scaling

Shopify engineering describes replacing Redis with MySQL for inventory reservations, using MySQL's SKIP LOCKED feature to achieve high-throughput scaling during peak traffic and eliminate consistency issues between separate systems.

Azure Friday episode with Scott Hanselman and Paula Berenguel demonstrates scaling read-heavy PostgreSQL workloads on Azure Database for PostgreSQL Flexible Server using read replicas and virtual endpoints for failover, following the same pattern that powers ChatGPT.

A detailed technical blog post explaining how to scale PostgreSQL-backed queues using SKIP LOCKED and proper transaction isolation levels, achieving 30K workflow executions per second.

This article discusses the phenomenon of software systems failing due to resource saturation as they scale, drawing an analogy with the limits of biological systems, and introduces the impact of physical resource (CPU, memory, disk, network) saturation and virtual limits on system reliability.

An interview with OpenHands CEO Robert Brennan on scaling AI agents from personal laptops to enterprise infrastructure, covering automation, governance, cost control, and model flexibility.

This paper introduces MemoryDecoder at Scale, scaling parametric long-term memory models to 6.9B parameters pretrained on 300B tokens, showing that independently scaling memory is more parameter-efficient than scaling base models alone.

SSI, the AI company led by Ilya Sutskever, has partnered with NVIDIA to scale a new discovery in machine learning and AI.

MCP has received a major update. The core protocol is now fully stateless, removing sessions and long-lived connections, making it better suited for serverless, edge deployment, and horizontal scaling. It's being called the biggest update since its release.

This paper introduces Explorative Modeling, a new generative modeling paradigm that factors the training loop by exploring candidate matches between model generations and data. It establishes a third pretraining axis beyond parameters and data, improves scaling efficiency across images, video, and language, and enables end-to-end generative modeling with far fewer inference steps.

MoLGE assigns dedicated expert modules to clusters of similar languages in a mixture-of-experts framework for large-scale multilingual ASR, achieving improvements across 495 languages with minimal parameter increase.

Miles Brundage shares a link about industrial scale distillation, likely referring to a research paper or discussion on large-scale model distillation in AI.

Ilya Sutskever's startup SSI announces a strategic partnership with NVIDIA, including a substantial investment that will enable a tenfold increase in compute over the next 12 months, signaling scaling of their AI research.

Intel's experiments offer five practical lessons for enterprise leaders building infrastructure for agentic AI, emphasizing that it is a systems problem beyond inference and providing metrics like agent density and task latency for effective deployment.

Discussion about the data sources labs may use to train 10T parameter models, including synthetic reasoning chains and human-generated traces, amid concerns about hitting the data wall.

Kimi's CEO Yang Zhilin advocates avoiding clever architectures and prioritizing scaling, exemplified by Moonshot's MuonClip fix that enabled stable training on 15.5 trillion tokens.

A research group is exploring a new AGI hypothesis that uses coordinated multiple models instead of a single ever-larger model.

Elon Musk responds to a post about the real constraint on scaling Tesla Robotaxi, emphasizing the need for caution to avoid accidents and harm.

DeepSeek founder Liang Wenfeng reportedly outlined an AGI roadmap focusing on chain-of-thought reasoning, agents, continual learning, AI self-improvement, and embodied intelligence. This roadmap explains the company's current priorities including coding agents and general-purpose agents, while deprioritizing vertical agents, video generation, and commercialization to focus on solving continual learning as the key bottleneck.

This article discusses the challenges of scaling voice agents, noting that failures occur at different layers, and identifies the most common bottleneck that limits performance first.

The U.S. Army consumed its entire year's AI token budget in one month, forcing reimposed usage caps and highlighting that token economics—not just GPU availability—is becoming a critical bottleneck for large-scale enterprise AI deployment.