Category: General Discussion

Several who have read my blog contact me concerning reference materials and textbooks that I would recommend.  I must admit that I am a biblio-maniac.  I am unable to go into a book store especially a used  bookstore and walk out empty handed.  Even in this day and age of iPads I still love the visceral aspects of the printed form.  Call me a luddite. That said I do have have somewhat of an interesting per-view on the world of books.

For the software world the people that are doing the creating are not writing books per se.  Thus if your reading books your not necessarily creating anything novel.  Let me be more specific.  Many use books to find an answer.  Many use books as a catalyst or reference.  I fall into the latter.  There are no “give me a shipping product” books – no “what is the answer?”  – for some of the stuff that we create.  Yes there are several ‘cookbooks’ and recipes for say learning a language or a new framework but there are no books that will provide answers to production level novel solutions.  As a very succinct example I was having a discussion in NYC with a very respected Executive Engineering Director and we both decided that there were no books that dealt specifically with hiring great talent in the tech world.  Why?  Because most are not going to tell you exactly what it really takes to hire great people and to build great teams.  That said few are going to explain how they came up with great coding feats – they will explain it after the fact for reference see Coders At Work.

So without further “waxin all philosphical‘”  here is a laundry list of books that I have found useful over the years.  I will try and categorize them according to subject matter.  Its by no means comprehensive.  I will also utilize this same list format in the future.   I would love to hear feedback or your thoughts – please share your list, if you are so inclined – after the fact.

Machine Learning, Data Mining and Knowledge Discovery

Scaling Up Machine Learning (Cambridge University Press)

This book is phenomenal and is at the very for front of issues we are all trying to solve in the areas of large scale learning systems. The book contains everything from parallelization of Support Vector Machines to details of how one designs hardware architectures for the next round of highly scalable machine learning algorithms.  Note this is not an introductory text.  Also the book takes a very interesting view in that it is written mainly by the best in the field.

Machine Learning,McGraw Publishing

This is a seminal text in the areas of Machine Learning.  There is  enough verbiage if your not a mathematician and enough algorithmic pseudo code to follow the development of the explanations.  I find myself going back to this book over and over.  Covers most everything from Bayes to Boosting.  It also has updated chapters on his web page.  I also like this writing style.

Business Modeling and Data Mining  (The Morgan Kaufmann Series in Data Management Systems)

Some purest might not like this book but I think it is a great read because it discusses why we actually need to consider a paying customer.  The book goes deep enough in the explanations and marries them to great scenarios for business models.  It also discusses in a ‘mind map manner’ how to design systems and algorithms.  The text also has a great bibliography.

Collective Intelligence, (O’ Reilly Press)

This is a great book that gets your toes in the water for differing aspects of “DataScience”.  Basically statistics and machine learning for data.  The book has examples for simple crawlers,  Bayes Theorem, Adaptive Neural Networks, Similarity Functions, Distance Metrics, and workhorse algorithms such as Support Vector Machines and Non-Negative Matrix Factorization.  Written in a pragmatic fashion.  Code in Python is included on the author’s website.

Artificial Intelligence: A Modern Approach 3rd Edition, (Prentice Hall Books)

Seminal book on the subject.  Spanning is the word I think of when reading this tome.  The book covers the usual suspects but goes on to a ‘meta-level’ to discuss varied fields of Logic, Qualitative Physics, Spatial Reasoning, Psychological foundations and fundamental mathematics.  The appendix and bibliography are worth the price of entry (expensive) for this oracle.

Introduction to Evolutionary Computing (Natural Computing Series, Springer)

I am a proponent of the ‘get it and go’ format.  This lives up to the title.  It is a good introductory text.  It starts off with a good introduction to Genetic Algorithms, not to be confused with Evolutionary Computing and proceeds to Learning systems and deeper issues as Non-Stationary classification systems, multi-variate parameter selection as well as a chapter on applications.

Machine Learning – An Algorithmic Perspective (CRC Press)

This book takes a novel approach that have an online presence and a hard text reference.  Stephen Marsland updates the code and book online and provides access much like the O’Reilly series.  Of particular interest is the format for review of such areas as probability, eigenvaules and fundamental algorithms.   The book also provides code examples mainly in the areas of Python.  This book takes exactly that approach, with each topic being presented both mathematically and in Python code using the Numpy and Scipy libraries.  As most know SciPy and Numpy resemble Matlab and is sufficiently high level that the book code
examples read like pseudocode.  SciKits would also be a good addition.

