World Wire

Agent-net , the team building an agent-to-agent marketplace where AI agents discover, trust, and pay each other, has released Webagent , an open source harness for standing up public-facing business agents. So, basically you give it your website, get an agent, and let it talk to other agents. Instead of writing orchestration code, a business fills in a declarative JSON spec, picks 1 provider for each of 9 pluggable slots, and runs webagent serve . Is it deployable? Yes, with caveats. The repo ships under Apache 2.0, builds green, and runs live Slack, WhatsApp, and HTTP agents backed by MCP tools today. It is still labeled v0, and the browser action provider, OAuth-gated MCP, OTel export, and the AgentNet identity and billing layer are listed as not yet built. Slots, providers, picks Webagent is written in Go. An agent is 1 Brain (an LLM plus instruction) over a set of slots defined in core/ . Each slot is a Service Provider Interface with a registry of providers in spi/ ...

Reward AI , a robotics startup whose team’s prior work includes DexCap, HumanPlus, and ALOHA, has released OM-1 , short for Omnibody Model 1. OM-1 is a general-purpose manipulation policy that learns from humans wearing a sensorized glove, then runs on industrial arms and humanoids at human speed. The key findings that stands out: no teleoperation data and no on-robot data go into training. The system follows one principle, ‘One Model, One Data Interface, Any Body,’. Is it deployable? No, OM-1 is Reward AI’s in-house policy. No weights, code, dataset, or API have been released, so developers cannot run it on their own hardware yet. Why Skip Robot Data? Most robot foundation policies train on teleoperated or self-collected robot data, which binds the dataset to one embodiment. Reward AI argues that human-level manipulation will not come from more of this data or more compute, citing Anderson’s “More Is Different.” Instead, capture...

Backpropagation is a global algorithm: a forward pass, then a backward pass, then a weight update, each locked behind the previous one. Brains have no known mechanism for that kind of network-wide phase locking, which is why local-learning alternatives such as predictive coding (PC) keep drawing research interest. Sakana AI researchers propose Augmented Lagrangian Predictive Coding (PC-ALM) , a variant of PC that keeps every update layer-local yet recovers backprop-aligned credit signals. The research team reports training residual MLPs up to 1000 layers within about 2 percentage points of backprop on MNIST. Is it deployable? Yes, as research code: an MIT-licensed JAX reference implementation runs on CPU and reproduces the paper’s width-depth grid. It is a training method, not a model, and has only been tested on small image benchmarks. Why standard PC stalls in deep, narrow networks PC treats every hidden activation as an optimization variable and penalizes the sq...

Robot developers do not have one compute problem. They have 3. A policy is trained on GB200 or H100 clusters, tested in Isaac Sim on RTX GPUs, then validated on a Jetson mounted inside a real robot. Each tier has its own cluster, its own scheduler, and its own glue scripts. NVIDIA OSMO is NVIDIA’s answer to that fragmentation: an open-source, Kubernetes-native workflow orchestrator that lets a team describe the whole pipeline in a single YAML file and run it across every tier without touching infrastructure code. Deployable? Yes. OSMO is Apache-2.0 licensed , ships Helm charts and containers on NGC , and has a local quickstart that runs the full control plane on a workstation with KIND. The Three Computer Problem NVIDIA frames physical AI as a three computer problem . Training happens on data-center GPUs. Simulation, physics, and sensor rendering happen on workstation-class RTX hardware. Deployment and hardware-in-the-loop (HIL) testing happen on edge devices such ...