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@baonhixinh02: #xuhuongtiktok #fpy #dance #vairal #nổitiếng

Vitamin Gái Xinh 🦋
Vitamin Gái Xinh 🦋
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Region: VN
Tuesday 06 October 2026 10:09:00 GMT
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nht.k.em12
Nhật kí em 🪝🦋 :
ĐỌC TÊN EM ĐI ANH
2026-10-06 11:19:06
0
nhi_1235677
nhị :
👍🥰❤
2026-10-06 18:17:00
0
van_ut_97
🐃 :
😳😳😳
2026-10-06 14:48:21
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kh0ng197
●●●Không○○○ :
🥰🥰🥰
2026-10-06 14:40:50
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hhbkbj
少年ㄟ :
😳😳😳
2026-10-06 12:56:25
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ivanharold280
tru van :
🥰🥰🥰
2026-10-06 20:25:29
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klime198
@Юрчес :
🥰🥰🥰
2026-10-06 10:49:15
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zaicungroi
N. :
🥰🥰🥰
2026-10-06 10:45:52
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nhacchillremix0
nhacchillremix0 :
@vippro549
2026-10-06 22:33:29
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Post 74 | Promo ni korang kena masuk live Kerepek Mastuki tau sebab akan ada sekali voucher dalam tu. Kita bagi harga kawan2 je ni kalau join geng live kita boleh tengok kita packing sekali 🔥
Post 74 | Promo ni korang kena masuk live Kerepek Mastuki tau sebab akan ada sekali voucher dalam tu. Kita bagi harga kawan2 je ni kalau join geng live kita boleh tengok kita packing sekali 🔥
Everyone's building AI agents. Few can explain what's inside one. Here are 9 AI agent concepts in one 8-second loop. 🤖 The model is the easy part. Everything around it decides whether your agent works. 🧠 1. Memory & State: memory recalls the past; state tracks where the task stands right now. 🎛️ 2. Orchestration: decides who does what, and in what order. 📚 3. RAG (retrieval-augmented generation): fetch the right docs, inject them into the prompt, give a grounded answer. 🔁 4. Harness: the loop that lets a model act: context → action → persist → control, repeated. 📏 5. Evals: score outputs against what you expected, then improve. Without them you're shipping on vibes. 🔌 6. MCP (Model Context Protocol): one open standard for connecting agents to tools and data. 📄 7. Skills: reusable instruction files (SKILL.md) an agent loads only when it needs them. 🤝 8. A2A (Agent2Agent protocol): an agent reads another agent's
Everyone's building AI agents. Few can explain what's inside one. Here are 9 AI agent concepts in one 8-second loop. 🤖 The model is the easy part. Everything around it decides whether your agent works. 🧠 1. Memory & State: memory recalls the past; state tracks where the task stands right now. 🎛️ 2. Orchestration: decides who does what, and in what order. 📚 3. RAG (retrieval-augmented generation): fetch the right docs, inject them into the prompt, give a grounded answer. 🔁 4. Harness: the loop that lets a model act: context → action → persist → control, repeated. 📏 5. Evals: score outputs against what you expected, then improve. Without them you're shipping on vibes. 🔌 6. MCP (Model Context Protocol): one open standard for connecting agents to tools and data. 📄 7. Skills: reusable instruction files (SKILL.md) an agent loads only when it needs them. 🤝 8. A2A (Agent2Agent protocol): an agent reads another agent's "agent card" to discover what it can do, then hands it tasks. 👥 9. Multi-agent systems: specialist agents sharing one task. 👀 What people miss: when an agent fails, it's usually not the model. It's a missing eval, a leaky harness, or the wrong context. Upgrading to a smarter model rarely fixes a broken loop. Better models raise the ceiling. Concepts 1–9 raise the floor. 📸 Screenshot this before your next AI engineering or system design interview. These 9 come up constantly. Follow @hackproduct for AI engineering concepts you can actually picture. ⚡ . . #AIagents #agenticAI #AIengineering #LLM #GenAI

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