coding

“Rock solid” aims to enhance Cursor's role as a software engineering companion, defining comprehensive policies for AI coding agents and users. It emphasizes task-driven development, strict adherence to product requirements, controlled file creation, and clear roles. Key principles include user authority, task granularity, and documentation standards. The policy details backlog management, task workflows, testing strategies, and change management to maintain project integrity, accountability, and automation, ensuring quality and compliance throughout the development process.

https://gist.github.com/boxabirds/4a8a8a16b1f8431fd64a790209452380

TLDR: After extensive use of LLMs for coding, I've reverted to manual coding due to chaotic code quality and frustration. I realized I'd been relying too much on AI instead of applying my own software engineering skills. Now, I limit AI's role to minor tasks while focusing on understanding and organizing code myself. I'm concerned about the detrimental effects of AI reliance on programming skills and worry that non-coders face greater challenges with AI tools.

https://albertofortin.com/writing/coding-with-ai

AI is increasingly generating code, with Microsoft and Google noting substantial contributions to their projects. However, AI won't fully replace human programmers; it will augment them by automating repetitive tasks. Skilled engineers will remain essential for complex problem-solving, oversight, and innovation in fields like finance. Future careers will focus on leveraging AI tools, creating new roles such as AI trainers and developers. The demand for high-level coding expertise will persist as AI enhances productivity rather than replacing the need for human insight.

https://thefinanser.com/2025/05/who-needs-coders-when-youve-got-ai

AI revolutionizes programming; narrative shifts from replacement to expansion. History shows programming evolves with technology, lowering barriers and increasing accessibility. AI tools democratize coding, allowing non-experts to innovate. A new spectrum emerges: “vibe coding” for rapid development and systematic AI engineering for complex systems. Increased collaboration between AI and human creativity leads to unprecedented opportunities in software innovation. Embrace this era of exploration and problem-solving.

https://www.oreilly.com/radar/ai-and-programming-the-beginning-of-a-new-era/

AI coding assistants excel in tasks like generating boilerplate code, simple functions, documentation, and debugging, but have limitations in complex coding scenarios. While 63% of developers leverage AI for productivity, reliance can increase technical debt and produce quality issues. AI performs best with straightforward code and popular libraries but struggles with novel or complex projects. Engineering leaders must understand AI's strengths and weaknesses to avoid pitfalls, as improper use can lead to wasted time and debugging challenges. Overall, AI tools are rapidly evolving, promising increased software development efficiency but requiring careful oversight.

https://www.infoworld.com/article/3973969/knowing-when-to-use-ai-coding-assistants.html

AI coding assistants are ineffective; programming requires thinking, not just code generation. Code lacks context and often leads to poor outcomes. AI produces bad code that appears correct but needs verification. Human understanding is key; utilizing well-documented resources is more beneficial than AI for quality programming. Writing code is easy; effective programming is challenging and a cognitive task.

https://www.doliver.org/articles/programming-is-a-thinkers-game

AI coding risks joy in programming, replacing deep engagement with efficiency. While productivity rises, the sense of fulfillment diminishes, leading to detachment from the craft. Maintaining moments of “flow” in traditional coding is essential for long-term happiness in the field. Redefining satisfaction may be necessary as programming evolves.

https://terriblesoftware.org/2025/04/23/the-hidden-cost-of-ai-coding/

AI revolutionizes coding, making it accessible but highlighting the importance of foundational programming knowledge for judgement and creativity. While AI tools can quickly generate code, understanding the principles behind coding is essential for effective problem-solving and preventing vulnerabilities. Learning to code fosters a mindset that can enhance various fields. Despite AI advancements, human programmers remain vital, adapting and working alongside AI, as new roles emerge requiring a blend of technical skills and AI fluency. Skipping coding education risks reliance on AI tools, emphasizing that learning to code is crucial, not obsolete.

https://www.ie.edu/insights/articles/a-brand-new-hello-world-of-coding/

GPT-4.1 API Launch Summary:
OpenAI introduces GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano, featuring significant improvements in coding, instruction following, long context comprehension (up to 1 million tokens), and enhanced performance metrics over previous models, particularly in coding and real-world applications. These models are crafted based on developer feedback, reducing costs and latency significantly. Early testers report higher accuracy and efficiency across various tasks, making GPT-4.1 suitable for complex tasks in coding and legal contexts. The models are exclusively available via API, with GPT-4.5 being deprecated as GPT-4.1 offers superior capabilities at lower operational costs.

https://openai.com/index/gpt-4-1/