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. 𝓾𝓷𝓴𝓷𝓸𝔀𝓷🍃
. 𝓾𝓷𝓴𝓷𝓸𝔀𝓷🍃
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Friday 04 September 2026 17:54:48 GMT
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jakes._004
💀𝗝𝟰𝗞𝗘𝗦!𝟬𝟬𝟰🇱🇷🌴🥷🏻 :
Big brrrrr😂🔥
2026-09-05 03:02:46
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I’m building an **Autonomous AI Research Agent** from scratch as part of my **AI with Hammad** project. Instead of giving one question directly to an AI and trusting the answer, my agent follows a process: **You ask a question 👇** ➡️ **Planner** — Understands what needs to be researched ➡️ **Web Search** — Finds information from the internet ➡️ **Reader** — Reads the sources ➡️ **Extractor** — Finds the important facts ➡️ **Fact Checker** — Compares information from different sources ➡️ **Summarizer** — Turns everything into an easy-to-read report ### 💡 Simple example: You ask: **“What are the best AI automation tools in 2026?”** Instead of simply answering: > “Here are 5 tools…” The agent will: 🔎 Search multiple sources 📖 Read the information 📊 Compare the data ✅ Check important claims 📝 Create a final research report That’s what makes it an **AI Agent**, not just a chatbot. ### 🛠️ What I’m using: 🐍 Python ⚡ FastAPI 🧠 Gemini API 📦 Pydantic 🗄️ SQLite 🌐 Vanilla JavaScript I’m intentionally building Version 1 **without heavy agent frameworks**. Why? Because I want to understand what actually happens behind the scenes: **Planning → Tools → Memory → Verification → Decision Making → Final Answer** This project is part of my journey to understand **Agentic AI by building it, not just studying it.** 🚀 **20 modules. One project. Built from scratch.** **Would you build an AI Agent from scratch, or use a framework like LangChain/LangGraph?** Let me know your approach 👇 #AI #AgenticAI #ArtificialIntelligence #Python #AIEngineering
I’m building an **Autonomous AI Research Agent** from scratch as part of my **AI with Hammad** project. Instead of giving one question directly to an AI and trusting the answer, my agent follows a process: **You ask a question 👇** ➡️ **Planner** — Understands what needs to be researched ➡️ **Web Search** — Finds information from the internet ➡️ **Reader** — Reads the sources ➡️ **Extractor** — Finds the important facts ➡️ **Fact Checker** — Compares information from different sources ➡️ **Summarizer** — Turns everything into an easy-to-read report ### 💡 Simple example: You ask: **“What are the best AI automation tools in 2026?”** Instead of simply answering: > “Here are 5 tools…” The agent will: 🔎 Search multiple sources 📖 Read the information 📊 Compare the data ✅ Check important claims 📝 Create a final research report That’s what makes it an **AI Agent**, not just a chatbot. ### 🛠️ What I’m using: 🐍 Python ⚡ FastAPI 🧠 Gemini API 📦 Pydantic 🗄️ SQLite 🌐 Vanilla JavaScript I’m intentionally building Version 1 **without heavy agent frameworks**. Why? Because I want to understand what actually happens behind the scenes: **Planning → Tools → Memory → Verification → Decision Making → Final Answer** This project is part of my journey to understand **Agentic AI by building it, not just studying it.** 🚀 **20 modules. One project. Built from scratch.** **Would you build an AI Agent from scratch, or use a framework like LangChain/LangGraph?** Let me know your approach 👇 #AI #AgenticAI #ArtificialIntelligence #Python #AIEngineering

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