Major_MB ⭐

IPL 2026 POINTS TABLE. 📈

what will you choose? > 40 lakh base + WLB + 5 days > 51 lakh base + no WLB + 5 days > 45 lakh base + slight WLB + 3 days > 55 lakh base + WLB + remote

@mufaddal_vohra That was direct... oooch!! Pant!! First Virat, then Rohit and now Pant!! injury not good!!

RISHABH PANT RETIRED HURT.

Mental Edit 🔥 Eppudaite Hero 1 2 ani Dips paddaayo akkadnundi Racha Racha Chesesaru 💥💥💥💥💥💥 @SunRisers

Daily Routine Food for Better Health:🍎🍆 1. Date

11 simple foods that naturally promote healthy testosterone levels 1. Pumpkin seeds

Agentic system design concepts I'd master if I wanted to build AI that doesn't blow up in prod. Bookmark this. 1. Agent Circuit Breaker 2. Blast Radius Limiter 3. Orchestrator vs Choreography 4. Tool Invocation Timeout 5. Confidence Threshold Gate 6. Context Window Checkpointing 7. Idempotent Tool Calls 8. Dead Letter Queue for Agents 9. LLM Gateway Pattern 10. Semantic Caching 11. Human Escalation Protocol 12. Multi-Agent State Sync 13. Replanning Loop 14. Canary Agent Deployment 15. Agentic Observability Tracing

Linux File Commands Cheat Sheet:

Eat clean. Train hard. Think clear. Discipline in your diet becomes discipline in your life.

Clean outfit combos

When someone says they’re a Fullstack developer in 2026 😂

MAN TO MAN.

Testosterone-boosting breakfast: -3 whole eggs -Raw honey -Beef liver -Black coffee -Pink salt -Morning sunlight No cereal. No soy milk. No weakness.

Men, it's YOU vs. YOU. Every single day.

MEN, LEARN HOW TO FLIRT ALREADY Save it, you'll need it✅ 1. Use statements, not questions (observe her from afar) Instead of asking boring questions like "What do you do?", make bold and fun observations about her.

MASTERING PLAN FOR LLMs Large Language Models (LLMs) are the foundation of modern AI systems. They power chatbots, copilots, search engines, automation tools, and intelligent agents. This mastering plan takes you from understanding how LLMs work to building scalable, production-grade AI applications. STEP 1: UNDERSTAND LLM FUNDAMENTALS → What LLMs are and how they work → Tokens and tokenization → Transformers architecture basics → Training vs inference → Pretraining and fine-tuning → Context windows and limitations Build a strong conceptual foundation before building applications. STEP 2: LEARN HOW TO USE LLM APIs → Working with OpenAI APIs → Prompting via SDKs (JavaScript, Python) → Chat vs completion models → Temperature, max tokens, top-p → Streaming responses → Handling API errors and retries APIs are the entry point to real-world LLM applications. STEP 3: MASTER PROMPT ENGINEERING → Zero-shot prompting → Few-shot prompting → Chain-of-Thought prompting → Role-based prompting → Output formatting (JSON, structured data) → Prompt optimization techniques Good prompts = better outputs. STEP 4: STRUCTURED OUTPUTS AND TOOL USAGE → Function calling → Tool integration → JSON schema outputs → Calling external APIs → Building tool-augmented agents This is how LLMs interact with real systems. STEP 5: RETRIEVAL AUGMENTED GENERATION (RAG) → What RAG is and why it matters → Embeddings and vector databases → Semantic search → Chunking and indexing data → Query pipelines → Improving answer accuracy RAG helps LLMs use your own data. STEP 6: MEMORY AND CONTEXT MANAGEMENT → Short-term vs long-term memory → Conversation history management → Summarization techniques → Vector memory systems → Context window optimization Memory enables more intelligent and personalized systems. STEP 7: BUILDING AI AGENTS → What AI agents are → ReAct pattern → Plan-and-execute systems → Tool-using agents → Autonomous workflows → Multi-agent systems Agents turn LLMs into decision-making systems. STEP 8: LLM FRAMEWORKS AND TOOLS → LangChain → LlamaIndex → AutoGen → Semantic Kernel → Prompt orchestration tools Frameworks help you scale development faster. STEP 9: EVALUATION AND TESTING → Evaluating LLM outputs → Prompt testing → Benchmarking → Human-in-the-loop evaluation → Automated evaluation pipelines You cannot improve what you don’t measure. STEP 10: SAFETY AND GUARDRAILS → Handling hallucinations → Content filtering → Prompt injection protection → Rate limiting → Moderation systems → Responsible AI practices Safety is critical in production AI systems. STEP 11: PERFORMANCE AND OPTIMIZATION → Latency optimization → Caching strategies → Token cost optimization → Model selection strategies → Batch processing → Streaming and responsiveness Efficient systems reduce cost and improve UX. STEP 12: DEPLOYMENT AND SCALING → API deployment strategies → Serverless vs microservices → Load balancing → Scaling LLM applications → Monitoring usage and costs → Multi-region deployments Production systems must be reliable and scalable. STEP 13: BUILD REAL-WORLD PROJECTS → AI chatbot with memory → Document Q&A system (RAG) → AI coding assistant → AI content generator → AI automation agent → Multi-agent collaboration system Projects turn knowledge into real-world skills. LLMS HANDBOOK Get the complete LLMs Handbook with deep explanations, prompt engineering strategies, RAG systems, AI agents, and production-ready AI architectures: codewithdhanian.gumroad.com/l/haeit

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