What is Manifest?

Manifest is a YAML-powered backend development platform that lets you build microservices, APIs, databases, storage, and even authentication—all from a single configuration file. No backend code required.

Who created it?

Manifest is an independent tool developed by the Manifest team to simplify backend development for modern applications.

Key Features (Based on the Review)

🧱 Microbackend with Just YAML
Define your entire backend (DB, APIs, Storage, Auth) in one declarative YAML file.

⚡ No Code Backend Setup
Avoid repetitive boilerplate—just configure and deploy.

🧩 Frontend-Ready APIs
Connect the backend directly to your own frontend using the auto-generated endpoints.

🔐 Built-in Authentication
Includes user auth out of the box without any extra setup.

📊 Auto Admin Panel
Instant admin UI with access to all your backend entities and data.

Other Features

• Playground: Try it instantly via the Manifest Playground for hands-on prototyping.

• Handles DB, APIs & Storage: Everything is scaffolded for you.

• Headless Ready: Perfect for CMS, blogs, and marketing sites.

• Great for Microservices: Ideal for tasks like payments, notifications, logs, file uploads.

• Ideal for CRUD-Heavy Apps: Think PIMs, directories, mobile apps, and MVPs.

Maturity

Medium – Already usable and quite powerful, especially for prototyping and internal tools.

Similar Tools

• Firebase: Backend-as-a-Service with realtime DB and auth.

• Supabase: Open-source Firebase alternative with SQL.

• Xano: No-code backend builder with API-first development.

• Appwrite: Backend server for web and mobile apps.

What is Firebase Studio? Firebase Studio is a newly launched development environment by Google that leverages AI to assist in building and prototyping applications, particularly those integrated with Firebase services.

Who created it?: Google

Key Features (Based on the Review)

AI-Powered Prototyping

Generates UI layouts and basic code based on natural language descriptions.

Generate a Blueprint

Creates a basic structure and suggests necessary components.

Real-time Rendering

Displays a live preview of the application as it's being built.

Natural Language UI Modification

Allows users to adjust UI elements (like button colors) using simple text commands.

Other Features

• Integrated Code Editor: Provides a full-fledged coding environment similar to VS Code.

• Firebase Integration: Likely designed for seamless integration with Firebase services (though not deeply explored in the review).

• Gemini Integration: Includes access to Google's Gemini AI model for tasks like code generation and potentially unit testing.

• Version Control: Offers basic version control functionalities (though the reviewer was initially unsure of its backend).

• Public Link Sharing: Enables sharing a public link to the prototyped application.

• Performance Analysis: Includes a Lighthouse integration for performance testing.

Maturity: Medium (Launched recently and currently in preview)

Other AI-Powered IDE Extensions:

• GitHub Copilot: An AI pair programmer that suggests code completions and entire code blocks within existing IDEs like VS Code.

• Cursor: An AI-first code editor built with features like AI-powered code generation, refactoring, and chat.

• Replit: An online IDE with AI code completion and generation features

When I started building my product, I was full of excitement, energy—and assumptions.

Looking back, I realize I could’ve saved myself months of time and a lot of unnecessary complexity if I had just done a few things differently. So I’m writing this in the hope that it helps someone else who’s building something new.

Here are the top mistakes I made—and what I’d do differently today:

Mistake #1: Overengineering the First Version

As an engineer, I had this itch to build everything right from day one. Clean architecture, best practices, scalable for millions of users—even though we barely had ten. I was thinking about edge cases, performance, reliability… all before figuring out if people actually wanted what we were building.

Looking back, I realize I was designing a skyscraper before even laying the foundation.

💡 What I’d Do Differently:

• Start scrappy. Make the MVP intentionally small and simple—just enough to validate with early users.

• Use low-code or no-code when possible. Speed matters more than perfect code.

• Leverage existing SaaS tools for common things like authentication, email, and payments.

• Minimize components—less moving parts means fewer things breaking.

• Use micro backends and DB-as-a-service to avoid infrastructure overhead.

• Skip full CICD initially. Basic version control and manual deploys are usually enough for the first few weeks/months.

You’ll end up rewriting anyway. But you’ll rewrite with clarity, feedback, and real usage in mind—which is way better than premature optimization.

