blackboy Perplexity AI is transforming how we search for information. Learn how to use this AI-powered search engine effectively. What is Perplexity? An AI search engine that provides direct answers with sources cited. Key Features ✅ Real-time information ✅ Source citations ✅ Follow-up questions ✅ Thread continuity Pro Features • Copilot mode for detailed research • … Read more
Integrate DALL-E 3 into your applications. Generate images programmatically with OpenAI’s API. API Setup import openai client = openai.OpenAI() Generate Image response = client.images.generate( model=”dall-e-3″, prompt=”A beautiful sunset over mountains”, size=”1024×1024″ ) Pricing • DALL-E 3: $0.04-$0.12 per image • DALL-E 2: $0.02-$0.12 per image Use Cases ✅ Marketing content ✅ Product visualization ✅ Creative … Read more
Master Midjourney V6 for AI art creation. Create professional-quality images with simple prompts. What’s New in V6 ✅ Improved photorealism ✅ Better text rendering ✅ Enhanced coherence ✅ More style control Basic Commands /imagine [prompt] – Generate image /settings – Adjust preferences /describe – Image to prompt Pro Tips • Use –ar for aspect ratio … Read more
Comparing the top AI code assistants in 2026. GitHub Copilot vs Cursor – which one should you use? Overview Both tools use AI to help you code faster, but they have key differences. Comparison Table Feature Copilot Cursor Price $10/mo $20/mo Context Awareness Good Excellent Codebase Chat No Yes VS Code Compatible Yes Yes Multi-file … Read more
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!