If you haven’t read Entangled Life: How Fungi Shape Our World, I can’t recommend it enough.

https://www.merlinsheldrake.com/entangled-life

Couldn’t agree more. And for what it’s worth, its aptly-named author Merlin Sheldrake is an absolutely charming human too. As quirky iconoclastic woodland enthusiasts go.

For a taste, Joanna Steinhardt for the LA Review:

https://lareviewofbooks.org/article/the-mycophiles-plea-on-m...

I suspect some folks here might also appreciate his early-career (2011) musical collaboration with brother Cosmo Sheldrake and friends, as the Gentle Mystics:

https://gentlemystics.bandcamp.com/album/gentle-mystics

Cosmo is his brother, wow! (have deeply adored his solo work since discovering it last year)

Aaah, it all connects - a web within a web indeed...

I have got some weekend reading and listening to get to now - thank you all kindly. As a contribution wanted to mention Paul Stamets and his works - it's all somehow about fungi (and bees sometimes!), and all deeply fascinating

As someone who's read a lot of pop sci on this topic as well as published research I went into this book expecting the tropes I've become very familiar with. Merlin Sheldrake actually brought some refreshing and unique takes and I walked away actually learning some new things. It's become my goto introductory book to get people interested in the world of fungi

I’ve been using MixedBread, which is a pretty old model at this point. Recently, I tried comparing it to some newer models and was disappointed that the results weren’t dramatically and uniformly better.

You probably can’t go wrong if you pick a recent one that scores decently well on benchmarks and is at the right price point (or memory requirement) for whatever you’re trying to do.

Developer here. Thanks for posting! I'm glad you've found it useful!

Let me know if you have any feedback on it.

I have feedback. My main piece of feedback is that it should be more obvious at signup that you can select interests from a list (because I prefer doing that). My other feedback is that the categories based on technology from a country (like “Swedish filesystems”) should probably be removed because there is probably noone who cares what country the filesystem came from.

Thanks for that! I added the first one to the feedback board here https://feedback.scour.ing/142 and I'm tweaking the recommendations to avoid topics like Swedish filesystems.

Let me know if you have any other suggestions!

Thanks for the write-up! Comprehension debt definitely feels like one of the biggest issues when I'm leaning heavily on coding agents.

It might fall into the too-heavy prior art category you mention at the end, but I quite like Superpowers (https://claude.com/plugins/superpowers and wrote a short review of why I like it: https://emschwartz.me/a-rave-review-of-superpowers-for-claud...).

Yeah I agree with the implementation plan not being that useful for me to read. I often just tell it to go after reading the design doc.

Seems kind of crazy, but I like it

This is great. It would be nice to be able to add a custom dictionary for words that are often misunderstood.

Pick an embedding model that supports binary quantization and then use a SIMD-optimized Hamming Distance function. I'm doing this for Scour and doing about 1.6 billion comparisons per second.

https://scour.ing

https://emschwartz.me/binary-vector-embeddings-are-so-cool/

Seems neat. Could you plug it in to DataFusion to use their SQL layer?

Yes! This is a great idea. Thanks for mentioning it! GlueSQL could be another cool target too (https://github.com/gluesql/gluesql). I think there's a fun exploration in taking a storage engine and seeing if it's compatible with different SQL layers — GlueSQL as a simpler starting point, DataFusion as the more complete option (https://github.com/apache/datafusion). Plugging into a real SQL engine seems like a great strategy for uncovering bugs, fixing correctness issues, and discovering what use cases the storage layer actually needs to support. It's also interesting how certain SQL engines and storage engines tend to align well with each other — the right pairing can unlock a lot. Definitely a direction I want to explore.

This is exciting work. I especially appreciate that it was trained to support binary quantization (I wrote about how cool binary quantized vector embeddings are in https://emschwartz.me/binary-vector-embeddings-are-so-cool/)