Mistake #2: Not Writing Down the Idea Clearly

I had the idea in my head. I spoke to friends about it, pitched it in conversations, and even started building it. But every time I described it, it sounded a little different. Why? Because I hadn’t taken the time to write it down clearly. And if you can’t write it in a sentence, you probably don’t understand it well enough.

💡 What I’d Do Differently:

Start by writing the idea in one clear sentence. If you can make it shorter, even better. Then expand that into a one-pager answering key questions:

1. What problem am I solving, and is it already being solved by others?
   Understand the existing solutions and identify your unique edge.

2. Why do I want to build this?
   Clarify your motivation—whether it’s passion, market gap, or a personal itch.

3. Who exactly are my users?
   Be specific. Narrow down to personas or segments instead of vague demographics.

4. How are people currently addressing this problem?
   Study current workarounds, tools, or behaviors to validate the need.

5. What’s my business model?
   Think about how this can sustain itself—free, freemium, B2B, subscriptions, etc.

Clarity isn’t just for pitching—it's the foundation for building, getting feedback, and keeping your team aligned.

Mistake #3: Building Based on Assumptions

Once a few people told me the idea was great, I was off to the races. I started building with confidence—without any real validation. Looking back, I now know I was mostly hearing what I wanted to hear.

It’s easy to fall into the trap of early compliments and friendly encouragement. But those aren't proof that people need—or will use—what you're building.

💡 What I’d Do Differently:

• Find where your users hang out. Whether it’s Reddit, LinkedIn, Discord, Twitter—your early users are somewhere. Go there.

• Run a short questionnaire or poll to collect early signals about the problem.

• Talk to strangers, not just friends. Your friends will often support you no matter what—and they may not be the target users.

• Look at your competitors. What are people saying in their reviews? Support forums? Communities? Those are real pain points you can learn from.

Assumptions are expensive. It’s better to “waste time” validating than to waste months building something no one needs.

Mistake #4: Building All the Features

I thought having more features would make my product more appealing. That it would attract more users, cover more use cases, and justify a higher price. In reality, it just made things more complicated, delayed launch, and confused users.

A bloated MVP is no longer an MVP—it’s just a product that hasn’t been validated.

💡 What I’d Do Differently:

• Start with the one core feature that truly solves the user’s main problem.

• Avoid unnecessary integrations. Focus only on the ones your early target users need right now.

• Define your user persona very clearly—then narrow it down even more.

• Ship early, even if it feels bare. Let users tell you what’s missing.

• Skip pricing at first. Let users experience the product, then ask them what it’s worth. Real feedback is better than guesses.

More features don’t mean more users. Solving one problem really well gets you your first ten real users—and they’ll guide you to the next ten.

Mistake #5: Spending Too Much Time on Branding, Design, and Website Polish (Too Early)

I wanted my product to feel unique—stand out with a distinctive brand, beautiful color palette, and a perfect website. So I spent weeks crafting it all. The problem? None of that mattered when the product wasn’t yet validated.

In the MVP stage, your focus should be speed and simplicity. If your product actually solves a real problem, users will use it—regardless of the logo or pixel-perfect UI. Ironically, I rebranded my product twice after launch, realizing all that early time spent on branding was premature.

💡 What I’d Do Differently:

• Use familiar UX patterns: Don’t reinvent the wheel. Design your MVP like the tools your users already love—it lowers the learning curve.

• Focus on the core feature: Strip everything else out. Let users experience the one thing your product does best.

• Keep the website dead simple: One homepage that explains what it does in 10 seconds is enough.

• Leverage UI frameworks: Use existing systems like Tailwind or component libraries to build faster and stay consistent.

• Think like a developer: Design for ease of development. A beautiful design that’s hard to implement will slow you down—make it easy to ship.

Wrapping Up

These were some of the biggest mistakes I made while building my first product—from overengineering to unclear ideas, building on assumptions, and trying to do too much too soon. The good news? Every mistake came with a lesson, and those lessons are shaping how I build today.

In the upcoming newsletter, I’ll go deeper into:

• How to deploy your MVP efficiently

• Quick ways to prototype (even without code)

• Tactics to reach your early users

• How to create and run user surveys that actually work

If you're building something, or planning to, stay tuned. I’ll be sharing practical steps that’ll save you time, energy, and maybe a few gray hairs.

