61% of developers spend more than 30 minutes a day looking for answers to work-related issues, citing the 2024 Stack Overflow Developer Survey.
In 2025, the solutions are obvious: AI tools. In this article, we’ve identified five technologies, including coding helpers and machine learning platforms, that may act as a helping hand for developers and improve their projects.
How to Select the Best AI Tool for Developers?
The selection of an AI tool is determined by the demands of your project and its compatibility with your existing tools. For example, if you need to automate repetitive coding chores, GitHub Copilot may be useful due to its real-time code recommendations. Furthermore, when evaluating cloud services, understanding the distinctions across platforms is critical; check our guide to selecting the best cloud service.
Consider the ethical implications of employing AI and practise appropriate usage. Remember that AI technologies should support, not replace, human knowledge, achieving a balance in which AI boosts efficiency while without limiting developers’ inventiveness.
To utilise an AI tool efficiently, choose one that is appropriate for your team’s workflow, project objectives, and ethical values.
Top 5 AI Tools for Programmers
1. GitHub Copilot.
GitHub Copilot is an AI-powered coding tool that offers code recommendations and autocompletion to boost productivity. It supports major IDEs such as Visual Studio Code, JetBrains, and Neovim.
GitHub Copilot supports a wide range of programming languages, including Python, JavaScript, and TypeScript, and can assist produce boilerplate code, documentation, and tests. Analysing huge code repositories provides meaningful code suggestions for both novice and experienced developers.
As of November 2024, it had over 1.8 million paying members and was used by over 77,000 organisations, indicating its efficacy and appeal. This AI helper accelerates coding operations and decreases developers’ workloads, allowing them to concentrate on issue solutions.
2. Cursor
Cursor is based on Visual Studio Code and utilises artificial intelligence to deliver context-aware recommendations as well as natural language processing for code creation and editing. You may define your requirements in simple language, and Cursor will convert them into executable code.
It works with VS Code’s current extensions, themes, and keyboard shortcuts. It also enables AI pair programming, which provides predicted code modifications and contextual suggestions to speed up your productivity. The chat interface allows you to ask enquiries and get real-time answers.
Cursor has pricing levels that are appropriate for different users: a Free Hobby plan, a Pro plan at $20/month, and a Business plan at $40/user/month.
3. v0 by Vercel.
V0 is an artificial intelligence tool, comparable to GitHub Copilot and Replit. It is likely to connect with your development environment, providing code completions and recommendations. It may also help with automating routine programming activities, debugging, and optimising code performance.
For teams utilising platforms such as Strapi, v0 may assist speed API development or content management processes by automating chores and delivering context-aware insights, freeing up developers to focus on more critical duties.
4. Bolt.New
Bolt.new, an AI tool for developers in 2025. While precise information is scarce, many have commended its performance, claiming that it outperforms competing programs.
It appears to simplify coding processes, decrease repetition, and improve code quality. An open-source headless CMS, may help developers increase productivity by offering capabilities such as quick API development, content management, customisation, collaboration, and solid security. These skills are very useful in complex tasks like creating a ChatGPT clone with them or improving data visualisation. Bolt.new is an alternative for developers who want to optimise their workflow.
5. Codeum
Codeium, an AI tool akin to GitHub Copilot and Cursor, aims to improve developer productivity. It may interface with common IDEs to provide code completion and context-aware recommendations.
Codeium contains features such as:
- Collaborative and agentic conversation experience.
- Supercomplete ideas based on the next purpose.
- Autocomplete offers quick inline code recommendations.
- Chat Refactor, explain, unit-test, document, and more. Command Inline instruction-based restructuring
More Quality AI Tools For Developers
Machine learning models are developed using TensorFlow, which is part of the Google Cloud AI Platform. It allows you to design, train, and deploy AI models since it is open source. It supports edge devices and federated learning, making it suited for a variety of AI projects.
The OpenAI GPT-4 API offers strong natural language processing capabilities, which are beneficial for language-related activities such as content production, translation, and sentiment analysis. Continuous updates improve its performance.
AutoML Platforms, such as Google Cloud AutoML, enable developers to build machine learning models without requiring substantial knowledge. They provide pre-built models that may be tailored to individual applications, easing AI integration.
AI-enhanced IDEs offer predictive coding and intelligent debugging features. They enhance code quality and speed up development by automating common operations, allowing engineers to focus on more critical issues.
By adding these AI technologies into your workflow, you may handle software development difficulties and complete projects more quickly and efficiently.
Conclusion
The shift from tasks to intelligence is redefining how developers approach coding and problem-solving. With AI-powered tools now capable of assisting with everything from autocompletion to machine learning integration, programmers can significantly boost productivity without compromising creativity. These technologies aren’t just helpful—they’re becoming essential. As AI continues to evolve, the smartest move for developers is to adopt tools that act as a helping hand for programmers, streamlining workflows and enhancing the quality of their output.
