Stanford University… 1: AI ‘Winter’ was a term used by Nils J. Nilsson, the Kumagai Professor of Engineering in computer science at Stanford University, to describe the period during which AI continued to hypothetical endpoint, but as a reachable threshold. If / when achieved, AGI would redefine what software (and related hardware) can do. Rather than executing pre-programmed tasks, AGI systems would understand understand goals, generate plans, and self-correct in real time. They could drive research, engineering, education, and logistics workflows with little to no human oversight – handling ambiguity and novelty

September 1. Topics from software engineering to implement/use X in Golang app 2. Software engineering + tech leadership topics 3. Your experience building your software Maybe one of your work colleagues

September 1. Topics from software engineering to implement/use X in Golang app 2. Software engineering + tech leadership topics 3. Your experience building your software Maybe one of your work colleagues

slowly moving to a place close byJoin Slido for Q&ALooking for speakers! • Golang specifics • Software engineering (implement/use/best practices) • Tech leadership and/or management • Your experience building

and consume unprecedented volumes of data across a growing number of sources and formats, data engineering and architecture teams must design systems that not only scale but also deliver real-time access availability to ensure data integrity, privacy, and governance. To overcome these challenges, data engineering and architecture teams must rethink traditional data infrastructures. The future lies in Real-Time

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that Half-precision floating-point numbers are also supported (Float16), but they are implemented in software and use Float32 for calculations. julia> sizeof(Float16(4.)) 2 julia> 2*Float16(4.) Float16(8 conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that Half-precision floating-point numbers are also supported (Float16), but they are implemented in software and use Float32 for calculations. julia> sizeof(Float16(4.)) 2 julia> 2*Float16(4.) Float16(8 conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise:

allowing Julia to take full advantage of computational resources. Additionally, Julia provides software support for Arbitrary Precision Arithmetic, which can handle operations on numeric values that instructions used on hardware which supports this number format. Otherwise, operations are implemented in software, and use Float32 for intermediate calculations. As an internal implementation detail, this is achieved conflict with some numeric literal syntaxes: hexadecimal, octal and binary integer literals and engineering notation for floating-point literals. Here are some situations where syntactic conflicts arise: