Welcome to the deep dive today. We're tackling
something pretty fundamental I think is the skill
of well writing code actually being replaced
by just writing a clear email Or something like
it. It's that feeling right that creative friction.
You get this this brilliant idea for an app Maybe
it's simple. Maybe it's a whole business But
then you think about learning JavaScript Python
dealing with all the setup and it just stops
you cold Yeah, that huge technical wall that
barrier that used to mean months, maybe years
of learning. Our source material today suggests
that barrier. It's mostly gone thanks to generative
AI. Exactly. And this big shift, people are starting
to call it vibe coding. It's more about focusing
on the feeling, the function you want, rather
than getting lost in confusing code syntax. So
our mission today is to really unpack how that
works. We'll define vibe coding, look at the
tools you need from simple chat interfaces right
up to these powerful AI agents, and we'll break
down the specific process, what the sources are
calling the golden prompt. Yeah, and we'll dive
into the money side too. New models this opens
up. And maybe most importantly, we'll hit those
crucial reality checks, things you really need
to know before you jump in. Okay, let's unpack
this idea then. Vibe coding. Before AI, Building
software felt, I don't know, like building a
house all by yourself. You're mixing the cement,
laying every single brick, checking the plumbing,
and if you mess up one tiny detail, the whole
thing could be shaky. Totally. Vibe coding flips
that role completely. You're not the one laying
bricks anymore, you're the architect, the visionary.
You still have control, absolutely, but you're
builders. They're this super fast, hyper -efficient
AI team working around the clock. So you just
tell the team, okay, I need a wall here, big
window, facing west for the sunset, and the AI
handles the framing, the materials, putting the
glass, and all that detail. Your focus is just
on the final vision. And what's really fascinating
is why this works now. It's not just slightly
better code. The tech behind tools like ChatGPT,
it's gotten incredibly good at understanding
context, exponentially better. A few years back,
AI could maybe write tiny bits of code, often
buggy, isolated snippets. Right, snippets that
didn't know about anything else, yeah, no state.
Precisely. Now these models can handle huge complex
instructions. Think like a whole floor plan,
not just a door frame. And crucially, they remember
context. They know what was built last week,
how the new roof needs to connect. This means
they manage complex dependencies, keep track
of the app's state across lots of requests. Ah,
that deep context memory. That's the key, then,
to speeding things up so much from months down
to days. It really compresses the whole creation
time scale dramatically. Let's people try ideas
that were just too expensive or time -consuming
before. Right. Lots more experimentation is possible
now. OK, so if the AI is the builder doing the
heavy lifting, what's the absolute first thing
the architect, the human, has to nail down? The
vision. It has to be crystal clear before you
even touch a tool. Leaving from that vision,
let's talk tools. For someone just starting out,
just curious, the sources say, start simple.
Yeah, stick with the standard chat tools first,
like your basic chat GPT or Claude. They're great
for, say, simple calculators or getting the code
for a basic one -page website, even simple text
games. You can copy the HTML and CSS they give
you, open it straight in your browser. It feels
kind of magical. But there's a clear limit, right?
That code is static. If you need users to log
in or you need to store data permanently or connect
to some financial API... These basic tools fall
short. Exactly. They can write the blueprint,
but they can't run the factory, as you put it.
For building real, dynamic products, apps that
handle user data, you got to step up to AI agents.
AI agents, OK. Which raises the question, what's
the real difference? Well, agents don't just
write code. They also run it. They execute commands,
fix errors automatically, and handle the tricky
stuff of putting it online. So an agent isn't
just spitting out text. It's like software using
other software, a mini CEO for the build. That's
a good way to think about it. Yeah. It has an
execution environment. It can use APIs, manage
secret keys, handle those compressed configuration
files. All the stuff that used to trip up beginners
constantly, it abstracts away the infrastructure
pain. Which lowers the barrier to entry, but
also raises the ceiling of what you can actually
build. Definitely. And we're seeing platforms
emerge to help with this. Replit's a big one.
Its agent feature handles tough things like setting
up databases, environment stuff. Bolt .new, as
mentioned, is great for web apps, runs right
in the browser, super fast prototyping. And lovable
gets a shout out for making things look really
modern and polished, aesthetically pleasing.
without needing design skills. What's the biggest
sort of inherent difficulty when you move from
just chatting with an AI to using one of these
more sophisticated agents? Agents handle complex
operational tasks, like actually running and
debugging code in real time. OK, let's get into
the core process. The sources lay it out in stages,
phase one. The blueprint. They're really strict
about this. Before you write a single prompt,
spend 10 minutes just writing down exactly what
the app should do. Yeah. because most people
fail by being vague. They just type, make a water
app. That's like a guaranteed way to get prompt
drift where the AI just wanders off. You need
absolute clarity. Take their hydro habits tracker
example to find the audience. Desk workers forgetting
water. The one core function, clicking a button
to add water intake. Nice to haves, like sharing
with friends. Clearly mark those for later. Right.
Nail the core first. Then, once that blueprint's
solid, you write the instruction, the golden
prompt. And there's a formula they suggest. Role,
goal, features, and rules. It forces clarity.
For HydroHabits, the role isn't just developer.
It's expert web developer, specialized in mobile
-first, high -conversion apps. Super specific.
The goal. Build a water intake tracker. Three
liter, daily target. No ambiguity. And the features
get really specific too, almost visual. Simple
login, circular progress bar, quick add buttons
for 250 milliliter, 500 milliliters, 1L, a street
counter, and that satisfying confetti animation
when you hit the goal, details matter. And rules
and style are just as vital. Use light blues
white, keep it clean, minimal, make sure it works
perfectly on a phone, stop the AI guessing. I
have to admit, I still wrestle with prompt drift
myself sometimes. You know, you start clear.
