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@tofik.abdo81: #ethiopian_tik_tok🇪🇹🇪🇹🇪🇹🇪🇹 #ertreantiktok🇪🇷 #ethiopian_tik_tok🇪🇹🇪🇹🇪🇹🇪🇹 @Tofik Abdo @ʙɪʟᴇᴇ 𝐌𝐮𝐬𝐢𝐜 🥀

Tofik Abdo
Tofik Abdo
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Friday 13 February 2026 13:41:32 GMT
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illma.buulcha.jedh
Tofa Th Genrse :
guttusa naff ergi mee
2026-02-13 14:05:52
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user32830578315097
5G :
mashalaaa Abaa Gadaa kenyaa🥰🥰🥰🥰
2026-02-13 22:04:43
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user34335434032278
Tofik jebel :
goota genaa🥰❤️
2026-02-15 14:09:12
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Better than the city 🍃. #laguna #roadtrip
Better than the city 🍃. #laguna #roadtrip




#کابلی #🦋 #🎶 #❤️ #غمگین____عاشقانه______شکسته__
#کابلی #🦋 #🎶 #❤️ #غمگین____عاشقانه______شکسته__
PrismML Bonsai runs a 27B model on an iPhone — a 1-bit LLM from a Caltech spin-off that squeezes Qwen 3.6 from 54 GB down to 4 GB and runs local AI on-device, no cloud. Everyone said a 27-billion-parameter model couldn't fit on a phone. PrismML did it with patented math, not normal compression. Bonsai stores each weight as a single sign bit — plus or minus one — bundling 128 of them under one shared float16 scale, so the math still runs in float16 but the memory footprint collapses. The claim: 14× less memory, 8× faster, 5× less energy, and over 10× the intelligence density of a standard model. And it's not iPhone-only — the open Bonsai demo runs on Mac, CUDA, Vulkan, ROCm, and plain CPU, with an 8B model at roughly 2.5 GB. You can test a 1-bit model on your own machine today. ---- 🚀 DYNAMOUS AI COMMUNITY Want to learn agentic coding with live daily events and workshops? Check out Dynamous AI: https://dynamous.ai/?code=646a60 Get 10% off here 👉 https://shorturl.smartcode.diy/dynamous_ai_10_percent_discount ⚡ HOSTINGER — RELIABLE HOSTING FOR YOUR PROJECTS (10% OFF) Whether you're shipping a portfolio, a side project, n8n flows, or AI agents — I use Hostinger for fast, affordable VPS + web hosting. Get 10% off here 👉 https://hostinger.com/DIYSMARTCODE (Affiliate link — costs you nothing, supports the channel.) ---- Resources: - heise:
PrismML Bonsai runs a 27B model on an iPhone — a 1-bit LLM from a Caltech spin-off that squeezes Qwen 3.6 from 54 GB down to 4 GB and runs local AI on-device, no cloud. Everyone said a 27-billion-parameter model couldn't fit on a phone. PrismML did it with patented math, not normal compression. Bonsai stores each weight as a single sign bit — plus or minus one — bundling 128 of them under one shared float16 scale, so the math still runs in float16 but the memory footprint collapses. The claim: 14× less memory, 8× faster, 5× less energy, and over 10× the intelligence density of a standard model. And it's not iPhone-only — the open Bonsai demo runs on Mac, CUDA, Vulkan, ROCm, and plain CPU, with an 8B model at roughly 2.5 GB. You can test a 1-bit model on your own machine today. ---- 🚀 DYNAMOUS AI COMMUNITY Want to learn agentic coding with live daily events and workshops? Check out Dynamous AI: https://dynamous.ai/?code=646a60 Get 10% off here 👉 https://shorturl.smartcode.diy/dynamous_ai_10_percent_discount ⚡ HOSTINGER — RELIABLE HOSTING FOR YOUR PROJECTS (10% OFF) Whether you're shipping a portfolio, a side project, n8n flows, or AI agents — I use Hostinger for fast, affordable VPS + web hosting. Get 10% off here 👉 https://hostinger.com/DIYSMARTCODE (Affiliate link — costs you nothing, supports the channel.) ---- Resources: - heise: "Start-up PrismML — so far, the largest local AI model for the iPhone" — https://www.heise.de/en/news/Start-up-PrismML-So-far-the-largest-local-AI-model-for-the-iPhone-11360516.html - PrismML: https://prismml.com - Bonsai demo (run it yourself): https://github.com/PrismML-Eng/Bonsai-demo - r/LocalLLaMA — Bonsai 1-bit explanation thread: https://www.reddit.com/r/LocalLLaMA/ Did you test a 1-bit model yet? One-bit weights sound like they should wreck quality — but the receipts say near-original. So: real breakthrough, or too good to be true? Drop your verdict below. #1BitLLM #LocalLLM #LocalAI #OnDeviceAI #PrismML #Bonsai #iPhoneAI #Qwen #LLM #AI #ArtificialIntelligence #MachineLearning #OpenSource #EdgeAI #Quantization #RunLLMLocally #AIonPhone #Caltech #TernaryLLM #PrivateAI #AIModels #DeepLearning #LLMQuantization

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