The Qwen team released Qwen-RobotSuite, a suite of three independent embodied AI foundation models for robotics. Qwen-RobotManip is a Vision-Language-Action model based on Qwen3.5-4B that aligns heterogeneous manipulation data into a unified 80-dimensional action vector, achieving 1st place on RoboChallenge Table30-v1 and strong cross-embodiment transfer. Qwen-RobotWorld is a language-conditioned video world model using a 60-layer dual-stream MMDiT and a frozen Qwen2.5-VL encoder, ranking 1st overall on EWMBench and DreamGen Bench. Qwen-RobotNav is a scalable navigation model built on Qwen3-VL with a parameterized observation interface, reaching 76.5% success rate on VLN-CE RxR and enabling agentic planning. RobotManip and RobotNav have public GitHub repositories; RobotWorld is presented as a research paper.
Nous Research’s open-source Hermes Agent now ships a non-blocking async_delegation toolset, tracked in GitHub issue #5586. The existing delegate_task, which froze the parent chat until all child subagents finished, is supplemented with asynchronous tools: delegate_task_async returns a task_id immediately, while check_task, steer_task, collect_task, cancel_task, and list_task manage the background run. Subagents remain strictly isolated—each gets a fresh conversation and only a final summary returns to the parent, preserving context windows. Background agents execute as in‑process threads using the same AIAgent machinery, model routing, and credential pool; users enable the update with `hermes update`.
The team behind the open-source MetaGPT framework has launched Atoms, a vibe coding platform that uses a team of AI agents to handle the entire product lifecycle. Agents include Iris (deep researcher), Emma (product manager), Bob (architect), Alex (engineer), Sarah (SEO specialist), Adrian (ads specialist), David (data analyst), and Mike (team leader), coordinating research, scoping, full-stack development, deployment, SEO, and Google Ads management. Every app comes with Atoms Cloud providing built-in authentication, real-time database, Stripe payments, scalable hosting, and one-click deployment. A Race Mode runs prompts across multiple frontier models to improve accuracy up to 3×, and users retain full code ownership with export to GitHub. Atoms offers a free tier with 15 credits per day and Pro plans starting at $20/month, differentiating itself from Lovable and Base44 by bundling market research and distribution tools into the build workflow.
Google Cloud announced Open Knowledge Format (OKF) v0.1, an open, vendor-neutral specification that formalizes the LLM-wiki pattern as a portable directory of markdown files with YAML frontmatter. OKF is not a service or platform—it requires no SDK, runtime, or registry—and a bundle renders on GitHub, ships as a tarball, or mounts on any filesystem. Each concept is one markdown file identified by its path, with only one required field (type) in the frontmatter; cross-links between files form a knowledge graph that agents can navigate without translation. Google released reference tools including a BigQuery enrichment agent, a static HTML visualizer, and sample bundles. The format targets the scattered internal knowledge problem, letting agents consume curated, version-controlled context directly, unlike retrieval-augmented generation (RAG).
This tutorial demonstrates a full parsing pipeline using Docling Parse to extract text cells (words, characters, lines) with page-level coordinates from a multi-element test PDF. It covers environment setup, generation of a PDF with columns, tables, vector shapes, and an embedded image, and extraction of structured JSON/CSV outputs. The workflow includes reconstruction of layout-aware reading order from word coordinates, rendering of cell overlays for inspection, and benchmarking of threaded parallel parsing. The resulting pipeline is suitable for document AI tasks such as layout analysis, table extraction, and preparation for retrieval-augmented generation (RAG).
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