Antigravity

Before heading for well-deserved vacations, I wanted to share something I’ve been cooking on and off for the past few weeks: an unofficial Java SDK for Antigravity.

If you’re familiar with Antigravity, you’ll know it’s available across different surfaces:

• Antigravity 2.0: an agent manager for the Antigravity harness, where you can work across multiple projects and sessions from a rich UI,
• Antigravity CLI: a CLI coding agent, where you can interleave running terminal commands, with active agent coding actions,
• Antigravity IDE: a VSCode fork, with an integrated Antigravity chat assistant, when you want to be deep into the code,
• Antigravity SDK: which is actually a toolkit you can integrate and manage yourself, to create your own Antigravity harness.

Antigravity surfaces

A few weeks ago, I wrote about building the Antigravity Brain Visualizer: a tool to parse raw JSONL transcript logs from Antigravity AI agent sessions and render them into an interactive web interface with proportional timelines and sequence groupings.

While visual timeline scrubbing and sequence filtering made it easier to inspect what an agent did, diagnosing complex tool failures or creating preventative guardrails still required manual investigation:

• Why did a tool call fail at step #38?
• What sequence of events led up to a specific error?
• Could I automatically turn a failure pattern into an Agent Skill to prevent Antigravity from repeating the mistake?

To address these questions directly within the application, I built the Interactive Session Assistant in v0.4.1 of the Antigravity Brain Visualizer. It transforms the visualizer from a passive log viewer into an interactive diagnostic co-pilot.

When AI coding agents tackle complex tasks, they often waste time making the same mistakes, running into environment quirks, or retrying failed approaches before finding something that works. If an agent encounters a domain-specific trap in one session, that lesson is lost when the next session starts, forcing the agent to repeat the exact same trial-and-error cycle.

I built the Open Reasoning Format (ORF) to fix this. ORF is a lightweight, file-based specification that lets AI agents record and retrieve operational learnings across sessions. With access to playbooks from previous runs, agents facing similar problems can skip known dead ends, reach working solutions faster, and use about half the steps (and tokens).

I constantly use a text editor to jot ideas, draft blog posts, or collate documents, but my usual options always felt a bit off. TextEdit is too bare-bones and defaults to a tiny font I can barely read. MacVim takes a few seconds too long to load. VS Code requires me to dismiss plugin updates and changelogs before I can even start typing.

I realized I didn’t want another bloated app from a big tech company. I wanted a personal tool tailored exactly to my workflow.

For the past few weeks, a lot has been written and said about Loop Engineering, and we’re seeing an explosion of Agent Skills out there for various tasks. These concepts are trendy, but how do they actually translate into day-to-day productivity?

Today, I want to testify with a concrete use case where skills and loops helped me become significantly more productive: authoring Google Codelabs.

About Codelabs

For those who might not be familiar, Google Codelabs are guided, hands-on coding tutorials that walk developers step-by-step through building an application, integrating an API, or learning a new technology. They are great educational resources, but crafting a high-quality one is far from trivial, and takes time.

When working with autonomous AI agents like Antigravity, understanding what they are doing in the background can be difficult. The agents construct reasoning chains, dispatch background tasks, and execute system commands over long sessions. All of this is recorded in detailed JSONL transcript files. Reading raw JSONL is inefficient, so I built the Antigravity Brain Visualizer to parse and render these transcripts into an interactive interface.

Antigravity CLI allows you to customize both the terminal window title and the bottom status line. This is done by passing a JSON payload of the current agent state to external shell scripts via standard input.

In this post, I will explain how I set up my environment, the specific scripts I use, and how to configure the CLI to load them.

The Principle

Both the title and the status line operate on the same principle:

My friend Leonard and I were collaborating over the weekend on some new updates for the Groovy Web Console. This console is an online playground where Apache Groovy users can run Groovy scripts online, with different versions of the language, from Groovy 3 up to the experimental Groovy 6. Additionally, there’s a specific integration with the Spock testing framework, which allows users to run tests written with the framework.

Here’s what the old console (well, the current one at the time of this writing) looks like:

What a wild title, isn’t it? It’s a catchy one, not generated by AI, to illustrate this crazy week of announcements by Google. Of course, there are big highlights like Gemini 3 Pro, Antigravity, or Nano Banana Pro, but not only, and this is the purpose of the article to share with you everything, including links to all the interesting materials about those news.

Gemini 3 Pro

The community was eagerly anticipating the release of Gemini 3. Gemini 3 Pro is a state-of-the-art model, with excellent multimodal capabilities, advanced reasoning, excellent at coding, and other agentic activities.