DEV Community: aichannode

Lovable vs Replit — Which One Is Actually Better?

aichannode — Tue, 05 May 2026 20:55:57 +0000

Everyone’s pitching faster builds and “no-code” lately. Two names I kept hearing were Lovable and Replit, so I actually used both while putting together a site for Radcrew.
This isn’t a spec-sheet comparison — it’s what stuck after real work.

What is Replit?

Replit is basically a full development environment in the browser: write code, run it, host it, without wrestling your laptop into the right shape first. It supports lots of languages, collaboration is built-in, and AI help (Ghostwriter) is available when you want it.

The parts I cared about were the boring but important stuff — environments spin up fast, you’re not chasing configuration for a day, and you’re not locked into a single “app template” mindset.

Bonus: Replit also lets you create a mock backend with PostgreSQL, which made prototyping full-stack features a breeze for Radcrew. No spinning up separate servers or juggling database connections — it’s all in the cloud.

What is Lovable?

Lovable sits closer to “tell the AI what you want and get UI back.” Prompts do most of the heavy lifting; you’re not wiring everything by hand from scratch.

The trade-off is simple: speed and a visual head start versus less room to customize the stack when things get complex.

My Experience Using Both

Same project, same goal: a Radcrew website that had to go past “cool demo.” I bounced between generating layouts, tweaking components, and trying to land something I could actually ship.

Where Lovable Shines

Lovable is genuinely fun when you’re still figuring out what the site should look like. You get screens fast. MVPs and “what if we tried this layout?” moments are where it earns its keep.

Where It Falls Short

Once I needed tighter control over structure and behavior, Lovable started to feel restrictive. It was harder to refactor the way I wanted and harder to grow the project without the tool getting in the way. Fine for opening moves; less convincing for finishing them.

Why Replit Felt More Powerful

Lovable couldn’t give me what I wanted most: full control over the project.

Real Development Environment

No guessing what the generator thought I meant. I could lay out folders my way, pull in dependencies, set breakpoints, and read errors — the normal development loop, just in the cloud.

Flexibility

UI tweaks, backend features, random APIs — Replit didn’t make me negotiate every step. Lovable often felt like a corridor; Replit felt like a workshop. Plus, being able to mock a PostgreSQL backend inside Replit meant I could build real features instead of just static layouts.

Stability for Real Projects

For Radcrew, I needed repeatability: run, test, change, run again without mystery regressions. That mattered more than any single flashy feature once I was past the first screen.

Visual Comparison (Radcrew Website)

This is generated by Lovable:

And this is generated by Replit:

I tried making a polished UI with Lovable for about three days, but it couldn’t reach my requirements.
By using Replit, I finished it within three hours :)

Quick summary: Lovable wins for “something on screen fast.” Replit wins for “something I’m not embarrassed to call done.”

Pricing at a Glance

Both tools have free tiers, so you can test without paying. Paid plans differ:

Replit: Core starts around $20/month (billed yearly) for heavier-duty dev features and more AI usage. Pro is ~$95/month if you need serious cloud horsepower.
Lovable: Pro is $25/month for individual workspaces, Business around $50/month for teams.

Replit’s plans scale with power and flexibility, while Lovable is simpler and focused on quick idea-to-screen. Bottom line: compare what you get for your workflow, not just the headline price.

Final Thoughts

Flattened down: Lovable for fast ideas and prototypes; Replit when I care about finishing something for real.

For Radcrew-style work, I’d pick Replit — not because Lovable is useless, but because I hit its ceiling earlier.

Conclusion

Lovable is part of a wave that’s changing how people start projects. For me, today, control, full-stack capabilities, and shipping still point to Replit.

Your mileage may vary depending on what you’re building. If you’ve used both, I’d love to hear whether your experience matches mine.

If you liked this article, please check out the Radcrew GitHub repo and give it a ⭐!

How I Structure a FastAPI Backend with LLM Features (From a Real Project)

aichannode — Wed, 29 Apr 2026 13:10:51 +0000

How I Structure a FastAPI Backend with LLM Features (From a Real Project)

I Don’t Start With Endpoints Anymore

When I used to start backend projects, I’d jump straight into writing routes.

