Latest news with #ClaudeCode
Arabian Post
a day ago
- Arabian Post
Claudia Brings User-Friendly GUI to Claude Code
Claudia, a newly released open‑source graphical user interface for Anthropic's Claude Code, brings a visually intuitive layer to the traditionally command‑line driven tool. Developed by YC‑backed Asterisk, this desktop application enables developers to manage projects, run interactive AI agents, and track progress—all through a local, privacy‑focused interface. The interface addresses growing frustrations among developers using Claude Code's text‑only environment. Asterisk's design employs Tauri for cross‑platform deployment, along with React, Rust, and Tailwind CSS for responsiveness on macOS, Linux and Windows. Its key functionalities include session history navigation, branching checkpoints, custom agent creation, and real‑time analytics—all critical improvements over the linear CLI workflow. Security and data control are central. Agents run in sandboxed processes, with filesystem whitelisting and network constraints to protect user environments. The local‑first philosophy ensures all data resides on the developer's machine, removing cloud‑based privacy concerns. Customisable security profiles and violation monitoring further enhance trust and transparency. ADVERTISEMENT Session time‑travel functionality allows users to create and revert to checkpoints, much like Git branching, simplifying experimentation and version control. This, paired with an integrated Markdown editor, offers a unified environment for coding, documentation and analysis. A real‑time dashboard displays token usage, cost metrics and session statistics, helping developers monitor API consumption efficiently. Claudia complements recent enhancements to Claude itself, including the Artifacts feature that enables code generation and shareable AI‑powered applications, and Claude Code's improved agentic capabilities. While Anthropic's platform now supports no‑code app building and complex workflows, Claudia enriches the experience for developers by offering offline, granular control over agentic coding sessions. The open‑source aspect draws attention. Hosted on GitHub, Claudia invites community involvement—developers can inspect the code, submit enhancements or customise features, aligning with open‑source principles valued in AI tooling. Executables are pending, but the tool is already buildable from source across all major desktop platforms. This launch fits a broader trend: as AI‑driven coding tools proliferate, demand grows for interfaces that balance power with usability. Unlike fully autonomous coding systems, Claudia augments developers' workflows, offering transparency, control, and adaptation to personalised needs. In comparing it to comprehensive IDEs like Eclipse Theia—which supports AI plugins and LSP integration—Claudia occupies a specialised niche focused specifically on Claude Code, agent orchestration, and local security. Early feedback on social channels reflects enthusiasm. Reddit users highlight its ability to tackle 'terminal chaos' and bring session management to Claude Code. A spirited open‑source community contributes via issue reports and architectural suggestions, indicating strong potential for rapid iteration and enhancement.
Geeky Gadgets
3 days ago
- Business
- Geeky Gadgets
Making Claude Code More Useful with TDD and XP Techniques
What if you could combine the power of artificial intelligence with time-tested development practices to not only write better code but also transform your workflow? AI tools like Claude Code are reshaping how developers approach software creation, offering unprecedented speed and automation. But here's the catch: without a structured approach, even the most advanced AI can introduce risks—like incomplete test coverage or subtle errors that slip through the cracks. This is where methodologies like test-driven development (TDD) and extreme programming (XP) step in, providing a framework to harness AI's potential while making sure your code remains reliable, maintainable, and adaptable. The result? A development process that's not just faster but smarter. In this piece, Feedback Driven Dev explore how pairing AI with proven practices like TDD and XP can transform your approach to coding. You'll discover how techniques such as incremental development, layered testing, and clean architecture can help you maintain control over your projects while using AI to automate repetitive tasks and improve efficiency. Along the way, we'll dive into real-world examples, like the 'Dev Context' project, to illustrate how these principles come to life in practical scenarios. Whether you're a seasoned developer or just starting to experiment with AI tools, this exploration will challenge you to rethink how you build software—and how to do it better. AI in Software Development The Importance of Combining AI with Proven Practices AI tools such as Claude are undeniably powerful, but they are not without limitations. While they can accelerate development and reduce manual effort, challenges like incomplete test coverage and occasional rule violations can arise. To fully harness the benefits of AI, pairing it with established methodologies like TDD and XP is essential. These practices ensure that your code remains reliable, maintainable, and adaptable, even as AI takes on a larger role in your workflow. By integrating these approaches, you can mitigate risks while maximizing the potential of AI-driven development. Practical Application: The 'Dev Context' Project A real-world example of this approach is the development of 'Dev Context,' a tool designed to enhance productivity by organizing workspaces, projects, contexts, and bookmarks. Built using the Tori framework, which functions similarly to Electron, this project addresses inefficiencies caused by frequent context-switching. By adopting a hexagonal architecture, the tool achieves a clean separation of concerns, making it easier to maintain and adapt over time. Claude Code, an AI tool, plays a pivotal role in automating coding tasks for the 'Dev Context' project. It assists in generating tests, implementing features, and maintaining coding standards. However, AI is not a standalone solution. Challenges such as reliance on mocks, occasional errors, and gaps in validation highlight the need for manual oversight. AI should be viewed as a complement to your expertise, enhancing productivity without replacing critical human judgment. Making Claude Code more useful with TDD and XP Techniques Watch this video on YouTube. Expand your understanding of Claude Code with additional resources from our extensive library of articles. Using TDD for