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@wdym_aqsa: دستانے وفا بس اتنی سی ہے کے اُسکے خاطر اُس کو ہے چھوڑ دیا ۔ #wdym_aqsa

𝙰𝙲𝙺𝙴𝚁𝙼𝙰𝙽~🕷️
𝙰𝙲𝙺𝙴𝚁𝙼𝙰𝙽~🕷️
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Region: PK
Saturday 10 October 2026 06:41:35 GMT
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Comments

always_tyb
Ꭻᴇʀʀ𝚢♛ :
hmmmmm🫠
2026-10-10 06:45:10
4
haniihaha_0099
haniya_faisal :
hayyyyyyyyyy
2026-10-10 12:14:04
1
ejaz.khan5040
pathan :
Dil se awaz aati hai ki wo laut aayega Aur dimaag kehta hai ye khwahish chhod de😣😑😫😒😭😭😭
2026-10-10 14:38:06
3
huma.gull86
😎 :
hyee I done🙃
2026-10-10 17:56:51
0
lover..zln..shn
𝑳𝒐𝒔𝒕 𝑰𝒍𝒍𝒖𝒔𝒊𝒐𝒏 🥀 :
[Teary eyed][Teary eyed][Teary eyed]
2026-10-10 06:45:10
4
khan804__47
Hassan ❤️ :
[Red heart][Red heart][Red heart]
2026-10-10 12:47:18
2
harisrauf483
RAUF🪐 :
🥰🥰🥰
2026-10-10 08:48:22
3
vibe_zaid
زید 🕷️ :
❤️‍🔥
2026-10-10 06:43:24
3
balochistani1233
𝕭𝖆𝖑𝖔𝖈𝖍𝖎𝖘𝖙𝖆𝖓𝖎 :
🥰🥰🥰
2026-10-10 20:21:10
0
drohibaloch3
Baloch :
😳😳😳
2026-10-10 17:14:09
2
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How to run your own private AI (Ollama + Open WebUI, step by step, on hardware you control) How to run your own private AI, where your sensitive files stay on hardware you control. A lot of businesses avoid AI because of their data. A paid business plan from OpenAI or Anthropic doesn't train on your business data by default, but your data still goes to their servers. The other option is a local AI or self-hosted AI: the models are smaller than the best paid ones and security becomes your job, but your files never leave your machine. This is the whole build, step by step. It's longer than my usual videos on purpose. The build: The memory rule for picking a model. Take the model's size in billions, multiply by about 0.6, and that's the gigabytes it needs. A 9 billion model needs about 7 GB, a 30 billion model about 20 GB. Leave room for the conversation and your other apps. Run it on your own Mac. The easy way is LM Studio: no terminal, search a model, download it, drag in a PDF and ask. For the full setup, Ollama runs the model and Open WebUI is the chat screen, installed with Docker Desktop. This is the most private way there is. Or build a private AI server your whole team can reach. On a rented server with plain Ubuntu, you install Ollama, Docker, Open WebUI and Docling with the official commands, so you know exactly what's running. Put it on your own domain with Caddy, so you get HTTPS that renews itself. Chat with your company documents using RAG. Set up the embedding model, turn on hybrid search, use Docling for scanned PDFs, build a Knowledge collection, and every answer shows citations back to the exact paragraph. Lock it down, in order: two-factor on your hosting account, SSH keys with password login off, a firewall with only three ports open, Ollama never exposed to the internet, updates, sign-ups off, and Tailscale so only your team can see it. The honest part. A server you rent is still someone else's computer, and running it yourself doesn't make you compliant with anything. It means you're responsible for everything. For the most private option, keep it on a machine in your own office with disk encryption on. Connect n8n right next to your AI. A private AI that only chats is half the value. With n8n and the Ollama chat model on the same server, you can pull key dates out of a contract uploaded through a form, sort every support ticket and draft a reply, or write a report for your team every Monday, and the AI never leaves your server. Every command is in the free blog post, link in my profile. #aiautomation #privateai #localai
How to run your own private AI (Ollama + Open WebUI, step by step, on hardware you control) How to run your own private AI, where your sensitive files stay on hardware you control. A lot of businesses avoid AI because of their data. A paid business plan from OpenAI or Anthropic doesn't train on your business data by default, but your data still goes to their servers. The other option is a local AI or self-hosted AI: the models are smaller than the best paid ones and security becomes your job, but your files never leave your machine. This is the whole build, step by step. It's longer than my usual videos on purpose. The build: The memory rule for picking a model. Take the model's size in billions, multiply by about 0.6, and that's the gigabytes it needs. A 9 billion model needs about 7 GB, a 30 billion model about 20 GB. Leave room for the conversation and your other apps. Run it on your own Mac. The easy way is LM Studio: no terminal, search a model, download it, drag in a PDF and ask. For the full setup, Ollama runs the model and Open WebUI is the chat screen, installed with Docker Desktop. This is the most private way there is. Or build a private AI server your whole team can reach. On a rented server with plain Ubuntu, you install Ollama, Docker, Open WebUI and Docling with the official commands, so you know exactly what's running. Put it on your own domain with Caddy, so you get HTTPS that renews itself. Chat with your company documents using RAG. Set up the embedding model, turn on hybrid search, use Docling for scanned PDFs, build a Knowledge collection, and every answer shows citations back to the exact paragraph. Lock it down, in order: two-factor on your hosting account, SSH keys with password login off, a firewall with only three ports open, Ollama never exposed to the internet, updates, sign-ups off, and Tailscale so only your team can see it. The honest part. A server you rent is still someone else's computer, and running it yourself doesn't make you compliant with anything. It means you're responsible for everything. For the most private option, keep it on a machine in your own office with disk encryption on. Connect n8n right next to your AI. A private AI that only chats is half the value. With n8n and the Ollama chat model on the same server, you can pull key dates out of a contract uploaded through a form, sort every support ticket and draft a reply, or write a report for your team every Monday, and the AI never leaves your server. Every command is in the free blog post, link in my profile. #aiautomation #privateai #localai

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