That worked… until the project grew.

Now, I start with something else:

“How will this project fall apart in 3 months?”

Because it will — especially if you’re using LLMs.

You’ll start seeing:

prompts copied across files
random LLM calls inside endpoints
parsing logic that no one wants to touch
“temporary” hacks that become permanent

So these days, I focus heavily on structure first, features second.

This post is how I structured a FastAPI backend with LLM integration for a real estate consultant system — and what actually held up.

FastAPI vs Express — Different Problems

Coming from Node.js + Express, I was used to this:

routes/
controllers/
services/
models/

Flexible, simple… and easy to mess up.

Over time:

controllers get bloated
services become dumping grounds
logic gets duplicated

With FastAPI, the issue is different:

It gives you powerful tools, but no strong opinion on structure.

So people end up with:

everything inside main.py
business logic inside route handlers
LLM calls scattered everywhere

And once LLM is involved, things get chaotic fast.

The Project (Real Context)

This is from a real project:

A backend that collects user preferences, uses an LLM to interpret them, and guides real estate search.

Not just CRUD. It includes:

multi-step intake flow
LLM-based parsing
dynamic question generation

The Structure I Landed On

api/
core/
llm/
models/
repositories/
schemas/
utils/

High-Level Flow

[Client]
   ↓
[API Layer]
   ↓
[Repositories + LLM]
   ↓
[Database]   [LLM Provider]

API Layer — Keep It Boring

api/v1/endpoints/

Responsibilities:

request/response
validation
calling repositories or LLM layer

@router.post("/intake")
def create_intake(...):
    return intake_repo.create(...)

Core — The Foundation

core/

Includes:

config
DB connection
dependency injection
external SDK wrappers

LLM Layer — Treat It as a Domain

llm/
  intake/
    prompts.py
    schema.py
    service.py
  providers/

All LLM-related logic lives here.

llm_intake_service.parse_user_input(text)

Why?

Because LLM is:

non-deterministic
sensitive to prompts
provider-dependent

Models vs Repositories

models/
repositories/

models/ → DB structure
repositories/ → queries

Keeps data access clean and testable.

Schemas — Critical for LLM

schemas/

LLMs:

hallucinate
return inconsistent formats

So:

define strict schemas
validate every response

Utils — Use Carefully

utils/

Good for:

small helpers

Bad when:

it becomes a dumping ground

LLM Flow

User Input
   ↓
LLM Prompt
   ↓
LLM Response
   ↓
Schema Validation
   ↓
Structured Data

One Key Idea

Treat LLM as its own domain, not just a tool.

Final Thoughts

This structure isn’t perfect.

But it worked in a real project.

Structure isn’t about being clean.

It’s about staying sane when things get messy.

This is the codebase — you can find the FastAPI architecture here:

github.com/radcrew/real-estate-consultant

If you found this helpful, please star the repo — it helps others discover it, and you’ll see updates as the project evolves.

How to Write Prompts When You’re Vibe Coding

aichannode — Fri, 24 Apr 2026 18:18:52 +0000

Cursor and ChatGPT already changed how I work

A few years ago, coding felt like a solo sport: you, the keyboard, and Stack Overflow when things went sideways. Now there’s an assistant in the editor, another tab for chat, sometimes a whole builder that spits out UI before you’ve finished your coffee.

I’m not here to argue whether that’s good or bad—it’s mostly here, and I’ve adapted. The part that surprised me is not “the model can code.” It’s that most of my pain now comes from communication, not syntax. Cursor and ChatGPT didn’t just speed up typing for me; they shifted the job toward directing: I say what I want, something gets drafted, I react, we iterate.

So if you ask what actually matters in vibe coding now, I’d say it’s boring and unsexy: the prompt. Not the biggest context window, not the trendiest stack—a clear ask, written like you mean it.

The trade nobody warned me about

When I started, I treated prompts like quick texts. “Make a dashboard.” “Fix this.” “Add auth.” I thought I was being efficient.