Reliable Development Test-driven development (TDD) is a cornerstone of this process, offering a structured approach to building reliable software. By writing tests before implementing code, you can: Ensure rapid feedback loops: Quickly identify and address issues during development. Quickly identify and address issues during development. Focus on behavior: Prioritize functionality over implementation details. Prioritize functionality over implementation details. Build confidence: Make changes with the assurance that existing functionality remains intact. To further enhance test reliability, mutation testing is employed. This technique introduces deliberate changes to the code to verify that your tests can detect errors effectively. By adhering to TDD principles, you can systematically address gaps in validation and improve overall code quality. XP Practices: Small Steps Toward Big Improvements Extreme programming (XP) practices complement TDD by emphasizing incremental development and frequent iterations. Key techniques include: Pair Programming: Encourages collaboration, reduces errors, and improves code quality through shared knowledge. Encourages collaboration, reduces errors, and improves code quality through shared knowledge. Automated Testing: Ensures consistency and minimizes the risk of regressions as the codebase evolves. These practices align seamlessly with AI integration, allowing you to iterate quickly while maintaining control over the development process. By combining XP principles with AI tools like Claude Code, you can achieve a balance between speed and precision. Hexagonal Architecture: A Framework for Clean Code Hexagonal architecture, also known as the ports and adapters pattern, is a critical component of maintaining clean and adaptable code. This approach separates domain logic from external systems like APIs and databases, simplifying testing and enhancing system flexibility. Testing strategies tailored to each layer of the architecture ensure comprehensive coverage: Domain Layer: Focuses on business logic with minimal reliance on external dependencies. Focuses on business logic with minimal reliance on external dependencies. Repository Layer: Uses test containers and Docker to simulate isolated database environments. Uses test containers and Docker to simulate isolated database environments. Controller Layer: Validates API behavior, including error handling and pagination. By adopting this architecture, you can create systems that are easier to maintain, test, and extend over time. Layered Testing: Making sure Comprehensive Validation Layered testing strategies are essential for making sure that every aspect of your system functions as intended. Each layer has a specific focus: Domain Tests: Validate business rules and logic to ensure they align with requirements. Validate business rules and logic to ensure they align with requirements. Repository Tests: Verify data interactions and database operations for accuracy and reliability. Verify data interactions and database operations for accuracy and reliability. Controller Tests: Focus on API endpoints, including error handling, response validation, and pagination. Tools like Bruno, which is similar to Postman, streamline API testing by managing collections and allowing version control. AI-generated collections can further simplify the process of verifying functionality, saving both time and effort. Overcoming Challenges and Lessons Learned While AI offers significant advantages, it also presents challenges that require careful management. Common issues include: Gaps in validation: AI-generated tests may overlook edge cases or complex scenarios. AI-generated tests may overlook edge cases or complex scenarios. Over-reliance on mocks: Excessive use of mocks can obscure real-world issues and lead to false confidence. Excessive use of mocks can obscure real-world issues and lead to false confidence. Occasional errors: AI-generated code and tests may contain inaccuracies that require manual correction. Addressing these challenges involves manual review, refinement, and adherence to best practices. Improvements in error handling, structured logging, and linting rules can further enhance the development process, making sure that AI remains a valuable tool rather than a potential liability. Future Directions for the 'Dev Context' Project Looking ahead, several enhancements are planned for the 'Dev Context' project to improve its functionality and reliability: Introducing mutation testing to validate the robustness of test suites. Refining error handling mechanisms to ensure greater reliability and user satisfaction. Improving code readability and maintainability to simplify future development efforts. Expanding functionality and exploring monetization opportunities to increase the tool's value. These improvements aim to create a more robust and user-friendly system while maintaining a focus on clean architecture and thorough testing. Final Thoughts AI tools like Claude Code have the potential to transform software development when paired with robust practices like TDD and XP. By maintaining clean architecture, using layered testing strategies, and iterating incrementally, you can build systems that are both reliable and adaptable. However, manual oversight remains essential. AI should augment your expertise, not replace it. With the right balance of automation and human judgment, you can achieve both efficiency and quality in your development projects. Media Credit: FeedbackDrivenDev 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.
The Star
4 days ago
- The Star
Google unveils open-source Gemini CLI
SAN FRANCISCO, June 25 (Xinhua) -- Google announced on Wednesday the launch of Gemini CLI, an agentic artificial intelligence (AI) tool designed to run locally from terminals. The new tool connects Google's Gemini AI models to local codebases, and it allows developers to make natural language requests, the company said. Google offers AI coding tools such as Gemini Code Assist. With the release of Gemini CLI, it competes directly with other command-line AI tools such as OpenAI's Codex CLI and Anthropic's Claude Code. The company said it designed the tool to handle other tasks as well. Developers can tap Gemini CLI to create videos with Google's Veo 3 model, generate research reports with the company's Deep Research agent, or access real-time information through Google Search. Google is also open-sourcing Gemini CLI under the Apache 2.0 license. Free users can make 60 model requests per minute and 1,000 requests per day. According to Google, it is roughly double the average number of requests developers made when using the tool.
