TERSE

Semantic state for AI systems

devtoolsintermediateFree tier

Overview

TERSE is a new state-management approach for AI systems that lets apps declare semantic state and query it in a single parallel call. It is positioned as a simpler alternative to combining RAG, graph databases, and extra LLM calls, which makes it appealing for agent and application developers building stateful AI workflows.

Best For

+ml-engineers
+backend-developers
+agent-builders

News & Updates

Hyperverse Explores AI Robot Control in New Technical Post

Hyperverse published a technical post titled 'What if AI could control robots?' on hyperverse.sh, exploring the intersection of artificial intelligence and robotics. The post does not disclose specific models, hardware, or benchmarks, leaving the scope of the proposal unclear. The core question—whether AI can directly control physical robots—touches on embodied AI, a field where companies like Google DeepMind and Figure are already testing large models on robotic platforms. Without concrete demos or code, the post reads as a conceptual prompt rather than a product announcement.

robots10/19/2026

ASEAN-Korea Forum in Manila Tackles Generative AI and Cultural IP Rights

The ASEAN-Korea Forum is convening in Manila to address how generative AI intersects with cultural intellectual property. The forum brings together policymakers, creators, and tech experts from Southeast Asia and South Korea to debate whether AI-generated content can preserve human creativity. Key topics include copyright frameworks for AI training data and the economic impact on cultural industries. The outcome could shape regional policies on AI-generated art, music, and text.

other10/16/2026

UC Berkeley Uses Diffusion Models to Generate Humanoid Robot Neural Weights

Researchers at UC Berkeley — Huixuan Cheng, Bike Zhang, and Shankar Sastry — published a paper showing that diffusion models can directly generate the neural network weights controlling humanoid robot policies, rather than relying on conventional reinforcement learning training. Instead of training a humanoid control network from scratch through millions of simulation steps, their approach learns the distribution of effective weight configurations and samples new ones via denoising. This could dramatically cut the compute cost of producing agile humanoid behaviors, which normally requires extensive reward engineering and sim-to-real transfer tuning. The work sits at the intersection of generative AI and robotics, treating neural weights themselves as a learnable data distribution.

breakthroughs10/15/2026