konghao

ttl

[konghao @konghao10]

@_FORAB 上海英伟达收入不高吧我记得

[konghao @konghao10]

@Alateten @tengfff 又回冷了

[konghao @konghao10]

@SaitoWu 笑死我了，等B卡和V卡大面积铺开你就知道有没有代差了

[konghao @konghao10]

@chownsb @blackanger 确实

[konghao @konghao10]

@Corrina101Young 这种盘口能有多大

[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.

[konghao @konghao10]

@karminski3 你确定不是过拟合？

[konghao @konghao10]

@wublockchain12 傻x害怕了

[iGeekbb @igeekbb]

这位叫李福克老师，不知道为什么，今天在抖音火了，网友说他教的是真东西。非常抽象，一个俄式大摆拳打出了残影，直接进击四个字沒念完，五六拳就打过来，这两下子一般年轻人还真接不住。

[Browser Use @browser_use]

Introducing: Browser Use CLI 2.0 🔥

The most efficient browser automation CLI tool
> 2x the speed, half the cost
> Easily connect to running Chrome
> Uses direct CDP

Try it now 🔗↓

[konghao @konghao10]

@cherylnatsu office 这狗屎软件没几年活了，做它干嘛

[konghao @konghao10]

@jefflijun 不去掀翻，而是绕过。

[konghao @konghao10]

@kugo_A10 什么傻逼系统，这种成交量能崩

[konghao @konghao10]

@EvanCrypto17 正常，我都不知道国内这些ai phd到底是什么水平，有一个ai phd 23年跟我说GPT是垃圾，想想真是。。。😅

[GitHubDaily @GitHub_Daily]

使用大语言模型时，稍微问一些敏感点的话题就直接拒绝回答，想要微调去除限制，颇为困难。

偶然看到 Heretic 这个开源项目，专门用来移除大模型的安全审查机制，而且全程自动化处理。

只需运行一条命令，就能帮我们自动解除 Transformer 架构模型的安全限制，同时对原模型智力水平影响不大。

GitHub：https://t.co/ZS4YfQ2KGR

还提供可视化分析功能，能生成残差向量的投影图和动画，帮助研究模型内部的语义结构。

如果你在研究大语言模型的内部机制，或者需要一个更自由的模型用于学术研究，这个工具值得一试。

[konghao @konghao10]

@otterpal24 造穷效应显著

[konghao @konghao10]

@vista8 这玩意耗token巨凶，被骗了

[konghao @konghao10]

@PartisanReview 很正常，这种看似反复无常的个人操作 完全打中中国官僚体制的死穴，中国就像一个行动缓慢的老人是无法应对这种快速的变化。

[konghao @konghao10]

@jessezheng 这玩意怎么过安检，搞笑呢

[konghao @konghao10]

@tokerumaisurii 我现在害怕中共军队掌握了这种技术咋办，不是玩完了