This topic surveys the current landscape of AI coding assistants and “vibe coding” platforms — tools that integrate large language models, agentic workflows, and runtime previews to reduce friction between idea and shipped code. It’s about how in‑editor copilots (GitHub Copilot, JetBrains AI Assistant), cloud IDEs with integrated hosting and assistants (Replit), agentic AI editors (Windsurf, formerly Codeium; Cursor), and prediction/completion engines (Tabnine, CodeGeeX) are shaping developer workflows. As of 2026, the field is defined by several trends: AI‑native IDEs that embed agents and live previews to preserve context and developer flow; multi‑model support and stronger on‑device or private‑cloud options driven by open code models (Code Llama, Salesforce CodeT5, CodeGeeX); and search/knowledge systems optimized for code (Phind). These shifts make the topic timely because teams balance faster iteration and automated refactorings with correctness, security, and licensing concerns. Platforms like Replit emphasize web‑first development and instant deployment, while Windsurf and Cursor focus on agentic features and seamless editor‑to‑web interactions. GitHub Copilot and JetBrains’ assistant bring context‑aware completions and refactorings inside established IDEs. Meanwhile, Tabnine and similar engines provide completions tuned for latency and scale. Understanding this space requires comparing capabilities (completion vs. agentic task orchestration), integration points (IDE plugin, cloud IDE, CLI), and trust factors (model provenance, data privacy, testing pipelines). For teams considering adoption, the critical tradeoffs are developer velocity, code quality, and operational control — and the choice increasingly depends on whether the priority is “flow” and rapid prototyping or rigorous, auditable code generation for production systems.