Experience Working with OpenClaw (Clawbot)
The author shares their experience as a developer working with OpenClaw (Clawbot), including a real-world setup and insights after using it in production-like scenarios.
Why it matters
This article provides valuable insights into the practical challenges and performance trade-offs when working with OpenClaw and local AI models.
Key Points
- 1Tested multiple local models using Ollama, but found cloud models like Kimi 2.5 and Anthropic's Claude to significantly outperform them
- 2Faced challenges with the opacity of the TUI, lack of cross-channel consistency, and configuration complexity
- 3Leveraged Ubuntu Server, Tailscale, and Claude Code to create a powerful personal AI infrastructure
Details
The author's setup includes an AMD Ryzen 5 5600X, 32GB RAM, RTX 3060, and Ubuntu Server. They experimented with Ollama local models and cloud models like Kimi 2.5 as part of a fallback strategy. While OpenClaw is a flexible and powerful system, the author found that the local models significantly underperformed compared to cloud models in terms of reasoning and speed. The author also faced challenges with the opacity of the TUI, lack of cross-channel consistency, and configuration complexity. Despite these issues, the author believes that the architectural foundation of OpenClaw is strong, with features like multi-model orchestration, fallback strategies, and multi-channel interaction. However, the ecosystem, especially around local models, still has room for improvement.
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