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.
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.
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