But after a few changes, the AI totally forgets
the original point and starts building, well,
something else entirely. This level of detail
up front feels absolutely necessary now. It really
is. It's like the contract with the AI. Yeah.
And it'll build that first version fast. Sources
say it's usually about 80 % right. Maybe the
buttons are too small, colors slightly off, that
kind of thing. Which leads straight into phase
three. This is the vibe part, right? You talk
to the agent like a human assistant molding it.
You say, hey, those buttons, too small, make
them 50 % bigger. Or that blue's too harsh, try
a softer sky blue. Exactly. It's a conversation.
You keep going back and forth, refining details,
shaping the app until you're happy. But isn't
that last 20 %? Isn't that where the real complexity
lies? If I still meet a human to fix tricky bugs
or device issues, have we really removed the
barrier or just shifted it? That's a critical
point. The barrier's been massively low. From
100 % build down to maybe the final 20 % polish.
The sources are clear. AI gets you 0 to 80 %
minutes. That's the revolution. But yeah, the
human architect still needs to guide that last
mile, refine it. And when it is ready, going
live seems almost anti -climactic. These modern
tools, Replet, Bolt, they just have a deploy
button. Pretty much. Click it. wait a bit while
the agent sorts out server stuff, and boom, you
get a real URL to share. A live product, just
like that. Just like that. OK, but how often
does the AI just completely whiff it on the first
try? Like, totally miss the mark, forcing you
to start over. It's usually about 80 % right.
That last 20 % needs that targeted human refinement.
Yeah. Sponsor reads. OK, so because you can build
things, maybe 10 times faster, prototype quickly.
You just, you get more shots on goal, right?
More chances to succeed. And this changes the
financial models available. Absolutely. The big
one this speed enables is the Microsoft model.
Sauce being software as a service. A Microsoft
is just a small, really niche app. Solves one
tiny problem for a very specific group. Like
a special invoice generator just for freelance
writers where that meeting note cleaner turning
messy text into a PDF. Exactly. Things like that.
And you might only charge five, maybe $10 a month.
You don't need a million users. If you find just
100 people paying $5 a month, that's $500 monthly
recurring revenue. for something you potentially
built in a weekend. Whoa. Okay, imagine scaling
a really solid Microsoft built that fast to hundreds,
maybe thousands of users. That whole rapid iteration
and growth potential, that feels fundamentally
new. Very exciting. Yeah. It is. The sources
point to a couple of other paths too. One is
freelancing. You take someone else's idea, business
owners maybe, run it through your agent tools.
You charge a premium for that result. Basically,
you're selling your prompting skill. And the
third one, which I think gets overlooked, is
just saving money or time at your existing job.
Building small internal tools. Automating some
repetitive tasks like that PDF cleaner example.
Maybe it saves your team two hours a week. That's
real value back to the company, even if you never
sell the tool itself. Definitely. Building those
internal efficiencies, often the quickest way
to show immediate value with these tools in a
workplace. OK, this all sounds amazing, like
pure gold, really, a tech dream. But every revolution
has its fine print, doesn't it? The caution tape.
Yeah. Before anyone rushes off to build their
Microsoft empire, we need those reality checks
from the sources. Right. Reality check number
one, the last 10 % is hard. We touched on this.
AI gets you, say, 90 % there super fast. But
that last 10 % Specific bugs making it work on
all devices, weird edge cases, that takes human
patience. Sometimes serious debugging. Yeah,
you might spend an hour chasing one line of code
the AI wrote, even if the 999 lines before it
were perfect. Sometimes you just have to delete
the last change and explain the problem differently
to the agent, right, to kind of unstick it. Exactly.
Reality check two. Security. This is huge. It's
explicitly your responsibility. The AI wants
to help you to complete the task. It's not automatically
building like Fort Knox for your logins. So if
you're handling sensitive stuff, credit cards,
personal info, you have to be incredibly careful.
For payments, the advice is clear. Use trusted
external tools. Stripe, lemon squeezy, things
like that. Don't try to build your own payment
system from scratch with AI. Just don't. Way
too risky. Way too risky. And reality check three,
cost. Building complex stuff, especially with
powerful agents using lots of computation that
uses credits or tokens. It costs money. Learning
in simple prototypes, usually cheap or even free
to start. But if your app gets high traffic or
it's really complex, your bill will go up. Always
check the pricing pages first. When should someone
who's stuck in that iteration phase going back
and forth just give up on that thread and start
fresh with a new golden prompt? If the AI sees
you stuck in a loop on one specific bug, like
the same air keeps popping up no matter how you
explain the fix. Maybe step back, try explaining
the fix differently, or simplify the goal for
that step. Yeah. That often breaks this cycle.
So stepping back, what does this all mean? Big
picture. It feels like we're in a really special
moment in time. That massive wall between just
having an idea and actually holding it, using
it, it's almost gone. It really has. You don't
need permission anymore. In the same way, you
don't necessarily need that computer science
degree to start building real things, even things
that make money. That's incredibly democratizing.
The core skill seems to have shifted entirely.
From execution, the sheer labor of typing code,
to clarity, the ability to think clearly and
write a precise, thoughtful prompt. That's the
valuable skill now. Yeah. The new currency is
just curiosity and a willingness to try things
out. To stop seeing software as this arcane,
complex thing and start thinking of it more like
molding clay, but with language. My best advice,
echoing the sources. Stop just reading about
it. Go build something silly today. Seriously.
Build a button that makes a funny noise. Build
a page that just shows random cat photos. Just
start vibing with the tools to see what happens.
And think about this. If you can get functional
software from just a text description, what really
complex, maybe multidisciplinary problem, something
totally out of reach a year ago, is now solvable
by the average curious person. That is a challenging
thought and a wonderful place to leave it.