control

A developer shares their experience moving from an agent platform to a self-managed stack after six months, citing better control over model selection, cost, and execution isolation, leading to a 60% drop in token costs.

OLO Robotics enables users to control robots directly from a browser with no setup required.

Treadmill Pro is an app that lets you control your treadmill wirelessly from your iPhone.

Mouseless is a cross-platform tool that enables keyboard-driven control of macOS, Linux, and Windows, allowing users to operate their computer without a mouse.

ToolGate is a lightweight external controller that predicts whether to execute or skip perceptual tool calls in vision-language agents, reducing token cost to 64–69% of baseline while preserving accuracy in cross-domain settings.

LongCat released WBench, a benchmark for video world models that tests control, memory, instruction-following, and physical plausibility across 289 cases and 20 models, finding that no model excels in all dimensions, highlighting the gap between video quality and true world simulation.

This paper introduces Parameterized Diffusion Policy (PDP), a framework that makes diffusion policies controllable by conditioning on low-dimensional latent parameters, enabling smooth behavior interpolation and adaptation without retraining. It demonstrates improved performance on complex multimodal robot tasks in simulation and real-world experiments.

This paper introduces a neuro-inspired framework called Inverter that uses Inverse Learning (IL) for fast and efficient planning and control, achieving significant improvements on D4RL benchmarks and quantum gate synthesis with orders of magnitude less inference computation.

A software developer questions the practical value of AI agents, expressing concerns about control, accountability, and whether manual automation combined with LLMs is more reliable than delegating to autonomous agents.

METR published its first Frontier Risk Report, assessing the risk of AI companies losing control of their own agents. The report involved testing the best internal models from Anthropic, Google, Meta, and OpenAI with chain-of-thought access and reviewing non-public information about capabilities and alignment.

A fluid dynamics PhD student used OpenAI's Codex 5.5 model to achieve fluid dynamics control purely through code generation, without training any neural network. It surpassed reinforcement learning baselines in multiple tests, with low cost and interpretable results.

Steven Brunton announces his new book 'Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control', with pre-order available and accompanying free PDF, YouTube videos, and Python code.