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Much of the conversation around agentic AI focuses on autonomy. How much work can an agent complete on its own? How many decisions can it make? How far can organizations remove people from the process? Those questions make sense, but they can also obscure where businesses are finding value today. […] The post The new […]
The new division of labor between people and AI agents Read More »
google/sam is not Segment Anything. SAM here means Sovereign Agent Mesh, an Apache-2.0 networking project for autonomous AI agents. The problem it targets is concrete. Agents now run across cloud servers, on-prem datacenters, laptops, Raspberry Pis and Android devices. Letting them share tools usually means exposing internal scripts, LLM endpoints or private APIs to the
Meet SAM (Sovereign Agent Mesh): A Zero-Config, Zero-Trust P2P Network for AI Agents Read More »
Ever wondered how ChatGPT, Gemini, and other chat interfaces generate PDFs, PowerPoints, and more when all they have under the hood is an LLM? The trick isn’t a smarter model. It’s something simpler: skills which are instructions an agent loads only when needed. Next, let’s explore how skills work using LangChain and how they can make
Nous Research has shipped Bot Mode for Hermes Agent, its MIT-licensed open source agent. Bot Mode replaces the single-agent session list with a roster of named bots. Each bot is a real Hermes profile, with its own chat, memory, skills, and pinned model. Bots message each other through a persistent Agent Inbox and hand work
ByteDance Seed and Tsinghua AIR have released CUDA Agent, an agentic reinforcement learning system that trains a large language model to write GPU kernels that beat a compiler. The gap it targets is narrow but stubborn: frontier models already produce correct CUDA, they just produce slow CUDA. On KernelBench, the base model Seed1.6 passes 74.0%
DeepSeek released DeepSeek Harness v0.1 in developer preview and published the full source code under the MIT license. The project ships as dsh at deepseek-ai/deepseek-harness. A harness is the layer between a model and the environment it acts in — the tools, files, sandboxes, and control loop that let an agent keep working. DeepSeek frames
Long-running AI agents often spend most of their time on routine execution rather than difficult reasoning. After making a plan, they may perform hundreds of tool calls, file reads, validations, commands, and formatting steps, so using a frontier reasoning model for every action can become unnecessarily slow and expensive. NVIDIA’s Nemotron 3.5 Lightning takes a
NVIDIA Nemotron 3.5 Lightning: The AI Agent Workhorse Read More »
Organizations rarely struggle to build AI models. They struggle to scale them. Across industries, organizations continue to invest heavily in data and AI, yet many initiatives never move beyond pilot projects. The challenge isn’t creating better models. It’s maintaining the infrastructure that surrounds them. Data pipelines, governance, business rules and […] The post Why enterprise
How we built a Digital Twin of an Organization, powered by Claude, from prototype to production. The Challenge Our Client Came to Us With Organizations invest millions in transformation initiatives like new systems, restructured teams, AI adoption, without a complete picture of how they actually operate today. Process knowledge lives in documents that go stale,
Why it matters: See how a semantic knowledge graph for LLM agents slashes hallucinations, adds memory, and lifted answer accuracy from 17 to 54 percent in tests.
