Experience Working with OpenClaw (Clawbot)
I want to share my experience as a developer working with OpenClaw (Clawbot), including a real-world setup and some practical insights after using it in production-like scenarios.
My setup is based on:
- AMD Ryzen 5 5600X
- 32GB RAM
- RTX 3060 (LHR)
- NVMe SSD storage
- Ubuntu Server (headless environment)
I also experimented with multiple Ollama local models as part of a fallback strategy, along with cloud models like Kimi 2.5.
From a configuration standpoint, OpenClaw is a powerful and flexible system — but that flexibility comes at a cost.
Real Setup (Explained)
Instead of showing raw configuration, here’s how my setup is structured conceptually:
- Primary model: Claude Opus 4.6
- Cloud alternative: Kimi 2.5
- Fallback #1: Local models via Ollama (GPU)
- Fallback #2: OpenAI models
- Interface: Telegram
- Remote access: Tailscale as a service
Flow:
Primary cloud model → Alternative cloud → Local fallback → Secondary cloud fallback
This setup aims to balance:
- Performance
- Reliability
- Offline capability
Infrastructure & Tooling
Ubuntu Server
I chose Ubuntu Server to fully utilize the machine:
- Lower overhead (no GUI)
- Better resource allocation for models
- More predictable performance for long-running processes
Tailscale (as a service)
I used Tailscale running as a background service to access the machine remotely.
The experience was excellent:
- Fast and stable connections
- Zero-config networking
- Secure remote access without exposing ports
This made it extremely easy to:
- Manage OpenClaw remotely
- Debug issues
- Interact with the system from anywhere
Claude Code for Setup
I used Claude Code to bootstrap and configure the environment.
This significantly reduced setup friction:
- Faster iteration
- Easier debugging
- Better guidance wiring models and fallbacks
Local Models Tested (Ollama)
I tested several local models using Ollama:
- Gemma 3 (12B)
- Qwen 3 (14B, abliterated)
- Qwen 3.5 (9B)
Model Performance Ranking
🧠 Overall Ranking (Reasoning + Speed)
- Kimi 2.5 (cloud) → Best overall performance
- Gemma 3 (local)
- Qwen 3 (local)
- Qwen 3.5 (local)
☁️ Provider Ranking
- Anthropic (Claude models) → Most reliable reasoning
- OpenAI models → Strong and consistent
- Ollama (local models) → Significantly weaker
My Experience with Local Models
Using Ollama with local models is a great idea in theory.
Pros:
- Works without internet
- Fully local
- Good fallback strategy
Reality:
Even running on an RTX 3060 and testing multiple models:
Local models were, in practice, a major downgrade.
It almost feels like the system gets “lobotomized” when switching to them:
- Weak reasoning
- Poor context handling
- Inconsistent outputs
This becomes very clear when compared to:
- Claude (Anthropic)
- OpenAI models
- Kimi 2.5 (which performed surprisingly well)
Key Challenges
1. Opacity in the TUI
The TUI feels like a black box.
You don’t know:
- Which model is active
- When fallbacks trigger
- Why decisions are made
This makes debugging painful.
2. Lack of Cross-Channel Consistency
- Telegram ≠ TUI
- No shared continuity
- Fragmented sessions
3. Configuration Complexity
You must carefully align:
- Models
- Providers
- Fallbacks
- Channels
Otherwise:
- Silent failures
- Weird behaviors
- Hard-to-debug issues
Final Thoughts
OpenClaw has a strong architectural foundation:
- Multi-model orchestration
- Fallback strategies
- Multi-channel interaction
But it needs improvements in:
- Transparency (TUI)
- Cross-channel consistency
- Developer experience
- Local model performance
Conclusion
Combining:
- Ubuntu Server
- Tailscale
- Cloud + local models
creates a very powerful personal AI infrastructure.
However:
Today, cloud models still massively outperform local ones, even on decent hardware like an RTX 3060.
The idea is solid.
The execution is promising.
But the ecosystem — especially around local models — still has a long way to go.
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