Latest news with #AIcoding
Geeky Gadgets
a day ago
- Geeky Gadgets
Best AI Coding Tools of 2025 : Say Goodbye to Coding Frustrations
What if writing code felt less like a chore and more like having a skilled collaborator by your side? With the rise of AI-powered coding tools, this vision is no longer a distant dream but an everyday reality for developers worldwide. From automating repetitive tasks to generating entire blocks of functional code, these tools are reshaping the way we approach software development. Whether you're a seasoned programmer or just starting out, the promise of faster workflows, fewer bugs, and smarter solutions is hard to ignore. But with so many options available, how do you know which tool is right for you? This exploration by of the best AI coding tools will guide you through the standout options in this rapidly growing space. Dr Alex Young uncovers tools tailored to beginners dipping their toes into coding, as well as advanced platforms designed for tackling complex, enterprise-level projects. Along the way, you'll learn how these tools use innovative technologies like large language models (LLMs) to enhance productivity and creativity. By the end, you'll not only have a clearer understanding of what's out there but also gain insights into how these tools can transform your development process. After all, the future of coding isn't just about writing lines of code—it's about working smarter, not harder. Understanding AI Coding Tools AI coding tools are designed to streamline the software development process by automating repetitive tasks and providing intelligent assistance. These tools can handle activities such as code generation, debugging, and deployment, allowing you to focus on creativity and problem-solving. By integrating AI into your workflow, you can reduce development time and improve code quality. Below is an evaluation of some of the leading tools in this space. Top AI Coding Tools and Their Features Here's an in-depth look at some of the most notable AI coding tools currently available: Cursor AI: Built on Visual Studio Code, Cursor AI offers extensive customization options and supports multiple coding models. It is particularly suitable for users with some coding experience. Pricing starts at $20/month , and a free trial is available for new users. Built on Visual Studio Code, Cursor AI offers extensive customization options and supports multiple coding models. It is particularly suitable for users with some coding experience. Pricing starts at , and a free trial is available for new users. Google Firebase Studio: Designed for advanced programmers, this platform supports repository imports and integrates seamlessly with Google's Gemini model. While it is free to use, it may struggle with simpler tasks such as regular expressions, making it better suited for complex projects. Designed for advanced programmers, this platform supports repository imports and integrates seamlessly with Google's Gemini model. While it is free to use, it may struggle with simpler tasks such as regular expressions, making it better suited for complex projects. Windsurf (formerly Kodium): Tailored for SaaS enterprise applications, Windsurf provides real-time code rendering and robust security features. It offers both free and paid versions, with pricing starting at $20/month . Tailored for SaaS enterprise applications, Windsurf provides real-time code rendering and robust security features. It offers both free and paid versions, with pricing starting at . Bolt: Optimized for Mac users, Bolt integrates seamlessly with development environments and delivers fast AI rendering. However, its browser-based nature limits accessibility for Windows users. Pricing is competitive, making it an attractive option for Apple enthusiasts. Optimized for Mac users, Bolt integrates seamlessly with development environments and delivers fast AI rendering. However, its browser-based nature limits accessibility for Windows users. Pricing is competitive, making it an attractive option for Apple enthusiasts. Replit: A web-based platform that supports the full development lifecycle, including deployment. While user-friendly, it lacks advanced coding capabilities, making it more suitable for simpler projects. Paid plans start at $25/month . A web-based platform that supports the full development lifecycle, including deployment. While user-friendly, it lacks advanced coding capabilities, making it more suitable for simpler projects. Paid plans start at . Lovable: A minimalist tool designed for beginners, Lovable excels in fast prototyping and built-in deployment features. However, it offers limited customization options for experienced developers. Pricing begins at $20/month . A minimalist tool designed for beginners, Lovable excels in fast prototyping and built-in deployment features. However, it offers limited customization options for experienced developers. Pricing begins at . V0 by Vercel: Specializing in front-end web development, V0 is both affordable and accessible, making it ideal for quick-start projects and developers seeking simplicity. Specializing in front-end web development, V0 is both affordable and accessible, making it ideal for quick-start projects and developers seeking simplicity. GitHub Copilot: Integrated into existing IDEs, GitHub Copilot provides intelligent code suggestions, making it a great choice for junior developers. However, it is less comprehensive compared to AI-first tools, which may limit its appeal for advanced users. Best AI Coding Tools for Developers in 2025 Watch this video on YouTube. Advance your skills in AI coding tools by reading more of our detailed content. The Role of Large Language Models (LLMs) in AI Coding Large language models (LLMs) form the backbone of many AI coding tools, allowing advanced capabilities such as code generation, optimization, and debugging. These models are trained on vast datasets, allowing them to understand and generate complex code structures. Below are some of the most