Welcome to the Deep Dive. Today, we're looking
at what feels like a foundational pivot in how
software is actually getting built. It really
is. Using old AI coding tools, you know, like
standard ChatGPT or Clod. It's kind of like using
a Ferrari to deliver pizza. Right. I mean, it
works, sure. Yeah. But you're leaving about 95
% of what that thing can do just sitting in the
garage. Yeah. The future is. Yeah. Well, it's
the agentic workspace. Exactly. So we're moving
past those single -threaded AI chat assistants,
the ones that just spit out code snippets, and
we're stepping into what we're calling the conductor
era. That's it. Yeah. And our mission today is
to really unpack Google anti -gravity, which
is this next -gen workspace. system that makes
it all click. The RAPS framework. Yeah. Rules,
armory, parallel agents, and serverless running.
And the promise here is, you know, building production
-ready apps in under half an hour. Okay. Let's
slow down and define that. What is anti -gravity
officially? So the official line is it's an agentic
AI -first IDE, an integrated development environment
powered by models like Gemini 3, Claude, and
it's designed from the ground up. to build real
software using specialized agents and deep tool
integrations. So an easier way to think about
it. Just imagine VS Code and ChatGPT had a kid.
Okay. And that kid was raised by a team of specialist
developers who never sleep, never complain, and
never ask for an hourly rate. That's anti -gravity.
And that's the critical distinction, right? Because
the tools people use now, like Cursor or just
a chatbot, they give you pieces of code. It's
a single threaded back and forth conversation.
The whole workflow changes. Yeah. Anti -gravity
brings like three huge advantages to the table.
First is long -term memory. It remembers. It
remembers. It maintains context across your whole
project. It knows what you built yesterday, which
file you're in. It's a massive relief. And the
second big piece is that you're not just chatting
with one a**. AI. You're managing a small AI
development team through these specialized agents.
Right. And it uses what we're calling structured
workflows. The system is building actual systems
from your answers, not just giving you the answers
themselves. Okay. And the third advantage, you
said it connects to the real world. Yeah. The
model context protocol or MCP. This is what lets
the AI connect directly to databases, to scraping
tools, to live documentation. It gives the AI
eyes and hands. So if the core difference is
managing a dev team, what is the architectural
blueprint for that management? The RAPS framework.
That is the blueprint. It defines the rules,
the tools, the roles, and the deployment for
orchestrating that team. Okay, so that brings
us to the methodology itself. RAPS. Let's start
with the R rules. Right. Think of rules as the
track conditions for your Formula One car. Okay.
You can have the fastest car, the best driver,
your AI. But if it doesn't know the track, it's
going to give you generic results. Rules set
the personality and the technical standards for
the race. So they're basically master instructions
that ensure consistent code quality. Because
without them, you get that chaos we've all seen.
Oh, yeah. Inconsistent naming, three different
CSS frameworks in one project. Just a mess. And
you can set these rules at different levels,
right? Three critical levels. You start with
global rules. These apply to every single project
you ever start. So this is like my personal default.
Exactly. Your persona, senior product engineer
at Stripe, and your go -to tech stack like Next
.js 14 and Tailwind. Then you drill down into
workspace rules. Yeah, these are project specific.
For an app, we might build, say, subreddit pulse.
We could mandate a specific color palette, a
certain design aesthetic, maybe glass morphism.
And finally, task rules. Super simple. One -off
commands like build a login component, but use
React hooks only. That's a task rule. You know,
I still wrestle with prompt drift myself. Oh,
me too. You start a conversation, it's going
great, and then three prompts later, it's completely
forgotten the style guide you gave it. That is
the exact pain point this solves. Right. Rules
let you hard code a definition of done. A definition
of done. Yeah. So you can require the AI to,
say, verify the UI, check for edge cases, and
update the docs before it can mark a task as
incomplete. It prevents it from giving you code
that just breaks on compile. That's a huge shift.
You're defining the goalposts, not just kicking
the ball. Right. And if rules were about consistency,
what external context do they need so they don't
build something that's already outdated? They
need eyes and ears. They need the armory. Exactly.
So let's talk about A for Armory. This is where
that MCP, the model context protocol, really
comes into play. It solves that disconnection
from reality that plagues so many AI models.
For sure. And the specific tools are what's so
fascinating. Let's take five essentials. First,
context seven. Why is that one so important?
Because AI training data is always stale. Always.
Context 7 gives the AI live access to the latest
API documentation. No more deprecated code errors.
Okay, that's huge. What's next? Firecrawl. For
an app that needs fresh data, like our subreddit
Pulse example, Firecrawl can go scrape websites
and just hand back clean, structured data. So
it turns the web into a database for the app.
Pretty much. And speaking of databases, you integrate
Supabase. This lets the AI team manage schemas
and query data with just plain English. You don't
write any SQL. The builder agent just handles
it. Then for Memre, you bring in Pinecone. This
is for RAG, right? Retrieval Augmented Generation.
Exactly. RAG is just a fancy way of saying the
AI can look things up in an external knowledge
base. Pinecone stores your old project data,
docs, design patterns. It gives the AI perfect
long -term memory. And the last one. Notion.