Semantics and Natural Language Processing

Programming the Semantic Web (O’ Reilly Press)

With the same eye towards ‘get in and get done’ Seagram wrote this because there was no middle ground for    Semantics or if there were such a text it was voluminous.  Great introductory text on Semantics with succinct code examples in Python.  The text emphasizes that you are not constrained to using data from only one database or server, but when the data is organized and stored in a standardized semantic format such as RDF, it may be made available for reading by anyone (if desired) without any risk of it being overwritten by using a query language like SPARQL instead of the better known SQL.  Once again complete code available via the InterWebz.

Semantic Web Programming (Wiley)

This book is a larger text that spans the areas of Semantics: FOAF, Reasoners, SPARQL, Linked Data, Microformats without getting too far in the weeds with theoretic gymnastics (read axiomatic proofs etc).  Written in mainly in JAVA.  The discussion of Semantics from a ‘non ivory tower’ standpoint is worth the purchase.  Then again it has been said in order to program semantics one has to believe semantics exist.

Natural Language Processing (O’ Reilly Press)

People often confuse Semantics with Natural Language Processing.  While the two are close cousins you can do one without the other and visa versa.  Historically NLTKas it is now known in the industry is becoming the “goto” Python Library for performing textual gymnastics.  This book is a wonderful introduction to Python, Text Analytics and analysis of grammer.  For example entity extraction which historically is extremely difficult is:

entities = nltk.chunk.ne_chunk(tagged)

I mean (pun intended) how nice is that?

Python Text Processing with NLTK 2.0 Cookbook (PAKT Publishing)

This is at a much lower level and assumes that you need to do perform basic NLP and is more text analytics based than extracting meaning, linguistic and grammatical behaviors.  One novel aspect to this book is the addition of creation of a corpora reader via MongoDb.  The book even addresses nuances like FileLocking during corpora edits.  It uses PyMongo as the front end interface with Python examples.  The examples go on the look at various aspects of NLTK and NoSQL architectures.

Network and Dynamic Systems

Network Crowds and Markets: Reasoning About A Highly Connected World (Cambridge University Press) 

At the moment this is one of the tomes that I am immersed within.  The book has a good introduction on Graph Theory but then quickly heads into territory such as calculations of Social Capital with emphasis on Closure within Networks.  The text also does a grand job of tying (pun intended) Graph Theory and Game Theoretic applications with emphasis on arbitrage environments.  The chapter on diffusion in networks is worth the price of purchase.  For anyone performing work in the area of Social Networks this text is a must.

Social and Economic Networks (Princeton University Press)

This book is a great mix of statistics, linear algebra and game theory as applied to social networks.  The economic emphasis is particularly cogent.  The detail and depth is world class.  The analysis of Stochastic and Random graph models is of particular interest for anyone dealing with adoption models in network environments.  Further the mathematical analysis of imitation in social networks is especially enjoyable. This is also one of the few text that I have seen that tie together Fat Tail, Long Tail and Poisson Distribution analysis.

Introduction to Dynamic Systems (Wiley)

This is an older text published in 1979.  Funny how those “maths” do not change much – eh?  I have owned this book since I have been involved with creating software systems.  The book while not “out of print” is commanding extremely large sums of money.  The objective as stated in the preface is: “to help one develop the ability to analyze real dynamic phenomena and dynamic systems.”  Seems very appropriate in this day and age of online behavior.  The book starts off with a review of differential equations, linear algebra and state space equations.  Then text then goes on to address both mechanical and electrical constructs with applications with tools such as markov models and control systems with z-transform theory.   You read this book and honestly you realize not much as changed with the InterWebz.

Math

Numerical Analysis, Sixth Edition  (Brooks/Cole Publishing)

Years ago when I was working on my Phd in Applied Science we had several levels of Numerical Analysis.  I love the aspects of Lagrange Polynomials, L2 Norms, Splines and the like.  This book covers them all and has a really cool glossary and index of defined terms for that moment when you literally forgot what a term means because your cross eyed.