notable LLMs used in AI coding: Claude Sonnet: Known for its speed and reliability, Claude Sonnet is highly effective for complex projects and code optimization. However, it may require occasional user intervention to align with specific prompts. Known for its speed and reliability, Claude Sonnet is highly effective for complex projects and code optimization. However, it may require occasional user intervention to align with specific prompts. Gemini 2.5 Pro: Integrated with Firebase Studio, this model excels at generating long lines of code but may occasionally deviate from user instructions, requiring careful review. Integrated with Firebase Studio, this model excels at generating long lines of code but may occasionally deviate from user instructions, requiring careful review. OpenAI Models: Renowned for their strong reasoning capabilities, these models are particularly effective in debugging and often suggest alternative solutions. However, they may produce occasional errors, necessitating user oversight. Renowned for their strong reasoning capabilities, these models are particularly effective in debugging and often suggest alternative solutions. However, they may produce occasional errors, necessitating user oversight. DeepSeek: An open source, budget-friendly model, DeepSeek is ideal for self-hosted setups and cost-conscious users. It delivers solid coding capabilities without compromising affordability. How to Choose the Right AI Coding Tool Selecting the right AI coding tool or model depends on your expertise, project requirements, and budget. Here are some recommendations to guide your decision: For Beginners: Tools like Lovable, Bolt, or Claude Sonnet's web interface offer simplicity and ease of use, making them ideal for those new to coding. Tools like Lovable, Bolt, or Claude Sonnet's web interface offer simplicity and ease of use, making them ideal for those new to coding. For Intermediate to Advanced Users: Cursor AI, Windsurf, or Google Firebase Studio provide robust features and advanced capabilities for tackling complex projects. Cursor AI, Windsurf, or Google Firebase Studio provide robust features and advanced capabilities for tackling complex projects. For Budget-Conscious Users: Open source options like DeepSeek or free tools such as Firebase Studio are excellent choices for those looking to minimize costs without sacrificing functionality. Maximizing the Potential of AI Coding Tools To fully use the benefits of AI coding tools, consider adopting the following strategies: Experiment with multiple tools and models to identify the ones that best align with your workflow and project requirements. Use AI-generated code as a learning opportunity by carefully reviewing and debugging the outputs to enhance your understanding of coding principles. Combine different tools and models to capitalize on their unique strengths, allowing you to achieve optimal results for your projects. Empowering Your Development Journey AI-powered coding tools and LLMs are transforming software development by offering solutions that cater to a wide range of skill levels and project needs. By understanding the strengths and limitations of each tool and model, you can make informed decisions that enhance your productivity and creativity. Whether you are building a simple prototype or a complex application, these technologies provide the support you need to succeed in today's fast-evolving development landscape. Media Credit: Dr Alex Young Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
4 days ago
- Business
- Yahoo
AI coding tools made some experienced software engineers less productive in a recent study
AI coding assistants decreased experienced software developers' productivity by 19%, a new METR study suggests. The study found developers were overconfident in the AI tools, expecting a 20% productivity boost even after using them. Critics caution that AI code editors have advanced since the February study period and that the results are site-specific. AI code editors have quickly become a mainstay of software development, employed by tech giants such as Amazon, Microsoft, and Google. In an interesting twist, a new study suggests AI tools made some developers less productive. Experienced developers using AI coding tools took 19% longer to complete issues than those not using generative AI assistance, according to a new study from Model Evaluation & Threat Research (METR). Even after completing the tasks, participants couldn't accurately gauge their own productivity, the study said: The average AI-assisted developers still thought their productivity had gained by 20%. METR's study recruited 16 developers with large, open-source repositories that they had worked on for years. The developers were randomly assigned into two groups: Those allowed to use AI coding assistance and those who weren't. The AI-assisted coders could choose which vibe-coding tool they used. Most chose Cursor with Claude 3.5/3.7 Sonnet. Business Insider reached out to Cursor for comment. Developers without AI spent over 10% more time actively coding, the study said. The AI-assisted coders spent over 20% more time reviewing AI outputs, prompting AI, waiting on AI, or being idle. METR researcher Nate Rush told BI he uses an AI code editor every day. While he didn't make a formal prediction about the study's results, Rush said he jotted down positive productivity figures he expected the study to reach. He remains surprised by the negative end result — and cautions against taking it out of context. "Much of what we see is the specificity of our setting," Rush said, explaining that developers without the participants' 5-10 years of expertise would likely see different results. "But the fact that we found any slowdown at all was really surprising." Steve Newman, serial entrepreneur and cofounder of Google Docs, described the findings in a Substack post as "too bad to be true," but after more careful analysis of the study and its methodology, he found the study credible. "This