This article guides you on creating a ChatGPT app, focusing on embedding your code into the platform. Colin Matthews, an experienced AI instructor, provides practical insights and resources to help you get started.

The article discusses Claude Code, an advanced AI tool that can autonomously generate software and websites based on user prompts. It highlights how Claude combines various techniques to manage tasks, analyze data, and improve its performance, making it a powerful resource for users with programming needs. However, the tool is primarily designed for programmers, limiting accessibility for non-technical users.

This article discusses how AI agents impact the choice of programming languages, emphasizing the importance of static type systems and code comprehension. It highlights the challenges of understanding and maintaining code generated by AI, as well as the necessity for clear source code to preserve knowledge within software projects.

This article argues against the idea that advancements in AI, particularly large language models, will replace software developers. The author reflects on historical trends where similar predictions proved wrong and emphasizes that programming involves complex human thinking that AI cannot replicate. The demand for skilled programmers will continue as businesses navigate current technological hype and economic challenges.

Greptile offers AI-driven code reviews that adapt to your code's context, helping teams maintain coding standards. It supports self-hosting on major cloud platforms, ensuring data privacy and compliance with regulations like GDPR and HIPAA. The service is compatible with popular version control systems and supports various programming languages.

Collins Dictionary has named "vibe coding" as its word of the year for 2025. This term describes the process of creating apps by simply describing them to AI instead of writing traditional code. While it simplifies programming for non-coders, the results can still be hit or miss.

The article examines the competitive landscape of open-source and proprietary AI models, highlighting that proprietary providers maintain pricing power despite cheaper alternatives. Open-source models have stabilized at about 22-25% market share, while programming use cases dominate among leading providers. Retention rates vary significantly, with some models showing stronger user engagement than others.

Armin Ronacher discusses the significant changes in his programming approach and the impact of AI tools like Claude Code throughout 2025. He explores the evolving relationship between developers and AI, the challenges of code review in the age of agentic coding, and the need for innovation in version control and observability.

This article chronicles the development and impact of the Ralph Wiggum Technique created by Geoff Huntley, detailing key events from its inception in June 2025 to early 2026. It discusses the tool's unique approach to coding, the challenges faced, and lessons learned from various experiments with the technique.

Metis is an open-source tool developed by Arm to enhance security code reviews using AI. It leverages large language models for semantic understanding, making it effective in identifying vulnerabilities in complex codebases. The tool is extensible and supports multiple programming languages.

The article discusses how the rise of AI tools like LLMs is diminishing the need for small open source libraries, such as blob-util. The author reflects on the loss of educational value in coding as instant solutions replace the learning process. While acknowledging the challenges, they express hope for more innovative open source projects that can't be easily replicated by AI.

This article discusses the challenges of using AI to generate code for distributed systems, emphasizing that traditional coding practices can lead to bugs that are hard to catch. It argues for frameworks like Hydro that make distributed behavior explicit and aim to reduce these bugs by design, rather than relying solely on testing.

This article explores the excitement and anxiety of working with AI as a beginner. It highlights the shared experiences of early adopters who find joy in experimentation and creativity, reminiscent of the early days of Python. The author calls for a more open and casual culture of communication in the AI community.

David Heinemeier Hansson argues that while AI can generate code, it lacks the quality and understanding that junior developers bring to the table. He emphasizes that coding isn't just about writing—it's about problem-solving and system design, areas where AI struggles. The future of software development relies on nurturing human talent, not replacing it with AI.

The article reviews a recent study on how AI tools impact learning new coding skills. It highlights that while AI users completed tasks faster, their retention of knowledge was poorer, especially among those who retyped AI-generated code. The author discusses the balance between speed and depth of learning in software engineering and calls for more research on long-term AI use.

This article explains how to create a basic AI coding assistant using Python. It outlines the core functionalities needed, such as reading, listing, and editing files, and provides a step-by-step guide to implementing these features. The author emphasizes that the underlying architecture is straightforward and can be adapted for various LLM providers.

The author shares their experience experimenting with AI code agents like Claude Code and Opus 4.5. They found that these agents can save time on coding tasks, but still require human oversight to ensure quality. The article highlights the practical benefits and limitations of using AI in programming workflows.

This article introduces NERD, a programming language designed for AI to write code with minimal human intervention. It highlights how NERD optimizes code structure for efficient machine processing while remaining legible for human review. The piece argues that as AI continues to dominate code generation, traditional human-readable formats will become obsolete.

This article discusses the impact of AI on formal verification, highlighting both its potential and limitations. It explains the challenges of creating formal specifications for most software and critiques the reliability of autoformalization and proof assistants in the verification process.