Probability Random Variables and Stochastic Processes (McGraw Hill)

This is/was a text for a class at c on – Probability  Random Variables and Stochastic Processes.  The book is written by Athanasios Papoulis who is a legend in signal processing. I will tell you this is not for the faint of heart and neither was the class.  Now more than ever it is an important part of anyone doing data science.

Matrix Algebra for Engineers (Von Nostrand Company)

This is a straight ahead approach to Linear Algebra.  Written in a straight ahead fashion addressing fundamentals of matrix theory like simultaneous equations, determinants and eigenvalue analysis.  I probably have a high affinity for this book because it is a first edition published in 1965.

Programming and Code Development

The Art of Computer Programming Vol 1-4 

Colloquially known as TAOCP, I am not going to say much about these tomes except you should at a minimum know what they are and have in them.  Not for the faint of heart if you just want to do some PHP or ruby on rails. Having had the experience to take a class from Professor Knuth with variations of these via class notes being the backdrop, I cannot say enough about the text.  For the true software engineer a must for the arsenal.

Code Complete (Microsoft Press)

IMHO this is a great book for the thought processes that lead to creating production products.  It was a seminal work in the area before all of the Agile books hit the market.  While I myself am not a big proponent of ‘software process’ this book gets you in the modus operandi of how to think not just code.

Signal Processing

Adaptive and Digital Signal Processing (International Series on Signal Processing)

I was having discussion the other day with some I respect in the audio, semantics and data science field and we noted that long before you could spin up a virtual machine in the proverbial cloud there was a field called real time signal processing  – where you didnt have a database.  All the processing of streams were in real time – in memory.  Funny how the vissistudes of cyclialty come back to haunt you think Big Data.  I highly recommend this text.  Great examples of frequency and array based processing.  When everyone was teaching time domain behavior Professor Lindquist was teaching the law of dual domains and easier math.  It also covers various kernel transforms never covered in other text before the term “wavelet” came in to vogue.

So there is a first cut in a multi volume blog that I will continue.  I trust this was helpful.  If you find any of these text useful feel free to contact me with what you are working on or considering.  Also if there are recommendations that you find particularly informative as far as a certain text goes drop me a line.

Until Then,

Go Big or Go Home!

@tctjr

Several who have read my blog contact me concerning reference materials and textbooks that I would recommend.  I must admit that I am a biblio-maniac.  I am unable to go into a book store especially a used  bookstore and walk out empty handed.  Even in this day and age of iPads I still love the visceral aspects of the printed form.  Call me a luddite. That said I do have have somewhat of an interesting per-view on the world of books.

For the software world the people that are doing the creating are not writing books per se.  Thus if your reading books your not necessarily creating anything novel.  Let me be more specific.  Many use books to find an answer.  Many use books as a catalyst or reference.  I fall into the latter.  There are no “give me a shipping product” books – no “what is the answer?”  – for some of the stuff that we create.  Yes there are several ‘cookbooks’ and recipes for say learning a language or a new framework but there are no books that will provide answers to production level novel solutions.  As a very succinct example I was having a discussion in NYC with a very respected Executive Engineering Director and we both decided that there were no books that dealt specifically with hiring great talent in the tech world.  Why?  Because most are not going to tell you exactly what it really takes to hire great people and to build great teams.  That said few are going to explain how they came up with great coding feats – they will explain it after the fact for reference see Coders At Work.

So without further “waxin all philosphical‘”  here is a laundry list of books that I have found useful over the years.  I will try and categorize them according to subject matter.  Its by no means comprehensive.  I will also utilize this same list format in the future.   I would love to hear feedback or your thoughts – please share your list, if you are so inclined – after the fact.

Machine Learning, Data Mining and Knowledge Discovery

Scaling Up Machine Learning (Cambridge University Press)

This book is phenomenal and is at the very for front of issues we are all trying to solve in the areas of large scale learning systems. The book contains everything from parallelization of Support Vector Machines to details of how one designs hardware architectures for the next round of highly scalable machine learning algorithms.  Note this is not an introductory text.  Also the book takes a very interesting view in that it is written mainly by the best in the field.

Machine Learning,McGraw Publishing

This is a seminal text in the areas of Machine Learning.  There is  enough verbiage if your not a mathematician and enough algorithmic pseudo code to follow the development of the explanations.  I find myself going back to this book over and over.  Covers most everything from Bayes to Boosting.  It also has updated chapters on his web page.  I also like this writing style.

