@veilharts: Dainveil edit #ytmci #ytmcindonesia #Minecraft #devastatedsmp #dainveil @YT: Dainveil

# ⋆˚ঌ.flowering azalea
# ⋆˚ঌ.flowering azalea
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Thursday 13 August 2026 08:22:21 GMT
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dainveil
YT: Dainveil :
YOOO W EDIT
2026-08-13 08:23:55
17
zaaanz7
ZanifyX :
what a edit
2026-08-13 09:02:03
1
fannnnnnn48
ϝαɳɳ :
berawal dari kehilangan 1 peliharaan (Sherby) & 2 teman (Gadink & Sleepy Felaz). bahkan jadi buronan, si bro (Dainveil) langsung jadi villain ampe membantai 1 kerajaan yg sekitar ada 25 orang ama ngancurin 3 fraksi (pasukan oren, musuhnya si oren yg di laut itu, ama musuhnya kerajaan salju yg 3 orang itu) ama ngalahin Reinwal ama pasukannya
2026-08-13 10:08:35
3
lexua.veil.xry
lexu sigma :
W
2026-08-18 14:15:48
0
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Local LLM Coding: Can a Base MacBook Handle It You can run a real AI coding setup on the MacBook you already own. This is the full local LLM coding stack: a runner, a coding agent, and an OpenRouter fallback, assembled on an ordinary 24 GB M-series Mac. Heavy Claude Code use costs $100 to $200 a month. This video builds the alternative piece by piece: which local LLM actually fits in 24 GB of unified memory (and why model file size lies to you), why your choice of coding harness matters more locally than it ever did in the cloud, what local models honestly handle today, and the SSD-streaming project that just put a 26-billion-parameter model in 2 GB of RAM. CHAPTERS 00:00​ A 26B model in 2 GB of RAM 00:42​ The three-piece stack: runner, harness, wire 01:45​ What actually fits in 24 GB 02:58​ The harness tax 03:53​ What local AI coding honestly handles 04:52​ OpenRouter and the real monthly bill 06:06​ Where to start (fix this Ollama default first) 06:53​ The tipping point: SSD streaming WHAT IS A LOCAL LLM? A local LLM is an AI model that runs entirely on your own hardware instead of a cloud API. You download the weights once, serve them on localhost with a runner like Ollama or LM Studio, and no code ever leaves your machine. That matters for privacy and NDA work, for offline coding, and for owning a model that can't be deprecated, repriced, or nerfed. On Apple Silicon, unified memory makes a MacBook one of the best machines to run an LLM locally. IN THIS VIDEO • The best local LLM for coding on a 24 GB Mac: Gemma 4 12B at 4-bit, and the Qwen coder line • How to run an LLM locally: Ollama, LM Studio, and llama.cpp as the runner layer • The harness tax: why Aider and lean agents beat Claude Code and OpenCode for local models • The one Ollama context length setting that silently breaks coding agents • Local AI coding costs: a realistic $20 to $40 per month hybrid setup • OpenRouter as the escape hatch when a task needs a frontier model • turbo-fieldfare and SSD streaming: running a 26B mixture-of-experts model in 2 GB of RAM #localllm​ #aicoding​ #ollama​ #localai​ #programming​
Local LLM Coding: Can a Base MacBook Handle It You can run a real AI coding setup on the MacBook you already own. This is the full local LLM coding stack: a runner, a coding agent, and an OpenRouter fallback, assembled on an ordinary 24 GB M-series Mac. Heavy Claude Code use costs $100 to $200 a month. This video builds the alternative piece by piece: which local LLM actually fits in 24 GB of unified memory (and why model file size lies to you), why your choice of coding harness matters more locally than it ever did in the cloud, what local models honestly handle today, and the SSD-streaming project that just put a 26-billion-parameter model in 2 GB of RAM. CHAPTERS 00:00​ A 26B model in 2 GB of RAM 00:42​ The three-piece stack: runner, harness, wire 01:45​ What actually fits in 24 GB 02:58​ The harness tax 03:53​ What local AI coding honestly handles 04:52​ OpenRouter and the real monthly bill 06:06​ Where to start (fix this Ollama default first) 06:53​ The tipping point: SSD streaming WHAT IS A LOCAL LLM? A local LLM is an AI model that runs entirely on your own hardware instead of a cloud API. You download the weights once, serve them on localhost with a runner like Ollama or LM Studio, and no code ever leaves your machine. That matters for privacy and NDA work, for offline coding, and for owning a model that can't be deprecated, repriced, or nerfed. On Apple Silicon, unified memory makes a MacBook one of the best machines to run an LLM locally. IN THIS VIDEO • The best local LLM for coding on a 24 GB Mac: Gemma 4 12B at 4-bit, and the Qwen coder line • How to run an LLM locally: Ollama, LM Studio, and llama.cpp as the runner layer • The harness tax: why Aider and lean agents beat Claude Code and OpenCode for local models • The one Ollama context length setting that silently breaks coding agents • Local AI coding costs: a realistic $20 to $40 per month hybrid setup • OpenRouter as the escape hatch when a task needs a frontier model • turbo-fieldfare and SSD streaming: running a 26B mixture-of-experts model in 2 GB of RAM #localllm​ #aicoding​ #ollama​ #localai​ #programming​

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