Channing Allen

Co-founder @IndieHackers, the world's largest online startup community.

Building @UseHatchAI, the world's most productive AI assistant.

[Channing Allen @ChanningAllen]

We built an app called Hatch

It enables Karpathy's entire LLM Knowledge Base workflow out of the box, in 2 or 3 clicks, in a single interface.

No need to stitch together Obsidian + plugins + markdown files + custom tools + etc.

Hatch is an AI workspace where files, docs, and chats all live together. Not only can the chats READ files and docs, but they can WRITE and ORGANIZE them as well.

This means you can set up an LLM Wiki in literally 3 steps, as I show in this video.

[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.

[Channing Allen @ChanningAllen]

Loving the algorithm!

Might get active on X again…

[Channing Allen @ChanningAllen]

@thesamparr Conor needs a lucky left hand in the first two rounds. His gas tank won't take him to the third, let alone championship rounds.

I've got Holloway by TKO in the third round

[Channing Allen @ChanningAllen]

@beffjezos should've gone with

fumble,
feeble,
foible

[Channing Allen @ChanningAllen]

@T_Zahil True but this applies to all business ideas everywhere always

[Channing Allen @ChanningAllen]

@JayVander_ Anthropic's valuation is reported to be $965B post-money.

Let's just call it $1T.

In 5 years they're 1/3 of Microsoft. In the top 20 of companies globally.

[Channing Allen @ChanningAllen]

This damn company didn't even exist in 2020. Kinda nuts.

[Channing Allen @ChanningAllen]

@signulll nah i much prefer claustrophobiamaxxing

there's nothing more beautiful than an overstuffed bookshelf

[Channing Allen @ChanningAllen]

@robkhenderson based on the time gap, my assumption is he reply-guy'ed you somewhere else (e.g. in the comment section of one of your posts from late may/early june) and got no response

[Channing Allen @ChanningAllen]

Until I was like 23 or 24 I didn't know this was an unusual amount of hours to be able to put in at a laptop.

At different phases I've put scary hours into online gaming, novel writing, coding, and indie hacking.

It's common in the startup world I think.

[meadowtreader @mudscryer]

If you claim to work more than 40 hours a week and you are solely a knowledge worker you’re just lying

[Channing Allen @ChanningAllen]

@VicVijayakumar Good idea, how old are they?

[Channing Allen @ChanningAllen]

@mudscryer there are lots of people like this. like, lots.

many startup founders, many software engineers. many gamer types.

less common among 9-5ers. super common among the self employed

[Channing Allen @ChanningAllen]

@damonchen For me it's profit now that I'm running an AI SaaS. Tokens are expensive!

[Channing Allen @ChanningAllen]

@jackfriks running @UseHatchAI means seeing $200/day… often multiple times per day

[Channing Allen @ChanningAllen]

@Austen Why not use a human assistant?

[Channing Allen @ChanningAllen]

A quarter million! 🥹

[Channing Allen @ChanningAllen]

Closing in on a quarter million email subs!

Good place to be now that distribution is the biggest moat

[jack friks @jackfriks]

hitting $35K MRR took 4+ years of struggling

AND I ALMOST GAVE UP 2 SUMMERS AGO...

but i didn't, and things worked out so far, so i did an interview about it ↓

[Channing Allen @ChanningAllen]

@bryan_johnson Sheesh you're so good at Twitter

[Channing Allen @ChanningAllen]

@AdamYamada True

[Channing Allen @ChanningAllen]

My executive assistant just inked us a $7.5k ad deal without any guidance, supervision, or even awareness from me. Just needed my final sign off.

The sales cycle included 10+ email exchanges and 2 Zoom calls. She upsold the client!

AI won't replace her for 18+ months

[Channing Allen @ChanningAllen]

AI tools are unique in the history of software in that if a user is confused about how to use one, the best solution is usually to ask the tool itself.

[Channing Allen @ChanningAllen]

@bryan_johnson @nntaleb This is a good tweet