Articles

AI-assisted coding evolved from autocomplete to copilots to autonomous agents in five years. The next phase — intent-driven development, self-healing systems, AI-native languages, and formal verification at scale — will transform not just how we write software but whether we write it at all.

AI in healthcare is shifting from single-purpose models to autonomous agents that reason across patient records, coordinate care workflows, and assist in diagnosis and treatment planning. The FDA has authorized over 1,500 AI-enabled devices, but no autonomous AI prescription system has been cleared. Here is how the transition is unfolding and what engineers building healthcare AI need to know.

80% of AI projects fail to deliver value. The top causes are incomplete requirements, missing user research, and skipping discovery. AI agents make building fast, but speed without direction produces throwaway code. Here is the planning framework that changes the odds.

AI interfaces generate reasoning chains, tool calls, confidence scores, and citations that overwhelm users when shown all at once. Progressive disclosure solves the complexity gap by showing the result first and letting users drill into the reasoning on demand.

92% of developers use AI coding tools daily. Only 29% trust the code they produce. AI-assisted code has 1.7x more major issues and 2.74x more security vulnerabilities. Here is what the data actually says about vibe coding.