I wasn’t. I was renting my future self a pile of cleanup. The model would hand back something plausible—almost always wrong in small, expensive ways: the layout was almost right, the state handling was almost right, the happy path worked and everything else melted. I’d burn an hour tracing behavior that would never have gone sideways if I’d spent one extra minute sharpening the request.

My rule of thumb now: one more minute on the prompt often buys back something like an hour of debugging and rework. Not always, but often enough that I’m embarrassed I ever skipped it.

What follows isn’t theory. It’s what I actually do when I want the output to land closer to finished the first time.

What I try to get right every time

Be specific.

I name what I’m building in plain pieces: features, rough layout, vibe of the UI, and what “done” looks like. “Dashboard” tells the model nothing useful. “Three KPIs on top, one line chart for last 30 days, table below with sortable columns, calm spacing like Notion, no neon gradients”—that’s a picture someone can draw.

Give it structure.

I split the prompt into chunks with headers or bullets so neither of us has to hunt. Usually something like: goal, must-haves, nice-to-haves, hard limits (tech, performance, what not to do), and how I’ll know it’s correct. It reads a bit like a tiny ticket. That’s the point.

Point at examples.

If I’ve got a screenshot, a product I like, or even a single phrase—“dense like Linear,” “quiet like Stripe’s docs”—I put it in. I’m not asking for a clone; I’m giving a target so the model stops free-styling typography and spacing I’ll hate.

Work in steps.

I stopped asking for the whole castle in one message. First the skeleton and the main user path. Then CRUD or filters. Then polish. Big all-in-one prompts feel productive for sixty seconds and expensive for the rest of the afternoon.

Refine like you mean it.

First output is a draft. I read it like code review: what’s wrong, what’s missing, what’s structurally off. If the same mistake shows up twice, I fix the prompt, not just the line—the spec was probably thin.

Prompts that wasted my time vs. prompts that didn’t

These are shortened, but the pattern is real.

Landing page

Bad (I’ve sent worse):

> Make a nice landing page for my SaaS.

That’s not a brief; it’s a mood. No audience, no sections, no tone—“nice” could mean anything, so the model guesses.

Better (still pasteable):

> Single-page site for a B2B analytics tool, audience = small ecommerce teams.  
> Sections: hero with headline, subhead, primary CTA, screenshot placeholder; logo strip; three feature blocks; one quote for social proof; pricing with two tiers; FAQ accordion; simple footer.  
> Look: lots of whitespace, neutral background, one accent, something like Inter, thin borders—no glassmorphism.  
> Must be responsive. Proper heading order, visible focus states on buttons. Use placeholder copy, don’t invent fake customer names.  
> I’m happy when mobile scroll has zero horizontal junk.

Dashboard

Bad:

> Create a dashboard with charts.

Better:

> Admin shell: sidebar (Dashboard / Users / Settings), top bar with search + user menu.  
> Dashboard body: four KPI cards—Revenue, Orders, Conversion, Refunds—then a 30-day line chart, then a bar chart for top products, then a sortable orders table with id, customer, total, status, date.  
> Data from mocked JSON in a `data/` folder. Show loading skeletons; empty state when the table has no rows.  
> No paid chart APIs. Small components, semantic HTML.  
> This step is only layout + fake data + route placeholders—no real auth yet.

Adding a feature mid-flight

Bad:

> Add user profiles.

Better:

> Profile screen from the user menu. Fields: name (required), avatar URL optional, bio max 240 chars, timezone dropdown.  
> Inline validation; Save disabled until valid; toast on success. Optimistic updates optional—don’t block on that.  
> Explicitly out of scope: password, email, billing.  
> Match whatever spacing and components the app already uses; if there’s no form primitive, add a single `ProfileForm` instead of copy-pasting markup everywhere.

A scrap of template I keep in Notes

When I’m blanking, I paste this and fill what I know:

Goal:
Who it’s for:
Main happy-path (a short paragraph):

Must ship:
Nice if there’s time:

Visual refs (apps, screenshots, adjectives):

Tech / limits / “do not”:

Empty & error behavior:

Definition of done:

Blank lines mean “model will improvise here” on purpose. I only leave them blank when I’m fine with that.

