Cursor is the most advanced AI code editor available today. Learn how to use Cursor to code 10x faster with AI. What is Cursor? Cursor is a VS Code-based editor with built-in AI capabilities. It features GPT-4, code completion, and intelligent refactoring. Key Features ✅ AI code completion ✅ Natural language to code ✅ Codebase-aware … Read more
Set and manage token budgets. Control costs in production applications. Budgeting Strategies 1. Set per-request limits 2. Track usage per user 3. Implement quotas 4. Alert on thresholds Implementation class TokenBudget: def __init__(self, max_tokens=10000): self.max_tokens = max_tokens self.used = 0 def check(self, tokens): return self.used + tokens
Process multiple requests efficiently. Batch requests to reduce overhead. Batching Strategies 1. Combine similar requests 2. Use batch endpoints 3. Parallel processing Code Example prompts = [“prompt1”, “prompt2”, “prompt3”] batch_response = process_batch(prompts) Conclusion Batching improves efficiency!
Implement caching to reduce API calls. Cache responses for efficiency and cost savings. Caching Types ✅ Response caching ✅ Embedding caching ✅ Semantic caching Implementation from functools import lru_cache @lru_cache(maxsize=1000) def get_response(prompt): return client.chat.completions.create(…) Conclusion Caching reduces redundant API calls!
Compress prompts without losing quality. Reduce token count while maintaining effectiveness. Techniques 1. Remove filler words 2. Use abbreviations 3. Combine instructions 4. Reference instead of repeat Example Before: “Please write a comprehensive article about…” After: “Write article about…” Conclusion Compression maintains quality while saving tokens!
Practical strategies to reduce token usage. Save money on API costs with these techniques. Strategies 1. Prompt compression 2. Remove redundancy 3. Use shorter examples 4. Optimize system prompts Before/After Before: 1000 tokens After: 400 tokens (60% reduction) Conclusion Token optimization significantly reduces costs!
Develop plugins to extend OpenClaw. Create powerful integrations with plugins. Plugin Architecture Plugins add new capabilities to OpenClaw. Creating a Plugin 1. Define plugin structure 2. Implement plugin logic 3. Add configuration 4. Test thoroughly Conclusion Plugins enable custom integrations!
Deploy OpenClaw in production environments. Run your AI assistant reliably at scale. Deployment Options ✅ Docker ✅ Kubernetes ✅ Cloud platforms Configuration Use environment variables for configuration. Monitoring Set up logging and monitoring for production. Conclusion Production deployment ensures reliability!
Manage memory in OpenClaw for context-aware responses. Build assistants that remember and learn. Memory Types ✅ Short-term memory ✅ Long-term memory ✅ Working memory Memory Files – MEMORY.md: Long-term memories – memory/YYYY-MM-DD.md: Daily notes Conclusion Memory enables context-aware assistants!
Build custom skills to extend OpenClaw. Create specialized capabilities for your assistant. Skill Structure – SKILL.md: Skill description – scripts/: Executable scripts – references/: Documentation Example Skill Create a skill that integrates with your favorite API. Best Practices ✅ Clear documentation ✅ Error handling ✅ Input validation Conclusion Skills make OpenClaw infinitely extensible!