does anyone have any thoughts on AI-native org structures?

Comments URL: https://news.ycombinator.com/item?id=47589525

Points: 3

# Comments: 1

does anyone have any thoughts on AI-native org structures?

Comments URL: https://news.ycombinator.com/item?id=47589485

Points: 2

# Comments: 1

I'm interested in ingesting this type of data at scale but I already treat any information as adversarial, without any future prompts in the initial equation.

There have been more advanced instances that I've noticed where they have one account generating response frameworks of text from a whitepaper, or other source/post, to re-distribute the content on their account as "original content"...

But then that post gets quoted from another account, with another LLM-generated text response to further amplify the previous text/post + new LLM text/post.

I believe that's where the world gets scary when very specific narrative frameworks can be applied to any post, that then gets amplified across socials.

I believe it is possible to reduce the data poisoning from these sources by applying a layered approach like the OP, but I believe it needs many more dimensions with scoring to model true adversaries with loops for autonomous quarantine->processing->ingesting->verification->research->continue to verification or quarantine->then start again for all data that gets added after the initial population.

Also, for: "1. Map every write path into your knowledge base. You can probably name the human editors. Can you name all the automated pipelines — Confluence sync, Slack archiving, SharePoint connectors, documentation build scripts? Each is a potential injection path. If you can’t enumerate them, you can’t audit them."

I recommend scoring for each source with different levels of escalation for all processes from official vs user-facing sources. That addresses issues starting from the core vs allowing more access from untrusted sources.

The Atomic Unit: 1 video (10s) = 1 Meme.

The Combinatorial Act: Sequencing 154 of them where the transition ($N \to N+1$) creates the meaning.

The Result: A "Macro-Meme" (the thread) that is greater than the sum of its parts.

Need your help, urgently! :)

Long-time lurker, newly and hopefully valuable HN poster soon.

My X account is currently classified as "Probable Spam" after I made 1,078 posts over the last 7 days (lol).

Any chance ya'll can explore the above, then if you are delighted to please interact with my account to counter the "Probable Spam" classification?

Thank you!

P.S. "Show HN is for something you've made that other people can play with. HN users can try it out, give you feedback, and ask questions in the thread." <-- I hope this qualifies when related to Combinatorial Memetics!

Comments URL: https://news.ycombinator.com/item?id=46928274

Points: 1

# Comments: 2

I'm curious what everyone thinks about this approach, especially from anyone who's worked on entity resolution at scale, physics-based visualization of non-physical systems, or applying GNNs to real-world financial graph data.

I'd appreciate any and all recommendations or comments. Thank you!