Business Modeling and Data Mining  (The Morgan Kaufmann Series in Data Management Systems)

Some purest might not like this book but I think it is a great read because it discusses why we actually need to consider a paying customer.  The book goes deep enough in the explanations and marries them to great scenarios for business models.  It also discusses in a ‘mind map manner’ how to design systems and algorithms.  The text also has a great bibliography.

Collective Intelligence, (O’ Reilly Press)

This is a great book that gets your toes in the water for differing aspects of “DataScience”.  Basically statistics and machine learning for data.  The book has examples for simple crawlers,  Bayes Theorem, Adaptive Neural Networks, Similarity Functions, Distance Metrics, and workhorse algorithms such as Support Vector Machines and Non-Negative Matrix Factorization.  Written in a pragmatic fashion.  Code in Python is included on the author’s website.

Artificial Intelligence: A Modern Approach 3rd Edition, (Prentice Hall Books)

Seminal book on the subject.  Spanning is the word I think of when reading this tome.  The book covers the usual suspects but goes on to a ‘meta-level’ to discuss varied fields of Logic, Qualitative Physics, Spatial Reasoning, Psychological foundations and fundamental mathematics.  The appendix and bibliography are worth the price of entry (expensive) for this oracle.

Introduction to Evolutionary Computing (Natural Computing Series, Springer)

I am a proponent of the ‘get it and go’ format.  This lives up to the title.  It is a good introductory text.  It starts off with a good introduction to Genetic Algorithms, not to be confused with Evolutionary Computing and proceeds to Learning systems and deeper issues as Non-Stationary classification systems, multi-variate parameter selection as well as a chapter on applications.

Machine Learning – An Algorithmic Perspective (CRC Press)

This book takes a novel approach that have an online presence and a hard text reference.  Stephen Marsland updates the code and book online and provides access much like the O’Reilly series.  Of particular interest is the format for review of such areas as probability, eigenvaules and fundamental algorithms.   The book also provides code examples mainly in the areas of Python.  This book takes exactly that approach, with each topic being presented both mathematically and in Python code using the Numpy and Scipy libraries.  As most know SciPy and Numpy resemble Matlab and is sufficiently high level that the book code
examples read like pseudocode.  SciKits would also be a good addition.

Semantics and Natural Language Processing

Programming the Semantic Web (O’ Reilly Press)

With the same eye towards ‘get in and get done’ Seagram wrote this because there was no middle ground for    Semantics or if there were such a text it was voluminous.  Great introductory text on Semantics with succinct code examples in Python.  The text emphasizes that you are not constrained to using data from only one database or server, but when the data is organized and stored in a standardized semantic format such as RDF, it may be made available for reading by anyone (if desired) without any risk of it being overwritten by using a query language like SPARQL instead of the better known SQL.  Once again complete code available via the InterWebz.

Semantic Web Programming (Wiley)

This book is a larger text that spans the areas of Semantics: FOAF, Reasoners, SPARQL, Linked Data, Microformats without getting too far in the weeds with theoretic gymnastics (read axiomatic proofs etc).  Written in mainly in JAVA.  The discussion of Semantics from a ‘non ivory tower’ standpoint is worth the purchase.  Then again it has been said in order to program semantics one has to believe semantics exist.

Natural Language Processing (O’ Reilly Press)

People often confuse Semantics with Natural Language Processing.  While the two are close cousins you can do one without the other and visa versa.  Historically NLTKas it is now known in the industry is becoming the “goto” Python Library for performing textual gymnastics.  This book is a wonderful introduction to Python, Text Analytics and analysis of grammer.  For example entity extraction which historically is extremely difficult is:

entities = nltk.chunk.ne_chunk(tagged)

I mean (pun intended) how nice is that?

Python Text Processing with NLTK 2.0 Cookbook (PAKT Publishing)

This is at a much lower level and assumes that you need to do perform basic NLP and is more text analytics based than extracting meaning, linguistic and grammatical behaviors.  One novel aspect to this book is the addition of creation of a corpora reader via MongoDb.  The book even addresses nuances like FileLocking during corpora edits.  It uses PyMongo as the front end interface with Python examples.  The examples go on the look at various aspects of NLTK and NoSQL architectures.

