847

5 Pikachu 209 105 104 1 851

6 Yoshi 223 112 111 1 803

7 Mario 203 101 102 -1 820

8 Wario 207 102 105 -3 760

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Samus (2nd) wins from Wario (3rd) 10 times

Page 42: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Samus 221 120 101 19 896

2 Kong 217 117 100 17 802

3 Luigi 186 95 91 4 841

4 Zelda 160 81 79 2 847

5 Pikachu 209 105 104 1 851

6 Yoshi 223 112 111 1 803

7 Mario 203 101 102 -1 820

8 Wario 207 102 105 -3 760

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Samus (2nd) wins from Wario (3rd) 10 times

Page 43: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Samus 221 120 101 19 896

2 Kong 227 117 110 7 732

3 Luigi 186 95 91 4 841

4 Zelda 160 81 79 2 847

5 Pikachu 209 105 104 1 851

6 Yoshi 223 112 111 1 803

7 Mario 203 101 102 -1 820

8 Wario 207 102 105 -3 760

9 Bowser 196 92 104 -12 845

10 Fox 208 95 113 -18 754

Kong (2nd) loses to Bowser (10th) 10 times

Page 44: Premature Optimization 2.0 - Intercon 2016
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Page 46: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Kong 217 117 100 17 802

2 Samus 211 110 101 9 842

3 Wario 197 102 95 7 824

4 Luigi 186 95 91 4 841

5 Zelda 160 81 79 2 847

6 Pikachu 209 105 104 1 851

7 Yoshi 223 112 111 1 803

8 Mario 203 101 102 -1 820

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Page 47: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Kong 217 117 100 17 802

2 Samus 211 110 101 9 842

3 Wario 197 102 95 7 824

4 Luigi 186 95 91 4 841

5 Zelda 160 81 79 2 847

6 Pikachu 209 105 104 1 851

7 Yoshi 223 112 111 1 803

8 Mario 203 101 102 -1 820

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Name Games Wins Loses Points Elo Rating

1 Pikachu 209 105 104 1 851

2 Zelda 160 81 79 2 847

3 Samus 211 110 101 9 842

4 Luigi 186 95 91 4 841

5 Wario 197 102 95 7 824

6 Mario 203 101 102 -1 820

7 Yoshi 223 112 111 1 803

8 Kong 217 117 100 17 802

9 Bowser 186 82 104 -22 785

10 Fox 208 95 113 -18 754

Page 48: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Kong 217 117 100 17 802

2 Samus 211 110 101 9 842

3 Wario 197 102 95 7 824

4 Luigi 186 95 91 4 841

5 Zelda 160 81 79 2 847

6 Pikachu 209 105 104 1 851

7 Yoshi 223 112 111 1 803

8 Mario 203 101 102 -1 820

9 Fox 208 95 113 -18 754

10 Bowser 186 82 104 -22 785

Name Games Wins Loses Points Elo Rating

1 Pikachu 209 105 104 1 851

2 Zelda 160 81 79 2 847

3 Samus 211 110 101 9 842

4 Luigi 186 95 91 4 841

5 Wario 197 102 95 7 824

6 Mario 203 101 102 -1 820

7 Yoshi 223 112 111 1 803

8 Kong 217 117 100 17 802

9 Bowser 186 82 104 -22 785

10 Fox 208 95 113 -18 754

Page 49: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Pikachu 209 105 104 1 851

2 Zelda 160 81 79 2 847

3 Samus 211 110 101 9 842

4 Luigi 186 95 91 4 841

5 Wario 197 102 95 7 824

6 Mario 203 101 102 -1 820

7 Yoshi 223 112 111 1 803

8 Kong 217 117 100 17 802

9 Bowser 186 82 104 -22 785

10 Fox 208 95 113 -18 754

Page 50: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Pikachu 209 105 104 1 851

2 Zelda 160 81 79 2 847

3 Samus 211 110 101 9 842

4 Luigi 186 95 91 4 841

5 Wario 197 102 95 7 824

6 Mario 203 101 102 -1 820

7 Yoshi 223 112 111 1 803

8 Kong 217 117 100 17 802

9 Bowser 186 82 104 -22 785

10 Fox 208 95 113 -18 754

Page 51: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Pikachu 209 105 104 1 851

2 Zelda 160 81 79 2 847

3 Samus 211 110 101 9 842

4 Luigi 186 95 91 4 841

5 Wario 197 102 95 7 824

6 Mario 203 101 102 -1 820

7 Yoshi 223 112 111 1 803

8 Kong 217 117 100 17 802

9 Bowser 186 82 104 -22 785

10 Fox 208 95 113 -18 754

Page 52: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Zelda 170 91 79 12 904