study doesn't expose AI coding tools as a fraud, but it does remind us that they have important limitations (for now, at least)," Newman wrote. The METR researchers said they found evidence for multiple contributors to the productivity slowdown. Over-optimism was one likely factor: Before completing the tasks, developers predicted AI would decrease implementation time by 24%. For skilled developers, it may still be quicker to do what you know well. The METR study found that AI-assisted participants slowed down on the issues they were more familiar with. They also reported that their level of experience made it more difficult for AI to help them. AI also may not be reliable enough yet to produce clean and accurate code. AI-assisted developers in the study accepted less than 44% of the generated code, and spent 9% of their time cleaning AI outputs. Ruben Bloom, one of the study's developers, posted a reaction thread on X. Coding assistants have developed considerably since he participated in February. "I think if the result is valid at this point in time, that's one thing, I think if people are citing in another 3 months' time, they'll be making a mistake," Bloom wrote. METR's Rush acknowledges that the 19% slowdown is a "point-in-time measurement" and that he'd like to study the figure over time. Rush stands by the study's takeaway that AI productivity gains may be more individualized than expected. "A number of developers told me this really interesting anecdote, which is, 'Knowing this information, I feel this desire to use AI more judiciously,'" Rush said. "On an individual level, these developers know their actual productivity impact. They can make more informed decisions." Read the original article on Business Insider
Yahoo
5 days ago
- Yahoo
AI coding tools may not speed up every developer, study shows
Software engineer workflows have been transformed in recent years by an influx of AI coding tools like Cursor and GitHub Copilot, which promise to enhance productivity by automatically writing lines of code, fixing bugs, and testing changes. The tools are powered by AI models from OpenAI, Google DeepMind, Anthropic, and xAI that have rapidly increased their performance on a range of software engineering tests in recent years. However, a new study published Thursday by the non-profit AI research group METR calls into question the extent to which today's AI coding tools enhance productivity for experienced developers. METR conducted a randomized controlled trial for this study by recruiting 16 experienced open-source developers and having them complete 246 real tasks on large code repositories they regularly contribute to. The researchers randomly assigned roughly half of those tasks as 'AI-allowed,' giving developers permission to use state-of-the-art AI coding tools such as Cursor Pro, while the other half of tasks forbade the use of AI tools. Before completing their assigned tasks, the developers forecasted that using AI coding tools would reduce their completion time by 24%. That wasn't the case. 'Surprisingly, we find that allowing AI actually increases completion time by 19%— developers are slower when using AI tooling,' the researchers said. Notably, only 56% of the developers in the study had experience using Cursor, the main AI tool offered in the study. While nearly all the developers (94%) had experience using some web-based LLMs in their coding workflows, this study was the first time some used Cursor specifically. The researchers note that developers were trained on using Cursor in preparation for the study. Nevertheless, METR's findings raise questions about the supposed universal productivity gains promised by AI coding tools in 2025. Based on the study, developers shouldn't assume that AI coding tools — specifically what's come to be known as 'vibe coders' — will immediately speed up their workflows. METR researchers point to a few potential reasons why AI slowed down developers rather than speeding them up. First, developers spend much more time prompting AI and waiting for it to respond when using vibe coders rather than actually coding. AI also tends to struggle in large, complex code bases, which this test used. The study's authors are careful not to draw any strong conclusions from these findings, explicitly noting they don't believe AI systems currently fail to speed up many or most software developers. Other large scale studies have shown that AI coding tools do speed up software engineer workflows. The authors also note that AI progress has been substantial in recent years, and that they wouldn't expect the same results even three months from now. METR has also found that AI coding tools have significantly improved their ability to complete complex, long-horizon tasks in recent years. However, the research offers yet another reason to be skeptical of the promised gains of AI coding tools. Other studies have shown that today's AI coding tools can introduce mistakes, and in some cases, security vulnerabilities. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
TechCrunch
5 days ago
- TechCrunch
AI coding tools may not speed up every developer, study shows
