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Using Blender with coding agents on macOS
Simon Willison demonstrates driving the Blender macOS app with ChatGPT Codex coding agents to render scenes via Python, costing roughly $4.24 at API rates.
Willison documents installing the full Blender application from blender.org and using ChatGPT Codex on macOS to render a pelican-riding-a-bicycle scene through Blender's Python API. He refined the output with follow-up prompts. AgentsView estimated the equivalent API cost at $4.24 for gpt-6-astra.
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AWS Introduces Pizza Bot: An Open Source Inbox for Background AI Agents
AWS open-sourced Pizza Bot, a self-hosted inbox app for background AI agents with approval gating and multi-provider model support.
AWS released Pizza Bot under Apache 2.0 after earlier versions served over 2,000 Amazon employees for meeting prep, email drafting, and research. The app provides macOS, Windows, and Linux desktop builds plus browser and terminal clients talking to a Hono API server, with LangGraph/DeepAgents checkpoints preserving thread state and approval pauses. It supports Amazon Bedrock, Anthropic, Google Gemini, OpenAI, OpenRouter, and Ollama, exposes external tools via MCP servers, and lets skill authors gate actions behind approve/edit/reject flows.
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27.5KB language-agnostic WebGPU syntax highlighter
A developer released gpu-lexer, a 27.5KB language-agnostic syntax highlighter that uses a tiny WebGPU model to label code tokens in the browser.
gpu-lexer splits source into words, whitespace, and symbols, then a small WebGPU model uses local and whole-file context to assign nine token classes, working on languages never seen in training. On held-out files, 12.57% of token labels differ from Shiki, though this measures agreement with Shiki rather than objective correctness. In benchmarks against Shiki 4.4.3, Prism.js, Highlight.js, Sugar High, and Starry Night, it highlighted 10 concatenated copies of three.min.js (5.56M characters) about 10x faster on an Apple M4 Pro in Chrome 152. The author frames it as an experiment, not a grammar-equivalent highlighter.
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Nous Research Adds One-Click Local Model Setup to Hermes Desktop
Nous Research's Hermes Desktop now offers one-click local model setup that reads hardware, picks a fitting quantization, downloads weights, and configures llama.cpp automatically.
Hermes Desktop, the MIT-licensed build of the open-source Hermes Agent, now sets up local models in one click: it reads the machine's hardware, selects a model that fits, downloads weights, and configures the inference runtime. It manages a pinned llama.cpp build with CUDA, Metal, Vulkan, HIP, and CPU backends, and shows green/amber/red memory-fit verdicts per catalog model before download. Quantization floors at 4-bit, and recommended models guarantee at least a 64K context window protected by ordered RAM offload (expert weights first, never the attention cache). It runs on macOS 12+, Windows 10/11, and Linux with no account required for local models.
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numbat - AI agent observability, (Fri, Sep 4th)
SANS reviews Perplexity's open source numbat, a Go-based tool giving security teams observability, detection rules, and enforcement for AI agents like Claude and Gemini.
Numbat, Perplexity AI's open source observability tool, monitors desktop, CLI, IDE, and gateway AI agents through local hooks, OTLP/HTTP logs, and on-disk session artifacts. It ships detection rules mapped to MITRE ATT&CK (e.g., recon.network_sweep / T1046), supports enforcement mode, and packages investigations with SHA256-verified manifests and timelines. The SANS review positions it as a response to unmanaged AI agent and MCP server sprawl highlighted by the OpenAI/Hugging Face incident.
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