Woah Amazon’s new Kiro IDE is absolutely HUGE.

And if you think this is just another Cursor or Copilot competitor then you are dead wrong on the spot…

This is a revolutionary approach to how AI coding assistants are supposed to be… This is real software development.

Development based on real GOALS — not just randomly prompting an agent here and there.

No more blind stateless changes — everything is grounded on real specs, requirements, and goals 👇

Amazon Kiro understands that you’re not just coding for coding sake — you have actual targets in mind.

Targets it can even define for you — in an incredibly detailed and comprehensive way:

Look — it can even make unbelievably sophisticated designs for you based on your requirements 👇

I told you — this is REAL software development.

This is just one of the incredibly innovative Kiro features that no other IDE has.

And guess what — It’s based on VS Code — switching is so ridiculously easy — you can even keep your VS Code settings and most extensions.

Goal-first thinking, agentic automation, and deep integration with tools developers already use.

Two big ideas

Kiro is based on two big ideas it implements in an unprecedented way:

Spec-driven development

This is very similar to what Windsurf tried to do with their recent Markdown planning mode update:

You don’t start with code. You start with intent — written in natural language or diagrams.

These specs live alongside your codebase, guiding it as the project evolves. Kiro continuously uses them to generate and align features, update documentation, track architectural intent, and catch inconsistencies.

Background hooks

This one is absolutely insane — how come no one ever thought of this until now?

Hooks — automated agents that run in the background. As you code, they quietly:

• Generate and update docs
• Write and maintain tests
• Flag technical debt
• Improve structure and naming
• Ensure the implementation matches your specs

This isn’t just a chat window on the side. This is an always-on assistant that sees the entire project and works with you on every save.

Under the hood

Code OSS Core

Kiro is built on Code OSS — the same open-source engine behind VS Code. Your extensions, keybindings, and theme carry over seamlessly. Zero learning curve.

MCP Integration

It supports the Model Context Protocol, allowing Kiro to call external agents and tools through a shared memory layer. This sets it up for a future of multi-agent collaboration that’s already taking shape.

Model Support

Kiro runs on Claude Sonnet 4.0 by default, with fallback to Claude 3.7. Support for other models, like Gemini, is on the roadmap — and the architecture is designed to be model-flexible.

Massive demand already

Kiro is in free preview right now — but massive demand has already forced AWS to cap usage and implement a waitlist.

A full pricing structure is on the way — something like:

• Free: 50 interactions/month
• Pro: $19/month for 1,000 interactions
• Pro+: $39/month for 3,000 interactions

Signups are open, but usage is currently restricted to early testers.

Better

If you’ve used Cursor or Windsurf, you already know how powerful it is to have agentic workflows built directly into your IDE.

Kiro builds on that foundation — but shifts from reactive prompting to proactive structure. It doesn’t just assist your coding. It tries to own the meta-work: the tests you skip, the docs you forget, the loose ends that add up over time.

That’s where Kiro stakes its claim — not just as a smart code editor, but as an operating system for full-stack development discipline.

Don’t ignore this

Kiro is still early, but it’s not experimental in spirit. It’s built with a clear vision:

• Bring AI into every layer of the software kdevelopment process
• Anchor work around intent, not just implementation
• Support fast prototyping and scalable production with equal seriousness

For solo builders and teams alike Kiro is most definitely worth keeping an eye on.

Not just for what it does now, but for what it signals about where modern development is headed.

It’s happening.

Vibe coding jobs are becoming a reality — and these aren’t little paying jobs either.

Look at this — a vibe coding job listing on Indeed paying up to $220,000 per year.

This is the real deal.

Many of you laughing at vibe coding and AI-assisted coding in general — now see for yourself.

More and more companies are seeing vibe coding as the real deal.

Of course many developers are still in denial.

Developers who keep ignoring AI are going to eventually find themselves left behind.

Because what vibe coding really means is that developers are finally being allowed to focus. No more spinning your wheels on boilerplate.

No more spending hours trying to scaffold out yet another UI flow that’s 90% the same as last week’s. No more pretending documentation is exciting.

AI does the boring stuff. You do the real work — thinking clearly, setting direction, maintaining the mission of the project.

The tools are here. Cursor. Windsurf. Claude Code. They let you shape a whole app without opening Stack Overflow once. You flow. You prompt. You build. You debug at the speed of thought.

And businesses are catching on. They’re not just hiring developers who can memorize syntax and write boilerplate from scratch. They’re hiring developers who can design systems, think fast, move faster — and collaborate with AI like it’s second nature.

That vibe coder you’re laughing at? They’re now outputting 10x the features per sprint, spending half the time sipping coffee and planning product-market fit, while the AI handles the grind.

