While severall open medical databases and open-source tools exist, they are often fragmented or built for academia. There is significant room to contribute by hacking together better toolsets, localized databases, or AI-driven interfaces to make this data truly accessible.

llm9p is a small, experimental 9P server that lets you interact with LLMs as a filesystem.

[0] https://news.ycombinator.com/item?id=47286408

Comments URL: https://news.ycombinator.com/item?id=47295708

Points: 17

# Comments: 3

You could RAG-feed this model the facts of WWII, and it would technically "know" about Hitler. But it wouldn't share the modern sentiment or gravity. In its latent space, the vector for "Hitler" has no semantic proximity to "Evil".

https://youtu.be/f3c4mQty_so?si=pkdj9q5ieoj7pzPc

I believe we are heading toward a world where AI offers easy mental shortcuts for nearly everything, similar to how cheap carbs became widespread in our diets. I do not yet know how I will deal with that. For now, I am just a kid in a candy store, enjoying the novelty.

That said, I think the project might be aiming a bit too directly at the highest level of complexity — the full integration of vibe-driven rules across assistants — without first grounding things in foundational concepts. Personally, I’d love to see a clearer breakdown of stages, such as:

1. Formal concepts of LLM-driven project management, akin to how we reason about conventional PM tools/processes.

2. Abstractions and interfaces to build structured rules/prompts (in the broad sense) that can be versioned, composed, and reasoned about.

3. Configuration management to deploy/adapt those rules across specific environments (LLMs, IDEs, agents).

Laying that groundwork could make the ambitious cross-assistant unification feel more achievable and less brittle.

Still, kudos — I think we need more of this kind of experimentation and discussion in the open.

It's like real-life Pokémon GO and field mycology has a "collect 'em all" vibe. You get out into nature, identify and catalog fungi — it scratches the same itch as exploring an open-world game.

Fungi are discrete, classifiable entities with tons of metadata: GPS location, substrate, time of year, morphology, spore prints, photos, microscopic features. Perfect for structured data nerds.

Unlike many branches of biology, you don’t need to go to the Amazon. You can walk into your backyard or a nearby forest and find species newly known for your country and sometimes even new for science.

Microscopes, macro lenses, chemicals, even DNA sequencing. There’s a hacker spirit in mycology.

Projects like iNaturalist, Mushroom Observer, and FungiMap are full of real scientific contributions from everyday people. The barrier to entry is low, the impact can be surprisingly high, and the community is genuinely welcoming. Many leading contributors — even those publishing in cutting-edge scientific journals — are passionate autodidacts rather than formally trained biologists.

High intra-species variance, subtle features — perfect playground for machine learning wich is not nearly "solved" here.

Cordyceps that zombify insects. Giant underground networks that share nutrients between trees. Bioluminescent mushrooms. Many weird stories.

MCP: I've gotten 2,415 times smarter since then.