2 Pikachu 209 105 104 1 851

3 Luigi 186 95 91 4 841

4 Wario 197 102 95 7 824

5 Mario 203 101 102 -1 820

6 Yoshi 223 112 111 1 803

7 Kong 217 117 100 17 802

8 Bowser 186 82 104 -22 785

9 Samus 221 110 111 -1 775

10 Fox 208 95 113 -18 754

Zelda (2nd) wins from Samus (3rd) 10 times

Page 53: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Zelda 170 91 79 12 904

2 Pikachu 209 105 104 1 851

3 Luigi 186 95 91 4 841

4 Wario 197 102 95 7 824

5 Mario 203 101 102 -1 820

6 Yoshi 223 112 111 1 803

7 Kong 217 117 100 17 802

8 Bowser 186 82 104 -22 785

9 Samus 221 110 111 -1 775

10 Fox 208 95 113 -18 754

Zelda (2nd) wins from Samus (3rd) 10 times

Page 54: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Zelda 170 91 79 12 904

2 Pikachu 209 105 104 1 851

3 Luigi 186 95 91 4 841

4 Wario 197 102 95 7 824

5 Mario 203 101 102 -1 820

6 Yoshi 223 112 111 1 803

7 Kong 217 117 100 17 802

8 Bowser 186 82 104 -22 785

9 Samus 221 110 111 -1 775

10 Fox 208 95 113 -18 754

Zelda (2nd) wins from Samus (3rd) 10 times

Page 55: Premature Optimization 2.0 - Intercon 2016

Name Games Wins Loses Points Elo Rating

1 Zelda 170 91 79 12 904

2 Luigi 186 95 91 4 841

3 Fox 218 105 113 -8 829

4 Wario 197 102 95 7 824

5 Mario 203 101 102 -1 820

6 Yoshi 223 112 111 1 803

7 Kong 217 117 100 17 802

8 Bowser 186 82 104 -22 785

9 Samus 221 110 111 -1 775

10 Pikachu 219 105 114 -9 766

Pikachu (2nd) loses to Fox (10th) 10 times

Page 56: Premature Optimization 2.0 - Intercon 2016

ELO Rating System

Microsoft TrueSkill Rating System

Google PageRank

Facebook EdgeRank

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.NETC

CoffeeScriptElixir

F#

Groovy

Java

JavaScriptObjective-C

PHP

Perl PythonRuby

Swift

Visual Basic .NET

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.NET

C

CoffeeScript

ElixirF#

Groovy

Java JavaScript

Objective-C

PHPPerl

Python

Ruby

Swift

Visual Basic .NET

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Speedcode (1953) John Backus

- interpreter took 310 memory words, about 30% of the memory available on a 701

- twenty times that of machine code

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Fortran (1957) John Backus

- first optimizing compiler - complex number types/

electric engineering

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CPL (1963) Christopher Strachey

- Combined/Cambridge/Combined Programming Language

- low and high level

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BCPL (1966) Martin Richards

- Basic CPL - Bootstrap CPL - popular choice

for bootstrapping a system

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occam (1983) INMOS

- concurrent programming language - Bootstrap CPL - communication between processes

work through named channels

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Algol (1958) ETH Zürich committee

- Algol 58, Algol 60, Algol 68, Algol W - Context-free Grammars (Backus-Naur Form/BNF) - "ALGOL 68 was the first (and possibly one of the last)

major language for which a full formal definition was made before it was implemented”

- “ALGOL 68 has been criticized, most prominently by some members of its design committee such as Hoare and Dijkstra, for abandoning the simplicity of ALGOL 60”

- "Steve Bourne, who was on the Algol 68 revision committee, took some of its ideas to his Bourne shell (and thereby, to descendant shells such as Bash) and to C (and thereby to descendants such as C++)."

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Modula (1960) Niklaus Wirth

- "One of the first languages designed from the start for modular programming"

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MUMPS (1966) Neil Pappalardo

- "Massachusetts General Hospital Utility Multi-Programming System”

- hierarchical ACID built-in database - InterSystems Caché (SQL driver)

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ADA (1966) MIL-STD

- strong typing, generics - modularity mechanisms (packages) - run-time checking

- access to unallocated memory, buffer overflow errors, range violations, off-by-one errors, array access errors, and other detectable bugs

- parallel processing (tasks, synchronous message passing, protected objects, and

- nondeterministic select statements) - exception handling - widely used in critical systems, where any anomaly might

lead to very serious consequences, e.g., accidental death, injury or severe financial loss. Examples of systems where Ada is used include avionics,ATC, railways, banking, military and space technology

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Hope (1970s) Edinburgh University

- NPL and Hope: first languages with call-by-pattern evaluation

- influenced Standard ML, Haskell

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SNOBOL (1962) AT&T Bell Labs

