AI for Developers
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LSP setup in Claude Code is broken out of the box. Here's how to fix it.
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How the declarative component model and AI-augmented development let me ship across web, desktop, mobile, email, video, and agent interfaces in a single week.
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A clear breakdown of AI models, products, and agents — the three layers of the AI coding landscape and how they relate to each other.
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Teams that succeed with AI are not just using better tools. They are working differently. Here's the workflow shift that matters.
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After watching teams adopt AI tools for months, here's the sustainable workflow that sticks: plan mode + iteration + continuous verification.
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When engineers complain AI tools are slow, it is usually not the AI. It is how they use it. Here are strategies for keeping Claude Code fast and responsive.
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Extensions multiply Claude's usefulness, but they add complexity. Here is when teams should invest in MCP servers and custom skills—and when to keep it simple.
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A super short primer on tokens in AI coding tools and why they matter for developers.
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Not every coding task needs AI. Here is how successful teams decide when Claude Code adds value and when traditional tools are faster.
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Every successful AI adoption I've seen starts here: plan mode transforms unpredictable code generation into a systematic, reviewable process teams can trust.
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When engineers struggle with AI tools, it is usually not the AI. It is how they frame tasks. Here is what makes the difference between success and frustration.
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The key insight for using AI coding tools effectively: Claude does not magically know your code. It explores systematically. Understanding this changes how you work with it.
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It is not about better answers. It is about a fundamentally different workflow that scales—from chat-based help to systematic development.