WEBVTT

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Welcome to the Deep Dive. Today, we're looking

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at what feels like a foundational pivot in how

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software is actually getting built. It really

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is. Using old AI coding tools, you know, like

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standard ChatGPT or Clod. It's kind of like using

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a Ferrari to deliver pizza. Right. I mean, it

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works, sure. Yeah. But you're leaving about 95

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% of what that thing can do just sitting in the

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garage. Yeah. The future is. Yeah. Well, it's

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the agentic workspace. Exactly. So we're moving

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past those single -threaded AI chat assistants,

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the ones that just spit out code snippets, and

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we're stepping into what we're calling the conductor

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era. That's it. Yeah. And our mission today is

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to really unpack Google anti -gravity, which

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is this next -gen workspace. system that makes

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it all click. The RAPS framework. Yeah. Rules,

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armory, parallel agents, and serverless running.

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And the promise here is, you know, building production

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-ready apps in under half an hour. Okay. Let's

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slow down and define that. What is anti -gravity

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officially? So the official line is it's an agentic

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AI -first IDE, an integrated development environment

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powered by models like Gemini 3, Claude, and

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it's designed from the ground up. to build real

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software using specialized agents and deep tool

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integrations. So an easier way to think about

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it. Just imagine VS Code and ChatGPT had a kid.

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Okay. And that kid was raised by a team of specialist

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developers who never sleep, never complain, and

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never ask for an hourly rate. That's anti -gravity.

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And that's the critical distinction, right? Because

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the tools people use now, like Cursor or just

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a chatbot, they give you pieces of code. It's

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a single threaded back and forth conversation.

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The whole workflow changes. Yeah. Anti -gravity

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brings like three huge advantages to the table.

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First is long -term memory. It remembers. It

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remembers. It maintains context across your whole

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project. It knows what you built yesterday, which

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file you're in. It's a massive relief. And the

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second big piece is that you're not just chatting

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with one a**. AI. You're managing a small AI

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development team through these specialized agents.

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Right. And it uses what we're calling structured

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workflows. The system is building actual systems

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from your answers, not just giving you the answers

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themselves. Okay. And the third advantage, you

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said it connects to the real world. Yeah. The

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model context protocol or MCP. This is what lets

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the AI connect directly to databases, to scraping

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tools, to live documentation. It gives the AI

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eyes and hands. So if the core difference is

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managing a dev team, what is the architectural

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blueprint for that management? The RAPS framework.

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That is the blueprint. It defines the rules,

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the tools, the roles, and the deployment for

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orchestrating that team. Okay, so that brings

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us to the methodology itself. RAPS. Let's start

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with the R rules. Right. Think of rules as the

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track conditions for your Formula One car. Okay.

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You can have the fastest car, the best driver,

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your AI. But if it doesn't know the track, it's

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going to give you generic results. Rules set

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the personality and the technical standards for

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the race. So they're basically master instructions

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that ensure consistent code quality. Because

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without them, you get that chaos we've all seen.

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Oh, yeah. Inconsistent naming, three different

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CSS frameworks in one project. Just a mess. And

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you can set these rules at different levels,

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right? Three critical levels. You start with

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global rules. These apply to every single project

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you ever start. So this is like my personal default.

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Exactly. Your persona, senior product engineer

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at Stripe, and your go -to tech stack like Next

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.js 14 and Tailwind. Then you drill down into

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workspace rules. Yeah, these are project specific.

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For an app, we might build, say, subreddit pulse.

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We could mandate a specific color palette, a

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certain design aesthetic, maybe glass morphism.

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And finally, task rules. Super simple. One -off

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commands like build a login component, but use

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React hooks only. That's a task rule. You know,

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I still wrestle with prompt drift myself. Oh,

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me too. You start a conversation, it's going

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great, and then three prompts later, it's completely

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forgotten the style guide you gave it. That is

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the exact pain point this solves. Right. Rules

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let you hard code a definition of done. A definition

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of done. Yeah. So you can require the AI to,

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say, verify the UI, check for edge cases, and

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update the docs before it can mark a task as

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incomplete. It prevents it from giving you code

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that just breaks on compile. That's a huge shift.

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You're defining the goalposts, not just kicking

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the ball. Right. And if rules were about consistency,

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what external context do they need so they don't

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build something that's already outdated? They

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need eyes and ears. They need the armory. Exactly.

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So let's talk about A for Armory. This is where

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that MCP, the model context protocol, really

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comes into play. It solves that disconnection

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from reality that plagues so many AI models.

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For sure. And the specific tools are what's so

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fascinating. Let's take five essentials. First,

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context seven. Why is that one so important?

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Because AI training data is always stale. Always.

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Context 7 gives the AI live access to the latest

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API documentation. No more deprecated code errors.

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Okay, that's huge. What's next? Firecrawl. For

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an app that needs fresh data, like our subreddit

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Pulse example, Firecrawl can go scrape websites

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and just hand back clean, structured data. So

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it turns the web into a database for the app.

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Pretty much. And speaking of databases, you integrate

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Supabase. This lets the AI team manage schemas

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and query data with just plain English. You don't

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write any SQL. The builder agent just handles

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it. Then for Memre, you bring in Pinecone. This

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is for RAG, right? Retrieval Augmented Generation.

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Exactly. RAG is just a fancy way of saying the

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AI can look things up in an external knowledge

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base. Pinecone stores your old project data,

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docs, design patterns. It gives the AI perfect

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long -term memory. And the last one. Notion.