- patterns as a first-class data type - operators for pattern

concatenation and alternation - early regular expressions/pattern

matching

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FLOW-MATIC (1955) Grace Hopper

- 1952 - A0 compiler - 1959 - COBOL - English-like high level

language

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Simula (1967) Ole-Johan Dahl, Kristen Nygaard

- object-oriented programming - inheritance and subclasses - virtual methods, coroutines - Garbage Collector

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CLU (1974) Barbara Liskov

- early object-oriented programming - classes with constructors and methods,

without inheritance - iterators, abstract data types, type-safe

parameterized/variant types - multiple return values (parallel

assignment)

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ISWIM (1965) Peter J. Landin

- "If you See What I Mean" - functional programming

(SASL, Miranda, ML, Haskell) - lazy evaluation

(SASL, KRC, Hope, Miranda, Haskell,

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Miranda (1985) David Turner

- lazy, purely functional programming - list comprehensions (Haskell)

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Clean (1987) Radboud University Nijmegen

- mutable state and I/O is done through a uniqueness typing system (Haskell Monads)

- performance comparable to Haskell

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Self (1987) David Ungar, Randall Smith

- dialect of Smalltalk (OOP) - prototype-based (Javascript) - Just in time Compiling (JVM)

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Prolog (1972) Alain Colmerauer

- logic programming (influenced Erlang) - natural language processing - relations, represented as facts and rules

- running a query over these relations

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Erlang (1986) Joe Armstrong, Robert Virding and Mike Williams

- distributed, fault-tolerant, soft-real-time, non-stop applications. It supports hot swapping

- processes communicate using message passing

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ML (1973) Robin Milner/University of Edinburgh

- Hindley–Milner type system - parametric polymorphism - “Type Inference”

- ML Family (Standard ML, OCaml), Clean, Haskell

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REBOL (1997) Carl Sassenrath

- "Relative Expression Based Object Language"

- dialecting: small, optimized, Domain-Specific Language (DSL) for code and data

- influenced JSON

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Page 95: Premature Optimization 2.0 - Intercon 2016

• APL (1964) introduced: array programming, influenced: functional programming

• ALGOL (1958) refined both structured procedural programming and the discipline of language specification.

• Simula (1967) first language designed to support object-oriented programming; Smalltalk (1972) followed with the first "purely" object-oriented language.

• C (1969 - 1973) popular system programming language

• Prolog (1972), first logic programming language. • ML (1978) built a polymorphic type system on top of

Lisp, pioneering statically typed functional programming languages.

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2015

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2015FORTRAN (1957) John Backus

- FORTRAN 2015

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2015

COBOL (1959) Conference on Data Systems Languages (CODASYL)

- COBOL 2014

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2015

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2015

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2015

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23%

32%

45%

Python Fortran C

500k LOC

Page 105: Premature Optimization 2.0 - Intercon 2016
Page 106: Premature Optimization 2.0 - Intercon 2016
Page 107: Premature Optimization 2.0 - Intercon 2016
Page 108: Premature Optimization 2.0 - Intercon 2016

Gordon Moore - Intel co-founder

Page 109: Premature Optimization 2.0 - Intercon 2016
Page 110: Premature Optimization 2.0 - Intercon 2016
Page 111: Premature Optimization 2.0 - Intercon 2016

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Intel1Processors1Transistor1Count

Page 112: Premature Optimization 2.0 - Intercon 2016

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Intel1Processors1Transistor1Count

Intel 400410 µm Intel 80386 Pentium

0.8 µm

Itanium 2

Six-core Xeon 7400

8-core Itanium Poulson32 nm

18-core Xeon Haswell-E522 nm

15-core Xeon Ivy Bridge-EX

Duo-core + GPU Core i7 Broadwell-U14 nm

Page 113: Premature Optimization 2.0 - Intercon 2016

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Intel1Processors1Transistor1Count

Intel 400410 µm Intel 80386 Pentium

0.8 µm

Itanium 2

Six-core Xeon 7400

8-core Itanium Poulson32 nm

18-core Xeon Haswell-E522 nm

15-core Xeon Ivy Bridge-EX

Duo-core + GPU Core i7 Broadwell-U14 nm

Apple A7

Apple A8

Apple A8X20 nm

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Page 116: Premature Optimization 2.0 - Intercon 2016
Page 117: Premature Optimization 2.0 - Intercon 2016

Gordon Bell - DEC early employee

Page 118: Premature Optimization 2.0 - Intercon 2016

$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Page 119: Premature Optimization 2.0 - Intercon 2016

$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Supercomputadores

Page 120: Premature Optimization 2.0 - Intercon 2016

$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Supercomputadores

Minicomputadores

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$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Supercomputadores

Minicomputadores

Workstations

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$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Supercomputadores

Minicomputadores

Workstations

PersonalComputers

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$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Supercomputadores

