scalability

A new moving-horizon approximate branch-and-reduce method for training near-optimal deep classification trees on large-scale datasets with continuous features, achieving better accuracy than heuristic baselines and far greater scalability than global optimal solvers.

Using the Devin AI agent to automate the creation of pixel-perfect product tutorials at scale, achieving results superior to human-made tutorials with improved consistency and parallel processing capabilities.

Boris Cherny, creator of Claude Code, emphasizes that AI tools work better with freedom and tools rather than rigid workflows, as general learning systems scale more effectively. This insight is discussed in the context of agent-led development.

Microsoft Research introduces Agensh, a self-organized multi-agent harness that scales to over 1,000 agents, demonstrating improved test-pass rates on coding tasks without a central orchestrator.

The article explains that PostgreSQL's SELECT DISTINCT clause does not scale efficiently, as it always scans all matching rows, leading to performance problems in certain workloads, and provides insights and workarounds.

Marin has built scalable infrastructure to process 25T tokens from open datasets on Hugging Face for training their 535B language model, emphasizing the power of the open community.

The blog post discusses a caching solution for routing engine estimates in a gig economy delivery app using Redis and H3 hexagonal coordinates, and highlights challenges with Redis cluster key distribution and multi-key commands.

This paper introduces methods to bound four key memory peaks in large Mixture-of-Experts training, enabling training at 1M context length with fixed GPU memory and up to 10.4× throughput improvement over baselines.

Toollery is a training-free candidate-compression framework that improves scalability and efficiency for LLM agent tool and skill selection through retrieval-based methods.

Agensh is a scalable self-organized multi-agent system without a central orchestrator that improves performance on complex tasks by scaling the number of agents, showing significant test-pass rate increases on benchmarks like ProgramBench and pandoc.

The author shares a novel use case for Jev in building custom verifiers for AI agent harnesses, enabling scalable test-time compute by combining System One and System Two models.

This paper introduces Compressed Active Subspaces (CAS), a method to enable scalable Bayesian inference in large neural networks by compressing parameter spaces and maintaining predictive performance.

EdgeReMIND is a scalable memorization baseline for temporal multi-relational link prediction that achieves top performance on the TGB 2.0 benchmark, particularly on large datasets where embedding methods fail due to computational limits.

Agent Substrate, an open-source agent execution runtime, is now available on Google Kubernetes Engine, providing high-density sandboxing with sub-second activation and security features for scaling AI agents.

The article discusses the operational challenges companies encounter when scaling from a few AI agents to dozens, proposing a solution that separates business logic from technical control to manage agentic workflows effectively.

The article questions the common practice of limiting AI agent runs for human verification and explores structural alternatives when task volumes exceed human oversight capacity.

The paper introduces a branch-and-bound framework for scalable verification of nonlinear neural feedback systems, improving state-of-the-art methods by combining combinatorial and propagative solvers through tools like rail and clipper.

The article discusses unconventional development choices in the open-source voxel RPG Veloren, highlighting the use of Entity Component System (ECS) for scalability and the unified player/NPC duality, offering insights for game developers.

The article argues that enterprise AI pilots often fail by creating fragmented tools, and emphasizes the need for a unified agentic operating system to build scalable AI capability.

This paper proposes a hybrid agentic AI framework for supply chain analytics that uses a coordinator agent and specialized agents to improve decision-making, achieving 90% accuracy and reducing token usage by fourfold.