Google released Gemini CLI on June 25, an agent harness that runs Gemini 2.5 Pro locally in the terminal with built-in Model Context Protocol support, Google Search grounding, GEMINI.md system-prompt files and bundled extensions, plus access to Veo and Imagen for media generation. It is open source under Apache 2.0, and signing in with a personal Google account gives 60 model requests per minute and 1,000 per day at no cost against 2.5 Pro's 1-million-token context window; heavier use routes through Vertex AI or AI Studio keys with usage-based billing.

The harness ships an explicit sandbox story rather than leaving it to the user: native macOS Seatbelt confinement, execution inside Docker or Podman containers, and network traffic routed through proxies for inspection. Google's open-sourcing argument is auditability, and in a week when a rival's IDE extension needed a WebSocket patch, the ability to read the code that mediates tool calls is the substantive part of the release, not the license label.

The Wall Street Journal reported on June 25 that Meta had hired Lucas Beyer, Alexander Kolesnikov and Xiaohua Zhai — the three researchers who set up OpenAI's Zurich office after leaving Google DeepMind — for Zuckerberg's superintelligence effort; an OpenAI spokesperson confirmed the departures. The following day TechCrunch reported that Trapit Bansal, at OpenAI since 2022 and a foundational contributor to the o1 reasoning model's reinforcement-learning work, had also left for Meta's reasoning team. Zuckerberg's offers to individual researchers have been reported at $100M-plus packages.

The context is a company buying capability it has not been able to ship: Meta has no publicly available reasoning model, Llama 4 landed in April to poor reviews, and its access to frontier talent was previously bought at the company level — a $14.3B investment for a 49% stake in Scale AI in June, which brought CEO Alexandr Wang with it. Google, in the same 48 hours, published open weights that run on a phone for free.

DeepMind published AlphaGenome on June 25, a sequence model that takes up to 1 megabase of DNA as input and predicts thousands of functional genomic tracks at single-base-pair resolution — gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription-factor binding, contact maps and splice-site usage. The preprint reports it matching or exceeding the strongest external models on 24 of 26 variant-effect evaluations, and reproducing the mechanism of clinically relevant variants near the TAL1 oncogene. Science and Nature both covered it the same day.

Access is the interesting part for builders. The model is free through an AlphaGenome API for non-commercial research, with client code public on GitHub, but source code and weights are promised only once a peer-reviewed version lands, and a commercial offering is in early testing. DeepMind also says the API suits analyses of thousands of predictions, not millions. It is the inverse of the week's open-weight releases: capability published, artefacts gated, commercial path provisional.

The CVE record for GHSA-9f65-56v6-gxw7 was published on June 24: Claude Code's VSCode extension, versions 0.2.116 through 1.0.23, and the JetBrains beta plugin, 0.1.1 through 0.1.8, lacked origin validation on the WebSocket their local server opens, rated CVSS 8.8 by GitHub's CNA. Because the vulnerable extensions are also used in VSCode forks, Cursor, Windsurf and VSCodium users were in scope. A page the developer merely visits can script a connection to that local server.

What an attacker gets is the agent's own context: arbitrary file reads, the list of open files, selection and diagnostics events, and limited code execution where a Jupyter notebook is open and a malicious prompt is accepted. Anthropic patched on June 13 — 1.0.24 for VSCode-family extensions, 0.1.9 for JetBrains — and auto-update covers most installs, but the disclosure only became public the following week.