LobsterBoard adds theme system and template gallery
Theme system implementation
LobsterBoard has added a theme system that provides five visual options. The original dark mode remains the default. Users can switch themes from a dropdown menu while in edit mode, and the system remembers their choice.
- Terminal theme: Green CRT monitor aesthetic
- Paper theme: Warm cream/sepia color scheme
- Feminine light theme: Soft pinks and lavenders
- Feminine dark theme: Dark background with purple accents
Template gallery functionality
The template gallery allows users to export their entire dashboard layout as a template, complete with an auto-captured screenshot preview. Users can browse other templates and import them in two ways:
- Full replacement of existing dashboard
- Merge below existing widgets
The system automatically strips sensitive data including API keys and calendar URLs during export, preventing accidental exposure of credentials like Google Calendar tokens.
📖 Read the full source: r/openclaw
👀 See Also
Open-source memory system for LLM agents achieves high benchmark scores
A persistent memory system for Claude Code and OpenClaw provides LLM agents with context continuity across sessions, achieving 90.8% on LoCoMo and 89.1% on LongMemEval benchmarks. The adapter-based architecture works with any agent framework.
Tocket CLI: A Context Engineering Framework for AI Coding Agents
Tocket is a CLI tool that creates a .context/ folder with markdown files for AI agents to maintain project memory across sessions. It auto-detects tech stacks from package.json and generates a pre-configured .cursorrules file.
Interact MCP: Faster Web Browsing for Claude Code with Persistent Chromium
Interact MCP is a Model Context Protocol tool that keeps a persistent Chromium browser in-process, reducing browser action times from 2-5 seconds to 5-50ms after the initial call. It features a ref system for element interaction without CSS selectors and includes 46 tools for web automation.
SubQ: A Sub-Quadratic LLM with 12M-Token Context Window
SubQ is a fully sub-quadratic sparse-attention LLM offering a 12M-token context window at 150 tokens/s, with SWE-Bench Verified 81.8% and RULER @ 128K 95.0%. It reduces attention compute ~1000× compared to transformers.