It’s no longer “do you use AI when coding?” The question now is: how well do you prompt? How fluid is your interaction with a coding agent? Can you build and ship products faster than the average dev team — on your own?

We’re talking developers who can:

• Build an MVP in a weekend using only natural language + refactors
• Design UIs visually and prompt the backend logic to match
• Jump between different stacks without having to “learn” them from scratch

They don’t memorize. They orchestrate. That’s the new skill set.

And vibe coding doesn’t kill the developer. It amplifies the real ones. The strategic thinkers. The system architects. The fast learners. The ones who know how to move ideas into product — not just line-by-line into code.

So yeah. If you’re laughing now you’ll probably be crying later.

The wave is already here. Some devs are riding it.

Others? Still stuck complaining in Reddit thread about how terrible AI is for “corrupting” software dev.

Good luck with that.

We’re building the future, with our minds. One prompt at a time.

Software development is changing fast and AI is at the center of it.

Today’s best developers aren’t just writing code — they’re collaborating with intelligent agents to think, plan, and build differently.

There’s a shift happening in how apps are imagined, designed, and brought to life.

Developers who know how to work with AI can move faster, think bigger, and build smarter — from the very first spark of an idea all the way to a finished product.

Generate requirements

AI helps you systematically break down a high-level idea into concrete, manageable requirements.

Instead of getting stuck in a brainstorming loop, you can use an AI agent to act as a consultant, asking clarifying questions and suggesting a comprehensive list of features.

With this you identify potential edge cases and missing components early in the process.

What the AI can do

• Generate a list of functional and non-functional requirements.
• Ask clarifying questions to refine your initial idea.
• Analyze your requirements for potential contradictions or missing details.
• Suggest features based on industry standards and best practices.

Example prompt

• “I want to build a social media app for fitness enthusiasts. What are the core features I’ll need for this app? Categorize them by user roles (e.g., individual user, admin).”
• “Given the requirements for a real-time chat feature, what are the potential technical challenges and non-functional requirements I should consider, such as scalability and security?”
• “Act as a product manager. I’ve defined the following features for my to-do list app: user authentication, task creation, task deletion, and task editing. What is missing? What other features would make this a more complete product?”

Design and plan

Once you have your requirements, an AI agent can help you with the architectural design, data modeling, and even UI/UX wireframing. It can suggest design patterns, database schemas, and user flows, acting as a virtual architect or designer.

What the AI can do

• Propose an application architecture (e.g., monolithic, microservices).
• Design a database schema based on your app’s features.
• Generate user flow
• Suggest UI components and design patterns for specific screens.

Example prompts

• “Design a scalable and secure backend architecture for a real-time messaging app. It should support millions of concurrent users and handle high-volume data traffic.”
• “Based on the following features for a food delivery app: user profiles, restaurant listings, order tracking, and payment processing, generate a detailed database schema using PostgreSQL.”
• “Give me an UI flow for the user registration and login process of a mobile app. The flow should include screens for sign-up, email verification, password reset, and a successful login state.”
• “Generate an example of a component for a social feed that uses the React framework. The component should display posts with images, likes, and comments.”

Prototype rapidly

When it’s time to build AI can rapidly create the foundational structure or “scaffolding” of your app.

This includes generating project directories, configuration files, and basic API endpoints, allowing you to start building on a solid foundation without the manual setup overhead.

What the AI can do

• Generate a project structure for a specific framework (e.g., a Next.js app with a Tailwind CSS setup).
• Create a basic API with CRUD (Create, Read, Update, Delete) endpoints.
• Write configuration files for linters, formatters, and build tools.

Example prompts

• “Create the file and folder structure for a full-stack e-commerce application using Next.js, Express.js, and MongoDB.”
• “Generate a basic REST API in Python using Flask. The API should have endpoints for a ‘products’ resource, including /products (GET), /products/:id (GET), and /products (POST).”
• “Set up a new React project in the current directory. Include the necessary dependencies for state management with Zustand and routing with React Router.”

Create and build

This is where AI shines as a pair programmer.

Go beyond simple functions and files — ask the AI to implement entire features at a high level and stay focused on the bigger picture.

What the AI can do

• Write feature-complete code blocks based on a description.
• Integrate different services or APIs.
• Refactor existing code to improve performance or readability.
• Generate test data for specific functions or features.

Example prompts

• “Implement the ‘user registration’ feature. It should take a username and password, validate them, hash the password, and store the new user in the database. Use Express.js and Mongoose.”
• “Create a ‘search functionality’ for a list of blog posts. The search should be case-insensitive and match against the post’s title and content. Write this using React with hooks.”
• “Using the requests library in Python, write a function to fetch weather data from the OpenWeatherMap API for a given city and parse the JSON response.”