What is Micro-Backend ?

A micro-backend is a low-code backend solution that provides out-of-the-box APIs and authentication on top of a database. Instead of spending months coding a full-fledged backend, you can simply set up your database, configure your APIs, and start building your frontend—your app is ready in no time!

Why Should You Care?

Most small SaaS solutions boil down to CRUD operations (Create, Read, Update, Delete) with authentication. Yet, many founders waste time and resources building a full-scale backend from scratch. With a micro-backend, you can launch faster and cheaper, focusing on your product instead of backend complexity.

Who Is This For?

✅ Solopreneurs & Indie Hackers – Get your product live quickly.
✅ Startups & Small Teams – Validate ideas without backend headaches.
✅ MVP Builders – Ship faster, iterate quicker.

How Does It Work?

1️⃣ Create a database via UI or a simple configuration file (like YAML).
2️⃣ Auto-generate APIs to perform CRUD operations.
3️⃣ Built-in authentication—no extra coding required.
4️⃣ Manage everything via UI or config, skipping complex backend code.

Will I Need to Rewrite It Later?

Many micro-backends can scale well enough for early growth. However, if your product takes off, you’ll eventually need to re-architect for scale. But that’s a good problem to have!

Popular Micro-Backend Solutions

🔹 Firebase : https://firebase.google.com/products/realtime-database
🔹 Appwrite : https://appwrite.io
🔹 Supabase : https://supabase.com
🔹 Pocketbase : https://pocketbase.io
🔹 Convex : https://www.convex.dev

Final Thoughts

If you're building a PoC, MVP, or an early-stage SaaS product that’s mostly CRUD-based, using a micro-backend can save time, money, and effort. Skip the heavy backend coding and get to market faster!

🚀 Start building smarter, not harder.

What is MCP?

MCP is a newly established standardized protocol designed for communication between LLMs and external APIs or data stores.

Who created it ? : Anthropic

Technical Overview

An SDK is available for various programming languages, enabling the implementation of an MCP server in the preferred language. This allows LLMs to perform function calls seamlessly.

Explain in simple way :

A middleware solution acts as a bridge between LLMs and different data sources like APIs, SDKs, and databases. This middle layer helps LLMs talk to these data sources, allowing them to get data and make changes or perform actions based on specific requests.

Git Repo :  https://github.com/modelcontextprotocol

Components

MCP Server: This server provides an MCP endpoint accessible to a Large Language Model (LLM) in a manner akin to a REST API or GraphQL server.

MCP Client: This refers to a client capable of directly accessing the MCP server.

How it works :

This approach separates data from the LLM, allowing it to process user queries, convert them into function calls, and execute actions in a standardized way. The LLM retrieves data through REST APIs or database queries and receives responses in a consistent format.

Consider the following examples:

Example 1: Calendar Integration

A calendar application, like Google Calendar, provides APIs for creating, updating, and canceling invites. Since the LLM does not inherently understand these APIs, an MCP server acts as a wrapper. It defines tools for invite creation, updating, and cancellation. When the LLM receives a related query, it calls the MCP server, which executes the appropriate function, interacts with the calendar API, and returns the response.

Example 2: SQL Database Integration

In a database storing user names and email addresses, an MCP server can expose functions for adding users and retrieving emails. The LLM's query is translated into an SQL command, executed on the database, and the response is returned to the LLM.

Who is using : https://opentools.com/registry

Advantages

• Standardization – Provides a unified approach for LLM tools and data exposure.

• Faster Implementation – Reduces the time needed to build LLM-based workflows.

• Enhanced Dynamism – Enables more dynamic and agentic LLM applications through external integrations.

Use Cases

• Allow users to connect their preferred LLM to your tool’s data via MCP.

• Integrate products with multiple tools to streamline workflows, provided MCP is supported.

Maturity : Early

Summary

MCP is a promising concept that acts as a standardized wrapper for APIs, CLIs, and SDKs. It ensures a unified authorization layer for LLM tools. While still in its early stages, it is gaining adoption and has strong potential.