You're probably treating Google anti -gravity
like just another AI chatbot. If you're opening
it up, using it like a glorified text editor
just to ask for a single function, you are completely
missing the point. You really are. The revolution
isn't about code generation. It's about coordination.
Welcome back to the Deep Dive. This session is
really for you, the developer who wants to move
beyond just asking an AI to write a function.
Right. and really start commanding a whole team
of autonomous agents. Exactly. Our sources show
that anti -gravity, it functions as a full orchestration
platform, not just a simple coding assistant.
So our mission today is to master that shift.
We're calling this new paradigm vibe coding.
I like that. Yeah, it's where you stop writing
lines of code and you become the tech lead managing
an entire AI team. And we've distilled this whole
process down to seven mission -critical features.
We're going to cover true parallel execution
with the agent manager, correcting agents in
real time with asynchronous feedback. ensuring
quality with artifacts, enabling self -healing
UIs through browser automation, systematizing
tasks with custom workflows, fixing that common
review policy mistake, and then optimizing your
credit costs with smart model selection. Okay,
let's unpack all of this. We have to start with
a single most critical change in architecture.
Yeah. The move from a linear chat history to
a full mission control dashboard. Let's do it.
So when we think about the current AI coding
tools, you know, GitHub Copilot, maybe Cloud
Code, what's the biggest frustration? It's the
black box. It's a total black box, right? You
put in a prompt, you wait, you get this huge
block of code back. Yeah. And if it fails...
You have zero visibility into why. None. You
don't know why it made that choice or where the
plan went wrong. It's completely opaque. And
that lack of visibility, it just kills any complex
project. It does. And anti -gravity flips that
script entirely with the agent manager. You're
not interacting with one single massive thread
anymore. It's an inbox -based system. So every
agent you create, let's say a researcher, a front
-end specialist, a back -end engineer. Each one
gets its own dedicated thread. And the beauty
of that, I assume, is the real -time monitoring.
That's it. You can click into any agent's thread
and you see three levels of transparency. First,
its thought process, the reasoning behind its
moves. Second, the step -by -step execution plan
it's building. And third, the real -time activity
log. You can literally watch it, browse documentation,
write code, run tests. So you aren't guessing.
You're actually watching. You're watching. We
saw this. Almost jaw -dropping demonstration
of this when our source started building a market
intelligence app. Let's just call it the MI app.
Okay. The user gave one broad prompt and then
spawned three agents at the same time. So you
had, what, a researcher agent that started browsing
SDK docs? Right. Instantly. And at the exact
same time, a front -end agent began scaffolding
the React components. And the backend agent.
Simultaneously setting up Python FASC API routes
for the data API. So that is the architectural
shift. You're not waiting for the researcher
to finish before the frontend can even begin.
No. They're all running in parallel, coordinated
by the main platform. I mean, this isn't just
multitasking. This is true orchestration. It
just eliminates the need for you to write thousands
of lines of, you know, glue code. So how does
this shift to parallel orchestration fundamentally
change the developer's day -to -day role? You
stop quoting lines and instantly start leading
a technical team. You're like a project manager
or a tech lead. Let's pivot to a frustration
I think everyone feels. Oh, this next one hits
close to home for me. Yeah. I mean, I still wrestle
with this myself sometimes. It's the prompt.
Drift. Right. You spend 10 minutes crafting this
highly detailed, perfect prompt. The agent gets,
say, 75 % of it right. But then it adds some
weird unwanted feature, maybe a user profile
section you didn't ask for. And in the old way,
that just kills your flow state. You have to
scrap the whole thing and restart the prompt
from scratch. Exactly. It's so frustrating. The
asynchronous feedback system in Anda Gravity
is designed to, like, surgically fix that. It
lets you inject corrections while the agent is
already working. The build doesn't stop. The
build doesn't stop. So imagine that MI app build.
Your front -end agent generates its first task
list, and you see it includes advanced charts
and graphs. You know that scope creep for your
MVP. Okay. You don't stop the agent. You just
click the little checkbox next to that task.
You leave an inline comment right there like,
remove this from the MVP entirely, and you hit
submit. And the agent just... Gets that signal
and adjusts on the fly. Immediately. It receives
that signal, it dynamically updates its scope,
and it adjusts its entire remaining plan without
failing or restarting. It's like gently steering
a ship instead of resetting the GPS every five
minutes. This process prevents starting over,
but if you're injecting changes mid -task, is
there a risk of, I don't know, breaking the agent's
internal logic flow? The agent is built to immediately
adapt its plan, ensuring you course -correct
the scope without triggering a catastrophic build
failure. That leads us right into artifacts.
