Generative AI coding assistants are systems that use large language models and instruction‑tuned code models to provide inline completions, whole‑function generation, conversational help, and autonomous developer workflows. By 2026 these tools span hosted services (GitHub Copilot, Amazon CodeWhisperer/Amazon Q Developer, Blackbox.ai), open and self‑hosted options (Tabby, CodeGeeX, Code Llama), and edge‑optimised models (Stable Code) designed for low‑latency, private inference. Anthropic’s Claude Opus 4.5 and other recent model families have improved instruction following and debugging support, enabling richer Copilot‑style chat and agentic automation within IDEs and terminals. Relevance: adoption has shifted from experimental completions to production developer workflows—teams prioritize security, compliance, and latency, driving interest in self‑hosted and edge deployments as well as in integrated agentic environments like Warp. Key trends include fill‑in‑the‑middle and whole‑function suggestions, chat‑based troubleshooting, model specialization for code, and tighter integration with CI, terminals, and code review. Enterprises are balancing hosted convenience with data residency and IP concerns; open models such as Code Llama and CodeGeeX and tools like Tabby lower the barrier for private deployments. Key tools and roles: GitHub Copilot — IDE/terminal pair programmer with inline completions and Copilot Chat; Amazon CodeWhisperer — inline and function suggestions folding into Amazon Q Developer; Anthropic Claude Opus 4.5 — high‑capability instruction model used for conversational coding and agent workflows; Stable Code — edge‑ready models for private, fast completion; Tabby, CodeGeeX, Blackbox.ai and Warp — provide self‑hosting, agent platforms, or integrated ADEs. Understanding these options helps teams choose tradeoffs between productivity, privacy, and operational cost.