12 popular technology stacks worth learning in 2025 and a guide to making money quickly
In 2025, technology is developing rapidly, and choosing the right technology stack is crucial. This article will introduce 12 technology stacks that will attract attention in 2025, and come with an AI Notion template guide to make money quickly. Whether you are a web developer, mobile app developer, or expert in the AI/blockchain field, you can find a direction that suits you.
1. MERN stack (MongoDB, Express.js, React, Node.js)
The MERN stack is the perfect solution for full-stack web development, allowing you to build the front-end and back-end of your website using JavaScript, streamlining the development process and increasing the scalability of your application.
Reasons to study MERN in 2025:
- Scalability: MERN’s components are powerful and fault-tolerant.
- Popularity: React and Node.js are popular choices among the developer community.
- Full-stack potential: Use a single language, JavaScript, for everything.
Tools worth exploring:
- React Server Component: Accelerate page loading speed.
- Prisma: Efficiently manage databases.
Related links:
- MongoDB
- Express.js
- React
- Node.js
2. MEVN stack (MongoDB, Express.js, Vue.js, Node.js)
If you're looking for simplicity and performance, the MEVN stack is your choice. It uses Vue.js as an alternative to React, providing a more accessible experience without sacrificing performance.
Reasons to study MEVN in 2025:
- The Simplicity of Vue.js: Ideal for beginners yet powerful enough to meet the needs of professionals.
- Growing Adoption: Enterprises favor Vue.js for its flexibility and efficiency.
Related links:
- Vue.js
- MongoDB
- Express.js
- Node.js
3. JAMstack (JavaScript, APIs, Markup)
JAMstack is not just a technology stack, it is a revolution in web architecture. Designed with speed, security, and simplicity in mind, it is the first choice for static websites.
Reasons to learn JAMstack in 2025:
- Performance: Static websites mean lightning-fast loading speeds.
- Security: Minimize server-side vulnerabilities.
Key tools:
- Next.js: Ideal for server-side rendering.
- Netlify and Vercel: Simplify the deployment process.
Related links:
- JAMstack
- Next.js
- Netlify
- Vercel
4. T3 stack (Next.js, TypeScript, tRPC, Tailwind CSS, Prisma)
For developers who love TypeScript and want to achieve type safety in their applications, the T3 stack is making waves.
Reasons to study T3 in 2025:
- Comprehensive TypeScript: Ensures type safety from front-end to back-end.
- Next.js Performance: Build extremely fast web apps.
Related links:
- Next.js
- TypeScript
- tRPC
- Tailwind CSS
- Prisma
5. Flutter stack (Flutter, Firebase)
Mobile application development is a hot field, and Flutter is the technology stack of choice for cross-platform development.
Reasons to learn Flutter in 2025:
- Cross-platform: Write once, deploy anywhere.
- Rapid development: Features such as hot reload speed up the development process.
Related links:
- Flutter
- Firebase
6. PERN stack (PostgreSQL, Express.js, React, Node.js)
For developers who prefer SQL databases, the PERN stack is a powerful tool for data-driven applications.
Reasons to study PERN in 2025:
- Powerful Database: PostgreSQL is a top-tier relational database management system (RDBMS).
- React Dominance: is a solid choice for modern user interfaces.
Related links:
- PostgreSQL
- Express.js
- React
- Node.js
7. Django stack (Django, PostgreSQL, React/Angular)
Python lovers, this stack is for you. The Django stack is great for rapid development and has built-in security features.
Reasons to learn Django in 2025:
- Quick Development: Get your app up and running quickly.
- Data Science Integration: Easily combine web development with data science.
Related links:
- Django
- PostgreSQL
- React
- Angular
8. DevOps stack (Docker, Kubernetes, Jenkins, Terraform)
Interested in infrastructure and automation? A DevOps stack is critical to simplifying deployment and scaling.
Reasons to learn DevOps in 2025:
- Infrastructure as Code: Automate everything.
- Containerization: Docker and Kubernetes are industry standards.
Related links:
- Docker
- Kubernetes
- Jenkins
- Terraform
9. AI/ML stack (Python, TensorFlow, PyTorch, FastAPI)
AI and ML are more than just buzzwords, they are shaping the future. The AI/ML stack is critical for anyone hoping to make a difference in this space.
Reasons to learn AI/ML in 2025:
- Growing demand: AI experts are needed in every industry.
- Python Ecosystem: Rich libraries make building models easier.
Related links:
- Python
- TensorFlow
- PyTorch
- FastAPI
10. Blockchain development stack (Solidity, Ethereum, Hardhat)
Blockchain technology will not disappear. Mastering this technology stack can put you at the forefront of decentralized applications.
Reasons to learn blockchain in 2025:
- Growth of DeFi: The decentralized finance space is booming.
- Smart Contracts: Solidity is the language of choice for blockchain development.
Related links:
- Solidity
- Ethereum
- Hardhat
11. Spring Boot React stack
For developers who like Java, the Spring Boot React stack provides a powerful backend and dynamic frontend.
Reasons to learn Spring Boot React in 2025:
- Enterprise Adoption: Trusted by large enterprises.
- Java Ecosystem: Take advantage of a wide range of tools and libraries.
Related links:
- Spring Boot
- React
12. Rust full stack (Rust, Actix Web, Yew)
Rust has gained attention for its performance and security, making it an excellent choice for both front-end and back-end development.
Reasons to learn Rust in 2025:
- High Performance: Ideal for memory-intensive applications.
- Evolving Ecosystem: Rust’s community and tooling are expanding rapidly.
Related links:
- Rust
- Actix Web
- Yew
Conclusion
Choosing the right technology stack depends on your personal goals and the type of project you want to create. Whether you’re working on web development, mobile apps, AI, or blockchain, there’s a technology stack that’s right for you. Dig in, keep learning, and start building amazing things! What is your go-to tech stack for 2025? Feel free to share your thoughts!
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By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.


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