Just for organization. The AI can create project
pages and docs automatically to keep the human
team in the loop. It sounds complex to set up.
It's surprisingly not. It's just a JSON config
file where you paste your API keys. But here's
the tip. Start small. Okay. Pick five to ten
essential tools. Don't go crazy and add 50, because
too many tools will actually slow things down
and shrink the AI's useful context. So how do
specialized tools like Context 7 address that
fundamental issue of AI training data getting
stale so fast? Live documentation access prevents
outdated API use and ensures the AI uses the
newest technical patterns. Got it. Okay, now
we're at the heart of it. P for parallel agents.
This is the conductor era in full effect. We
said telling one generalist AI to do everything
just gives you mediocre everything. So anti -gravity
deploys specialized agents that work at the same
time. And this is where you, the human, stop
being the coder and become the architect. You
set the vision and the agents handle the details.
And we can define, say, four main archetypes.
First up is the design lead. Yep. Its mission
is purely visual. UI, UX, making things look
amazing. Yeah. We tell it to use modern principles
like glass morphism. And its lane is clear. It
only owns the front -end folder. It can't touch
anything else. Can't touch the back -end. That's
for the builder. The builder's job is functionality,
logic, API routes. It owns the back -end and
API folders. Third is the nerd. I like that name.
The nerd is your QA lead. Its job is to break
things. It writes unit tests, audits the other
agents' work, and lives inside the tests folder.
It's your safety net. And the last one, the researcher.
The researcher is read -only. It gathers intel.
It might find the best libraries to use or the
most efficient data structures, and it writes
its findings into a planning document, like plan
.md. So you, as the conductor, might tell the
design lead, make this dashboard look like Linear's
UI, while you're also telling the builder, implement
the API for Firecrawl. Exactly. You orchestrate.
Okay, but if the agents are working in parallel,
what guarantees their changes won't conflict
or just overwrite each other's work? Assigning
those clear domain ownerships, those lanes, for
each agent is what prevents the conflicting code
changes. The system literally stops the builder
from messing with the front -end folder. And
that clarity is the whole game. It did everything.
Which brings us to the final letter, S, for serverless
running. The team built the app, but how does
it run when your laptop is closed? S is for deployment.
You deploy it straight to serverless platforms,
modal. Vercel, AWS Lambda, whatever, using built
-in tools. And the process is simple. It is.
The agents naturally build modular code. So you
just pick a platform. Modal is great for Python
jobs, Vercel for React frontends. And then you
deploy it with a natural language prompt. Something
like, deploy this app to Modal. Run it every
hour. Store results in Subabase. And anti -gravity
just handles it. The packaging, the scheduling,
all of it. It's really a set it and forget it
system. Totally. Okay, let's walk through that
subreddit pulse example again, step by step,
using RAPS. Go. A dashboard scraping two subreddits,
making LinkedIn content, updating hourly. So
first we set our global rules. Next .js, TypeScript,
Senior Engineer Persona. Then we create the initial
design doc. And here's the pro tip. Feed the
AI -specific design screenshots. Yes. Don't just
describe it. Show it examples from Linear or
Vercel. It gets the look and feel instantly way
better than text alone. Then the agents get to
work. Yep. The app design lead adds those cool
glassmorphism effects. The app builder hooks
up the firecrawl logic. And the app nerd runs
audits to make sure the hourly job won't crash.
And the final deployment step. We use the Supabase
integration to create the database tables with
a prompt. And then the final command. Deploy
the scraper to modal. Run hourly. Save to Supabase.
Done. Whoa. Just imagine scaling that. A system
that tracks a thousand sources with hourly updates
all maintained by an AI team. Superhuman speed.
Yeah. And zero human burnout. That's the moment
of wonder right there. It is. So anti -gravity
shines where speed and structure matter. Rapid
prototypes, MVPs, internal dashboards, automation
jobs. It's a cheat code for common development
patterns. But we have to talk about the boundaries.
When should you absolutely not use this? Absolutely.
Avoid it for production -critical systems. Anything
in banking, healthcare, core infrastructure.
Why is that? The higher the risk, the more you
need manual control. Same goes for really niche
domains like embedded systems. Or if you're working
with a super custom, outdated tech stack, the
AI is best at modern, standard workflows. So
for a first -time user building that subreddit
pulse app, what's the most important takeaway
from that example? Feeding the AI design screenshots
or rules guides the results far better than relying
solely on text prompts. So what does this all
mean for the future of development? It feels
like the era of the lone coder writing every
single line of CSS is, well, it's ending. It
is. We're seeing reports that developers using
these agentic tools can work up to 55 % faster.
It's a massive leap. The role itself is shifting
then. Completely. You go from being the implementer,
the person worried about every semicolon, to
being the architect, the strategist. You define
the vision. The RAPS agents implement, debug,
and deploy. The advantage isn't just writing
code faster. It's designing a system that builds
itself. That's the real unlock. The tools are
getting there so fast. So here's a final challenge
for you, the listener. Pick one small project
you've been putting off. Spend a weekend building
it with an agentic IDE and this RAPS framework.
By Monday morning, you'll probably have a working
prototype. And it will likely change how you
think about building software forever. Until
that's time.