taf.codes

tl;dr: I can start doing real analysis of trends in semantic-space, but slowly.

tl;dr: The podcast content I’ve been indexing was diluted with advertisements.

tl;dr: I’ve been processing podcast audio to build a semantic index.

tl;dr: I’ve switched my personal notes over to Logseq, even though I’ve heard complaints it can lose data.

tl;dr: I transcribe audio to get ~20 million words of text, then load this into my ‘knowledgebase system’ with several different chunking configurations and embedding models.

tl;dr: I implemented semantic search over a collection of documents I had handy.

tl;dr: I build a small service to automatically fetch web documents, push PDFs to my e-reader, extract markdown text content, and store metadata in Mongo.

tl;dr: I use a microcontroller to emulate a USB keyboard, stream audio to a transcription model, and then ‘type’ the keystrokes from the resulting text.

tl;dr: I annotate some data, and try out Promptfoo to compare some small and small-ish language models.

tl;dr: I’d like an easy way to do batch jobs on remote GPUs.

Everything you need to start welding, below.

I threw together a quick prototype to demonstrate how large multimodal AI models might be used to supplement human TAs in delivering educational courses.

A simple backup solution for Linux desktops. tl;dr: I have a keypress combination to put my desktop to sleep.

I wanted a comfortable way to read for extended periods, and this is what I came up with.

I reverse-engineered my Wiz smart-bulbs so I could control them without the Wiz app.