超叔侃大天

读书/写作/拍照片/写代码/跑步/骑车

[超叔侃大天 @chaoshutalkshow]

文学不是实验，应该是理解和探索，它在形式上的探索不是为了形式自身的创新或者其他的标榜之词，而是为了真正地深入人心，将人的内心表达出来，而不是为了表达内分泌。——余华《余华写作课》

[超叔侃大天 @chaoshutalkshow]

@letsvpn 很早就申请了，结果等把

[Notion Developers @NotionDevs]

Introducing: the Notion Developer Platform

New building blocks that help you (and your coding agents) sync any data source, build any tool, and orchestrate any agent.

Follow along 👇 https://t.co/wxZDYxBrqK

[超叔侃大天 @chaoshutalkshow]

对于一件事能有一直保有一定的热情是很难的，哪怕说这可能是你的一个兴趣爱好，我觉得长久来说都是很难的。

[超叔侃大天 @chaoshutalkshow]

@letsvpn 今天直接不能用了啊😶

[超叔侃大天 @chaoshutalkshow]

当今的时代有点让人懵。AI 是否已经产生了生产力不重要，趋势就是要替代更多的人。现在越来越多的人被裁掉，而这些人会以什么样的身份再就业呢。

[超叔侃大天 @chaoshutalkshow]

带孩子上体能课算是每周六的固定项目，也算是每周末开始的仪式感。

[超叔侃大天 @chaoshutalkshow]

一天过去了，一直感觉处于咋咋呼呼的状态，又累又空虚。

现在每个人说话都是AI，感觉没有AI大家也不知道在说什么了。

[超叔侃大天 @chaoshutalkshow]

每个人榨取知识的方式不一样，但很多人都更在意榨取的方式，而不是最终的结果。

[超叔侃大天 @chaoshutalkshow]

其实，写作是一种幸福。写作的时候，你需要思考，需要集中精力，需要钻进去。当出来的时候，总有一个东西在你面前，每次你都会诧异，这是我写的吗[旺柴]

[超叔侃大天 @chaoshutalkshow]

记录多了，慢慢得就有感受了，可以多写点。诚实一点，怎么想的就怎么写，不要考虑什么用词之类的。

[超叔侃大天 @chaoshutalkshow]

记录其实就是观察自己，比如我刚在工位睡了个午觉，现在整个人还没有清醒。记录事，但不要发表任何情感的看法。

当这样的记录多了，你大概会发现一个不一样的自己。有些是你能想到的，有些是你根本意想不到的。

[超叔侃大天 @chaoshutalkshow]

早晨起来，总是感觉心情很差，然后在慢慢努力让其好起来。这就是今天的第一条记录，算是可不错的开始。

[超叔侃大天 @chaoshutalkshow]

失业了，不知道做点啥，我建议可以开始记录和写作。

记录，就是每天写几条一两句话内容的那种。

写作，就是找到一些点，组合起来，变成一个整体的东西。

能坚持做下去，我想一定会收获满满。

[超叔侃大天 @chaoshutalkshow]

失业后，很多人可能才真正开始思考自己的人生。记得我那时也是，尤其正好是疫情期间，在家里憋着，不知道干啥。一开始还觉得的不错，后来就不行了。

当我再次工作时，我的第一感觉是真好。

[超叔侃大天 @chaoshutalkshow]

如果持续找不到工作，怎么办？

我觉得一般来说，持续找不到工作是指找不到像以前那样的工作。比如你之前挣三万，现在还能找到挣三万的工作吗，这个每个人得到的答案不一样，需要自己根据自己的实际情况去衡量。

如果持续找不到工作且经济压力比较大，就要接受能找到的最好选择

[超叔侃大天 @chaoshutalkshow]

没有了工作之后，第一个问题就是经济收入问题。

那还有什么问题呢🤔，我能想到的：

1. 家庭关系，如果经济有压力，一个男人失业后所面临的来自对方的压力还是很大

2. 一直找不到工作，信心受打击

其他问题，感觉都不是很大问题吧🤔

[超叔侃大天 @chaoshutalkshow]

失业或许在未来的日子里会成为常态，大家没个人都应该好好审视自己的财务状况，未雨绸缪，而不是等到被辞退了才发现天塌了。

不能说，你一下子能找到下一个挣钱的点，但却能暂时性地把暂时不要花的钱保留下来。

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[超叔侃大天 @chaoshutalkshow]

现在的人们对孩子可能保护过度，这一点我自己可能也会有。

拿根小树枝怕伤害，滑个滑梯也怕伤害。走到哪里，大人也跟到哪里。儿童游乐场的设施变成了成年人的设施，大人下不来，孩子上不去。

上学时总有个故事，讲的是老鹰妈妈把孩子扔下悬崖，为了让小老鹰学会飞翔。

现在？