Claude Code, Codex, opencode (Z.ai GLM-5.x), sometimes Gemini -- most of my work is at Mac iTerm2 CLI or Arch Linux terminal. I sometimes use emacs as the coding agent, but rarely JetBrains IDE or vscode (more for reading/debugging code).
I supplement this with my own vibe-coded tools that help agents plan, perform sagas/steps, stay on track, tools that check the code produced, tools that check the produced documentation, tools/process to limit AI coding agent write access to files outside their assigned project, and tools that allow coding agents to collaborate (shared wiki, mailboxes, gh cli issues, etc.). I have written test frameworks that my AI agents use to detect functional regressions.
On top of this I used web-based or Desktop: Claude, ChatGPT, and/or Gemini to do research, architecture, requirements, planning, design, and later review; to suggest improvements, answer questions, etc.
And I am trying to switch to opencode using local LLMs, served by llama.cpp, Freetoken, or vLLM, on 12G, 16G, and 24G VRAM GPUs. So far the cloud LLMs are faster/better, but recent qwen3.x MoE models look very promising.
vishnuaniyan
I use agent IDE - onorca.dev
It helps me to use multiple models simultaneously in the CLI environment and make the work 10x faster
Software1st
Clause Code intergrated with VSCode, but use VS2026 for dev testing
I supplement this with my own vibe-coded tools that help agents plan, perform sagas/steps, stay on track, tools that check the code produced, tools that check the produced documentation, tools/process to limit AI coding agent write access to files outside their assigned project, and tools that allow coding agents to collaborate (shared wiki, mailboxes, gh cli issues, etc.). I have written test frameworks that my AI agents use to detect functional regressions.
On top of this I used web-based or Desktop: Claude, ChatGPT, and/or Gemini to do research, architecture, requirements, planning, design, and later review; to suggest improvements, answer questions, etc.
And I am trying to switch to opencode using local LLMs, served by llama.cpp, Freetoken, or vLLM, on 12G, 16G, and 24G VRAM GPUs. So far the cloud LLMs are faster/better, but recent qwen3.x MoE models look very promising.