Because the speed of autonomous AI coding is,
I mean, it's only useful if the underlying architecture
is actually sound. Absolutely. Letting the AI
fly completely blind, that can result in some
pretty mediocre code. Oh, yeah. And really questionable
architectural decisions. You know, vibe coding
isn't chaos. It's controlled speed. We need human
judgment applied where it matters most. Which
is at the planning stage. Exactly. And artifacts
are these structured documents that the agents
generate as checkpoints before any major code
gets committed. They include task lists, but
also detail implementation plans and walkthroughs.
Walkthroughs are like change logs. Yeah, essentially
dynamic change logs. So think about the MI app
again. If you ask the agent to create a feature
with heavy data processing. It might first generate
an implementation plan. Okay. And that artifact
might say, you know, we will use a SQL -based
database for this. And here's where the human
comes in. This is the moment. If you know that
app is going to handle massive, unstructured,
real -time data, you review that plan and just
comment, no, SQL is too rigid, pivot to Mongo
Atlas immediately. And that 30 -second intervention
just saved the agent hours of writing useless
code. Hours. You refine the plan with your taste
and your judgment before a single line of final
code is written. That's the core of the whole
plan, refine, orchestrate cycle. So artifacts
represent the critical application of human taste
and judgment to counter raw AI speed. They ensure
core architectural decisions are validated and
corrected by the human, preventing major refactors
later. All right, let's talk about the absolute
drudgery of post -build verification. Oh, this
part is my favorite. You finish coding the MI
app UI. And then what? You spend hours manually
clicking through everything, testing every button,
taking screenshots. It's tedious. It's painful.
Anti -gravity gets rid of that by integrating
a persistent, controllable Chrome browser. It
can run headless in the background, or you can
watch it. The agents themselves control it to
verify their own work. So that's a paradigm shift
for QA. Huge. The user can just give a command
like, launch the browser, audit the entire UI,
and provide a grade from 1 to 10. And the agent?
It just does it. It opens the browser to your
local host, Bunt 300. It navigates the app, and
it generates a formal audit recording. And here's
the crazy part. It critiques its own creative
output. What does that look like? It might say,
current UI grade, 6 out of 10. Error. The data
charts are still using the old dark color scheme,
violating the new design system. It finds its
own bug. It finds the bug. Recommends a fix like
update the CSS variables. And if you approve,
it automatically executes the fix and then reaudits
the UI to confirm. Wait, hold on. That sounds
amazing. But isn't relying on the AI to critique
its own work just asking for confirmation bias?
Like how reliable is an AI grading its own homework?
That's a fair question. But the audit is based
on functional requirements, not opinion. It's
testing against the artifacts it already generated.
Plus, you can watch the whole recording of the
audit yourself. Whoa, honestly, I'm... I'm still
processing this feature. I mean, imagine scaling
that self -healing ability across a massive complex
application. You could eliminate entire QA sprints.
That saves weeks. It does. It closes the loop,
allowing the agent to test, critique, and automatically
fix its functional issues based on defined objectives.
Which brings us to process efficiency. Right.
If you're anything like me, you suffer from prompt
fatigue. You're constantly retyping these detailed
methodological instructions. Oh, the busy work.
Perform systematic debugging or refactor this
entire class following the Airbnb style guide.
Yeah, those 200 -word prompts, they just become
exhausting to repeat. That is where custom workflows
bring in some real rigor. They let you store
these high -leverage, structured processes as
reusable assets, and then you can trigger them
instantly with a simple slash command, like a
debugging workflow. So you're institutionalizing
best practices. You are. If the file upload feature
on the MI app breaks, you don't have to panic.
Our sources showed a systematic debugging skill
that forces the agent through a predefined four
-phase process. What are the phases? Root cause
investigation, pattern analysis, hypothesis testing,
and only then implementation, which always ends
with a regression test. And the big benefit there
is the methodology. It's the methodology. Instead
of typing that huge paragraph, you just type
at debugging workflow. The MI app file upload
is broken. And that prevents the classic whack
-a -mole debugging where a quick fix just creates
three new bugs somewhere else. Exactly. How does
having a systematized forced debug task fundamentally
prevent the creation of new bugs? It forces the
agent to methodically analyze patterns and dependencies
instead of just applying a rushed quick fix solution.
