AI最严厉的父亲
@dashen_wang
马斯克研究所合作伙伴 @MuskPapa_x @razzhood
当AGI睁开眼睛的那一刻，人类的存在便失去了所有意义
构架散人。
反编译 / 站群 / 量化 / Agent,二十年自学。
现在只带 Agent 干活,不雇人。
731 フォロー中 29.1K ファン
是老大
支持一下北京的朋友。
哦吼。
🚀Qwen3.8-Max just got upgraded. Meet Qwen3.8-Max-0902!
2.4T parameters. 1M context tokens. Built for real world complexity.
Further post trained on Coding & Cowork, Qwen3.8-Max-0902 now delivers stronger performance across complex enterprise tasks, scientific research, and long horizon workflows.
💰Pricing per 1M tokens:
$2 input, $6 output.
$0.17 explicit cache hit, $0.25 implicit cache hit.
Now live via API on QwenCloud. Come try it! 🙌
API:
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哦？
Now in preview: The ChatGPT desktop app for Linux.
Use ChatGPT, ChatGPT Work, and Codex where you already work and build, with your projects and browser workflows on supported Linux systems.
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歪比歪比歪比巴布。
@dashen_wang 我的头快爆炸了，但是真的看不太懂
可以参照
running Ornith-1.0-35B on a single RTX 3060. 12GB VRAM, 16GB RAM.
35B params. 170K context. ~52 tok/s.
been running it as the engine behind my Hermes agent and Qwen Code, and honestly it feels better than Qwen3.6-35B-A3B for that kind of agentic/coding work.
full llama-swap config:
llama-server
-m ornith-1.0-35b-Q4_K_M.gguf
-ngl 99 --n-cpu-moe 24 -c 170000 -fa on
-np 1
--cache-type-k q8_0 --cache-type-v q8_0
-b 2048 -ub 1024
--temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.0 --presence-penalty 0.0 --repeat-penalty 1.0
--reasoning on
@ornith_
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含金量？
kimi k3 逆向 强
oHHHHHHHHHHHHHHHHHHHH
DATA DATA DATA GO!!!
世界需要你
New prediction :
We will be able to run Mythos level local AI in 4 months.
On a single Macbook with 32GB RAM.
Mark this and come back.
我会出手
If you don’t have at least 1TB VRAM and a VPN to torrent model weights from China, you are asleep at the wheel, and ignoring the uncomfortable truth that open weight models may be banned this year.
Losing access to frontier models will be depressing and make a lot of people feel helpless - a few will have access to 100x productivity, and you will be stuck. Personally I cannot imagine doing work without frontier-level models right now.
Think of the narrative: Chinese model (GLM-6?) with Fable capabilities, no guardrails. It’s plausible.
Everyone is underpricing the chance that open models get banned. What are you going to do if it happens?
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优秀
This model can fit in a 12-16gb VRAM consumer GPUs and laptops and it's almost as good as Nvidia's Qwen3.6-27b NVFP4 for agentic workflows, which is insane 🤯
@PrismML's Bonsai-27B 2-bit is performing exceptionally well!
Coding tests next.
Full eval results link:
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太酷了
Bonsai 27B just changed the local LLM game forever.
1-bit quantization shrinks it from 54GB to just 3.8GB (-93%), while retaining 90% of its intelligence. That's insane.
With custom WebGPU kernels written by Fable 5 and GPT 5.6 Sol, the model now runs locally in your browser!
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是的。
@dashen_wang 我很赞同这篇文章的主旨，不过还是很想问一下，这篇文章是不是也用到了AI辅助，因为感觉很多句式也还是有些ai的痕迹。。。
杭州。
