World Wire

Nous Research has shipped a Profile Builder for Hermes Agent. It lives inside the project’s local web dashboard. Standing up a distinct agent used to mean several CLI steps. The builder now walks you through one guided flow. In that flow you define an agent’s identity. You pick a model and provider. You choose built-in and optional skills. You install skills from the hub. You attach MCP servers. Hermes Agent is Nous Research’s open-source, self-improving agent. It runs on the CLI, a desktop app, and messaging platforms. Profiles were previously assembled mostly through terminal commands. The Profile Builder brings those pieces into a browser form. Profile Builder A profile in Hermes is a separate home directory. Each profile holds its own config.yaml , .env , and SOUL.md . It also keeps separate memory, sessions, skills, cron jobs, and a state database. Profiles let you run isolated agents on one machine. A coding agent and a research agent never s...

This week, Cohere AI team shipped its first developer-facing coding model named ‘ North Mini Code ‘. ‘North Mini Code’ is open-weight and focused at software engineers. It is a mixture-of-experts (MoE) model with 30B total parameters. Only 3B of those parameters activate per token. The release is positioned around “sovereign” AI. The idea is simple: run capable models on your own terms. Small, efficient coding models let teams self-host without large GPU clusters. North Mini Code targets that gap directly. North Mini Code North Mini Code is a 30B-A3B parameter model. The A3B stands for three billion active parameters per forward pass. Cohere optimized it for three jobs: code generation, agentic software engineering, and terminal tasks . The model is text-in, text-out. There is no image or video input. The context window is 256K tokens. Maximum output length is 64K tokens. Cohere lists a minimum hardware bar of one H100 at FP8. Weights...

Google AI team including the Google DeepMind researchers have just released DiffusionGemma, an experimental open model for text generation. It uses text diffusion instead of standard autoregressive decoding. The model ships under a permissive Apache 2.0 license. Google positions it for devs and researchers exploring speed-critical, interactive local workflows. Examples include in-line editing, rapid iteration, and generating non-linear text structures. Most language models in use today are autoregressive. They generate one token at a time, left to right. Each new token depends on the token before it. DiffusionGemma works differently. It generates entire blocks of text simultaneously, in parallel. On dedicated GPUs, this delivers up to 4x faster generation. What is DiffusionGemma DiffusionGemma is a 26B Mixture of Experts (MoE) model. It activates only 3.8B parameters during inference. It is built on the Gemma 4 backbone, specifically the 26B-A4B architecture. Google integr...