By: Kris Range, Ankush Gulati, Jim Isaacs, Jennifer Shin, Jeremy Kelly, Jason Tu This is part 3 in a series called “Behind the Streams”. Check out part 1 and part 2 to learn more. Picture this: It’s seconds before the biggest fight night in Netflix history. Sixty-five million fans are waiting, devices in hand, hearts pounding. The …
Read more “Behind the Streams: Real-Time Recommendations for Live Events Part 3”
Many of today’s multimodal workloads require a powerful mix of GPU-based accelerators, large GPU memory, and professional graphics to achieve the performance and throughput that they need. Today, we announced the general availability of the G4 VM, powered by NVIDIA’s RTX PRO 6000 Blackwell Server Edition GPUs. The addition of the G4 expands our comprehensive …
Read more “The G4 VM is GA: Expanding our NVIDIA GPU portfolio for visual computing and AI”
Deploying LLM workloads can be complex and costly, often involving a lengthy, multi-step process. To solve this, Google Kubernetes Engine (GKE) offers Inference Quickstart. With Inference Quickstart, you can replace months of manual trial-and-error with out-of-the-box manifests and data-driven insights. Inference Quickstart integrates with the Gemini CLI through native Model Context Protocol (MCP) support to …
Read more “Use Gemini CLI to deploy cost-effective LLM workloads on GKE”
Understanding the nuances of speech emotion dataset curation and labeling is essential for assessing speech emotion recognition (SER) model potential in real-world applications. Most training and evaluation datasets contain acted or pseudo-acted speech (e.g., podcast speech) in which emotion expressions may be exaggerated or otherwise intentionally modified. Furthermore, datasets labeled based on crowd perception often …
Read more “Switchboard-Affect: Emotion Perception Labels from Conversational Speech”
Paul Graham’s “Founder Mode” paradigm misses the power of choosing missions that matter. Editor’s Note: In this blog post, Noam Perski reflects on his 15 years at Palantir and why “Mission Mode” — organizing companies around customer missions rather than founder involvement — is key to tackling problems that truly matter. Silicon Valley agrees that conventional management wisdom is destructive for …
How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data Streams at Internet Scale Authors: Adrian Taruc and James Dalton This is the first entry of a multi-part blog series describing how we built a Real-Time Distributed Graph (RDG). In Part 1, we will discuss the motivation for creating the RDG and the …
This post is co-written with Ross Ashworth at TP ICAP. The ability to quickly extract insights from customer relationship management systems (CRMs) and vast amounts of meeting notes can mean the difference between seizing opportunities and missing them entirely. TP ICAP faced this challenge, having thousands of vendor meeting records stored in their CRM. Using …
Read more “How TP ICAP transformed CRM data into real-time insights with Amazon Bedrock”
The retail media landscape has reached an inflection point. What started as a way for retailers to monetize their digital real estate has become the fastest-growing segment of digital advertising, with projections showing 21.9% growth in 2025 and a three-year compound annual growth rate of 19.7% through 2027, according to Dentsu’s Global Ad Spend Forecasts …
Read more “How Moloco is powering the future of retail media with AI Vector Search”
We’re partnering with Commonwealth Fusion Systems (CFS) to bring clean, safe, limitless fusion energy closer to reality.
We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a codebase with an executable runtime environment, unit tests, and a task specified in natural language. We use SWE-Gym to train language model based SWE agents, achieving up to 19% absolute gains in …
Read more “Training Software Engineering Agents and Verifiers with SWE-Gym”