Software engineer workflows have been transformed in recent years by an influx of AI coding tools like Cursor and GitHub Copilot, which promise to enhance productivity by automatically writing lines of code, fixing bugs, and testing changes. The tools are powered by AI models from OpenAI, Google DeepMind, Anthropic, and xAI that have rapidly increased their performance on a range of software engineering tests in recent years. However, a new study published Thursday by the non-profit AI research group METR calls into question the extent to which today's AI coding tools enhance productivity for experienced developers. METR conducted a randomized controlled trial for this study by recruiting 16 experienced open-source developers and having them complete 246 real tasks on large code repositories they regularly contribute to. The researchers randomly assigned roughly half of those tasks as 'AI-allowed,' giving developers permission to use state-of-the-art AI coding tools such as Cursor Pro, while the other half of tasks forbade the use of AI tools. Before completing their assigned tasks, the developers forecasted that using AI coding tools would reduce their completion time by 24%. That wasn't the case. 'Surprisingly, we find that allowing AI actually increases completion time by 19%— developers are slower when using AI tooling,' the researchers said. Notably, only 56% of the developers in the study had experience using Cursor, the main AI tool offered in the study. While nearly all the developers (94%) had experience using some web-based LLMs in their coding workflows, this study was the first time some used Cursor specifically. The researchers note that developers were trained on using Cursor in preparation for the study. Nevertheless, METR's findings raise questions about the supposed universal productivity gains promised by AI coding tools in 2025. Based on the study, developers shouldn't assume that AI coding tools — specifically what's come to be known as 'vibe coders' — will immediately speed up their workflows. Techcrunch event Save up to $475 on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $450 on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | REGISTER NOW METR researchers point to a few potential reasons why AI slowed down developers rather than speeding them up. First, developers spend much more time prompting AI and waiting for it to respond when using vibe coders rather than actually coding. AI also tends to struggle in large, complex code bases, which this test used. The study's authors are careful not to draw any strong conclusions from these findings, explicitly noting they don't believe AI systems currently fail to speed up many or most software developers. Other large scale studies have shown that AI coding tools do speed up software engineer workflows. The authors also note that AI progress has been substantial in recent years, and that they wouldn't expect the same results even three months from now. METR has also found that AI coding tools have significantly improved their ability to complete complex, long-horizon tasks in recent years. The research offers yet another reason to be skeptical of the promised gains of AI coding tools. Other studies have shown that today's AI coding tools can introduce mistakes, and in some cases, security vulnerabilities.
Geeky Gadgets
5 days ago
- Business
- Geeky Gadgets
Claude Code vs Cursor : Why Developers Are Making the Switch
Why are some developers abandoning Cursor in favor of Claude Code? It's not just a matter of preference—it's about solving real frustrations in the coding world. Imagine wrestling with a massive codebase, only to find your tools lagging behind your needs. Or spending hours on repetitive tasks that could be automated. For many developers, tools like Cursor and Windsurf have been reliable companions, but they often fall short when it comes to tackling complex projects or offering the flexibility required for local development. Enter Claude Code—a tool that's not just another AI coding assistant but a fantastic option designed by the same minds behind the Claude AI model. Its seamless integration, precision, and ability to handle large-scale challenges are prompting developers to rethink their workflows. Riley Brown uncovers why Claude Code is becoming a favorite among developers and how it stacks up against competitors like Cursor and Codex. You'll discover the key advantages that make it stand out, from its ability to autonomously manage repetitive tasks to its unmatched performance with extensive codebases. Whether you're curious about its local development support or wondering how it addresses the limitations of other tools, this discussion will shed light on why it's capturing attention in the AI coding landscape. As developers continue to seek tools that enhance productivity without compromising control, the rise of Claude Code signals a shift in how we approach modern software development. Claude Code Overview Key Advantages of Claude Code Claude Code addresses several common challenges faced by developers, offering features that significantly enhance productivity and streamline workflows. Its direct integration with the Claude model ensures precision, reliability, and high performance. Below are the standout benefits that set Claude Code apart: Seamless Integration: As a product of the same team that developed the Claude AI model, Claude Code offers unmatched compatibility and optimized functionality, making sure a smooth and efficient development experience. As a product of the same team that developed the Claude AI model, Claude Code offers unmatched compatibility and optimized functionality, making sure a smooth and efficient development experience. Local Development Support: Unlike cloud-based tools such as Codex, Claude Code supports local development, providing developers with greater security , control , and privacy over their projects. Unlike cloud-based tools such as Codex, Claude Code supports local development, providing developers with greater , , and over their projects. Autonomous Task Execution: Features like auto-mode allow Claude Code to independently handle repetitive or complex tasks, saving time and reducing manual effort. Features like auto-mode allow Claude Code to independently handle repetitive or complex tasks, saving time and reducing manual effort. Large Codebase Management: The tool excels at navigating and analyzing extensive codebases, making it particularly well-suited for enterprise-level projects and large-scale development teams. These features have driven widespread adoption within Anthropic itself, where an impressive 95% of engineers reportedly use Claude Code internally. Its ability to simplify workflows and boost productivity makes it an essential tool for modern developers. How Claude Code Stacks Up Against Competitors While Claude Code is gaining