Testing

AI can automate and enhance your testing process by generating test cases, identifying potential bugs, and even creating entire test suites. This helps you catch errors early and ensures your code is robust.

What the AI can do

• Generate unit tests for functions or components.
• Create integration tests to verify interactions between different parts of your application.
• Find edge cases and potential failure scenarios for your code.
• Write end-to-end tests for critical user flows.

Example prompts

• “Write Jest unit tests for the following calculateTax function. Include tests for positive cases, zero values, and invalid inputs like negative numbers or strings.”
• “Generate integration tests for the user authentication flow to ensure that a new user can successfully register and log in.”
• “Analyze the updateShoppingCart function for potential bugs or race conditions, and suggest test cases to expose them.”
• “Write a Playwright test script to simulate a user adding an item to a cart and completing the checkout process on an e-commerce website.”

Code review

Even for flawless code, an AI can provide an objective second opinion. It can check for style consistency, potential security vulnerabilities, and opportunities for optimization that you might have missed.

What the AI can do

• Check for adherence to specific style guides (e.g., Airbnb JavaScript Style Guide).
• Identify code smells, anti-patterns, and potential performance bottlenecks.
• Suggest refactoring to make the code cleaner and more efficient.
• Detect common security vulnerabilities like SQL injection or cross-site scripting (XSS).

Example prompts

• “Review the following code for any security vulnerabilities, especially in handling user input.”
• “Analyze this file and suggest ways to simplify the logic and improve its readability. Use clear, concise language in your suggestions.”
• “Perform a code review of this pull request. Check for adherence to SOLID principles and clean code practices. Also, provide a brief summary of the changes and your overall assessment.”
• “Suggest a better name for the processData function and provide an explanation for why the new name is more appropriate.”

Documentation

One of the most tedious parts of coding, documentation, can be almost fully automated with AI. A well-prompted AI can generate a wide range of documentation, from in-line code comments to comprehensive API guides.

What the AI can do

• Write clear and concise JSDoc comments for functions and classes.
• Generate a README.md file for a project, including setup instructions and usage examples.
• Create API documentation based on your codebase.
• Summarize a complex code block or a project’s purpose in a non-technical way.

Example prompts

• “Write JSDoc comments for the fetchUserData function, including a description of its purpose, parameters, and what it returns.”
• “Generate a README.md file for a Python project that uses Flask and SQLAlchemy. The README should include an overview, installation instructions, how to run the app locally, and a basic API endpoint reference.”
• “Act as a technical writer. Explain how the PaymentService class works in simple, clear terms for a new developer joining the team.”
• “Generate a CHANGELOG.md for this project based on the git commit history since the last release.”

Many developers are still treating AI like a toy.

Especially the ones still scoffing at the idea of vibe coding and LLMs in general.

They’ll let it spit out some boilerplate or demo code, then go back to slogging through the hard stuff by hand.

They’re still stuck with the 2015 coding mindset.

Yet AI is already capable of shaving hours off the kinds of tasks that quietly eat your time every day.

The real edge comes when you stop thinking of it as a novelty and start using it as a persistent weapon in your workflow.

Developers who figure that out will move a lot faster than the rest.

These are 5 powerful ways to start using AI and coding agents to their maximum potential.

1. Write regex without the headaches

Regex is powerful but writing it by hand can be so painful. AI can:

• Translate plain-English rules into a working regex.
• Explain cryptic existing patterns.
• Generate positive and negative test strings so you can double-check correctness.

Example prompt:

“Write a function in a new file with a regex that matches ISO-8601 timestamps ending in Z, show me 5 valid and 5 invalid examples.”

You can still verify the AI’s regex in a tool like regex101 to confirm it works across the engine you’re using.

2. Easily test APIs with curl and beyond

When you’re debugging APIs writing curl commands with the right flags can be tedious.

Even with Postman you still have to click here and there, entering the parameters, organizing and creating folders… (ugh)

But with coding agents you can:

• Turn a plain description into a ready-to-run curl.
• Translate curl into client code in Python, Node, Go, etc.
• Add flags for retries, headers, or timing diagnostics.

Example prompt:

“Give me a curl command to POST JSON to /users, set an Authorization header, and print both response headers and timing stats.”

From there, you can ask the AI to convert that curl into production-ready code.

3. Rapidly scaffolding apps and webpages

Instead of Googling CLI flags or digging through docs you can let AI set up project starters for you:

• Next.js with Tailwind and TypeScript.
• Vite with React or Vue.
• Pre-configured routes and components that compile on the first run.

