AI in the Enterprise Lessons from seven frontier companiesContents A new way to work 3 Executive summary 5 Seven lessons for enterprise AI adoption Start with evals 6 Embed AI into your products models 13 Get AI in the hands of experts 16 Unblock your developers 18 Set bold automation goals 21 Conclusion 22 More resources 24 2 AI in the EnterpriseA new way 
 to work As an AI research and do their best work with sophisticated, complex, interconnected workflows and systems. We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance Helping people

`University of Massachusetts Amherst Powered by AI:
 A Cambrian Explosion
 for C++ Software Development Tools Emery BergerCretaceous–Paleogene (K-Pg) extinction eventCretaceous–Paleogene (K-Pg) extinction ALLOCATED MEMORY USAGE GPU UTIL %, PEAK MEMORY (MB/s) MEMORY PYTHON NATIVE AI-powered optimizations!AI-powered optimizations... COMING SOON!evolveevolve profiler that suggests optimizationsevolve

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

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model DeepSeek-AI research@deepseek.com Abstract We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model consisting of 8.1T tokens. Compared with the corpus used in DeepSeek 67B (our previous release) (DeepSeek-AI, 2024), this corpus features an extended amount of data, especially Chinese data, and higher data quality function in FFNs), unless specifically stated, DeepSeek-V2 follows the settings of DeepSeek 67B (DeepSeek-AI, 2024). 2.1. Multi-Head Latent Attention: Boosting Inference Efficiency Conventional Transformer

Files are harder to integrate. Flags (GCC, Clang) would be easier. Ideally, the same names. Moonshot: cross-language, cross-toolchain standard way. Point already raised to a few LLVM & kernel folks Retrieving the source code: pipe it out, TokenStream, …? How to make it useful for other projects? Moonshot: rust-analyzer support (e.g. “▶ Run Test | Debug”). Language Library Tooling Tooling Codegen

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

© 2023 Apex.AI, Inc. | All rights reserved. Apex.AI, Apex.OS, Apex.Grace, Apex.Ida, Apex.Alan, Apex.Ray are trademarks of Apex.AI. CppCon 2023 Building bridges: Leveraging C++ and ROS for simulators Divya Aggarwal October 5, 2023© 2023 Apex.AI, Inc. | All rights reserved. Apex.AI, Apex.OS, Apex.Grace, Apex.Ida, Apex.Alan, Apex.Ray are trademarks of Apex.AI. Need for Speed? Asphalt? GT? This is an driving simulator. 2 CppCon© 2023 Apex.AI, Inc. | All rights reserved. Apex.AI, Apex.OS, Apex.Grace, Apex.Ida, Apex.Alan, Apex.Ray are trademarks of Apex.AI. 3 Agenda 1. ROS and simulators 2. What

T y p e c ap t u r i n g . . . . . . . . . . . . . . . . . . . . . . . . 55 2. 1. 5 T y p e c on t ai n m e n t . . . . . . . . . . . . . . . . . . . . . . 60 2. 1. 6 F u n c t i on t y p e s . . . . 4. 2. 7 Loc al f u n c t i on d e c l ar at i on . . . . . . . . . . . . . . . . . . 119 4. 2. 8 T ai l r e c u r s i on op t i m i z at i on . . . . . . . . . . . . . . . . . 119 4. 2. 9 F u n c t i c on t ai n m e n t - c h e c k i n g e x p r e s s i on s . . . . . . . 172 8. 11. 1 T y p e - c h e c k i n g e x p r e s s i on s . . . . . . . . . . . . . . . . . . 173 8. 11. 2 C on t ai n m e n

c h t o t h e e x t e n t p os s i b l e u n d e r l aw , T h e I d r i s C o m m u n i t y h as w ai v e d al l c op y r i gh t an d r e l at e d or n e i gh b or i n g r i gh t s t o D oc u m e n t at 0 c an b e f ou n d on l i n e at : h t t p : //c r e at i v e c om m on s . or g/ p u b l i c d om ai n /z e r o/1. 0/ 1 C H AP T E R 1 T h e I d r i s T u t o r i a l T h i s i s t h e I d r i s T c h t o t h e e x t e n t p os s i b l e u n d e r l aw , T h e I d r i s C o m m u n i t y h as w ai v e d al l c op y r i gh t an d r e l at e d or n e i gh b or i n g r i gh t s t o D oc u m e n t at

c h t o t h e e x t e n t p os s i b l e u n d e r l aw , T h e I d r i s C o m m u n i t y h as w ai v e d al l c op y r i gh t an d r e l at e d or n e i gh b or i n g r i gh t s t o D oc u m e n t at 0 c an b e f ou n d on l i n e at : h t t p : //c r e at i v e c om m on s . or g/ p u b l i c d om ai n /z e r o/1. 0/ 1 C H AP T E R 1 T h e I d r i s T u t o r i a l T h i s i s t h e I d r i s T c h t o t h e e x t e n t p os s i b l e u n d e r l aw , T h e I d r i s C o m m u n i t y h as w ai v e d al l c op y r i gh t an d r e l at e d or n e i gh b or i n g r i gh t s t o D oc u m e n t at