Bonny

😎01年 BUAA CS ｜ HKUST BDT
😴专注于AI｜投资｜传统文化
🥰pre-intern in Amber Group/Ant Group/AI Startup/Xiaomi
🏝️base杭州 欢迎coffee chat!

[Bonny @xsocoool]

体验地址：https://t.co/qduOhKe22b 邀请码评论区dd

[Bonny @xsocoool]

https://t.co/jO2h2CwCUD

[Bonny @xsocoool]

抽老象了

[Stephanie Palazzolo @steph_palazzolo]

Lilian Weng, the Thinking Machines cofounder who announced her departure earlier this week, citing startup-related stress and illness, is rejoining OpenAI.

She'll be working on using AI to develop new models (recursive self-improvement).

w/ @amir

https://t.co/rart1w6KkM

[Bonny @xsocoool]

这几天下班回家分了三天看完了《华尔街之狼》这部电影，未删减版总共有三个小时，看完简直震碎了我的三观。
说几点感受：
销售是讲��事：创造焦虑、制造需求、给予情绪价值、拉进距离，最后一锤定音
形式先于内容：说话的方式、话术、语音语调等等都决定了对方想不想buy in
需要坚定信念：jordan的演讲能力太强了，十分会鼓动情绪、抑扬顿挫、自己有坚定的信念
任何都可交易：包括sex, 身份等
在这个信息繁杂错乱的时代，会造势的能力太重要了，包括标题要怎么取，内容要怎么写，如何吸引别人的注意力，只有被看见才有卖出去的可能。
上面说的是充分条件，必要条件是有边界和长期信用。

[Bonny @xsocoool]

@Jiaxi_Cui 感觉hermes效果更好

[Bonny @xsocoool]

其实我觉得现在的路径不一定不对，Mainstream AGI thesis其实是alternative outcome的积分结果啊。

只有在各个不同的领域不断突破，最后一个积分，就实现AGI了。毕竟让一家公司直接实现AGI是不现实的，应该是全人类的共同推进。

[Adam Hunt @RealAdamHunt]

Recently I've flipped from being bullish to being bearish about AI.

I think I'm updating my bearishness to be more solidly bearish. Early thoughts (which I hope to be disproven in the next year or so, I would prefer progress) and my reasoning:

The whole 'it turns out if you keep training and scaling the models more they develop broad new capabilities in lots of domains' thesis is wrong (sorry Demis). The recent batch of models haven't got more general, they've got less general. This is most obvious in the fact that their language outputs have got much worse in comparison to e.g. o3. If they were gaining generalist capacities we would expect them to be describing their work in ever more graceful and comprehensive prose!

The image that was being shared as the AGI thesis (November 2025, Tomas Pueyo) was the spiky bubble that has a current spike or two out past human capabilities (e.g. on coding or math) but below human on other capabilities on the other spikes - the future prediction was that as the models scale/advance, every spike would grow bit by bit until the whole center encompasses the human capabilities, with super-superhuman on some spikes. I think it seems like what's actually happened in the last few models has been that the coding/math spike has grown, but leaving behind or even at the cost of the other spikes. The models are no better at some simple logic, language (and sometimes worse!).

This makes sense from a simple RL perspective; you can't RL something endlessly on one domain of tasks and expect it to improve on the other tasks. The fact that early LLMs did seem to improve generally was a byproduct of the written language corpus covering everything - that corpus is general, so training it on that gave the appearance of something generally intelligent and becoming more generally intelligent as it got better at replicating that corpus. But the actual logic and underlying ground truths behind the language aren't captured efficiently enough and weren't effectively RLd in - they top out at some point (I guess this happened around the time that there was the 'has scaling hit a wall' discussion in late 2024). Chain of thought was then a genuine breakthrough, along with web search, which plugged into that general LLM global-corpus intelligence to lead to post 2024 gains.

The AI companies have since worked out that coding works (and pays) really well (basically this is because the entire job is nearly perfectly recorded and exists as training data, and you can set up clear benchmarks and rewards). The recent models (and benchmarks) have been maxxing that and we've seen degradation on normal English use for that reason. This could still be transformative, leading to extremely powerful (and potentially dangerous, particularly in cyber security) models but it's not a pathway to AGI.

I'm probably at about 40% confidence about this. It fits my current observations of AI progress and has a basic explanatory model. It doesn't account for potential breakthroughs, which is a major reason for discounting.
To make some predictions, I guess if I'm right this will become broadly apparent and more widely acknowledged in the next year or two, as we see how the spikiness of models that keep getting released develops.
Maybe there will be efforts to concentrate on specific spikes e.g. health or law which require going back to earlier models and RLing on a different data set/with different rewards/benchmarks. Maybe those separate models can be linked together to give a more apparently general model. How capital intensive that is/the potential profitability will be a defining question. But I just don't see general abilities emerging atm, and I don't think we will any time soon. Good news - a whole industry of tackling important specific problems/sectors can open up!

[Bonny @xsocoool]

@AYi_AInotes 专业教师，都用千问办公。我的妈，和workbuddy一样吗，农村包围城市，武装夺取政权？？但是感觉入场又晚了啊

[Bonny @xsocoool]

@huxlab scaling law还是有用啊

[Bonny @xsocoool]

@dotey 16-bit偷偷改成8-bit这操作太离谱了，GPT是认真的吗

[Bonny @xsocoool]

@Khazix0918 早就抛弃claude了，纯恶心人

[Bonny @xsocoool]

开源项目地址：https://t.co/OJdgMumGw4

[Bonny @xsocoool]

一张图看懂最近很火的Lil's Blog里说的MCE，Meta Context Engineering。

一句话改概括就是从原本训练模型，优化模型参数，改为自动优化prompt/context/SKILL来让模型在当前数据集上进行拟合。
#ai #blog #claude #MCE

[Bonny @xsocoool]

自己尝试了一个视频分割的例子，输入image list + initial prompt，要求输出符合条件的若干frame list。此处用的base model是Kimi-K2.6。

做了一个dashboard，会显示每条数据的正确与否，以及meta-agent会根据这些负样本写进一些新的rules到base-agent的prompt中。

同时，可以实时监控当前batch的指标等等：

[Bonny @xsocoool]

OSworld v2.0最近发布了，和v1最大的区别在于其操作时���从v1的2min，延长到了1.6h，并且平均使用的tool calls的数量从30扩大到了318。

对于metrics上来看，从v1的binary来判断结果对错与否，改进成了更细粒度的partial reward，平均会保存27.25 个 checkpoints。

v1大多数是单个app，或者简单的workflow，v2会横跨两个以上的apps。

v1基本都是静态的环境，但是v2是动态环境，即会在执行的过程中收到一些邮件/team chat，需要agent临时判断和做决定。

#ai #guiagent #gui #agent #osworld

[Bonny @xsocoool]

完善等于终结，终结等于死亡。

[Bonny @xsocoool]

《眨眼之间》这本书提出了一个新的思维方式，就是现在我们都很注重理性分析、量化以及判断。
但是我们忽视了一个很重要的思维方式：
我们的直觉、我们的瞬间判断力。

[Bonny @xsocoool]

@gkxspace kimi实在是太好用了...

[Bonny @xsocoool]

@Aoyi21 牛逼

[Bonny @xsocoool]

@Saccc_c 这个挺好玩的啊，顺便把视频也剪了，把知识也整合了，直接塞进自己的知识库