The article discusses the implications of using large language models (LLMs) in software development, arguing that while LLMs may simplify coding through natural language prompts, they don't eliminate the need for managing complexity and control. It highlights that programming languages are still essential for addressing this complexity, regardless of advancements in AI.

This article discusses three new features for AI agents that improve their ability to work with multiple tools efficiently. The Tool Search Tool allows tools to be discovered on-demand, Programmatic Tool Calling streamlines workflows through code, and Tool Use Examples help agents learn proper tool utilization.

Rue is an early-stage research project aimed at creating a programming language that offers memory safety without garbage collection, while being easier to learn than Rust. The project is a collaboration between developer Steve Klabnik and AI assistant Claude, and is still in development with many features yet to come.

The author reflects on the decline of traditional programming as AI tools take over coding tasks. He addresses the emotional impact on seasoned developers, highlighting a sense of loss for the artistry and satisfaction of coding by hand. The piece invites readers to mourn the craft that defined their careers.

This article discusses the evolving role of SQL in the context of AI-generated code, highlighting the tension between writing code for efficiency and reading it for comprehension. It proposes the need for tools that help those familiar with SQL understand queries better, suggesting that current solutions often cater to those who don’t know SQL at all.

The article discusses how TypeScript, created to improve JavaScript's scalability for large projects, has become the most-used programming language on GitHub in 2025. Anders Hejlsberg explains its evolution, performance improvements, and how its static typing makes it ideal for AI-assisted coding.

This article discusses the benefits and challenges of using AI in programming from the perspective of a senior engineer. It shares practical tips and personal insights on how to effectively integrate AI tools into workflows while addressing common concerns about code quality and understanding.

DeepCode is an AI platform that automates the conversion of research papers and natural language prompts into production-ready code. It excels in implementing complex algorithms and generating both front-end and back-end code while outperforming existing commercial code agents and human experts.

Stephen Ramsay critiques the concept of vibe coding, expressing his discomfort with it while acknowledging its effectiveness. He questions why vibe coding uses traditional programming languages designed for human readability when a language tailored for machine efficiency could be more suitable. Ramsay explores the potential for a new "vibe-oriented programming language" that could streamline the coding process further.

The author discusses the transformative impact of AI on programming, highlighting how advanced language models can now handle substantial coding tasks with minimal human intervention. While acknowledging the potential for job displacement, the author emphasizes the importance of adapting to these changes and using AI as a tool to enhance creativity and productivity in software development.

The author shares their experience of creating Beep, a fictional programming language, in collaboration with the AI tool Claude Code. Through detailed examples, they explore features like lexical and dynamic scoping, as well as the AI's assistance in solving complex programming challenges and refactoring code.

The article explores whether artificial intelligences (AIs) exhibit different modes of thinking based on their programming and environment. It delves into the implications of these differences for understanding AI behavior and decision-making processes. The analysis highlights the complexity of AI thought and its potential impact on human-AI interactions.

OpenAI has released an upgraded version of Codex powered by the new GPT-5 model, enhancing its capabilities in code generation and understanding. The improvements aim to make the AI more efficient and accurate in assisting developers with programming tasks. This upgrade reflects OpenAI's ongoing commitment to advancing AI technology for practical applications in software development.

GitHub Copilot and similar AI tools create an illusion of productivity while often producing low-quality code that can hinder programming skills and understanding. The author argues that reliance on such tools leads to mediocrity in software development, as engineers may become complacent, neglecting the deeper nuances of coding and system performance. There's a call to reclaim the essence of programming through active engagement and critical thinking.

Stack Overflow has not been significantly harmed by the rise of AI, as the platform continues to thrive with its community-driven Q&A format. While AI tools can assist developers, they do not replace the need for human input and expertise found in Stack Overflow's vast resources. The article emphasizes the enduring value of human knowledge and interaction in programming communities.

Rust, Python, and TypeScript are emerging as the dominant programming languages due to their strong fundamentals and compatibility with the idea-oriented programming paradigm, which emphasizes a focus on project concepts over specific code syntax. This shift, driven by advancements in AI coding assistants, allows programmers to delegate tasks and streamline the development process while enhancing the importance of type systems and robust ecosystems. The article argues that this new approach makes programming more accessible and less dependent on deep technical knowledge.

Learn how to create a simple yet effective AI agent using Orkes Conductor, which streamlines the development process for developers. The article provides a step-by-step guide to building the agent, showcasing the capabilities of the Orkes platform.

Gemini Coder is an open-source AI pair programming tool that enhances coding efficiency by enabling developers to interact with various AI chatbots for code generation and editing. It integrates with popular code editors and offers features like multi-file changes, context selection, and intelligent code completions, all while ensuring user control and adherence to chatbot usage terms.