Network and Dynamic Systems

Network Crowds and Markets: Reasoning About A Highly Connected World (Cambridge University Press) 

At the moment this is one of the tomes that I am immersed within.  The book has a good introduction on Graph Theory but then quickly heads into territory such as calculations of Social Capital with emphasis on Closure within Networks.  The text also does a grand job of tying (pun intended) Graph Theory and Game Theoretic applications with emphasis on arbitrage environments.  The chapter on diffusion in networks is worth the price of purchase.  For anyone performing work in the area of Social Networks this text is a must.

Social and Economic Networks (Princeton University Press)

This book is a great mix of statistics, linear algebra and game theory as applied to social networks.  The economic emphasis is particularly cogent.  The detail and depth is world class.  The analysis of Stochastic and Random graph models is of particular interest for anyone dealing with adoption models in network environments.  Further the mathematical analysis of imitation in social networks is especially enjoyable. This is also one of the few text that I have seen that tie together Fat Tail, Long Tail and Poisson Distribution analysis.

Introduction to Dynamic Systems (Wiley)

This is an older text published in 1979.  Funny how those “maths” do not change much – eh?  I have owned this book since I have been involved with creating software systems.  The book while not “out of print” is commanding extremely large sums of money.  The objective as stated in the preface is: “to help one develop the ability to analyze real dynamic phenomena and dynamic systems.”  Seems very appropriate in this day and age of online behavior.  The book starts off with a review of differential equations, linear algebra and state space equations.  Then text then goes on to address both mechanical and electrical constructs with applications with tools such as markov models and control systems with z-transform theory.   You read this book and honestly you realize not much as changed with the InterWebz.

Math

Numerical Analysis, Sixth Edition  (Brooks/Cole Publishing)

Years ago when I was working on my Phd in Applied Science we had several levels of Numerical Analysis.  I love the aspects of Lagrange Polynomials, L2 Norms, Splines and the like.  This book covers them all and has a really cool glossary and index of defined terms for that moment when you literally forgot what a term means because your cross eyed.

Probability Random Variables and Stochastic Processes (McGraw Hill)

This is/was a text for a class at c on – Probability  Random Variables and Stochastic Processes.  The book is written by Athanasios Papoulis who is a legend in signal processing. I will tell you this is not for the faint of heart and neither was the class.  Now more than ever it is an important part of anyone doing data science.

Matrix Algebra for Engineers (Von Nostrand Company)

This is a straight ahead approach to Linear Algebra.  Written in a straight ahead fashion addressing fundamentals of matrix theory like simultaneous equations, determinants and eigenvalue analysis.  I probably have a high affinity for this book because it is a first edition published in 1965.

Programming and Code Development

The Art of Computer Programming Vol 1-4 

Colloquially known as TAOCP, I am not going to say much about these tomes except you should at a minimum know what they are and have in them.  Not for the faint of heart if you just want to do some PHP or ruby on rails. Having had the experience to take a class from Professor Knuth with variations of these via class notes being the backdrop, I cannot say enough about the text.  For the true software engineer a must for the arsenal.

Code Complete (Microsoft Press)

IMHO this is a great book for the thought processes that lead to creating production products.  It was a seminal work in the area before all of the Agile books hit the market.  While I myself am not a big proponent of ‘software process’ this book gets you in the modus operandi of how to think not just code.

Signal Processing

Adaptive and Digital Signal Processing (International Series on Signal Processing)

I was having discussion the other day with some I respect in the audio, semantics and data science field and we noted that long before you could spin up a virtual machine in the proverbial cloud there was a field called real time signal processing  – where you didnt have a database.  All the processing of streams were in real time – in memory.  Funny how the vissistudes of cyclialty come back to haunt you think Big Data.  I highly recommend this text.  Great examples of frequency and array based processing.  When everyone was teaching time domain behavior Professor Lindquist was teaching the law of dual domains and easier math.  It also covers various kernel transforms never covered in other text before the term “wavelet” came in to vogue.

So there is a first cut in a multi volume blog that I will continue.  I trust this was helpful.  If you find any of these text useful feel free to contact me with what you are working on or considering.  Also if there are recommendations that you find particularly informative as far as a certain text goes drop me a line.

Until Then,

Go Big or Go Home!

@tctjr

Recently I received an award for best presentation at a BarCamp entitled “Quick and Dirty Startups.”  I had a blast both creating the slideware and presenting it – although during the presentation the projector was intermittently shorting out which I feared would cause the audience to enter into epileptic fits.  Maybe the gnashing of teeth and wringing of hands during my presentation was that or maybe the audience members realized how damn difficult it is to create company from the spark of an idea.   In the presentation I said do not create a company if the number one reason is to make a ton of money (that confused many…).

At the same time I have been in varying degrees of discussions in and around the T’s of a Startup: Talent.  Most who have followed my blog realize the importance I place on talent.  I have also been speaking with various software engineers of varying skills sets from those who love CSS, to those who love HDFS, to those who love creating ensemble machine learning algorithms.  These two events had me reflect on something that I used to consider seperate entitites.  It was though I had an epiphany.  What if what you do to create and who you are –  in fact are one in the same?

I wrote another blog on the great human endeavor of software.  I would also consider music to be in that league as well.  The two closely align.  There are rules yet there are no rules.  There are fundamental symbolics and syntactics that allow singular and crowd sourced orchestration to create great works of art.  Those that truly create great music and software have one thing in common: Passion.  Which is the number one attribute I look for when either wanting to work with someone or adding someone to a team.  I also reflected on the views people have of software engineering writ large and realized that most like the music world look at as us as though there is no work involved.  Why?  If you look at anyone who loves what they do and who they are within the same instance performing a task for the pure desire of doing it – is not work.  It is what you do and who you are that create that perception.  I have had the extreme pleasure of working in many aspects of the music industry as well as the software industry and in fact some cases one in the same.  No one sees  the 25/8  (25 hrs a day – 8 days a week) schedule.  All anyone sees are the end results: Concert: On stage women and men throwing themselves at the stage, Software Company: Pulling up to your yacht in your Modena with a personal assistant. They do not see the massive amount of preparation, practice and planning involved.  However while it is grueling for those that love the process it is not work.  On the contrary for us it is pure adulterated freedom.  Why do i say adulterated?  Many would consider that to do what we do with as much fun as it is – IT MUST BE  A CRIME!  Sure there are incredibly difficult situations, many that are life changing, but what of it?  For those that are truly passionate about creating we really dont know what else to do.  I will leave you with a great clip by a legendary guitar player Steve Vai as he discusses the secret to his success.  Know what you want.  See it happen and the “Is To Be” will ring true.

“Get into a line that you will find to be a deep personal interest, something you really enjoy spending twelve to fifteen hours a day working at, and the rest of the time thinking about.”~ Earl Nightingale ~

Until then,

Go Big Or Go Home!

@tct

Recently I received an award for best presentation at a BarCamp entitled “Quick and Dirty Startups.”  I had a blast both creating the slideware and presenting it – although during the presentation the projector was intermittently shorting out which I feared would cause the audience to enter into epileptic fits.  Maybe the gnashing of teeth and wringing of hands during my presentation was that or maybe the audience members realized how damn difficult it is to create company from the spark of an idea.   In the presentation I said do not create a company if the number one reason is to make a ton of money (that confused many…).

At the same time I have been in varying degrees of discussions in and around the T’s of a Startup: Talent.  Most who have followed my blog realize the importance I place on talent.  I have also been speaking with various software engineers of varying skills sets from those who love CSS, to those who love HDFS, to those who love creating ensemble machine learning algorithms.  These two events had me reflect on something that I used to consider seperate entitites.  It was though I had an epiphany.  What if what you do to create and who you are –  in fact are one in the same?

I wrote another blog on the great human endeavor of software.  I would also consider music to be in that league as well.  The two closely align.  There are rules yet there are no rules.  There are fundamental symbolics and syntactics that allow singular and crowd sourced orchestration to create great works of art.  Those that truly create great music and software have one thing in common: Passion.  Which is the number one attribute I look for when either wanting to work with someone or adding someone to a team.  I also reflected on the views people have of software engineering writ large and realized that most like the music world look at as us as though there is no work involved.  Why?  If you look at anyone who loves what they do and who they are within the same instance performing a task for the pure desire of doing it – is not work.  It is what you do and who you are that create that perception.  I have had the extreme pleasure of working in many aspects of the music industry as well as the software industry and in fact some cases one in the same.  No one sees  the 25/8  (25 hrs a day – 8 days a week) schedule.  All anyone sees are the end results: Concert: On stage women and men throwing themselves at the stage, Software Company: Pulling up to your yacht in your Modena with a personal assistant. They do not see the massive amount of preparation, practice and planning involved.  However while it is grueling for those that love the process it is not work.  On the contrary for us it is pure adulterated freedom.  Why do i say adulterated?  Many would consider that to do what we do with as much fun as it is – IT MUST BE  A CRIME!  Sure there are incredibly difficult situations, many that are life changing, but what of it?  For those that are truly passionate about creating we really dont know what else to do.  I will leave you with a great clip by a legendary guitar player Steve Vai as he discusses the secret to his success.  Know what you want.  See it happen and the “Is To Be” will ring true.

