World Wire

Apple research team recently released the container project . It is an open-source command-line tool written in Swift. It creates and runs Linux containers as lightweight virtual machines on a Mac. The project ships under the Apache 2.0 license and targets Apple silicon. Containers are how you ship reproducible environments from a laptop to a datacenter. Apple now offers a native path that avoids a single always-on Linux VM. What is Apple’s container ? container is a CLI tool that can be used to build images, run containers, and move images to and from registries. It consumes and produces OCI-compatible container images. So you can pull from Docker Hub or GitHub Container Registry and run those images. You can also push images you build to any standard registry. container uses the open-source Containerization Swift package. That package handles low-level container, image, and process management. The tool requires a Mac with Apple silicon. Intel Macs are not su...

In this tutorial , we build a lightweight personal AI agent inspired by the core architecture of nanobot , while keeping every part understandable and runnable in Google Colab. We start from the provider abstraction, then move through tool registration, session memory, lifecycle hooks, skills, and an MCP-style tool server. As we progress, we do not just use an external agent framework; we recreate the core building blocks ourselves so we can clearly see how messages, tools, memory, and model responses work together within a practical agent loop. Building the Provider Abstraction and Mock LLM Copy Code Copied Use a different Browser import subprocess, sys def _pip_install(*pkgs): try: subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=True) except Exception as e: print(f"(pip install skipped/failed for {pkgs}: {e})") _HAVE_OPENAI = False try: import openai _HAVE_OPENAI = True e...

DeepReinforce has released Ornith-1.0 , an open-source model family built for agentic coding. The lineup spans four sizes, from a 9B dense model to a 397B mixture-of-experts flagship. Every checkpoint ships under the MIT license on Hugging Face. The models are post-trained on top of pretrained Gemma 4 and Qwen 3.5. Most coding agents pair a model with a fixed, human-designed harness. Ornith-1.0 instead learns to write its own. The DeepReinforce research team reports state-of-the-art results among open models of comparable size. TL;DR Ornith-1.0 ships in 9B, 31B, 35B-MoE, and 397B-MoE sizes under MIT, built on Gemma 4 and Qwen 3.5. The model learns its own scaffold during RL, jointly optimizing the harness and the solution. Ornith-1.0-397B tops Claude Opus 4.7 on both headline benchmarks, but not Opus 4.8 or the larger GLM-5.2-744B. Three layers — fixed trust boundary, deterministic monitor, frozen LLM judge — guard against reward hacking. What is Ornith-1...