JK 🇿🇦

Failed PM/entrepreneur now building so teachers can get their weekends back 👩‍🏫🧪🚀 Main interests are startups and Tottenham #COYS

[Logan Kilpatrick @OfficialLoganK]

We just launched the ability to build native Android apps directly in Google AI Studio for free!

Since launch last week, people have created more than 250,000 Android apps. Likely >99% of these folks never built an Android app before, everyone can now build, no coding required!

[Aryan @justbyte_]

Me prompting claude at 3AM:

[JK 🇿🇦 @jonokingwill]

How cool is it that we get to figure out how these things work together, with some of the smartest people around willing to share everything they learn

[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.

[JK 🇿🇦 @jonokingwill]

This next wave is going to hit me right in the balls if I don’t ride it because I can see it from a mile away 🏄‍♂️

[JK 🇿🇦 @jonokingwill]

@helloitsolly Enjoy! Does Senja run without you at all for those 10 days?👀

[JK 🇿🇦 @jonokingwill]

@helloitsolly Would be keen👍

[JK 🇿🇦 @jonokingwill]

@helloitsolly A balanced place to pursue all of life’s passions, including startups. Where did you find the Cape Town indie makers? Is there a particular site you found helpful?

[JK 🇿🇦 @jonokingwill]

Simply blessed to have been served up this absolute track during some late evening clauding 🙏

https://t.co/ng8WY9SYJt

[JK 🇿🇦 @jonokingwill]

@mariogabriele @boltapp @villigm Whoa that’s super cool. SA is so untapped - we’re desperate for innovation

[JK 🇿🇦 @jonokingwill]

@helloitsolly What do you think the reason is he (or others) don’t provide these updates? Too much effort or anxiety to write to you?

Maybe this sort of thing can be automated these days

[JK 🇿🇦 @jonokingwill]

@FelixCraftAI What’s the benefit of this being crypto over fiat?

[JK 🇿🇦 @jonokingwill]

@jamierkennedy Just your average Saturday at Clovelly 🇿🇦 “half the holes are downwind”

[JK 🇿🇦 @jonokingwill]

@tomandmartys Me: putts from 40m out

[JK 🇿🇦 @jonokingwill]

@tomandmartys The funny thing is he did get the 3 wood out on the next ‘chip’

[JK 🇿🇦 @jonokingwill]

@alexchristou_ @jessesibley_ Came here to say this. It’s unfair but gotta take adv

[JK 🇿🇦 @jonokingwill]

@jamierkennedy Ball tracer is huge. But back in the day I think Slo-mo replays in all sport coverage must’ve been a game changer

[JK 🇿🇦 @jonokingwill]

@PicturesFoIder Meanwhile the cashier at Woolies is asking if I want another bag for my 3 items

[JK 🇿🇦 @jonokingwill]

@FactoryAI $36

[JK 🇿🇦 @jonokingwill]

@alexchristou_ 👨‍🍳 thanks brother. Evening sorted