Hey, we have to talk about what you call the
autonomy paradox. Yes. This is so important.
If the agent has too much freedom, it can make
really destructive changes. But if you micromanage
it, you kill the whole speed advantage of using
AI in the first place. Right. And the critical
insight from the sources is that these agents
are inherently over -optimistic about their own
skills. They don't ask for help. They rarely
ask for a review, even if they're about to do
a major refactor on a critical file. So Google
simplified the policy down to just two options.
Always proceed or request review. And the takeaway
here is vital. It is. When you're starting the
MI app or any new project, you have to toggle
request review on. It forces the platform into
that plan, then execute rhythm, making you validate
the artifacts. And the strategy here is dynamic.
Totally. You toggle it on in the early stages
to make sure your architecture is solid. But
once the project stabilizes, you can toggle it
OBIF and just rely on that asynchronous feedback
for maximum speed. So since the agent is typically
over -optimistic, when is request review most
critical to ensure quality? It must be enabled
early in the project lifecycle to validate and
lock down. foundational architectural decisions.
All right, our final feature. This one is all
about optimization. Which really means cost control,
let's be honest. It does, and the single biggest
mistake we see people make is running every single
task through the newest, most powerful, and,
uh... Most expensive model. Like Gemini 3 .0
Pro. You're just burning credits unnecessarily.
You really need to adopt a strategic three -model
approach. Okay. Different models are good at
different things. Running a massive model for
documentation is like digital malpractice. Gemini
3 .0 Pro should be your orchestrator. It's optimized
for this architecture, multi -agent stuff, artifact
generation, browser automation. And then you
have the deep thinkers. Right. Think of models
like Cloud Sonnet 4 .5. This model excels at
pure logical reasoning. So complex algorithms,
heavy debugging, refactoring legacy code. It
might be a bit slower, but the logical output
is better for those specific tasks. And the third
category. The janitors. Yeah. Or utility models
like GPT -OSS. These are perfect for the low
-stakes, high -volume stuff, generating markdown
docs, basic code formatting, simple boilerplate.
It's cheaper, it's faster for those jobs, and
it's good enough. So your workflow becomes highly
strategic. It does. You're using Pro for the
MI Apps UI build. You switch to Sonnet for that
really complex debugging task. And then you use
GPT -OSS to write the re -admys. We saw one user
save over 80 % on their bill just by doing this.
So what's the core rationale behind switching
models for different parts of the development
process? Strategic model switching leads directly
to faster builds, measurably better code quality,
and significantly lower costs. So if we pull
all seven of these features together. You get
the complete vibe coding workflow. It's a systematic
framework for building production apps at, I
mean, breakneck speed. It's how you move from
being a coder to a commander. It starts with
the plan. You use the agent manager to spawn
your agents, and you make sure that review policy
is set to on. Then you refine. You review those
key artifacts, like the implementation plan,
and you apply your human judgment using those
inline comments. Next, you orchestrate. The agents
execute those plans, working in true parallel,
coordinating the front -end, back -end, and research
all at once. And then you verify. The agent launches
Chrome, audits the build, self -grades its own
work with the automation feature, and fixes any
errors it finds. You systematize. For any repetitive
or complex task, like debugging, you use the
custom workflows with a simple let command. And
finally, you optimize. You strategically switch
models based on the task row for orchestration.
Sonnet for deep logic, and OSS for the janitorial
tasks. The central theme we uncover today is
that Google anti -gravity isn't just an evolutionary
step for the chatbot. It's a fundamentally different
paradigm. Really is. We're watching the shift
from a linear single input assistant to a complete
distributed orchestration platform. And this
is all realized through true parallel processing.
The ability to inject real -time feedback and
these self -testing capabilities that can eliminate
weeks of... traditional QA. You're no longer
waiting for the AI. You're directing it like
a highly efficient team lead. Exactly. The developers
who are going to win in this new era, they won't
be the ones who can write the most code. They'll
be the ones who orchestrate the best. The singularity
is, I mean, it's already here in the development
workflow. The only choice left is whether you're
going to master orchestration and manage your
AI teams or keep writing code line by line like
it's 2020. Thank you for joining us on this deep
dive. We encourage you to think about how you
can immediately apply this plan, refine, orchestrate,
verify framework to your own projects, regardless
of the tools you're currently using. We'll see
you next time.