global internet rails that are accessible to 5.5B citizens via connected devices; ever-growing digital datasets that have been in the making for over three decades; breakthrough large language models ~Six Centuries Printing Press – Invented 144024 …Knowledge Distribution – 1993-2021 = Active + Digital Delivery… *The internet is widely agreed to have been ‘publicly released’ in 1993 with release of Knowledge Distribution Evolution = Over ~Six Centuries25 …Knowledge Distribution – 2022+ = Active + Digital + Generative Delivery *We define the public launch of ChatGPT in November 2022 as the public release

Secretary of Defense for Acquisition) https://www.acq.osd.mil/evm/ Incremental process, i.e. Rolling Wave Planning; Agile Release/Program Increment Planning. Scenario describes one method of how Agile can Agile Contracting – Dr. Stephen Mayner SAFe https://techfarhub.cio.gov/ Handbook for Procuring Digital Services Using Agile Processes All structures (standard C, IDIQ, BPA, GWAC, set-aside, etc.) can

shift from industrial age processes to a modern management and development approach suited to the digital age. Agile practices help to make progress and development more transparent, enabling improved of the requirements in a sprint, thus requirements continue to shift to the right creating a bow wave effect. One way to manage this requirements shift is to make the 4th or 5th sprint in each release

dropping any arguments that correspond to potential defaults. For example, suppose you're writing a digital filtering algorithm and you have a method that handles the edges of the signal by applying padding: values: julia> sum(skipmissing([1, missing])) 1 This convenience function returns an iterator which filters out missing values efficiently. It can therefore be used with any function which supports iterators: with a 2.7 GHz Intel Core i7 processor, this produces: $ julia wave.jl; 1.207814709 seconds 4.443986180758249 $ julia --math-mode=ieee wave.jl; 4.487083643 seconds 4.443986180758249 Here, the option

dropping any arguments that correspond to potential defaults. For example, suppose you're writing a digital filtering algorithm and you have a method that handles the edges of the signal by applying padding: values: julia> sum(skipmissing([1, missing])) 1 This convenience function returns an iterator which filters out missing values efficiently. It can therefore be used with any function which supports iterators: with a 2.7 GHz Intel Core i7 processor, this produces: $ julia wave.jl; 1.207814709 seconds 4.443986180758249 $ julia --math-mode=ieee wave.jl; 4.487083643 seconds 4.443986180758249 Here, the option

dropping any arguments that correspond to potential defaults. For example, suppose you're writing a digital filtering algorithm and you have a method that handles the edges of the signal by applying padding: values: julia> sum(skipmissing([1, missing])) 1 This convenience function returns an iterator which filters out missing values efficiently. It can therefore be used with any function which supports iterators: with a 2.7 GHz Intel Core i7 processor, this produces: $ julia wave.jl; 1.207814709 seconds 4.443986180758249 $ julia --math-mode=ieee wave.jl; 4.487083643 seconds 4.443986180758249 Here, the option

dropping any arguments that correspond to potential defaults. For example, suppose you're writing a digital filtering algorithm and you have a method that handles the edges of the signal by applying padding: values: julia> sum(skipmissing([1, missing])) 1 This convenience function returns an iterator which filters out missing values efficiently. It can therefore be used with any function which supports iterators: with a 2.7 GHz Intel Core i7 processor, this produces: $ julia wave.jl; 1.207814709 seconds 4.443986180758249 $ julia --math-mode=ieee wave.jl; 4.487083643 seconds 4.443986180758249 Here, the option

dropping any arguments that correspond to potential defaults. For example, suppose you're writing a digital filtering algorithm and you have a method that handles the edges of the signal by applying padding: values: julia> sum(skipmissing([1, missing])) 1 This convenience function returns an iterator which filters out missing values efficiently. It can therefore be used with any function which supports iterators: with a 2.7 GHz Intel Core i7 processor, this produces: $ julia wave.jl; 1.207814709 seconds 4.443986180758249 $ julia --math-mode=ieee wave.jl; 4.487083643 seconds 4.443986180758249 Here, the option

dropping any arguments that correspond to potential defaults. For example, suppose you're writing a digital filtering algorithm and you have a method that handles the edges of the signal by applying padding: values: julia> sum(skipmissing([1, missing])) 1 This convenience function returns an iterator which filters out missing values efficiently. It can therefore be used with any function which supports iterators: with a 2.7 GHz Intel Core i7 processor, this produces: $ julia wave.jl; 1.207814709 seconds 4.443986180758249 $ julia --math-mode=ieee wave.jl; 4.487083643 seconds 4.443986180758249 Here, the option

dropping any arguments that correspond to potential defaults. For example, suppose you're writing a digital filtering algorithm and you have a method that handles the edges of the signal by applying padding: values: julia> sum(skipmissing([1, missing])) 1 This convenience function returns an iterator which filters out missing values efficiently. It can therefore be used with any function which supports iterators: with a 2.7 GHz Intel Core i7 processor, this produces: $ julia wave.jl; 1.207814709 seconds 4.443986180758249 $ julia --math-mode=ieee wave.jl; 4.487083643 seconds 4.443986180758249 Here, the option