In the last week of April, sitting in front of my screen, I caught myself thinking: "Wait — I haven't done that morning routine in a while."
The morning git diff scan, the rerunning of tests, the news-summary draft. Tasks I used to do every morning had quietly slipped into Antigravity's hands. At the same time, certain moments now stand out where I think, "this one I really do have to do myself." A month of using Antigravity daily has helped me draw a clearer line between what I can hand off and what I still need to hold onto.
This isn't a feature breakdown or a comparison piece. It's a personal log — written by an artist and indie developer who has been touching Antigravity every day — of small discoveries and lingering frustrations from April 2026.
What the Agent Took Over — The Morning Diff Scan Disappeared
The biggest relief in April was the morning repo check.
I used to brew coffee, open four repositories one by one, and run git status and git log --oneline -5 to review whatever the overnight automation had committed. Two or three minutes per site, a little over ten minutes total. Modest, but it had calcified into a daily ritual.
Now I just hand a single task to an Antigravity agent: "Summarize anything notable from last night's automated updates." The agent reads diffs across all four repos and returns a two- or three-line summary covering new article slugs, categories, and likely traffic keywords. I just read the summary and decide whether to approve or course-correct.
It sounds small, but it added up. Replacing a ten-minute ritual with a two-minute decision frees up morning focus for actual code review and content quality checks.
This automation is an extension of the setup I described in Fully Automating an Antigravity Workflow with MCP and GitHub Actions. When I wrote that post I half-doubted whether anyone needed automation at that scale. I didn't expect the daily payoff to come from such ordinary morning friction.
What I Still Did By Hand — Local LLM Tuning and MDX Cleanup
There were also moments in April where I clearly couldn't hand work off.
The first was local LLM configuration. The Antigravity-plus-Gemma-4 setup I documented in The Complete Setup Guide for Local LLMs in Antigravity does work. But once you live with it daily, you accumulate small judgment calls: how to trim the context size per request, which files to give the agent priority access to. The model can't decide these for you. They depend on your style, your project, and what's on your plate that day. I ended up tuning by hand most weeks.
The second was MDX cleanup. When I read articles drafted by an agent, I keep finding the same kinds of slips: link text that's actually the slug, code blocks missing the language tag, or polite and plain Japanese mixed in the same paragraph. The structural skeleton comes from the agent — but reading as a human and noticing "this one sentence feels off" is still very much my job.
I don't think of these as weaknesses. They're "tasks the agent isn't built for." Trusting AI well, I think, requires knowing exactly where it falls short — and being willing to cover those gaps yourself.
The Real Lesson — Distance from the Agent Matters
Looking back, the biggest lesson from April wasn't about features. It was about distance.
Running multi-agent flows in Antigravity's Manager Surface, it's easy to slip into "I can hand off everything" mode. And many routine tasks really can be handed off. But when I move on to the next task without checking what the agent finished, small misalignments quietly pile up.
Mid-April, I had exactly that kind of incident. An agent picked a slightly wrong category for an article, and the post never appeared in the right category listing. The root cause was me: I'd told it "decide the category yourself" and never verified the result. I wrote about wrangling Manager Surface in Five Tips for Mastering Manager Surface, but the harder lesson is that "mastering" matters less than building a rhythm of checking results.
Working with an agent feels a little like managing a junior teammate. Hand off completely, and you've moved from trust to abandonment. Hand off, then check, then return short feedback. That short loop is what makes the next round more accurate. So I added a tiny ritual: every night, before closing my laptop, I look at three things the agent shipped that day and read them with my own eyes.
What I Want to Try in May
A few experiments are queued up for May.
First, I want to read this April log alongside last month's. I left a March recap at The March 2026 Antigravity Monthly Recap, and putting the two side by side should reveal habits I haven't noticed yet. AI tools change quickly, and the only way to separate "the tool changed" from "I changed" is to keep monthly notes.
Second, I want to widen the agent's lane by one notch. Right now it handles drafts and diff summaries. In May I'll let it draft the social post that announces a new article. If it goes badly, I'll write that down honestly too.
Finally, a note for whoever made it this far: how an AI IDE feels depends entirely on your own development rhythm. My April won't be your April. But the habit of writing down "what I handed off" and "what I held onto" might help you see your own rhythm more clearly.
Tomorrow morning, before you open Antigravity, try spending one minute writing down where your time actually goes. The most useful preparation for working with an agent, I've found, is putting your own workflow into words first.