scaling

OpenHandsDev launched the Agent Control Plane, a system for controlling, observing, and scaling hundreds of AI agents across an organization.

A newcomer's observation that AI discussion is polarized between doom and hype, questioning whether enough effort is going into user experience and smaller-model system design versus pure scaling.

Two ICLR 2026 papers show how small RL-trained agents outperform frontier models on machine-learning engineering tasks and how MLE-Smith automatically scales MLE workloads.

YouTube talk by @sedielem offering a concise state-of-the-art overview of scaling generative image and video models, covering modeling, architecture, distillation and control.

McKinsey report finds that while two-thirds of global enterprises are experimenting with agentic AI, fewer than 10% achieve scalable impact, blaming weak data foundations.

OpenAI announces $110B in new investment at a $730B pre-money valuation, including major funding from SoftBank, NVIDIA, and Amazon, along with strategic partnerships to expand compute capacity and global reach for AI products. The funding aims to accelerate deployment of frontier AI across consumers, developers, and enterprises worldwide.

OpenAI shares technical insights on scaling PostgreSQL to support 800 million ChatGPT users and millions of queries per second, using a single-primary architecture with 50 read replicas while managing challenges from write-heavy workloads through sharding and optimization strategies.

OpenAI outlines its business strategy centered on scaling monetization with the value delivered by AI intelligence, detailing how ChatGPT evolved from a research preview to essential infrastructure across consumer, enterprise, and developer markets. The company reports significant growth metrics with compute scaled 9.5X and revenue reaching $20B+ ARR from 2023-2025.

OpenAI introduces GPT-4.5, their largest and best chat model yet, available as a research preview to Pro users and developers. The model advances unsupervised learning through scaling compute and data, showing improved factuality, reduced hallucinations, and better understanding of human intent compared to GPT-4o.

OpenAI shares detailed lessons learned from scaling a single Kubernetes cluster to 7,500 nodes to support large machine learning workloads, covering networking, scheduling, and infrastructure challenges. The post builds on their earlier experience scaling to 2,500 nodes and aims to help the broader Kubernetes community.

OpenAI researchers discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training across a wide range of tasks. They found that more complex tasks and more powerful models tolerate larger batch sizes, suggesting future AI systems can scale further through increased parallelization.

OpenAI shares infrastructure lessons from scaling Kubernetes to 2,500 nodes, detailing optimizations for container image pulls including kubelet configuration changes, Docker overlay2 migration, and preloading strategies to resolve Pending pod issues.