commoditization and driving diminishing returns, as output quality converges across players and differentiation becomes harder to sustain. At the same time, the cost of applying/using these models – known commoditization and driving diminishing returns, as output quality converges across players and differentiation becomes harder to sustain. At the same time, the cost of applying/using these models – known

JavaScript string literal (including template strings), only in the top-level syntactic context. The automatic escaping behavior can be disabled globally by passing autoescape=None to the Application or tornado name of an alternative escaping function may be used instead of None. Note that while Tornado’s automatic escaping is helpful in avoiding XSS vulnerabilities, it is not sufficient in all cases. Expressions location /static/ { root /var/friendfeed/static; if ($query_string) { expires max; } } Debug mode and automatic reloading If you pass debug=True to the Application constructor, the app will be run in debug/development

and ports Running behind a load balancerStatic files and aggressive file caching Debug mode and automatic reloadingIntroduction Tornado [http://www.tornadoweb.org] is a Python web framework and asynchronous JavaScript string literal (including template strings), only in the top-level syntactic context. The automatic escaping behavior can be disabled globally by passing autoescape=None to the Application or tornado name of an alternative escaping function may be used instead of None. Note that while Tornado’s automatic escaping is helpful in avoiding XSS vulnerabilities, it is not sufficient in all cases. Expressions

to define function behavior across many combinations of argument types via multiple dispatch • Automatic generation of efficient, specialized code for different argument types • Good performance, approaching limitations and is generally not recommended unless no other method works. For example, there is no automatic update mechanism for Juliaup with this installation method. The 64 bit version of the MSI installer 00000000 However, type promotion between the primitive types above and BigInt/BigFloat is not automatic and must be explicitly stated. julia> x = typemin(Int64) -9223372036854775808 julia> x = x -

to define function behavior across many combinations of argument types via multiple dispatch • Automatic generation of efficient, specialized code for different argument types • Good performance, approaching limitations and is generally not recommended unless no other method works. For example, there is no automatic update mechanism for Juliaup with this installation method. The 64 bit version of the MSI installer 00000000 However, type promotion between the primitive types above and BigInt/BigFloat is not automatic and must be explicitly stated. julia> x = typemin(Int64) -9223372036854775808 julia> x = x -

to define function behavior across many combinations of argument types via multiple dispatch • Automatic generation of efficient, specialized code for different argument types • Good performance, approaching limitations and is generally not recommended unless no other method works. For example, there is no automatic update mechanism for Juliaup with this installation method. The 64 bit version of the MSI installer 00000000 However, type promotion between the primitive types above and BigInt/BigFloat is not automatic and must be explicitly stated. julia> x = typemin(Int64) -9223372036854775808 julia> x = x -

to define function behavior across many combinations of argument types via multiple dispatch • Automatic generation of efficient, specialized code for different argument types • Good performance, approaching 00000000 However, type promotion between the primitive types above and BigInt/BigFloat is not automatic and must be explicitly stated. julia> x = typemin(Int64) -9223372036854775808 julia> x = x - definition is only used in the first case, while the 2x - y definition is used in the others. No automatic casting or conversion of function arguments is ever performed: all conversion in Julia is non-

to define function behavior across many combinations of argument types via multiple dispatch • Automatic generation of efficient, specialized code for different argument types • Good performance, approaching NUMBERS 23 However, type promotion between the primitive types above and BigInt/BigFloat is not automatic and must be explicitly stated. julia> x = typemin(Int64) -9223372036854775808 julia> x = x - definition is only used in the first case, while the 2x - y definition is used in the others. No automatic casting or conversion of function arguments is ever performed: all conversion in Julia is non-

to define function behavior across many combinations of argument types via multiple dispatch • Automatic generation of efficient, specialized code for different argument types • Good performance, approaching limitations and is generally not recommended unless no other method works. For example, there is no automatic update mechanism for Juliaup with this installation method. The 64 bit version of the MSI installer 00000000 However, type promotion between the primitive types above and BigInt/BigFloat is not automatic and must be explicitly stated. julia> x = typemin(Int64) -9223372036854775808 julia> x = x -

to define function behavior across many combinations of argument types via multiple dispatch • Automatic generation of efficient, specialized code for different argument types • Good performance, approaching limitations and is generally not recommended unless no other method works. For example, there is no automatic update mechanism for Juliaup with this installation method. The 64 bit version of the MSI installer 00000000 However, type promotion between the primitive types above and BigInt/BigFloat is not automatic and must be explicitly stated. julia> x = typemin(Int64) -9223372036854775808 julia> x = x -