Auto Added by WPeMatico
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 […]
On September 12, 2026, Anthropic CEO Dario Amodei published a writeup ‘We Must Pace the Frontier’. Its core message is blunt: ‘We must slow the pace at which we improve the capabilities of AI models.’ Within hours, OpenAI’s Sam Altman and xAI’s Elon Musk endorsed it. The next day, Microsoft CEO Satya Nadella welcomed ‘deliberate
In this tutorial, we build an end-to-end hierarchical Neural Radiance Field (NeRF) using JAX, Flax, Optax, and the volume-rendering primitives provided by jax3d. We first construct a synthetic multi-view dataset from an analytic scene containing volumetric geometry and view-dependent radiance, using sample_along_rays and volume_rendering to establish the forward rendering process. We then implement a NeRF
In most decoder-only LLMs, nothing computed at the last layer of token t feeds the first layer of token t+1; positions communicate only through attention over cached keys and values. A Princeton researcher’s (Yifan Zhang) technical report, Recurrent Looped Transformer (RLT), proposes closing that loop. The decoder’s final hidden state and its layerwise sliding-window attention
AWS introduced Pizza Bot, as a self-hosted application for AI tasks that continue while users work elsewhere. It organizes completed results and pending decisions into an email-style inbox. Earlier versions served more than 2,000 people inside Amazon, supporting meeting preparation, email drafting, Slack summaries, CRM logging, and research. The public application was rebuilt as an
AWS Introduces Pizza Bot: An Open Source Inbox for Background AI Agents Read More »
An agent, in its simplest form, is an LLM calling tools in a loop. That loop works for short jobs. Give it a task that runs for an hour and 200 tool calls, and it breaks in 2 predictable ways. The AWS Samples design guide for autonomous cloud coding agents names them directly: shallow agents
In this tutorial, we implement NVIDIA cuML as a GPU-accelerated machine learning framework and build a practical workflow that demonstrates how RAPIDS can accelerate familiar data science and machine learning tasks. We begin by configuring the GPU environment and examining cuml.accel, which lets us accelerate existing scikit-learn workloads with minimal code changes, before moving to
Cognition, the company behind the Devin coding agent, has released SWE-2, its most capable coding model to date. SWE-2 is post-trained with reinforcement learning from Kimi K3, Moonshot AI’s 2.8T-parameter open model. Cognition reports a score of 50.0% on FrontierCode 1.1 Main, within 1 point of Fable 5.1 at 64% lower cost. It is also
The Fly Language Model (FLM) is a public chatbot that couples the complete retained MaleCNS v1.0 fruit fly connectome to a frozen LiquidAI LFM2.5-1.2B-Instruct backbone. The developer who created the FLM calls it the world’s first Fly Language Model, built on an architecture called GPF (Generative Pre-trained Fly). It does not use the GPF label,
An agent harness is the code around a model: execution loop, tools, context, state, recovery, and verification. Per the Terminal-Bench 2.1 leaderboard, GPT-5 solves 35.2% of tasks inside Terminus 2 but 49.6% inside Codex CLI with identical weights. Most benchmarks keep that harness fixed. HarnessDev proposed by team of researchers from ByteDance Seed, Singapore University