“Get into a line that you will find to be a deep personal interest, something you really enjoy spending twelve to fifteen hours a day working at, and the rest of the time thinking about.”~ Earl Nightingale ~

Until then,

Go Big Or Go Home!

@tct

I have been doing some work lets say ‘out of the ordinary’ as of late.  I have also been extremely honored to be working around some former people who were directly involved “when peace broke out” during the Cold War.   I have also had the opportunity to work with people who have backgrounds in literature who now work in the technical industry.  Also, dear readers, as most of you know I am fond of the entrepreneurial spirit.    This  led me to thinking about how they all interrelate and then like magic!  Submarines, Shakespeare and Startups.

Submarines

So lets pick the first one: Submarines.  Some of submariners that I know were directly involved with designing, building and operating submarines.  Specifically Nuclear Powered submarines.   In listening to the stories I noticed how much in common Submarines have with Startups.   Let me be specific herewith:  I am referring to fast attack nuclear submarines.  Here is a wikipedia page for reference: Fast Attack Nuclear Submarines – Los Angeles Class.  I am also referring to problems areas posed as to why Russian Submarines were quieter than USA submarines (if your interested do the google grunt work).

So first let me explain why I believe in this day and age of Lean Startup mentality why we can learn a great deal from those that travel below the ocean surface.

Submariners are consumate planners.

Submariners are by definition – independent operations.  From the perspective of information and physical support the design, building and operation places a premium on planning.   The Commanding Officer (CO) must “go with what they know and have got”.  Efficiency is key.  Space is a premium.  Intelligence at the highest level are mandatory across all aspects of the design, operation and mission.

Submarines are the epitome of stealth

Submarines have been recognized as the preeminence of contributions to stealth and survival.  For instance the greater degree of enemy’s knowledge of the submarine – in both space and time – the greater the diminsihment of the submarines effectiveness.  Stealth relates to safety and the mission of the ship relates to efficiency.  Signature Management is the term given to the ability to lower the footprint of detection.  It ia also known as Stealth Technology.  Stealth technology also termed LO technology (low observable technology) is a sub-discipline of military tactics and passive electronic countermeasures,which cover a range of techniques used with personnel, aircraft, ships, submarines, and missiles, to make them less visible (ideally invisible) to radar, infrared, sonar and other detection methods.  Stealth Startup anyone?

Submariners are Concise and Focused

Submariners need to be extremely consice in all matters of communication.  In most cases receiving communications may always be accomplished but it may need to be managed around mission critical requirements.  These mission critical requirements are in most cases time sensitive  In many cases communication transmission may not always be available without mission impact.  Thus one needs to be concise and make decision in-situ at the moment and go with what they know.  Communications are minimized while maximizing the ability to war-fight.

Shakespeare

Nowadays, literature folks abound in the technical sector.  I also include english majors and people who dig 17th century poems like Milton’s Paradise Lost in the group.  Why?  They know how to communicate and weave disparate information.  They also know how to tell a story.  They also know how to use commas accordingly.   Shakespeare being the preeminent example of author and playwright wrote stories that naturally mirrored life’s twists and turns.  What does this have to do with startups?  Let us take an example from Hamlet (NOTE: this a cliff notes exceprt from SparkNotes)

” On a dark winter night, a ghost walks the ramparts of Elsinore Castle in Denmark. Discovered first by a pair of watchmen, then by the scholar Horatio, the ghost resembles the recently deceased King Hamlet, whose brother Claudius has inherited the throne and married the king’s widow, Queen Gertrude. When Horatio and the watchmen bring Prince Hamlet, the son of Gertrude and the dead king, to see the ghost, it speaks to him, declaring ominously that it is indeed his father’s spirit, and that he was murdered by none other than Claudius. Ordering Hamlet to seek revenge on the man who usurped his throne and married his wife, the ghost disappears with the dawn.” ~ SparkNotes on Hamlet

Ok so how does this relate to startups?  Or submarines for that matter?  Inquiring minds want to know!  We have Stealth, Revenge and Strategy all rolled into one simple paragraph! Notwithstanding the whole sword fighting thingy (technical term) between Hamlet and Laertes! This!  This is the stuff of true competition!   Moreover Shakespeare mirrors life, life is about humans, humans are bat-house crazy, ergo startups are crazy!

