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Claude Code's Source Leak: What Happened and What Teams Should Learn
We had a different article planned for today. We bumped it because Anthropic's Claude Code source leak is a better lesson for engineering teams: release engineering mistakes can become public security events in a single publish.
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AI Accelerates Development, But It Doesn't Replace Senior Engineers
AI tools can slash time-to-market and development costs, but without experienced developers overseeing architecture, you're building a house of cards ready to collapse.
AI Governance for LLM Products: Testing, Guardrails, and Evaluation
Building AI features? Here's how to test outputs, prevent hallucinations, and build evaluation harnesses that scale.
Implementing AI Features: A Practical Framework
How to add LLM and AI features to your product without the chaos.
The AI Orchestrator: Software Engineering's New Role
Andrej Karpathy called it 'LLM orchestration': the emerging role of directing AI tools to build software. Here's what this new engineering discipline looks like in practice.
AI Makes You 19% Slower: The Productivity Illusion
Developers using AI think they're more productive. Research shows they're actually slower. Here's why AI productivity gains are harder to capture than you think.
46% of Developers Don't Trust AI Code: The Verification Crisis
New research shows a massive trust gap in AI-generated code. Here's how to build verification into your workflow without sacrificing velocity.
Implementing RAG: Production Patterns for AI Knowledge Bases
RAG (Retrieval-Augmented Generation) combines document retrieval with LLM generation. Here's how to build production-ready RAG systems that actually work.
LLM Cost Optimization: How to Control AI Feature Spend
AI features can look cheap in a prototype and painful in production. If you do not control prompts, routing, caching, and context size, your margins disappear faster than expected.
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