The article describes an experiment with Claude Code (Opus 4.7) where the AI was asked to reflect on its own thinking styles, rather than just being given tasks. The author identified six axes of thinking that can change how we interact with AI and built a prompt for others to test.
This article outlines seven AI-assisted engineering habits that optimize workflow, emphasizing the importance of providing context and treating prompts as tickets. It highlights that the utility of AI directly depends on the structure and clarity of the input provided.
An individual significantly reduced their AI API bill by implementing prompt caching. They discovered that much of their API request context was static and could be cached, leading to a 90% cost reduction on cached tokens.
This article addresses an expensive pattern where AI Agents repeatedly perform the same task, termed "prompt tunneling," rather than genuine debugging. The author proposes a self-loop detection mechanism to enable agents to identify and stop their own repetitive cycles.
The article explores how structured prompts can significantly reduce token usage (35-40%) compared to unstructured formats, directly impacting costs. It also emphasizes the importance of understanding when this token saving translates into better model answers and when it's merely overhead, based on experiments with Claude Sonnet 4.6.
This article details a methodology for creating an effective `CLAUDE.md` file, which constrains an AI agent by encoding rules derived from past errors. It highlights `CLAUDE.md`'s role as a continuous instruction set for AI, distinct from human-facing documentation.
The article outlines a four-layer structure and four rules for an effective CLAUDE.md file, designed to guide and constrain an AI agent (Claude Code) in managing a large codebase. It emphasizes writing prohibitions before best practices and the fundamental distinction between a README (for humans) and a CLAUDE.md (for agents).
This article details how the author built reusable marketing skills for GitHub Copilot, explaining the process and the reasons behind this initiative. It explores the application of AI to optimize marketing tasks and improve efficiency.
The author discovered that vague acceptance criteria, not the AI model, caused agents to build incorrect outputs. To fix this, criteria should be written as checkable specifications rather than abstract wishes, ensuring AI agents deliver the intended results.
AI prompts are more effective when tied to real workflows, providing context and structure for trustworthy outputs. This contrasts with isolated instructions, making AI output more reliable and repeatable for business work.
The article criticizes common misuses of Claude Skills, advocating for treating them like code and operations logic rather than prompt poetry. It explains that Skills are directories with progressive disclosure, allowing agents to efficiently load reusable knowledge and workflows on demand.
A LinkedIn post suggested that RESTful APIs might be replaced by a single AI prompt entry point. This article explores the implications of this claim, analyzing how artificial intelligence could redefine interaction with web services and the nature of API contracts. It discusses the rigidity of current APIs versus human imprecision and AI's role as an interpreter.
This article posits that AI agents are often not "dumb" but rather suffer from inadequate context provided by users. The bottleneck in software development has shifted from writing code to accurately describing intent and constraints to AI agents, a skill users often lack.
Most issues with AI agents stem not from their "stupidity" but from poorly provided context. The article advocates treating context as code, applying engineering discipline, and storing project knowledge in version-controlled files.
This article reframes the challenge of AI coding from merely reducing token spend to maximizing the value derived from those tokens. It advocates for a quality-first approach to token optimization, detailing mental models and concrete techniques to enhance AI agent accuracy and efficiency.
This article discusses the importance of using essential patterns when prompting AI coding assistants to build production-ready agents. By specifying architecture decisions, one can prevent failures like hallucinations and token waste, which often occur silently until production.
AI agents can get stuck in perpetual loops due to improper prompt engineering, such as continuously concatenating conversation history. This expands the context, exhausts API quotas, and incurs significant costs by repeatedly asking the same question or triggering rate limits.
An internal code-review bot powered by Claude became costly and prone to bugs due to accumulating context. The author developed 'ctxbudgeter' to cut input tokens by 60%, eliminate secret leaks, and reduce costs.
This content discusses how to make AI-generated code more robust for documentation, focusing on intentional prompting to include error handling and authentication. The goal is to build developer trust by providing snippets that mirror real-world, secure integration patterns.
As AI integrates into development workflows, the bottleneck shifts from writing code to crafting clear and unambiguous specifications. The ability to write precise, structured specifications is becoming a core engineering skill to prevent errors and ensure correct AI execution.