The author reflects on their 30-year programming career, suggesting that advancements in AI and tools like Amplifier are making traditional programming roles increasingly obsolete. They highlight how AI is evolving to handle complex programming tasks autonomously, indicating a future where machines may perform programming without human intervention.

Grok has launched `grok-code-fast-1`, a fast and cost-effective reasoning model tailored for agentic coding. Designed for usability and optimized for various programming languages, it promises rapid tool integration and a responsive user experience, currently offered for free through select partners.

A developer created a programming language called "cursed" by running AI model Claude in a loop for three months, combining Gen Z slang with traditional programming concepts. The project highlights both the potential of AI in software development and the importance of learning while using such technologies, as well as the community-driven nature of evolving the language further.

The author shares their journey of enhancing AI's understanding of codebases, revealing that existing code generation LLMs operate more like junior developers due to their limited context and lack of comprehension. By developing techniques like Ranked Recursive Summarization (RRS) and Prismatic Ranked Recursive Summarization (PRRS), the author created a tool called Giga AI, which significantly improves AI's ability to analyze and generate code by considering multiple perspectives, ultimately benefiting developers in their workflows.

The author shares insights on a favorite programming interview centered around implementing an async queue to handle server requests in a single-threaded environment. The discussion highlights the importance of reasoning through tricky coding challenges and the evolving role of AI in technical interviews, suggesting that candidates should be allowed to use AI tools to enhance their coding efficiency.

The article discusses the application of AI research utilizing Codex, a powerful model for code generation and understanding. It highlights various use cases, including improving programming efficiency and enabling new ways of interacting with code through natural language queries. The potential implications for developers and the programming community are also examined.

As AI technology advances, the way developers seek help is shifting, with StackOverflow's decline in popularity leading to a rise in reliance on blogs and guides. While AI tools like ChatGPT and Copilot are valuable, they have limitations, particularly in providing real-time information and accurate solutions. The article emphasizes the importance of critical thinking and knowledge-sharing among engineers in bridging the knowledge gap left by traditional platforms.

The article discusses the development of an AI Programming Assistant called Sketch, highlighting the simplicity of its main operational loop when interacting with a language model (LLM). It emphasizes the effectiveness of using LLMs with specific tools for automating programming tasks, improving developer workflows, and handling complex operations like git merges and stack trace analysis. The author expresses optimism about the future of agent loops in automating tedious tasks that have historically been challenging to automate.

CoRT enhances AI models by enabling them to recursively evaluate their responses, generating multiple alternatives and selecting the best one through a competitive process. This approach significantly improves performance, particularly in programming tasks, transforming initial responses from mediocre to impressive. Users can implement it easily with provided installation instructions and are encouraged to contribute improvements.

GitHub Copilot aims to enhance collaboration and productivity among developers by introducing agentic workflows that allow for more intuitive coding experiences. The vision emphasizes a shift from traditional pair programming to a more interactive, peer-based approach, leveraging AI to support developers in real-time. This evolution aims to create a more engaging and efficient software development process.

Armin Ronacher shares his insights on agentic coding, emphasizing his use of Claude Code and the Sonnet model for efficient tool usage. He discusses the importance of optimizing workflows, selecting programming languages like Go for backend projects, and ensuring effective tooling and logging practices to enhance AI agent performance in coding tasks.

The "am-i-vibing" library detects whether CLI tools and Node applications are being executed by AI agents, allowing them to adjust outputs and error handling accordingly. It provides functions for detecting different types of environments—agentic, interactive, and hybrid—and can be used via CLI for quick checks and detailed diagnostics.

Cognition has launched a new low-cost plan for its AI programming tool Devin, reducing the entry price to $20, with a pay-as-you-go option. Despite initial praise and claims of improved performance in Devin 2.0, the tool still struggles with complex tasks and has faced criticism for introducing bugs and security issues in its code output.

The content of the article appears to be corrupted or unreadable, making it impossible to extract meaningful insights or summarize its key points. It seems to be an attempt to discuss topics related to AI, software development, or technology, but the text is not coherent.

The article reflects on the author's journey from experimenting with reactive graphics in the 1990s to the current "Agent Era," where AI acts as a collaborative partner in design and technology. It discusses the shift from traditional user interfaces to agent experiences, highlighting the importance of human insight amidst evolving tools.

A Stanford University study reveals that the launch of ChatGPT has led to a significant decline in entry-level programming jobs in the U.S., particularly affecting workers aged 22 to 25. The research indicates that AI-driven automation is a major factor behind this trend, with younger workers in software development and other exposed fields experiencing job losses, while older workers in less affected fields see employment growth.

Frontier LLMs like Gemini 2.5 PRO significantly enhance programming capabilities by aiding in bug elimination, rapid prototyping, and collaborative design. However, to maximize their benefits, programmers must maintain control, provide extensive context, and engage in an interactive process rather than relying on LLMs to code independently. As AI evolves, the relationship between human developers and LLMs will continue to be crucial for producing high-quality code.

