Can anyone share agent skills that specifically help with the organizing, structuring, and linking of Obsidian files like a Karpathy style wiki? (Eg taking /raw content and processing it following some protocol)
> None of it was enough. When you can't trust that the votes, the comments, and the engagement you're seeing are real, you've lost the foundation a community platform is built on.
I used to love HN. Lots of interesting stuff, great articles, novel projects. Now it feels like the frontpage is always around 70% LLM-related stuff. And not breakthrough research or projects, just "new Claude version X" and shit like that. Eternal September I guess?
It's not, hence the "don't post AI slop as your comment" posting a few days back that had 1000+ comments.
Currently an unsolved problem - just stealthier on some platforms than others. Trigger the right topic on HN and the bots come out in-force together with humans sloppily copy/pasting LLM content.
Look at how many updoots it has. Look at how many vacuous, enthusiastic replies it got. That post is especially egregious, but you see stuff like that on a lesser scale every day here, now. My favorite bit is when they go out of their way to shill specific plans/pricing, e.g.:
This is an interesting product, thanks for sharing. Can you elaborate on some of your competitors in this landscape and what you might do differently compared to each one?
Thanks! The largest alternative to Captain is folks trying to build file search themselves. As mentioned in the post, it is a lot to manage.
The most similar product I've seen is Vertex File Search. They're hosted inside of GCP which can fit nicely into existing cloud deployments. Captain indexes from more sources (like R2 for example) and anecdotally provides faster indexing.
OpenSearch provides general search infrastructure and they recently added vector search. It's a low level engine so users would still need to build their own ingestion, parsing, chunking, embeddings, re-ranking, permissions, etc.
Onyx, Sana, and Glean are closer to application-layer enterprise AI products. Their internal knowledge assistants can search across SaaS tools but the interface is more graphical and seats are purchased as end-user software.
Captain sits in between because it's an API-first retrieval system to fully-manage file workloads. This adds search capabilities to existing AI agents but the agents are managed by the developers, outside of Captain.
Kore.ai however is more of an agent platform. Their focus is building and orchestrating agent workflows (which can include document retrieval, but that's not their main focus).
Unable to reproduce the recipe image in the iOS app. It first gave a normal text answer. Then when referencing this blog post it produced a wonky HTML artifact.
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