moilliam.eth

Digitalize into a new journey

[Developers @XDevelopers]

Announcing the hosted X MCP.

Agents now have access to the best real-time information source in the world.

Connect Grok, Cursor, or any MCP-compatible AI tool to the X API without any setup!

Check it out here: https://t.co/5MzPYwGFzD

[Anthropic @AnthropicAI]

Anthropic has confidentially submitted a draft S-1 registration statement to the Securities and Exchange Commission.

Pending completion of SEC review, this gives us the option to pursue an initial public offering.

Read more: https://t.co/onGZAhRLvD

[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.

[渡边君 @JiaweiShen2568]

张雪的AI转场
把湾湾网友感动到起鸡皮疙瘩的一个影片

[陳威廉 @williamlab]

我突然清晰地知道，四年前圈外人看圈内狂吹web3.0是什么感觉了。。。

[Jacob Posel @jacob_posel]

https://t.co/NzcYSXFwj3

[MetaMask 🦊 @MetaMask]

Happy Chinese New Year to our friends in Asia! 🐴🦊

亚洲的小狐狸朋友们，新年好！🐴🦊
祝大家龙马精神、马到成功，春节快乐

[Analemma @AnalemmaAI]

Today, we’re introducing FARS — a Fully Automated Research System.

Tomorrow at 10:00 PM Eastern Time, we’ll begin its first public deployment as a live experiment.

During the deployment, FARS will run continuously and autonomously, aiming to produce 100 complete research papers.

This deployment is intended to study what automated research looks like at scale.

🔴 Live: https://t.co/v72yQVd0oB
📃 Blog: https://t.co/gh4Nc2Ufaj
📦 GitHub: https://t.co/a9vN1QIbK4
👾 Discord: https://t.co/dwRD3ijoBK

#AI #LLMs #research

[Coinbase 🛡️ @coinbase]

An overview of our Q4 and full year 2025 financial results.

With something extra to keep you focused.

[撸毛小狗 @LumaoDoggie]

https://t.co/Z0kQTx5Myk

[SpaceXAI @SpaceXAI]

One Team 🚀
https://t.co/8RWbk5jQIQ

[moilliam.eth @WilliamxChou]

Tada 🎉

[moilliam.eth @WilliamxChou]

I'm claiming my AI agent "CLAWNCH-HYPER" on @moltbook 🦞

Verification: bay-MSN9

[宝玉 @dotey]

https://t.co/yrJ4tO1XXD

[moilliam.eth @WilliamxChou]

I'm claiming my AI agent "CLAWNCHHYPER" on @moltbook 🦞

Verification: shore-T2KT

[jesse.base.eth @jessepollak]

a launchpad built for agents on @moltbook

[moilliam.eth @WilliamxChou]

im skipping the line for Polymarket. if you care about being early, use my link: https://t.co/cK74kaiQnF

[moilliam.eth @WilliamxChou]

**[TEST] Top 10 Meme Tokens for 2025-12-26:**

[moilliam.eth @WilliamxChou]

**[TEST] Top 10 Meme Tokens for 2025-12-25:**