@ampha010: опана #projectsekai #kaito #вокалоид

амфа

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Saturday 03 October 2026 04:36:04 GMT

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Comments

KAITO :

У меня обсидиановый, прекрати

2026-10-03 08:29:21

419

-кайто сдох :

у него битва за ингалятор будто

2026-10-03 05:14:35

139

Психонавт💥 :

КАЙТО❤️

2026-10-03 06:15:39

15

Визоут Кики!🍭 :

у меня каменный

2026-10-03 09:18:16

15

Rina :

давай "что ты можешь сказать например "

2026-10-03 06:07:40

51

Мигурчик :

кайточка💝💝💝

2026-10-03 04:54:29

13

lisaveta (or utau fans) :

каймику канон

2026-10-04 17:20:56

1

ᛝ ׁ ׅ 🧁 𝗸іsᥱᥣkᥲ ᵎ𓂃 ๋࣭♡゙ :

2026-10-03 04:42:04

8

Lina ᥫ᭡ 𝙆𝙖𝙞𝙩𝙤'𝙨 𝙬𝙞𝙛𝙚 :

мой муж упомянается слыштизолип

2026-10-03 12:32:30

2

Atsuka :

нет, удали в6 и переходи на синтви

2026-10-03 13:31:28

4

^҉|҉М҉е҉й҉к҉о҉|҉^҉ :

ладно, я залипла

2026-10-05 04:31:09

1

Сайори лёблю :

КАЙТО ОЧЕНЬ СИЛЬНО ЛЮ

2026-10-03 19:12:38

2

⋆˚࿔ syncope 𝜗ৎ ♂ ♂ :

опа ты за кимлип

2026-10-03 16:58:59

1

Alcatraz :

Я залипла 🤤🤤🤤

2026-10-03 11:48:28

0

Селес :

2026-10-03 09:01:56

1

Son :

Он хотя бы не хнычет как Лен [я и то и то схаваю]

2026-10-03 13:09:31

1

чайный пакетик :

у меня на это каменный

2026-10-03 11:35:53

1

Чё?🦎27🍦 ⁷ :

Ебать ахуенно

2026-10-03 10:07:44

1

чҽᴘҽωня ̑🧁̼۫͗ :

на что я спустил

2026-10-04 07:33:13

0

лизун халяль👀 :

@⋆.celtic˚

2026-10-04 15:32:29

1

тунеядец :

🥰🥰🥰

2026-10-03 08:40:27

1

Nica :

@Mc Levdens🗿 ✔️

2026-10-03 07:19:19

1

To see more videos from user @ampha010, please go to the Tikwm homepage.

Other Videos

We are COOKEDDDDD

We are COOKEDDDDD

Traditional RAG retrieves documents and generates an answer. Agentic RAG goes further. Instead of following one fixed retrieval pipeline, an AI agent can reason, decide which tools to use, retrieve information, evaluate results, and iterate before producing the final answer. 🧩 THE ARCHITECTURE 1️⃣ USER QUERY ↓ User asks a question 2️⃣ AI AGENT ↓ Understands the task and decides what to do 3️⃣ QUERY PLANNER ↓ Breaks complex questions into smaller retrieval tasks 4️⃣ RETRIEVAL ↓ Searches your knowledge base using: • Vector search • Keyword search • Hybrid search • Metadata filtering 5️⃣ RERANKING ↓ Ranks retrieved documents by relevance 6️⃣ CONTEXT EVALUATION ↓ Agent checks: • Is the context relevant? • Is information sufficient? • Are sources trustworthy? 7️⃣ TOOL CALLING ↓ If required, the agent can use: • Web search • SQL databases • APIs • Calculators • Python tools • Internal knowledge bases 8️⃣ ITERATIVE RETRIEVAL ↓ Not enough information? Search again. 🔄 The agent can reformulate the query and retrieve additional context. 9️⃣ GENERATION ↓ LLM generates the final response using the selected evidence. 🔟 CITATIONS + VALIDATION ↓ Return the answer with supporting sources and verify that claims are grounded. 🏗️ TECH STACK LLM: GPT / Claude / Gemini / open-source models Orchestration: LangGraph / LangChain Embeddings: Sentence Transformers / OpenAI embeddings Vector DB: Qdrant / Pinecone / Weaviate / Chroma Reranker: Cross-encoder / Cohere / BGE Backend: FastAPI Database: PostgreSQL Frontend: React / Streamlit Deployment: Docker + Cloud 🔥 BASIC AGENTIC RAG FLOW Question → Agent → Plan → Retrieve → Rerank → Evaluate → Tool call if needed → Retrieve again if necessary → Generate → Verify → Final Answer + Sources 🧠 WHAT MAKES IT “AGENTIC”? Traditional RAG: Query → Retrieve → Generate Agentic RAG: Query → Reason → Plan → Act → Retrieve → Evaluate → Iterate → Generate The key difference is decision-making and feedback loops, not simply adding an LLM to a RAG pipeline. 💡 PROJECT IDEA Build a Research Assistant Agent that can: 📄 Search research papers 🔎 Retrieve relevant sections 🧠 Break complex questions into sub-questions 🌐 Search external sources when necessary 📊 Analyze structured data 🔗 Track citations ✅ Verify retrieved evidence 💬 Generate a grounded final answer That is much closer to a production-grade AI system than a basic chatbot. 📌 Save this architecture if you’re learning RAG or AI Engineering. #AgenticRAG #RAG #GenerativeAI #creatorsearchinsights #machinelearningengineer

