Cool to see that it pays off to have split things up by season!
having worked on the AI Playlist feature at Spotify (which is agentic) -- one thing you can try to prompt is "give me songs that sound <vibe> from my <liked songs | playlist called "X">.
An extension of this is "give me a sequence of N tracks from each of <list of playlist names for each past year> that all evoke <vibe / memory / activity>". Could be a fun prompt to play with
A friend of mine uses Apple Reminders to rank his most favorite sentences of all time. It struck me that authority emerges through which ones continue to stick around (as forwarded ideas).
I wonder if the premium on consistent writing quality is different than the premium we place on consistent novelty.
I'd hazard a guess that from the writer's perspective, novelty scales with volume of thought / connections, which is (at present) a fragmented process and not that well-assisted by AI. OTOH, can "writing quality" be better approximated by LLMs?
It's possible to do both. I write in a small field journal to think better, then periodically use Wispr Flow to quickly transcribe it to Obsidian (where I can use LLMs on the writing).
The plumbing metaphor is interesting. The most adjacent place I've heard this metaphor is in theology (Mako Fujimura)
The goal of plumbing is to fix / repair; certainly it's possible to enjoy fixing and repairing. But is the joy of writing in "repairing ideas"? How is that a separate concept from creating new ones?
Awesome stuff. Seems to me like one great use case of an MCP for Ableton would be to develop muscle / workflow memory for music production workflows. Adjacently, I've started using Perplexity a bunch for this sort of thing because it indexes YouTube tutorial transcripts. Any thoughts on how to design MCPs for learning Ableton better?
One thing I don’t cover in the blog post is ensuring your MCP tool calls are well documented. (If it’s an existing MCP you can do this with a README or instructions file.) I saw a jump in efficiency when I manually edited the docstrings with examples of when each tool would be used, how to call it, and better argument descriptions.
That's cool! Have you thought about some docstring augmentation loop -- either by having the agent log the tool uses or by auto querying (e.g. perplexity API) for example use cases in the wild?
having worked on the AI Playlist feature at Spotify (which is agentic) -- one thing you can try to prompt is "give me songs that sound <vibe> from my <liked songs | playlist called "X">.
An extension of this is "give me a sequence of N tracks from each of <list of playlist names for each past year> that all evoke <vibe / memory / activity>". Could be a fun prompt to play with