@alfatina93: #خواطر_للعقول_الراقية #الفاتنة #CapCut

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Tuesday 29 September 2026 01:02:16 GMT

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Jana ❤️جوجو :

جميل ذوقك

2026-10-01 10:52:54

1

ضياء السويعدي :

ال

2026-10-01 22:42:20

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ابو جود🥰 :

2026-10-01 16:14:27

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زهره البنفسج :

🥰🥰🥰

2026-10-03 22:04:41

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F💔♕FF :

2026-09-29 09:54:14

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Necati Şaşmaz :

2026-10-01 23:31:23

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اخر العظماء :

❤️❤️❤️

2026-09-29 22:47:48

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يوميات الذئب :

[وردة][وردة][وردة][وردة][وردة][وردة][وردة]

2026-10-03 20:05:22

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🕋 الہياقوت 🕋 :

@💜 لُـِـِِـِِِـِِـِـيْن 💜 🫣💜

2026-10-07 21:09:33

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عصام :

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2026-10-01 20:05:13

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احمد الهمص :

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2026-10-02 21:42:16

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How to build a private RAG on your Mac (Ollama + AnythingLLM, step by step, free models) How to build a private RAG on your Mac: an AI that answers from your own business files, shows you where it found each answer, and keeps every file on your Mac. Two free apps (Ollama and AnythingLLM Desktop), free open models, no Docker, no cloud account. By the end you'll know what every piece of a RAG system does, so when it gets something wrong you know exactly where to look. RAG in plain words: filling the shelf, then asking. A reader pulls the text out of your files, a chopper cuts it into small passages, an embedding model turns each passage into numbers that capture its meaning, and they all sit on a shelf called a vector database, on your disk. When you ask, your question gets turned into numbers by the same embedding model, the shelf hands back the closest passages, a second reader (a reranker) keeps the best few, and the chat model answers only from those and names the file. The build: 1. Check your Mac. Apple menu, About This Mac. You want an Apple M-series chip and at least 16 GB of memory. 8 GB is too tight. Install Ollama from ollama.com. 2. In Ollama's settings, switch Cloud off, so nothing you ask goes to Ollama's servers, and leave

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