momentum, it's important to evaluate how it compares to other AI coding tools such as Cursor, Windsurf, and Codex. Each of these tools has unique strengths, but Claude Code's deep integration with its AI model gives it a distinct edge in terms of functionality and performance. Cursor and Windsurf: Both tools use the Claude 4 model but focus on proprietary user interfaces and specific features. However, they lack the seamless integration and optimized performance that Claude Code offers. Both tools use the Claude 4 model but focus on proprietary user interfaces and specific features. However, they lack the seamless integration and optimized performance that Claude Code offers. Codex: Known for its user-friendly design, Codex is a cloud-based tool that struggles with flexibility for local development and is less effective when managing large-scale projects. Known for its user-friendly design, Codex is a cloud-based tool that struggles with for local development and is less effective when managing large-scale projects. Specialized Tools: Alternatives like Devon and Code Rabbit cater to niche tasks such as code review but lack the comprehensive development capabilities and versatility of Claude Code. By addressing the limitations of these tools, Claude Code positions itself as a versatile and powerful solution for a wide range of development needs, from small-scale projects to enterprise-level applications. Why Developers Prefer Claude Code Over Cursor for Complex Projects Watch this video on YouTube. Browse through more resources below from our in-depth content covering more areas on Claude Code. Enhancing Developer Workflows Claude Code is designed to integrate seamlessly into your existing development process, complementing other tools like Cursor or Windsurf. Its features are tailored to enhance productivity and simplify complex tasks. Here's how it can transform your workflow: Codebase Analysis: Simplifies the navigation and understanding of large codebases, allowing developers to quickly identify issues, optimize performance, and uncover opportunities for improvement. Simplifies the navigation and understanding of large codebases, allowing developers to quickly identify issues, optimize performance, and uncover opportunities for improvement. Automated Documentation: Generates clear and concise Markdown documentation, reducing the time spent on manual writing and making sure consistent project documentation. Generates clear and concise Markdown documentation, reducing the time spent on manual writing and making sure consistent project documentation. Iterative Development: Supports modular development by allowing developers to start with templates and incrementally expand functionality, fostering a more efficient and organized approach to coding. These capabilities make Claude Code an invaluable tool for developers aiming to optimize their workflows, reduce manual effort, and deliver high-quality results with greater efficiency. Trends Shaping the AI Coding Landscape The growing popularity of tools like Claude Code reflects broader trends in the software development industry. These trends highlight the increasing demand for specialized, user-friendly, and high-performance AI coding solutions. Key developments shaping the market include: SDK Customization: Claude Code's SDK enables developers to create custom interfaces and tools, fostering innovation and addressing niche requirements within specific industries or projects. Claude Code's SDK enables developers to create custom interfaces and tools, fostering innovation and addressing niche requirements within specific industries or projects. Specialized Frameworks: The rise of tools tailored to specific programming needs, such as React or WordPress, demonstrates the growing demand for targeted solutions that enhance productivity and usability. The rise of tools tailored to specific programming needs, such as React or WordPress, demonstrates the growing demand for targeted solutions that enhance productivity and usability. Competitive Evolution: While tools like Cursor and Windsurf focus on model development, Claude Code prioritizes integration and performance, positioning itself as a leader in the programming space. These trends underscore the importance of balancing user experience, model optimization, and developer needs in the rapidly evolving AI coding landscape. Future Implications for Software Development The adoption of AI coding assistants like Claude Code has far-reaching implications for the software development industry. By making advanced coding agents more accessible, these tools are poised to provide widespread access to software development and drive innovation across the field. Here's what the future may hold: Empowering Smaller Teams: Smaller development teams and individual developers can use Claude Code to compete with larger organizations by enhancing their productivity and capabilities. Smaller development teams and individual developers can use Claude Code to compete with larger organizations by enhancing their productivity and capabilities. Niche Applications: Developers may use Claude Code's SDK to create specialized tools and wrappers tailored to unique use cases, opening up new possibilities for innovation. Developers may use Claude Code's SDK to create specialized tools and wrappers tailored to unique use cases, opening up new possibilities for innovation. Boosting Productivity: The integration of AI coding tools into everyday workflows could lead to significant gains in efficiency, allowing developers to focus on creative and strategic aspects of their work. As the AI coding landscape continues to evolve, tools like Claude Code are likely to play a pivotal role in shaping the future of software development. Their ability to streamline workflows, enhance productivity, and support innovation positions them as essential assets for developers navigating the challenges of modern programming. Media Credit: Riley Brown Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