Chris Lattner, creator of LLVM and the Swift language, discusses the development of Mojo, a new programming language aimed at optimizing GPU productivity and ease of use. He emphasizes the importance of balancing control over hardware details with user-friendly features, advocating for a programming ecosystem that allows for specialization and democratization of AI compute resources.

The article discusses the pivotal role of coding in advancing artificial intelligence, emphasizing how programming languages and frameworks are foundational to AI development. It highlights the necessity of strong coding skills for professionals in the AI field to drive innovation and solve complex problems. The integration of coding with AI technologies is portrayed as essential for future advancements.

A 6-week bootcamp designed for programmers aims to enhance skills in building AI-powered applications, emphasizing hands-on experience and practical knowledge. Participants will create a working AI project and acquire essential techniques to improve their coding and development processes, ensuring they stay relevant in the evolving tech landscape.

GitHub Copilot CLI is a command-line interface that allows developers to easily integrate AI-assisted coding into their workflows. Users can get started by installing the CLI, authenticating with their GitHub account, and using various commands to leverage Copilot's capabilities for code suggestions and completions directly in the terminal. The article provides a step-by-step guide for setup and usage.

OpenAI has launched Codex, a coding tool designed for developers to delegate simple programming tasks to an AI agent that generates production-ready code. Accessible via the ChatGPT web app, Codex operates within a user’s development environment and can be customized through an “AGENTS.md” file for improved effectiveness. Built on a fine-tuned model, Codex leverages reinforcement learning to enhance its coding capabilities.

The author critiques the perception that AI can effectively code, arguing that current AI tools function similarly to compilers and highlight existing deficiencies in programming languages and tools. He suggests that the hype around AI coding is misleading, as it often masks the need for better foundational technologies in programming rather than representing a true advancement in coding capabilities. The article calls for a recognition of AI's limitations and advocates for a more thoughtful approach to its integration into workflows.

The author shares their experience of developing and releasing a macOS application entirely built using Claude, an AI programming assistant. They discuss the challenges faced during the development process and the unique aspects of using AI for coding. The article highlights the potential of AI in streamlining software development and enhancing productivity.

The author discusses the benefits of using AI for writing logs, particularly highlighting JetBrains’ Full Line Code Completion feature in PyCharm. This tool streamlines the logging process, reduces cognitive overhead, and enhances debugging efficiency by inferring log statements based on context. The article outlines the technical foundations of the AI model used and praises its thoughtful implementation for developers.

After years of experience as a software developer, the author reflects on the shift between intuitive and analytical thinking in programming. As technology evolves, particularly with the rise of AI tools that automate coding tasks, there is a growing concern about losing essential learning and problem-solving skills. The article emphasizes the need for developers to maintain a balance between utilizing automation and ensuring they understand the underlying principles of their craft.

AI coding tools are revolutionizing the way developers transition between programming languages, enabling them to contribute to projects in languages like C++ and Rust without extensive prior knowledge. By acting as a collaborative partner rather than just a code generator, AI helps developers overcome learning barriers and enhances the coding experience, allowing for more productive contributions from the outset. This shift signifies a broader trend towards reduced specialization in programming languages.

The author expresses concern over the increasing enforcement of AI tools like Copilot in programming, arguing that it transforms programmers into mere approvers of AI-generated code rather than creative contributors. This shift not only threatens the integrity of the profession but also places the responsibility for errors solely on programmers, even as they rely on AI assistance. The article questions the motivations behind making AI usage mandatory and the implications for the future of programming as a craft.

Vibe coding and reliance on AI tools like Claude Code are leading to a decline in deep understanding among developers, particularly impacting junior programmers who may skip the essential problem-solving struggles that foster growth. The author emphasizes the importance of intentional AI use, advocating for a balance between leveraging technology and maintaining the cognitive engagement that coding inherently demands.

In his article, Alexandru Nedelcu expresses his frustration with AI and LLMs in programming, arguing that they detract from the joy and satisfaction of the coding process. He emphasizes that while AI can handle simple tasks, it often fails at more complex problems, leading to a frustrating trial-and-error experience that lacks the fulfillment of traditional programming. Ultimately, he believes that relying on AI takes away the valuable learning journey inherent in programming.

The article discusses the author's approach to coding, likening it to the precision of a surgeon who focuses on critical tasks while delegating secondary tasks to AI tools. The author emphasizes the importance of leveraging AI to handle grunt work, thus allowing for greater concentration on meaningful design work and enhancing productivity. Additionally, the article reflects on the evolving role of AI in programming and its implications for team dynamics and status hierarchies.