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”
Organizations often face challenges when implementing single-shot fine-tuning approaches for their generative AI models. The single-shot fine-tuning method involves selecting training data, configuring hyperparameters, and hoping the results meet expectations without the ability to make incremental adjustments. Single-shot fine-tuning frequently leads to suboptimal results and requires starting the entire process from scratch when improvements are …
Read more “Iterative fine-tuning on Amazon Bedrock for strategic model improvement”
As generative AI applications grow in sophistication, development workflows become more fragmented. Although AI can be a force multiplier, teams may design prompts in one environment, manage versions in spreadsheets or text files, and then manually integrate them into their code. This leads to inefficiencies, versioning chaos, and collaboration bottlenecks. Vertex AI Studio is designed …
Read more “Announcing prompt management in the Vertex AI SDK”
We’re rolling out significant updates to Veo that give people even more creative control.
We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent …
Read more “Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration”