Layers: A New Era for Scalable Analytics, Search, and AI v 1.1Table of Contents Introduction 1. The Interconnection of Analytics, Search, and AI 2. What is a Real-Time Unified Data Layer? 3. Why Do experiences and ensure performance. 32. The Interconnection of Analytics, Search, and AI Analytics, search, and AI are deeply interconnected in how they process, interpret, and extract value from data information, enhancing discoverability, accelerating decision-making, and improving operational efficiency. AI acts as the intelligence layer, optimizing both search and analytics by making them faster, smarter

IntelligenceTrends – Artificial Intelligence (AI) May 30, 2025 Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey2 Context We set out to compile foundational trends related to AI. A starting collection of several ’ At the time, the pace of change catalyzed by the internet was unprecedented. Consider now that AI user and usage trending is ramping materially faster…and the machines can outpace us. The pace and OpenAI’s ChatGPT with its extremely easy-to-use / speedy user interface. In addition, relatively new AI company founders have been especially aggressive about innovation / product releases / investments

argument has the same meaning as for socket.listen(). flags is a bitmask of AI_* flags to getaddrinfo, like socket.AI_PASSIVE | socket.AI_NUMERICHOST. reuse_port option sets SO_REUSEPORT option for every socket can be used to pass additional flags to getaddrinfo. • tornado.netutil.bind_sockets no longer sets AI_ADDRCONFIG; this will cause it to bind to both ipv4 and ipv6 more often than before. • tornado.netutil HTTPServer can now be run on a unix socket as well as TCP. • Fixed exception at startup when socket.AI_ADDRCONFIG is not available, as on Windows XP IOLoop and IOStream • IOStream performance has been

socket.listen]. flags is a bitmask of AI_* flags to getaddrinfo [https://docs.python.org/3/library/socket.html#socket.getaddrinfo], like socket.AI_PASSIVE | socket.AI_NUMERICHOST. reuse_port option sets that can be used to pass additional flags to getaddrinfo. tornado.netutil.bind_sockets no longer sets AI_ADDRCONFIG; this will cause it to bind to both ipv4 and ipv6 more often than before. tornado.netutil HTTPServer can now be run on a unix socket as well as TCP. Fixed exception at startup when socket.AI_ADDRCONFIG is not available, as on Windows XP IOLoop and IOStream IOStream performance has been improved

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * biCHAPTER 12. METHODS 163 ## this is insufficient because it assumes `one(eltype(a))` `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * biCHAPTER 12. METHODS 163 ## this is insufficient because it assumes `one(eltype(a))` `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi)CHAPTER 13. METHODS 172 # this is wrong, since depending on the return value # of type-inference length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/pCHAPTER 79. LINEAR ALGEBRA 1517 with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi)CHAPTER 13. METHODS 172 # this is wrong, since depending on the return value # of type-inference length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/pCHAPTER 79. LINEAR ALGEBRA 1517 with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi)CHAPTER 13. METHODS 172 # this is wrong, since depending on the return value # of type-inference length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/pCHAPTER 79. LINEAR ALGEBRA 1517 with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using

pseudo-code might look like: function matmul(a::AbstractMatrix, b::AbstractMatrix) op = (ai, bi) -> ai * bi + ai * bi ## this is insufficient because it assumes `one(eltype(a))` is constructable: # R `+` calls `promote_type` ## but this is not true for some types, such as Bool: # R = promote_type(ai, bi) # this is wrong, since depending on the return value # of type-inference is very brittle (as the corresponding length. The p-norm is defined as ∥A∥p = � n � i=1 |ai|p �1/p with ai the entries of A, |ai| the norm of ai, and n the length of A. Since the p-norm is computed using the norms of