ktan1010

[Mind Matter @MindMatter28]

It took me 4 years to understand this; but I will teach in just 2 minutes:

1. Don't go to a party you aren't invited to.

[Mind Matter @MindMatter28]

NOBODY WARNED ME AT 30. SO I'M WARNING YOU.

1. Your parents are not side characters.

[猫叔的职场笔记 @haowue]

改变人生最重要的年龄是26、28、30、32、35、40和45岁。

如果你正处于这些年龄，请做这12件事：

1. 停止同时追求所有东西。

[ktan1010 @ktan1010]

@vulturetrades $spy

[ktan1010 @ktan1010]

@SRxTrades Strategy

[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.

[True market Leader @TmarketL]

Game theory

Most people are playing the wrong game.

If you want to get rich, there are only 3 games that actually matter.

Everything else is a distraction

[ktan1010 @ktan1010]

@clintoptions Let’s gooo $SPX

[ktan1010 @ktan1010]

@thedankoe Bookmarking so I can look at it tomorrow

[Min Choi @minchoi]

Nano Banana Pro is pretty wild.

People just dropped some new insane use cases

10 examples. Bookmark this.

[Charlie Bilello @charliebilello]

“Far more money has been lost by investors preparing for corrections, or trying to anticipate corrections, than has been lost in corrections themselves.” - Peter Lynch

Video: https://t.co/LsAWVOA05y

[TheBetterPath @TheBetterPath_]

15 Hard Truths about Life:

1.

[ktan1010 @ktan1010]

@sergitosergito Meebits group please

[柴郡🔔｜Crypto+AI Plus @0xCheshire]

如何修复你被多巴胺烧坏的大脑？

停止做这一件事，它正在悄悄摧毁你大脑的奖赏系统……

这个你意想不到却必须戒掉的习惯，将帮助你重启多巴胺系统，并真正迈向目标：🧵

[ktan1010 @ktan1010]

The harder you work on yourself, the easier life becomes.

[Adam @AdamColb_]

Hit the gym. Avoid drama. Smell nice. Make money. Level up your circle. Talk less. Pray more. Think highly of yourself.

[NASA @NASA]

It’s okay if you don’t feel sparkly today. It's cool if you're not feeling it. Step back for a bit. There’s just one problem.

You already sparkle. Maybe you don’t see it, but others do. You belong, just like every star in this star cluster belongs.

[比特傻 @bitfool1]

PowerPoint死了。

不再乏味的PowerPoint滑动创作。

现在，AI可以在短短2分钟内生成幻灯片。

这是使用AI生成PowerPoint幻灯片的方法（100％免费）