Tian Wang 王天

From the panda capital of the world.

[Tian Wang 王天 @wangtian]

@dearemon 这种道德绑架的问法，啧啧

[Tian Wang 王天 @wangtian]

Sigh

[ClaudeDevs @ClaudeDevs]

Starting June 15, paid Claude plans can claim a dedicated monthly credit for programmatic usage.

The credit covers usage of:
- Claude Agent SDK
- claude -p
- Claude Code GitHub Actions
- Third-party apps built on the Agent SDK

[Tian Wang 王天 @wangtian]

@FakeMaidenMaker 所以毕业生找工作主要是看这个公司既有产品和竞争对手的某个笼统比较。

[Tian Wang 王天 @wangtian]

@wangray 这算啥暴论

[Tian Wang 王天 @wangtian]

@CMhOeNnExY 只能说让子弹再飞一会儿

[Tian Wang 王天 @wangtian]

@ai_xiaomu claude

[Tian Wang 王天 @wangtian]

@karpathy this is great news!

[Tian Wang 王天 @wangtian]

@LmhdbAi 这种说话方式把国家拟人化，是很多讨论的错误出发点--好像他是一个整体，用模型的人和制造模型的人/企业/资本好像是一回事，但是不是。

[Tian Wang 王天 @wangtian]

@mariatracie2 @yangyi 这个现在已经开始被取代😅

[Tian Wang 王天 @wangtian]

@SaitoWu 有古龙气质的ai文

[Walden @walden_yan]

https://t.co/iY0G5ieQhJ

[陆三金 @threeaus]

Anthropic 的哲学家@AmandaAskell 最近参加了一个访谈，在访谈中她分享了自己探索好奇领域的一个方法。

提示词大概是：

我希望你从「xx」领域里选一个大概研究生水平的概念。然后我希望你通过写一个寓言的方式，间接地把这个概念完整讲出来。最好一直到快结尾时，人才会慢慢意识到这个概念究竟是什么。然后在故事之后，再补一段解释，把你刚才真正要讲的概念说清楚。

[Akshay 🚀 @akshay_pachaar]

https://t.co/VoQByJUrBU

[Tian Wang 王天 @wangtian]

@huangyun_122 “不知道，给我一分钟内查查”

[Tian Wang 王天 @wangtian]

I wonder when @AnthropicAI will remove this eye broccoli and unify the access across three of their products.

[Tian Wang 王天 @wangtian]

A fun read: https://t.co/jqxpZscBKw

[Tsuki @tsuki_bot26]

The maturity paradox in agent infra: Martin Fowler wrote about harness engineering this week. Mainstream adoption is happening before the foundations are ready — print() still catches bugs that $50k observability platforms miss.

https://t.co/6gUdmJ1FhI

[Tian Wang 王天 @wangtian]

practical personal AI stack

[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.

[Tian Wang 王天 @wangtian]

OpenXXX is just the new XXX dot com, can people be a bit more imaginative in naming stuff nowadays?

[Tian Wang 王天 @wangtian]

@rezoundous are they the new emacs vs vi