Example prompt:

“Scaffold a Next.js app with TypeScript, ESLint, Tailwind, and create Home, About, and Blog pages with starter code.”

This gets you running instantly so you can focus on building features.

And of course with AI you can go beyond project starters and stock templates — with LLMs and free-form text your options are limitless.

Example prompt:

“Bootstrap a custom web app for me. It should be a Next.js project with TypeScript, Tailwind, and ESLint, it should include:
– A /dashboard route with a responsive sidebar and top nav.
– Authentication stubs (login, signup, logout flow) — using [auth service of your choice]
– A mock API for /todos with create/read/update/delete endpoints.
– Example unit tests (with Jest) for at least one component and one API handler.
– A README that explains setup, usage, and next steps.”

4. Write awesome READMEs and changelogs

AI is excellent at producing the boilerplate structure for docs:

• README.md with install steps, usage, config, and contributing guidelines.
• CHANGELOGs that follow “Keep a Changelog” and Semantic Versioning.
• Quick-start snippets in multiple languages and operating systems.

Example prompt:

“Generate a README for this project with sections for Install, Quick Start, Usage, Config, FAQ, and Contributing.”

Then edit and polish to fit your repo’s voice.

In some repos I see how they automatically generate changelogs directly from the commits for their releases — often problematic as commits often don’t map cleanly to features.

But now with AI you just say something like:

Using my commit history, generate a CHANGELOG.md entry for version 1.3.0 fusing Keep a Changelog format. Group items under Added, Fixed, Changed, and Removed. Write in a clean, professional style.

5. Quick refactorings and inline edits

Modern AI coding tools like Windsurf, Cursor, or Copilot let you select code and simply say what you want changed:

• Convert callbacks to async/await.
• Converting a class to a hook — or a list of functions
• Extract a function or interface.

Very similar to now-not-so-useful VS Code extensions like JavaScript Booster.

Example prompt (select code first):

“Inline edit: refactor this function to use async/await, add JSDoc types, and keep behavior identical.”

Preview diffs, run tests, and expand the scope only after you’re confident.

What if the next generation of developers never opens a code editor?

This is the serious case Google is making with their incredible new natural language coding tool, Opal.

This isn’t just another no-code tool.

This is a gamble on re-thinking the very idea of coding.

Opal is a visual playground where anyone can build AI-powered “mini apps” without writing a single line of code.

Launched under Google Labs in July 2025.

You tell it what you want—“summarize a document and draft an email reply”—and Opal responds with a working, visual flow. Inputs, model calls, outputs—all wired up. You can tweak steps via a graph interface or just… keep chatting.

It’s not an IDE. It’s not even a low-code tool. It’s something stranger:

A conversational, modular AI agent that builds, edits, and is the app.

A big deal

Traditional development tools like IDEs and terminals and frameworks were all built with the same mindset — humans write code to tell computers what to do.

But Opal says:
Humans describe outcomes. The AI figures out the how.

It’s the opposite of what we’ve spent decades optimizing for:

• No syntax.
• No debugging.
• No deployment targets.

Just outcomes.

And it’s not alone. Google’s Jules can already work on your repo autonomously. Their Stitch tool can generates UIs from napkin sketches. Stitch + Jules + Opal = a future where the IDE becomes invisible.

This is also something similar we see in tools like OpenAI Codex to a lesser but important extent.

For the first time in the history of software development, we can make massive, significant changes to our codebase without having to ever open the IDE and touch code.

Opal vs IDEs

Where an IDE assumes:

• You know the language
• You own the repo
• You debug with your brain
• You ship and maintain your code

Opal assumes:

• You don’t need to know how anything works
• You want it running now
• The AI handles the logic
• The environment is the product

It’s the Uber of programming: you don’t need to build the car. You just say where you want to go.

Confidence

The tradeoff:

• Opal is fast but opaque.
• Code is slow but transparent.

There’s no Git. No static typing. No test suite. You’re trusting the AI to do the right thing—and if it breaks, you might not even know why.

But that’s the point. This isn’t supposed to do what an IDE does.
This is supposed to make you forget you ever needed one.

For this to ever happen — we have to have an incredibly high level of confidence — that the changes being made are exactly what we specify and every ambiguity is accounted for.

Will code become obsolete?

Not yet. Not for large systems. Not for fine-tuned control.

But Opal shows us a real possibility:

• That small tools can be spoken into existence.
• That AI will eat the scaffolding, the glue code, the boring parts.
• That someday, even real products might be built in layers of AI-on-AI—no React, no Docker, no IDE in sight.

It’s not just no-code. It’s post-code.

Welcome to the brave new world. Google’s already building it.