AI-built applications often work in development but fail at scale in production due to underlying infrastructure assumptions. This leads to issues with data ownership and migration, as AI builders are optimized for iteration speed, not production constraints.
This article discusses the danger in AI-assisted development where AI-generated test suites, written after the code, can fail to identify bugs, instead documenting existing behavior. This leads to passing tests and shipped bugs, masking real problems and silently violating specifications.
This article discusses how a junior dev AI agent, initially limited to a single bug-fixing skill, can be made significantly smarter. By implementing a structured set of nine distinct skill categories, the agent can move beyond one-dimensional output.
AI-built applications often fail at scale due to the inherent limitations of builder platforms, which prioritize rapid iteration over production scalability. Founders encounter debugging challenges and vendor lock-in as the inherited architecture leads to timeout errors and lack of visibility under high traffic.
This article reflects on Google I/O 2026's AI announcements, such as the Managed Agents API, highlighting their impressive features. However, the author raises critical questions about their real-world impact and challenges for the majority of developers in a cloud-first world.
Big Tech firms are accelerating AI investments and integration, while regulators and companies focus on safety and responsible adoption. The AI landscape is experiencing unprecedented growth, driven by massive investments, its integration into software development, and a critical focus on ethical and safe deployment.
The author stopped relying on Claude AI to follow code rules defined in CLAUDE.md due to the AI's inconsistency over long tasks or in different contexts. The solution found was to use "hooks" to enforce these rules, ensuring the development environment uses the correct configurations, such as the Node version.
This article details a developer's experience building self-improving Python scripts with Large Language Models (LLMs). It offers a step-by-step guide, covering LLM basics, environment setup, and code generation techniques using `llm_groq` and `transformers`.
The article warns against the "vibe coding hangover," where AI-generated MVPs, created by describing goals in natural language to LLMs, lead to spaghetti code, unauthenticated APIs, and hardcoded secrets due to a lack of disciplined engineering. It highlights the rapid adoption of AI coding tools but stresses the importance of underlying engineering rigor to avoid pitfalls.
The author initially intended to build a coding agent but shifted to creating a supervisor for existing AI coding tools like Codex and Claude Code. This supervisor handles dispatch, memory, and task management, focusing on orchestration rather than direct coding.
This post details Anthropic's Messages API, Claude models, and the use of tools for structured actions in applications. It emphasizes validating arguments, treating model output as untrusted, and API security practices.
This article details six essential 'code hooks' that the author integrates into every AI project, specifically with Claude, to proactively catch errors before content goes live. These hooks address limitations of Claude's memory files by automating checks for brand compliance, layout, accessibility, SEO, and post-publish verification, ensuring high-quality output.
AI coding agents are revolutionizing software development, evolving from simple autocomplete suggestions to autonomous pull request generation. Teams are leveraging these agents to inspect repositories, plan fixes, edit files, run tests, and open review-ready PRs, fundamentally changing the development workflow.
The author, after two months with Claude Code, switched to opencode, concluding that code quality is a myth when using the same underlying model. Key differences are workflow and cost: Claude Code is faster, but opencode is more thorough, running full test suites for regressions, while provider flexibility and local model support also matter.
Agent Harness Engineering is a methodology focused on proactively addressing AI agent errors by engineering solutions to prevent future occurrences of the same mistakes. It emphasizes an iterative approach to improve agent reliability and performance over time.
Vibe coding is an AI-assisted software development method where developers use natural language prompts to generate and modify code. This article reviews the top 10 vibe coding tools of 2025, detailing their strengths, weaknesses, and ideal use cases.
The author developed a 7-agent prompt framework named C.E.H. running on local LLMs, which successfully built a complex RAG system. Faced with 14 failures in the generated code, the author uniquely used the C.E.H. framework itself to debug and fix its own output.
In 2026, OpenAI reportedly faced a near-implosion due to internal squabbles and a frantic power grab, threatening the very foundation of AI progress. This crisis, far more severe than public perception, almost derailed the future of Artificial General Intelligence.
This content proposes treating AI agent 'skills' as markdown invocation contracts rather than code, allowing humans to review the contract's intent while an agent handles the underlying implementation. This method enables scaling to dozens of agents by maintaining review authority on the contracts, making the implementation interchangeable without human re-review.
This roadmap outlines how to build AI-powered applications with Google Gemini by 2026, emphasizing meeting user expectations for fast and useful experiences. It details Gemini's multimodal API capabilities and its integrations across Google AI Studio, Vertex AI, Android, and Firebase.