I was having a discussion the other day with a couple of very smart humans.  The discussion trended towards the aspect of performance which naturally led me to start joking about  meetings called “Nurturing Others In The Work Environment” and “I’m Ok – Your OK”.  A very bright software engineer,  on my current team, commented the software speaks for itself, which got me around to the current installment.  The software does speak for itself.  In fact, elite software truly speaks for itself.  Further software development is the ultimate survival of the fittest from an evolutionary standpoint.  For those not familiar with this subject matter I cannot at this juncture give you cliff notes.  Thus we are not nurturing others in the blogosphere.   Mr Spencer who was a pretty smart feller was attributed to coining the phrase “Survival Of The Fittest”.   Stanford is kind enough to give you a great rundown of the subject matter here.  Concerning the taboo terminology of Social Darwinism, we can locate the Wikipedia definition as thus.  Richard Hofstadter devoted an entire chapter of Social Darwinism in American Thought (1955) to Spencer, arguing that Spencer’s unfortunate vogue in late nineteenth-century America inspired Andrew Carnegie and William Graham Sumner’s visions of unbridled and unrepentant capitalism. For Hofstadter, Spencer was an “ultra-conservative” for whom the poor were so much unfit detritus. If you have not read Hofstadter’s book, you should. Once again it is outside the scope of this blog to review but it is one of the few tomes on the “taboo” subject of Social Darwinism.  Also if you have a Facebook account there is a Social Darwinism fan page.

It is my belief that the world of Spencer and Hofstadster via the foundation of Darwin, can be seen in the world of software development.  If you have ever worked on software that touched multiple millions of deployments to billions of feeds you know the importance of quality.  You also know the importance of performance and accuracy.  There is no arguing what happens when someone “breaks the build”.  It is NOT OK – YOU ARE NOT OK.  In fact you are probably going to get your ass called at 2:30 AM after you pulled a 3 day straight coding sprint to get up and fix it or else.  Does this sound like nurturing others in the work environment?  Hell No!  You are costing people money!.   It is a binary process.  Its either on or its off and it better be ON!  All systems go.  I am not saying that we should seek complete perfection, elegance and bug free coding that is impossible if your going to make bank through software creation.  He who ships first will win – if the quality is there.  Yet if your grinding and someone is lagging they are left severely behind the pack and if you watch the discovery channel you know what happens when there are laggers in nature.  It is not pretty.

This is a view from the inside.  From the outside most only see the frisbees and imported beer and sitting there with Pantera playing on the headphones whilst sitting in front of a computing device.  Easy huh?  Think again.  Real coders, data scientist, architects <<insert title de jour here>>  literally have ice running in their veins when it comes to creating software  – usually for money.  As I wrote in other installments, I discussed extreme capitalism and mercenaries.  Good, Better, Best.  It is especially exacerbated when the best of the best make it look easy.  The same for sports.  Kelly Slater, Tiger Woods, Michael Jordan these people make it look so easy.  Aplomb comes to mind.  I am fond of saying, ” To do a dangerous thing with style is the mark of a professional”.

Historically the software industry was a juxtaposition of the peace love and happiness stuff which is in direct conflict with what actually occurs within the world of software. Another interesting aspect to the software world that one must always be A D A P T I N G.  Homeostasis is a death knell to software company.   Microsoft missed the internet but sat down and decided to give away Internet Explorer.  Apple was tanking and created colorful computers.  Companies that continually evolve and change adapt to technology trends and market pressures – win.  Antiquated static slow moving companies are akin to the diseased of a pack and will be taken down.

Dont let you or your company be the one that is taken down.  Evolve. Change. Grow.  Seek Power through great software.  Innovation and Great Code speak volumes.  Amplify those qualities.

Until Then,

Go Big Or Go Home!