奶酪君 @0xCheeseFinder
Independent AI Researcher & Open Source Contributor | Ex-Head of Research @ VC & Crypto Exchange Crypto @ianwu2017 Singapore Joined April 2025-
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The GenAI Divide: State of AI in Business 2025 artificialintelligence-news.com/wp-content/upl…
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奶酪君 retweeted
Hugging Face quietly dropped FREE courses with certification It cover everything from LLMs to diffusion models. Here are the best ones you should bookmark today 🧵👇
奶酪君 retweeted
The growth of LLM context length with time: - GPT-3.5-turbo → 4k tokens - OpenAI GPT4 → 8k tokens - Claude 2 → 100k tokens - Llama 3 → 128k tokens - Gemini → 1M tokens Let's understand how they extend the context length of LLMs:
奶酪君 retweeted
最近在网上看到一位大神 Chris Dzombak，用 Claude Code 在短时间内写了整整12个项目，效率高到吓人。 我深挖了一下他的方法，发现终极秘诀不是什么花哨的Prompt，而是给 Claude 植入一个“资深工程师”的灵魂。 他创建了一个全局配置文件 CLAUDE .md，也就是AI的个人操作系统。这个文件里定义了： >…
昨天有消息说 ai下跌 跟MIT的一份新AI报告有关：尽管企业投入了400亿美元，但95%的生成式AI项目都未能实现盈利。这种炒作与现实之间的“GenAI鸿沟”，很可能就是昨天AI股票大跌的原因。 我倒是觉的非ai 公司大买ai 显卡算力还真不一定回的了本。
最近回国了一趟跟不同ai 朋友交流下，还有很大一部分人觉得学习ai 或者使用ai的前提是要很多显卡。真的有那么多业务数据只能本地吗？
本地大模型折腾了一晚上，最终选择出炉： 1.主用模型：Qwen-30B-Instruct，日常干活用它足够了。（指令遵循非常） 2. 推理备用：留了个 8-bit 魔改版的GPT OSS mlx。GPT-OSS 的 4-bit 版不太行，推理预算拉满三档意义不大。 3. coder 全部方向，打算不考虑本地模型直接用 SOTA旗舰（毕竟干活嘛）
看起来中小开源模型还是 @Alibaba_Qwen 3 30b 2507 resaoning 屠榜! @OpenAI gpt oss 20b 紧随其后. 意外的是LG 出的稠密模型exaone 杀入前三。 硬件角度来说小模型moe 还是有巨大成本硬件友好优势！ artificialanalysis.ai/models/open-so…
奶酪君 retweeted
Self-evolving agents are an important component to artificial superintelligence. Finally, there is a survey paper on the topic. I believe it covers a lot of the important literature on self-evolving agents. A top read for the weekend!
奶酪君 retweeted
Hierarchical Reasoning Model This is one of the most interesting ideas on reasoning I've read in the past couple of months. It uses a recurrent architecture for impressive hierarchical reasoning. Here are my notes:
Claude 4 量继续增长，可以说明干活的人还是选用最好的虽然最贵。另外就是玩玩或者非干活级别的，竞争比较激烈，完全是看价格，还有性能的平衡。
Claude 4 量继续增长，可以说明干活的人还是选用最好的虽然最贵。另外就是玩玩或者非干活级别的，竞争比较激烈，完全是看价格，还有性能的平衡。
虽然最近忙没怎么用cc，但是感觉是不好的信号。
感觉 能研究 claude code 为什么好用，对于agent 开发应该有极大帮助。 github.com/shareAI-lab/an…
奶酪君 retweeted
你知道 Claude Code 怎么控制思考时间长短的吗？在提示词里面说：“ultrathink”或者“think harder”或者“仔细思考”，那么它就会真的多思考一会。 不是玄学，而是写在代码里面的，Claude Code 把思考级别分成了 4 档： - HIGHEST： 32K - MIDDLE：10K - BASIC：4K - NONE： 0…
大模型厂商提供方都一样，Agent的机会就是对于Context engineering 的优化： 1.输出更好的产品效果。 2. 节约token。
转给我的基友了
奶酪君 retweeted
This is literally the best AI tool roadmap, It saved me hours.
刚看了 @Kimi_Moonshot 的训练损失曲线。毫无疑问，这是一次巨大的成功！尤其是在12-14万亿token区间那段显著的下降，进一步印证了Scaling Law。 @nvidia
Qwen3这个版本 上下文256k比 kimi翻倍了，模型大小是1/4 。理论成本大幅降低，且上下文有大幅进步，理论上 agent生成式更强接近SOTA 旗舰模型
Qwen3这个版本 上下文256k比 kimi翻倍了，模型大小是1/4 。理论成本大幅降低，且上下文有大幅进步，理论上 agent生成式更强接近SOTA 旗舰模型
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