Fresh off its Wiz payout, Index Ventures raises $2B across three funds
Index Ventures has raised $2 billion across three funds, bringing its total available investing capital to $3.5 billion.
The auto-updated technology brief
Index Ventures has raised $2 billion across three funds, bringing its total available investing capital to $3.5 billion.
Uber has partnered with and invested in about 30 autonomous vehicle companies over the past two years, expanding its presence in self-driving technology.
India's app market generated a record $345 million in Q2 2026, indicating a shift from just downloading apps to paying for them.
Meta's smart glasses are designed to alert bystanders when recording, but their safety features can be easily bypassed with a simple $2 sticker.
Snapchat will no longer feature videos made with AI in its public recommendations on Spotlight.
NVIDIA introduces solutions for running autonomous AI agents locally using faster models and multi-node clustering on DGX Spark, addressing compute demands and privacy concerns.
DynoSim addresses the complexity of tuning modern LLM serving deployments by simulating the Pareto frontier across interacting system choices such as model backend, tensor-parallel shape, and autoscaling thresholds.
NVIDIA RTX introduces DLSS 4.5 as a plugin for Unreal Engine 5 and expands multilingual AI character capabilities with NVIDIA ACE, enhancing AI-driven game development.
NVIDIA introduces DSX OS, an open and modular software platform designed to operate AI factories at scale, improving efficiency and lowering costs across AI infrastructure layers.
NVIDIA CUDA 13.3 introduces tile programming in C++, compiler autotuning, and Python updates to improve GPU development performance and portability.
DiffusionGemma, developed by Google DeepMind and optimized for NVIDIA platforms, offers a new approach to accelerate token-by-token text generation for real-time AI applications like chat assistants and copilots, improving responsiveness and reducing serving costs.
NVIDIA CompileIQ addresses the challenge of finding optimal compiler options to enhance performance for specific workloads, helping teams further optimize GPU-based LLM inference pipelines beyond traditional tuning methods.