Tripti Tripathi

Casually livestreaming at night… until something with glowing red eyes suddenly appears behind you. 😳🐺 I was NOT ready for that! 💀 Would you run or keep streaming? 👀 Made with @PixVerse #PixVerseTrend #EscapeCam #AIvideo #Horror #Livestream #PixVerse

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 𝗔𝗜 = 𝗖𝗵𝗮𝘁𝗚𝗣𝗧. Not even close. ChatGPT is what you see. The real AI revolution is the massive ecosystem being built underneath it. This AI Stack Map captures 100+ tools powering modern AI applications. 𝗧𝗵𝗲 𝗺𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 𝘀𝘁𝗮𝗰𝗸 𝗹𝗼𝗼𝗸𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: LLMs → OpenAI, Claude, Gemini, Llama, Mistral Agentic AI → LangGraph, CrewAI, AutoGen, CAMEL, Agno RAG → LangChain, LlamaIndex, Haystack, GraphRAG Embeddings → OpenAI, Voyage, Cohere, BGE MCP → Connecting models with tools, data and systems AI Security → Guardrails, Presidio, Lakera, Prompt Security Observability & Evals → LangSmith, Langfuse, Phoenix, Ragas Memory → Redis, Mem0, Zep, Neo4j, Chroma Agent Frameworks → OpenAI SDK, Semantic Kernel, Google ADK, Bedrock Automation → n8n, Zapier, Make, Airflow, Prefect Vector Databases → Pinecone, Weaviate, Qdrant, Milvus, pgvector But here’s the part that matters more than the logos. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗹𝗶𝘀𝘁. It’s a dependency graph. Your RAG is only as good as your retrieval + embeddings. Your agent is only as reliable as its tools + evaluations + guardrails. Your memory is useless if you can’t observe when context becomes stale or wrong. Your MCP layer becomes dangerous if permissions and governance are an afterthought. And a “best-in-class” stack can still become a terrible production system. Why? Because AI systems rarely fail only inside one component. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗶𝗹 𝗮𝘁 𝘁𝗵𝗲 𝗵𝗮𝗻𝗱𝗼𝗳𝗳𝘀. Model → Retrieval Retrieval → Context Context → Agent Agent → Tool Tool → Memory Memory → Evaluation That’s where latency compounds, context gets lost, permissions leak, hallucinations propagate and reliability starts falling apart. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗺𝗼𝗮𝘁 𝗶𝘀𝗻’𝘁 𝗵𝗮𝘃𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀. It’s designing how they work together. A stack map tells you 𝗪𝗛𝗔𝗧 exists. Production engineering decides 𝗛𝗢𝗪 it all survives contact with reality. Which tool in your AI stack has become indispensable? Save this map for your next AI build. Repost it for someone designing an AI architecture. #AI #ArtificialIntelligence #GenAI𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 𝗔𝗜 = 𝗖𝗵𝗮𝘁𝗚𝗣𝗧. Not even close. ChatGPT is what you see. The real AI revolution is the massive ecosystem being built underneath it. This AI Stack Map captures 100+ tools powering modern AI applications. 𝗧𝗵𝗲 𝗺𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 𝘀𝘁𝗮𝗰𝗸 𝗹𝗼𝗼𝗸𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: LLMs → OpenAI, Claude, Gemini, Llama, Mistral Agentic AI → LangGraph, CrewAI, AutoGen, CAMEL, Agno RAG → LangChain, LlamaIndex, Haystack, GraphRAG Embeddings → OpenAI, Voyage, Cohere, BGE MCP → Connecting models with tools, data and systems AI Security → Guardrails, Presidio, Lakera, Prompt Security Observability & Evals → LangSmith, Langfuse, Phoenix, Ragas Memory → Redis, Mem0, Zep, Neo4j, Chroma Agent Frameworks → OpenAI SDK, Semantic Kernel, Google ADK, Bedrock Automation → n8n, Zapier, Make, Airflow, Prefect Vector Databases → Pinecone, Weaviate, Qdrant, Milvus, pgvector But here’s the part that matters more than the logos. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗹𝗶𝘀𝘁. It’s a dependency graph. Your RAG is only as good as your retrieval + embeddings. Your agent is only as reliable as its tools + evaluations + guardrails. Your memory is useless if you can’t observe when context becomes stale or wrong. Your MCP layer becomes dangerous if permissions and governance are an afterthought. And a “best-in-class” stack can still become a terrible production system. Why? Because AI systems rarely fail only inside one component. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗶𝗹 𝗮𝘁 𝘁𝗵𝗲 𝗵𝗮𝗻𝗱𝗼𝗳𝗳𝘀. Model → Retrieval Retrieval → Context Context → Agent Agent → Tool Tool → Memory Memory → Evaluation That’s where latency compounds, context gets lost, permissions leak, hallucinations propagate and reliability starts falling apart. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗺𝗼𝗮𝘁 𝗶𝘀𝗻’𝘁 𝗵𝗮𝘃𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀. It’s designing how they work together. A stack map tells you 𝗪𝗛𝗔𝗧 exists. Production engineering decides 𝗛𝗢𝗪 it all survives contact with reality. Which tool in your AI stack has become indispensable? Save this map for your next AI build. Repost it for someone designing an AI architecture. #AI #ArtificialIntelligence #GenAI

Building AI agents? Don't miss this! Before launching your next AI agent, test how well it handles repetitive tasks. Does it become faster and smarter with every run, or does performance stay the same? The real power of an AI agent lies in continuous improvement!

A 19-year-old Japanese student built a trading bot with Claude Code in 2 days. Used his iPad as a second monitor. First night: $6,732 profit. Starting capital: $68. Total profit so far: $750,000. Here's how it works: The bot scans over 50 markets simultaneously. Syncs live BTC data from Binance every second. Spots price errors before humans even notice. The edge is pure speed + pattern recognition. While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets. No guessing. No emotions. No hesitation. Just Claude Code logic finding gaps that close in seconds. He built the entire system in 48 hours: → Claude Code handles the trading logic → Binance API feeds real-time BTC data → iPad displays multi-market monitoring → Executes trades when arbitrage windows open The system runs 24/7. Every price dislocation = profit opportunity. Most people are still trading manually, refreshing charts, second-guessing entries. Meanwhile this 19-year-old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear. Why are people still trading manually? 💡I'm giving away the exact Claude Code setup for free. 24 hours only. To get it: 👇 1️⃣ Comment "Claude" 2️⃣ Like Comment and Repost Follow @triptitips I'll DM you the complete setup.

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A 19-year-old Japanese student built a trading bot with Claude Code in 2 days. Used his iPad as a second monitor. First night: $6,732 profit. Starting capital: $68. Total profit so far: $750,000. Here's how it works: The bot scans over 50 markets simultaneously. Syncs live BTC data from Binance every second. Spots price errors before humans even notice. The edge is pure speed + pattern recognition. While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets. No guessing. No emotions. No hesitation. Just Claude Code logic finding gaps that close in seconds. He built the entire system in 48 hours: → Claude Code handles the trading logic → Binance API feeds real-time BTC data → iPad displays multi-market monitoring → Executes trades when arbitrage windows open The system runs 24/7. Every price dislocation = profit opportunity. Most people are still trading manually, refreshing charts, second-guessing entries. Meanwhile this 19-year-old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear. Why are people still trading manually? 💡 I'm giving away the exact Claude Code setup for free. 24 hours only. To get it: 1️⃣ Comment "Fable" 2️⃣ Like and Repost 3️⃣ Follow @triptitips I'll DM you the complete setup.