The Bitcoin Army

Be careful messing with the Bitcoin Army.

[The Bitcoin Army @qjbtc]

Bitcoin community is really the strongest community in the world. Beyond race, nationality, ethnicity.

If you are a Bitcoiner, I’ll literally cook you a meal if needed.

We believe in freedom.

History is on our side.

[The Bitcoin Army @qjbtc]

All in @rayq_ai

[小明同学 @xiaoming_xxx]

都 2026 年了！

竟然还有人不知：外版 eSIM 手机 + 境外 eSIM 套餐 = 无需 VPN 直连国际互联网。

[Michael Saylor @saylor]

Volatility tests every capital structure. Strategy remains focused on Bitcoin, disciplined capital allocation, credit quality, and long-term value creation. We appreciate our investors and will continue to execute with transparency and resolve. $MSTR

[Michael Saylor @saylor]

Digital Credit is income for investors who believe in Bitcoin. $STRC

[The Bitcoin Army @qjbtc]

@PeterSchiff nope

[SatsRush @satsrush]

@mattkratter @MasterTheGame @TFTC21 @SullyMichaelvan @saylor As @LynAldenContact says. If one entity is able to be a problem that kills it, it’s never meant to be more.

[𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 @techwith_ram]

https://t.co/qhM7NpYJZD

[a16z @a16z]

"Action produces information"

[Tim Denning @Tim_Denning]

Not having extreme urgency is the most common way to waste decades of your life.

[The Bitcoin Army @qjbtc]

@AutismCapital If you are a chinese, you know Jiang is for Jiang to survive in China he has very strict rules and tremendous restraints. I admire Jiang for many reasons, he’s actually the true silent dissent, he can’t change everything, asking Jiang extremely sensitive qxns put him in trouble

[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.

[Elon Musk @elonmusk]

Stand By Me

[DAIR.AI @dair_ai]

https://t.co/43O5kazLdH

[The Bitcoin Army @qjbtc]

@WallStWolfdog @JohnMappin 鱿鱼 yóu yú 犹太 yóu tài, not necessarily Zionist, it just means Jewish people, respectfully just adding some contexts.

[The Bitcoin Army @qjbtc]

@garrytan keep going!! you are doing great work!

[Zara Zhang @zarazhangrui]

Almost every AI power user I know is MORE stressed and busier after using AI, not less

What people thought AI would do: 10x productivity so that we can finish work earlier & relax more

What it’s actually doing: 10x productivity so that we end up with 20x more things to do cos of the sheer possibilities

[The Bitcoin Army @qjbtc]

@garrytan you open sourced the best GitHub skill repo and made millions of people more productive. Thank you, and happy birthday!

[Garry Tan @garrytan]

It's my birthday and on my birthday I want to recognize all my haters.

Haters do the best marketing. Love your haters.

[Elon Musk @elonmusk]

Major update to the 𝕏 AI recommendation algorithm rolling out next week.

This will be open sourced at the same time.

[Jiang Xueqin @xueqinjiang]

My Impact Theory interview on why Trump can't end this war: https://t.co/atptOfDfvj