When the VIBEs Start to Fade (www.mindruptive.com)

This analysis studies three real-world codebases (a few-hundred-line Python optimizer, a ~2,000-line C# beam-search heuristic, and a ~50k-line SvelteKit app) using modern assistants (ChatGPT‑5, Claude Sonnet/Opus, Gemini 2.5 Pro and IDE “vibe” agents like JetBrains Pro AI, GitHub Copilot, Claude Code). Its central claim: because these tools are LLMs that predict next tokens probabilistically rather than performing formal reasoning, they are structurally limited in maintaining correctness and global architectural consistency. Assistants are constrained by finite token-based context windows, operate in either “complete-bundle” or incremental workflows, and extend reach via embedding-based retrieval (RAG). Embeddings provide semantically similar fragments but not precise control‑flow, type, or dependency guarantees, producing a read–search–edit cycle where project state is repeatedly reconstructed and partial. Practical implications: developers will see plausible but incorrect code (non-compiling snippets, undefined symbols, cross-layer violations), opaque failures when context budgets are exceeded (silent “compaction”/memory loss), and coordination overhead as humans become the external consistency check. Protocols like Model Context Protocol (MCP) can raise precision by delivering curated, org-specific indexes to the model, reducing hallucination and improving consistency, but they don’t change the underlying statistical generation—so deterministic validation (builds, tests, formal checks) and security review remain essential. The net takeaway: context management is the key bottleneck; better protocols help but do not eliminate the need for human-driven verification and architecture-aware tools.