Minicomputadores

Workstations

PersonalComputers

ScalableClusters

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$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Supercomputadores

Minicomputadores

Workstations

PersonalComputers

Small Devices

ScalableClusters

Page 125: Premature Optimization 2.0 - Intercon 2016

$100M

$10M

$1M

$100K

$10k

$1k

$100

$10

1940 1960 1970 1980 1990 2000 2010

Supercomputadores

Minicomputadores

Workstations

PersonalComputers

Small Devices

ScalableClusters

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Vaticano 2005

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Vaticano 2013

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2010 2011 2012 2013 20142006 2007 2008 2009

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2010 2011 2012 2013 20142006 2007 2008 2009

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2010 2011 2012 2013 20142006 2007 2008 2009

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Page 139: Premature Optimization 2.0 - Intercon 2016

2017 60 anos de FORTRAN

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2017 60 anos de FORTRAN

2014 55 anos de COBOL

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2017 60 anos de FORTRAN

2014 55 anos de COBOL

2013 55 anos de LISP

Page 142: Premature Optimization 2.0 - Intercon 2016

2017 60 anos de FORTRAN

2014 55 anos de COBOL

2013 55 anos de LISP

2012 40 anos de Smalltalk

Page 143: Premature Optimization 2.0 - Intercon 2016

2017 60 anos de FORTRAN

2014 55 anos de COBOL

2013 55 anos de LISP

2012 40 anos de Smalltalk

2015 35 anos de ADA

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Page 145: Premature Optimization 2.0 - Intercon 2016

Séc XVII Cálculo Newton/Leibniz

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Séc XVII Cálculo Newton/Leibniz

1930's Lambda Calculus Alonzo Church

Page 147: Premature Optimization 2.0 - Intercon 2016

Séc XVII Cálculo Newton/Leibniz

1930's Lambda Calculus Alonzo Church

1939 Elo Rating System Arpad Elo

Page 148: Premature Optimization 2.0 - Intercon 2016

Séc XVII Cálculo Newton/Leibniz

1930's Lambda Calculus Alonzo Church

1939 Elo Rating System Arpad Elo

1975 Vector Space Model Gerald Salton

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SPA e-commerce

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SPA e-commercehttp://www.loja.com.br/#!/produtos/item-001

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SPA e-commercehttp://www.loja.com.br/#!/produtos/item-001

http://www.loja.com.br/?_escaped_fragment_=/produtos/item-001

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High Level Numbers

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Client #1

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$ 4,000

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$ 4,000($ 7 MI/yr revenue - 0.68%)

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Client #2

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$ 100,000+

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$ 100,000+($ 800 MI/yr revenue - 0.15%)

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• Minimum IT Cost:

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• Minimum IT Cost:

• USD 1000 - Cloud Services

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• Minimum IT Cost:

• USD 1000 - Cloud Services

• USD 3000 - 1 Developer

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• Minimum IT Cost:

• USD 1000 - Cloud Services

• USD 3000 - 1 Developer

• USD 4000 - 1 “Responsible” (Manager, Marketing, etc)

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• Minimum IT Cost:

• USD 1000 - Cloud Services

• USD 3000 - 1 Developer

• USD 4000 - 1 “Responsible” (Manager, Marketing, etc)

• Total: USD 8.000/month (USD 96.000/year)

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• Minimum IT Cost:

• USD 1000 - Cloud Services

• USD 3000 - 1 Developer

• USD 4000 - 1 “Responsible” (Manager, Marketing, etc)

• Total: USD 8.000/month (USD 96.000/year)

• Minimum Business Requirement:

Page 175: Premature Optimization 2.0 - Intercon 2016

• Minimum IT Cost:

• USD 1000 - Cloud Services

• USD 3000 - 1 Developer

• USD 4000 - 1 “Responsible” (Manager, Marketing, etc)

• Total: USD 8.000/month (USD 96.000/year)

• Minimum Business Requirement:

• Revenue: > USD 2 MI / year

Page 176: Premature Optimization 2.0 - Intercon 2016

• Minimum IT Cost:

• USD 1000 - Cloud Services

• USD 3000 - 1 Developer

• USD 4000 - 1 “Responsible” (Manager, Marketing, etc)

• Total: USD 8.000/month (USD 96.000/year)

• Minimum Business Requirement:

• Revenue: > USD 2 MI / year

• IT Cost / Revenue ratio: < 5%

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Page 178: Premature Optimization 2.0 - Intercon 2016

Increase Revenue>

Lower Costs

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Innovation

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Impossible Yesterday

Possible Today

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Page 186: Premature Optimization 2.0 - Intercon 2016

Innovation

Constraints

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PREMATUREOPTIMIZATIONis the Root of all Evil

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THANKS!slideshare.net/akitaonrails


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