Mint
4 days ago
- Business
- Mint
Gemini CLI debuts as Google's open-source AI coding assistant: How it works
Alphabet Inc.'s Google has unveiled a new artificial intelligence-powered coding assistant called Gemini CLI (Command Line Interface), designed to streamline the development process by allowing users to interact with their systems through natural language. The global launch signals Google's intent to close the gap with competing AI tools such as OpenAI's Codex and Anthropic's Claude Code. Announced on Wednesday, Gemini CLI is positioned as an open-source tool that brings the capabilities of Google's Gemini AI directly to the terminal, the text-based interface widely used by developers. It enables users to perform a variety of tasks using conversational commands, ranging from writing and debugging code to building simple websites or even generating videos. "With Gemini CLI, you can have a natural language conversation with your computer to solve problems and weave complex workflows together, to do far more than was previously possible," said Taylor Mullen, Senior Staff Software Engineer at Google, during a press briefing. One of the defining features of Gemini CLI is its open-source nature. Google said the decision reflects a broader goal of democratising access to AI tools, enabling developers to inspect, adapt, and contribute to the codebase. This move also supports transparency, allowing users to understand the mechanics of the tool and assess its security. Gemini CLI builds on Google's existing legacy of open AI development, including the TensorFlow engine and several foundational transformer models, the architecture behind leading AI systems like ChatGPT. The launch of Gemini CLI also follows Google's recent release of Gemma, its open large language models made publicly available earlier in 2024. These efforts point to a shift in strategy, with the tech giant increasingly engaging with external developers after years of keeping much of its proprietary AI under wraps. Gemini CLI users with personal Google accounts will be granted a free Gemini Code Assist licence, offering access to the Gemini 2.5 Pro model. This tier includes a generous usage limit of up to 60 requests per minute and 1,000 per day. Paid plans expand those limits to 120 requests per minute and 1,500 per day, while enterprise users can make up to 2,000 requests daily. Ryan J. Salva, Senior Director of Product at Google, emphasised the broad applicability of the new tool: 'We believe that these tools are going to dominate the way not just developers, but creators of all kinds, work over the next decade. Whether you're a student, a freelancer, or a seasoned professional, you deserve access to the same cutting-edge resources.'
Indian Express
4 days ago
- Business
- Indian Express
Google powers up developer workflows with new open-source AI tool Gemini CLI
Google has announced Gemini CLI, its open-source AI agent to rival Claude Code and Codex CLI. Developers rely on the command-line interface (CLI) terminal for almost everything. According to the tech giant, Gemini CLI's ubiquity and portability make it a go-to utility for getting work done. The latest offering from Google addresses developers' demand for integrated AI assistance. Gemini CLI harnesses Gemini directly into the terminal. It essentially offers a lightweight access to Gemini, offering developers the most direct path from their prompt to the model. 'While it excels at coding, we built Gemini CLI to do so much more. It's a versatile, local utility you can use for a wide range of tasks, from content generation and problem solving to deep research and task management,' the company said in its official release. Further, Google has also integrated Gemini CLI with Google's AI coding assistant, Gemini Code Assist. This will allow all Code Assist Developers on free, standard, and enterprise plans to get prompt-driven and AI-first coding in VS Code and Gemini CLI. With Gemini CLI, Google is offering unmatched usage limits for individual developers. In order to use Gemini CLI for free, users simply need to log in with their personal Google account to get a free Gemini Code Assist licence. This free licence brings free access to Gemini 2.5 Pro and its massive one million token context. In case a user ever hits a limit during this preview, Google is offering the industry's largest allowance with 60 model requests per minute and 1,000 requests per day at no charge. For professional developers required to run multiple agents at the same time, they can opt for Google AI Studio or Vertex AI key for usage-based billing or get a Gemini Code Assist Standard or Enterprise licence. Gemini CLI, now in preview, offers powerful AI capabilities, from code understanding and file manipulation to command execution and dynamic troubleshooting. The AI agent offers an upgrade to one's command line experience, allowing them to write code, debug issues, and streamline their workflow with natural language. According to Google, Gemini CLI's powerful capabilities are owing to built-in tools. Users can fetch web pages and offer real-time, external context to the model with ground prompts with Google Search. They can extend Gemini CLI's capabilities with built-in support for the Model Context Protocol (MCP) or bundled extensions. With customised prompts and instructions, developers can tailor Gemini for specific needs and workflows. Further, one can automate tasks and integrate the same with existing workflows by summoning Gemini CLI non-interactively with their scripts. Since Gemini CLI is fully open source (Apache 2.0), developers can inspect the code to understand how it works and verify its security implications.
