World Wire

Meta has released Muse for Mac , the first version of Muse that can complete things on a user’s computer. The agent works with local files and native apps, where your data already lives. It adds a desktop layer to an agent that launched on phones, the web and WhatsApp earlier this month. Is it deployable? Yes, for end users. It is a free macOS download today, only available in the US for now. It is a hosted consumer agent, not an open model you can self-host. What Meta Shipped Mark Zuckerberg announced the Mac app on X, noting that it works across apps, files, calendar, notes, and messages. His post added, “The team is shipping fast.” Meta’s Chief AI Officer Alexandr Wang also announced the launch the same day. Muse itself is not new this week. Muse launched on September 8th in the US across iOS, Android, the web and WhatsApp. According to TechCrunch , it quickly rose to the top of the U.S. App Store charts after that launch. Muse for Mac...

PrismML has released Ternary Bonsai 2 27B , a ternary-weight version of Qwen3.8 27B . The language model occupies 5.93 GB, against 53.80 GB in FP16. PrismML reports that it keeps 98.2% of the parent model’s average across 20 benchmarks. The model accepts text and images and supports a 262K-token context. PrismML demos it driving Cline coding agents and computer use on an RTX 5090. It arrives 2 months after the first Bonsai 27B , whose ternary variant retained about 95%. Is it deployable? Yes. The Apache 2.0 weights run today on a 16 GB laptop or a single 24 GB GPU. You need PrismML’s llama.cpp fork or its MLX runtime. What is Ternary Bonsai 2 27B? The model keeps the Qwen3.8 27B architecture unchanged. It has 27.36B parameters. That splits into a 24.35B language backbone, 2.54B in embeddings and LM head, and a 0.47B vision tower. The backbone uses hybrid attention, with about 75% linear-attention and 25% full-attention layers. Ternary weights cover embedd...

An agent is a model and a harness. The harness runs tools, holds state, manages permissions, and feeds context back to the model. With a local model, the harness matters more. Small context windows and weaker tool calling expose every design flaw. This guide ranks 11 open-source harnesses by how well they document local inference. All repo facts were read from GitHub on September 18, 2026. The ranking weighs 4 things: OSI-approved license, documented local runtimes, maintenance status, and safety controls. The 3 rules that apply to every harness 1. Raise the context window first : Per Ollama’s context length docs , defaults depend on VRAM: 4k under 24 GiB, 32k from 24 to 48 GiB, and 256k at 48 GiB or more. The same page says agents and coding tools should get at least 64,000 tokens. The fix is one line: OLLAMA_CONTEXT_LENGTH=64000 ollama serve . 2. Pick a model that supports tool calling : Goose’s provider docs state that models without tool calling can onl...