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Just for organization. The AI can create project

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pages and docs automatically to keep the human

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team in the loop. It sounds complex to set up.

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It's surprisingly not. It's just a JSON config

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file where you paste your API keys. But here's

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the tip. Start small. Okay. Pick five to ten

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essential tools. Don't go crazy and add 50, because

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too many tools will actually slow things down

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and shrink the AI's useful context. So how do

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specialized tools like Context 7 address that

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fundamental issue of AI training data getting

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stale so fast? Live documentation access prevents

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outdated API use and ensures the AI uses the

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newest technical patterns. Got it. Okay, now

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we're at the heart of it. P for parallel agents.

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This is the conductor era in full effect. We

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said telling one generalist AI to do everything

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just gives you mediocre everything. So anti -gravity

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deploys specialized agents that work at the same

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time. And this is where you, the human, stop

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being the coder and become the architect. You

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set the vision and the agents handle the details.

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And we can define, say, four main archetypes.

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First up is the design lead. Yep. Its mission

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is purely visual. UI, UX, making things look

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amazing. Yeah. We tell it to use modern principles

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like glass morphism. And its lane is clear. It

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only owns the front -end folder. It can't touch

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anything else. Can't touch the back -end. That's

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for the builder. The builder's job is functionality,

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logic, API routes. It owns the back -end and

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API folders. Third is the nerd. I like that name.

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The nerd is your QA lead. Its job is to break

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things. It writes unit tests, audits the other

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agents' work, and lives inside the tests folder.

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It's your safety net. And the last one, the researcher.

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The researcher is read -only. It gathers intel.

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It might find the best libraries to use or the

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most efficient data structures, and it writes

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its findings into a planning document, like plan

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.md. So you, as the conductor, might tell the

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design lead, make this dashboard look like Linear's

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UI, while you're also telling the builder, implement

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the API for Firecrawl. Exactly. You orchestrate.

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Okay, but if the agents are working in parallel,

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what guarantees their changes won't conflict

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or just overwrite each other's work? Assigning

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those clear domain ownerships, those lanes, for

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each agent is what prevents the conflicting code

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changes. The system literally stops the builder

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from messing with the front -end folder. And

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that clarity is the whole game. It did everything.

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Which brings us to the final letter, S, for serverless

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running. The team built the app, but how does

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it run when your laptop is closed? S is for deployment.

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You deploy it straight to serverless platforms,

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modal. Vercel, AWS Lambda, whatever, using built

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-in tools. And the process is simple. It is.

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The agents naturally build modular code. So you

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just pick a platform. Modal is great for Python

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jobs, Vercel for React frontends. And then you

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deploy it with a natural language prompt. Something

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like, deploy this app to Modal. Run it every

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hour. Store results in Subabase. And anti -gravity

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just handles it. The packaging, the scheduling,

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all of it. It's really a set it and forget it

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system. Totally. Okay, let's walk through that

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subreddit pulse example again, step by step,

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using RAPS. Go. A dashboard scraping two subreddits,

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making LinkedIn content, updating hourly. So

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first we set our global rules. Next .js, TypeScript,

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Senior Engineer Persona. Then we create the initial

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design doc. And here's the pro tip. Feed the

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AI -specific design screenshots. Yes. Don't just

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describe it. Show it examples from Linear or

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Vercel. It gets the look and feel instantly way

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better than text alone. Then the agents get to

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work. Yep. The app design lead adds those cool

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glassmorphism effects. The app builder hooks

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up the firecrawl logic. And the app nerd runs

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audits to make sure the hourly job won't crash.

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And the final deployment step. We use the Supabase

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integration to create the database tables with

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a prompt. And then the final command. Deploy

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the scraper to modal. Run hourly. Save to Supabase.

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Done. Whoa. Just imagine scaling that. A system

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that tracks a thousand sources with hourly updates

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all maintained by an AI team. Superhuman speed.

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Yeah. And zero human burnout. That's the moment

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of wonder right there. It is. So anti -gravity

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shines where speed and structure matter. Rapid

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prototypes, MVPs, internal dashboards, automation

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jobs. It's a cheat code for common development

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patterns. But we have to talk about the boundaries.

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When should you absolutely not use this? Absolutely.

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Avoid it for production -critical systems. Anything

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in banking, healthcare, core infrastructure.

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Why is that? The higher the risk, the more you

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need manual control. Same goes for really niche

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domains like embedded systems. Or if you're working

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with a super custom, outdated tech stack, the

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AI is best at modern, standard workflows. So

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for a first -time user building that subreddit

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pulse app, what's the most important takeaway

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from that example? Feeding the AI design screenshots

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or rules guides the results far better than relying

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solely on text prompts. So what does this all

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mean for the future of development? It feels

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like the era of the lone coder writing every

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single line of CSS is, well, it's ending. It

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is. We're seeing reports that developers using

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these agentic tools can work up to 55 % faster.

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It's a massive leap. The role itself is shifting

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then. Completely. You go from being the implementer,

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the person worried about every semicolon, to

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being the architect, the strategist. You define

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the vision. The RAPS agents implement, debug,

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and deploy. The advantage isn't just writing

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code faster. It's designing a system that builds

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itself. That's the real unlock. The tools are

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getting there so fast. So here's a final challenge

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for you, the listener. Pick one small project

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you've been putting off. Spend a weekend building

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it with an agentic IDE and this RAPS framework.

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By Monday morning, you'll probably have a working

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prototype. And it will likely change how you

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think about building software forever. Until

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that's time.
