The latest articles on DEV Community by Steven Aragón Urrea (@stevearagonsite). https://dev.to/stevearagonsite https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F1580013%2Ffc0a3b5a-bbdd-4dd9-8e66-1defcb6718d5.jpg https://dev.to/stevearagonsite en Steven Aragón Urrea Wed, 18 Mar 2026 18:58:24 +0000 https://dev.to/stevearagonsite/experience-working-with-openclaw-clawbot-298g https://dev.to/stevearagonsite/experience-working-with-openclaw-clawbot-298g <h1> Experience Working with OpenClaw (Clawbot) </h1> <p>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.</p> <p>My setup is based on:</p> <ul> <li><strong>AMD Ryzen 5 5600X</strong></li> <li><strong>32GB RAM</strong></li> <li><strong>RTX 3060 (LHR)</strong></li> <li><strong>NVMe SSD storage</strong></li> <li><strong>Ubuntu Server (headless environment)</strong></li> </ul> <p>I also experimented with multiple <strong>Ollama local models</strong> as part of a fallback strategy, along with <strong>cloud models like Kimi 2.5</strong>.</p> <p>From a configuration standpoint, OpenClaw is a powerful and flexible system — but that flexibility comes at a cost.</p> <h2> Real Setup (Explained) </h2> <p>Instead of showing raw configuration, here’s how my setup is structured conceptually:</p> <ul> <li> <strong>Primary model:</strong> Claude Opus 4.6 </li> <li> <strong>Cloud alternative:</strong> Kimi 2.5 </li> <li> <strong>Fallback #1:</strong> Local models via Ollama (GPU) </li> <li> <strong>Fallback #2:</strong> OpenAI models </li> <li> <strong>Interface:</strong> Telegram </li> <li> <strong>Remote access:</strong> Tailscale as a service </li> </ul> <p>Flow:</p> <blockquote> <p>Primary cloud model → Alternative cloud → Local fallback → Secondary cloud fallback</p> </blockquote> <p>This setup aims to balance:</p> <ul> <li>Performance</li> <li>Reliability</li> <li>Offline capability</li> </ul> <h2> Infrastructure &amp; Tooling </h2> <h3> Ubuntu Server </h3> <p>I chose <strong>Ubuntu Server</strong> to fully utilize the machine:</p> <ul> <li>Lower overhead (no GUI)</li> <li>Better resource allocation for models</li> <li>More predictable performance for long-running processes</li> </ul> <h3> Tailscale (as a service) </h3> <p>I used <strong>Tailscale</strong> running as a background service to access the machine remotely.</p> <p>The experience was <strong>excellent</strong>:</p> <ul> <li>Fast and stable connections</li> <li>Zero-config networking</li> <li>Secure remote access without exposing ports</li> </ul> <p>This made it extremely easy to:</p> <ul> <li>Manage OpenClaw remotely</li> <li>Debug issues</li> <li>Interact with the system from anywhere</li> </ul> <h3> Claude Code for Setup </h3> <p>I used <strong>Claude Code</strong> to bootstrap and configure the environment.</p> <p>This significantly reduced setup friction:</p> <ul> <li>Faster iteration</li> <li>Easier debugging</li> <li>Better guidance wiring models and fallbacks</li> </ul> <h2> Local Models Tested (Ollama) </h2> <p>I tested several local models using Ollama:</p> <ul> <li><strong>Gemma 3 (12B)</strong></li> <li><strong>Qwen 3 (14B, abliterated)</strong></li> <li><strong>Qwen 3.5 (9B)</strong></li> </ul> <h2> Model Performance Ranking </h2> <h3> 🧠 Overall Ranking (Reasoning + Speed) </h3> <ol> <li> <strong>Kimi 2.5 (cloud)</strong> → Best overall performance </li> <li> <strong>Gemma 3 (local)</strong> </li> <li> <strong>Qwen 3 (local)</strong> </li> <li> <strong>Qwen 3.5 (local)</strong> </li> </ol> <h3> ☁️ Provider Ranking </h3> <ol> <li> <strong>Anthropic (Claude models)</strong> → Most reliable reasoning </li> <li> <strong>OpenAI models</strong> → Strong and consistent </li> <li> <strong>Ollama (local models)</strong> → Significantly weaker </li> </ol> <h2> My Experience with Local Models </h2> <p>Using <strong>Ollama with local models</strong> is a great idea in theory.</p> <h3> Pros: </h3> <ul> <li>Works without internet</li> <li>Fully local</li> <li>Good fallback strategy</li> </ul> <h3> Reality: </h3> <p>Even running on an RTX 3060 and testing multiple models:</p> <blockquote> <p>Local models were, in practice, a <strong>major downgrade</strong>.</p> </blockquote> <p>It almost feels like the system gets “lobotomized” when switching to them:</p> <ul> <li>Weak reasoning</li> <li>Poor context handling</li> <li>Inconsistent outputs</li> </ul> <p>This becomes very clear when compared to:</p> <ul> <li>Claude (Anthropic)</li> <li>OpenAI models</li> <li>Kimi 2.5 (which performed surprisingly well)</li> </ul> <h2> Key Challenges </h2> <h3> 1. Opacity in the TUI </h3> <p>The TUI feels like a black box.</p> <p>You don’t know:</p> <ul> <li>Which model is active</li> <li>When fallbacks trigger</li> <li>Why decisions are made</li> </ul> <p>This makes debugging painful.</p> <h3> 2. Lack of Cross-Channel Consistency </h3> <ul> <li>Telegram ≠ TUI</li> <li>No shared continuity</li> <li>Fragmented sessions</li> </ul> <h3> 3. Configuration Complexity </h3> <p>You must carefully align:</p> <ul> <li>Models</li> <li>Providers</li> <li>Fallbacks</li> <li>Channels</li> </ul> <p>Otherwise:</p> <ul> <li>Silent failures</li> <li>Weird behaviors</li> <li>Hard-to-debug issues</li> </ul> <h2> Final Thoughts </h2> <p>OpenClaw has a <strong>strong architectural foundation</strong>:</p> <ul> <li>Multi-model orchestration</li> <li>Fallback strategies</li> <li>Multi-channel interaction</li> </ul> <p>But it needs improvements in:</p> <ul> <li>Transparency (TUI)</li> <li>Cross-channel consistency</li> <li>Developer experience</li> <li>Local model performance</li> </ul> <h2> Conclusion </h2> <p>Combining:</p> <ul> <li>Ubuntu Server</li> <li>Tailscale</li> <li>Cloud + local models</li> </ul> <p>creates a <strong>very powerful personal AI infrastructure</strong>.</p> <p>However:</p> <blockquote> <p>Today, <strong>cloud models still massively outperform local ones</strong>, even on decent hardware like an RTX 3060.</p> </blockquote> <p>The idea is solid.</p> <p>The execution is promising.</p> <p>But the ecosystem — especially around local models — still has a long way to go.</p> ai linux llm openclaw