Last thing

I used to think long prompts were overhead. They’re not—they’re where the work moved. The model is fast either way; I slow down when my ask is fuzzy.

So yeah: spend the extra minute. Your future you, the one who isn’t diff-hunting at midnight, will notice.

Vibe Coding Is Powerful, but Technical Skills Still Matter

aichannode — Tue, 24 Mar 2026 20:49:20 +0000

Vibe Coding Is Powerful, but Technical Skills Still Matter

AI-assisted development is changing how we build software. Today, it's possible to describe an idea in plain language and get working UI, components, or even entire features within seconds. This new workflow — often called vibe coding — is becoming more common among developers.

Instead of starting from scratch, we now guide AI tools, refine their outputs, and move forward much faster. It feels like development speed has multiplied overnight. For prototyping and iteration, this approach is incredibly powerful.

But this shift raises an important question:

If AI Can Build So Much, Do Technical Skills Still Matter?

At first glance, it might seem like technical depth is becoming less important. If AI can generate UI, logic, and structure, maybe developers only need to describe what they want.

However, real-world projects are rarely built in isolation. They already have existing architectures, dependencies, styling systems, and constraints. Generated code doesn’t always fit perfectly into those environments.

I ran into exactly this situation today while working on my own project.

My Attempt to Upgrade My Blockchain Project UI

I spent some time working on my personal blockchain project. The goal of this project is to support multiple EVM-compatible chains as well as the Solana ecosystem. The functionality was already in place, so I decided to improve the user interface.

Before making any changes, my UI looked like this:

To speed things up, I used an AI-powered UI generator to redesign the interface. Within minutes, it produced a modern and polished layout. The spacing, typography, and components all looked significantly better than my original version.

Here’s what the generated UI looked like:

The result was impressive. It felt like I could dramatically upgrade my project without spending hours designing everything manually. This is exactly the promise of vibe coding — fast iteration and high-quality output.

But then I checked the implementation.

When AI-Generated Code Meets a Real Project

My project was built with React using Create React App and styled with plain CSS. The generated UI, however, used Tailwind CSS and React + Vite. The component structure was also quite large and heavily dependent on utility classes.

At first, I considered copying the entire UI directly into my project using an AI coding assistant. But I quickly realized that doing so would likely break my existing structure. Mixing different build tools, styling systems, and architectures would create a messy codebase.

This is where vibe coding meets reality. AI-generated code works great in isolation, but integration into an existing project requires understanding how things fit together.

A Step-by-Step Migration Instead of Copy-Paste

Instead of copying everything at once, I decided to migrate the design piece by piece. I reviewed each section, understood the layout, and recreated it within my own structure. Sometimes I translated Tailwind utilities into my CSS. Other times I simplified components to better match my architecture.

During the process, the UI gradually improved:

Insert Image 3 — Mid-Migration Progress (Optional)
(Optional screenshot showing partial migration)

Because I understood the fundamentals of React and CSS, I could adapt the generated design quickly. Surprisingly, within about an hour, I had successfully migrated the key parts of the UI into my project.

Here’s the final result:

Insert Image 4 — Final Updated UI
(Place your final UI screenshot here)

What This Experience Taught Me

This small experience reinforced an important idea. Vibe coding is powerful, but it doesn’t eliminate the need for technical skills. AI helped me generate a great design, but integrating it required understanding my stack, architecture, and styling approach.

Without that knowledge:

I wouldn’t have recognized the incompatibilities
I wouldn’t have avoided breaking my project
I wouldn’t have migrated the UI cleanly
I wouldn’t have maintained consistency

AI accelerated the process, but technical skills made it successful.

Final Thoughts

We are clearly entering the vibe coding era. AI tools are becoming essential for faster development, better prototyping, and improved productivity. But this shift doesn’t reduce the importance of technical ability — it actually makes it more valuable.

The better your fundamentals, the more effectively you can use AI. Instead of being limited to copying generated code, you can adapt it, improve it, and integrate it into real-world systems.

Vibe coding is powerful.
But technical skills are what make it truly work. 🚀