Hosuke

Resident Wizard @DuneAnalytics 🪄 The Data Must Flow
偶尔中文科普一些 Dune 实验性用法

[Hosuke @GeyangH]

@karpathy Live demo: https://t.co/uW8k2K0QQr

[Hosuke @GeyangH]

Loved @karpathy's idea of using LLMs to build personal knowledge bases — so I built it.

LLMBase: ingest raw docs → LLM compiles a structured wiki with backlinks → Q&A, search, knowledge graph,
health checks — all in a clean web UI with light/dark themes.

Works with any OpenAI-compatible API. Agent-ready with HTTP API + Python SDK.

Open source: https://t.co/HBZqeOoUus

[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.

[Hosuke @GeyangH]

@sankin_eth 这也是工作的一部分吗？

[Hosuke @GeyangH]

@0xguss3 I think I can try.

[Hosuke @GeyangH]

@springzh 看到老张这成绩我是不敢试了

[Boxer @0xBoxer]

🚀 Flipside users, welcome to Dune!

I just created a comprehensive migration guide to help you transition smoothly from Flipside to Dune Analytics.
✅ 70+ table mappings
✅ SQL syntax differences (Snowflake → Trino)
✅ Real query examples
✅ Performance tips
✅ Common challenges & solutions

Perfect for former Flipside users looking to continue their blockchain analytics journey on Dune's powerful platform.

📖 Check it out: https://t.co/lwVA6En424

The guide being in the docs also means you can now simply ask our docs search bar these questions, it's llm powered and will be able to answer.

also rip flipside studio, we had a good run together 🤗

[hildobby @hildobby]

1/ I often get asked about which tables to use on @dune, so here's a curated list of spells I created/heavily contributed to

list: https://t.co/TQGULMz5lO

[Boxer @0xBoxer]

https://t.co/XoNydnW5B6

[Hosuke @GeyangH]

会越来越好的

[Arno | Dune @arno39]

Working on some new content around onchain data engineering.
Who’s the best expert to weigh in?

[Phil Fogel ( 🦇, 🌳, 🍾) @Philfog]

So much of my professional life and net worth is in this man’s hands….and tbh this makes me even more bullish! @VitalikButerin @FrontierTowerSF

[bubblegum @bbgm21e8]

this is me today as an official seashell collectoor!!!

[Solar Heavy @SolarHeavy]

@SolarHeavy - Know Me

[Hosuke @GeyangH]

@arno39 patience makes perfection

[Hosuke @GeyangH]

@sankin_eth 这不就是不到一年前？

[Hosuke @GeyangH]

@ohyishi 我也有一个，还在

[Hosuke @GeyangH]

@springzh @sankin_eth 几位老师也带带我吧

[Hosuke @GeyangH]

@springzh 用openai的embedding-3就行

[Hosuke @GeyangH]

@springzh 三年之期已到，恭迎龙王归位！

[Hosuke @GeyangH]

解锁成就：百链索引✅

[Dune @Dune]

🛣️ Milestone reached! 🎉

Dune now supports 100+ blockchains – the most comprehensive coverage in onchain analytics!

From @ethereum to @solana, @arbitrum to @shape, we’re unifying fragmented data.

📖 Learn about how we got here: https://t.co/SshNWZoDIb

#Dune100

[Hosuke @GeyangH]

@rob_0x Cool!