Traditional RAG retrieves documents and generates an answer. Agentic RAG goes further. Instead of following one fixed retrieval pipeline, an AI agent can reason, decide which tools to use, retrieve information, evaluate results, and iterate before producing the final answer. 🧩 THE ARCHITECTURE 1️⃣ USER QUERY ↓ User asks a question 2️⃣ AI AGENT ↓ Understands the task and decides what to do 3️⃣ QUERY PLANNER ↓ Breaks complex questions into smaller retrieval tasks 4️⃣ RETRIEVAL ↓ Searches your knowledge base using: • Vector search • Keyword search • Hybrid search • Metadata filtering 5️⃣ RERANKING ↓ Ranks retrieved documents by relevance 6️⃣ CONTEXT EVALUATION ↓ Agent checks: • Is the context relevant? • Is information sufficient? • Are sources trustworthy? 7️⃣ TOOL CALLING ↓ If required, the agent can use: • Web search • SQL databases • APIs • Calculators • Python tools • Internal knowledge bases 8️⃣ ITERATIVE RETRIEVAL ↓ Not enough information? Search again. 🔄 The agent can reformulate the query and retrieve additional context. 9️⃣ GENERATION ↓ LLM generates the final response using the selected evidence. 🔟 CITATIONS + VALIDATION ↓ Return the answer with supporting sources and verify that claims are grounded. 🏗️ TECH STACK LLM: GPT / Claude / Gemini / open-source models Orchestration: LangGraph / LangChain Embeddings: Sentence Transformers / OpenAI embeddings Vector DB: Qdrant / Pinecone / Weaviate / Chroma Reranker: Cross-encoder / Cohere / BGE Backend: FastAPI Database: PostgreSQL Frontend: React / Streamlit Deployment: Docker + Cloud 🔥 BASIC AGENTIC RAG FLOW Question → Agent → Plan → Retrieve → Rerank → Evaluate → Tool call if needed → Retrieve again if necessary → Generate → Verify → Final Answer + Sources 🧠 WHAT MAKES IT “AGENTIC”? Traditional RAG: Query → Retrieve → Generate Agentic RAG: Query → Reason → Plan → Act → Retrieve → Evaluate → Iterate → Generate The key difference is decision-making and feedback loops, not simply adding an LLM to a RAG pipeline. 💡 PROJECT IDEA Build a Research Assistant Agent that can: 📄 Search research papers 🔎 Retrieve relevant sections 🧠 Break complex questions into sub-questions 🌐 Search external sources when necessary 📊 Analyze structured data 🔗 Track citations ✅ Verify retrieved evidence 💬 Generate a grounded final answer That is much closer to a production-grade AI system than a basic chatbot. 📌 Save this architecture if you’re learning RAG or AI Engineering. #AgenticRAG #RAG #GenerativeAI #creatorsearchinsights #machinelearningengineer

#policesketch #creepiest #horrortok #explained

#policesketch #creepiest #horrortok #explained

Làm sao tỏa sáng trong

Làm sao tỏa sáng trong "cái bóng" của thế hệ sáng lập? Từ chuyên viên phân tích cấp cao tại Cục Dự trữ Liên bang Mỹ (Fed) đến CEO BNI Việt Nam – mạng lưới khoảng 10.000 doanh nghiệp do cha mình gây dựng, chị Christine Hồng Phúc bước vào một bài toán không chỉ là tiếp quản, mà là tự định vị mình trong một di sản đã có sẵn. Chị không cố thoát khỏi cái bóng của thế hệ sáng lập. Chị chọn tỏa sáng ngay trong đó, và học cách "nhận thông tin chứ không nhận năng lượng" để không bị cuốn theo cảm xúc của người khác. Trên The Quoc Khanh Show tuần này, Vietsuccess chào đón chị Christine Hồng Phúc với câu chuyện của một F2 lớn lên cùng BNI, trở về Việt Nam sau nhiều năm học tập và làm việc tại Mỹ, rồi từng bước nhận trách nhiệm điều hành tổ chức do cha mình – ông Hồ Quang Minh, Nhà sáng lập BNI Việt Nam xây dựng. Nếu bạn là F2 – thế hệ tiếp nối một doanh nghiệp gia đình, có lẽ đây là một trong những câu hỏi khó nhất: Làm sao để kế thừa một di sản lớn mà vẫn trở thành chính mình? ▶ Đón xem The Quoc Khanh Show 139: Làm sao tỏa sáng trong "cái bóng" của thế hệ sáng lập? ▶ Phát sóng vào lúc 19h ngày 08/10 trên các nền tảng Podcast của Vietsuccess. #Vietsuccess #TheQuocKhanhShow #TQKS

🇦🇹 Kleinwalsertal 🇦🇹

🇦🇹 Kleinwalsertal 🇦🇹

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