Traversal

“Because most of the data you're looking at is like time series data. And these LLMs are really bad at processing time series data, right? And that's really where like good statistics comes in.”

“For some of the agentic part of the stack, we are shifting towards Anthropic because they're agentic and the tool calling, especially the unstacking part, you know, when you go down the wrong path and you build context that forces you to keep going down the wr…”

“Our first VC backer was Sequoia, and I think they had met, I think, like, I think like 20 VC, 20, ah, companies before this, but unbeknownst to us, and that was the first question, like Bogomol, who's a board member from there asked, like, I've heard this pitc…”

“Now for, then there is this level of 30 to 40% of the incidents or issues where you need to involve you know, a senior engineer for sanity checking. I think that healing will appear in, I don't know, six months to a year that will be comfortably, the technolog…”

“And I don't think we've seen any other company in our space having something actually work at production.”

“You cannot just Put all of it into context of an LLM and hope something great happens. You have to search the data sequentially and adaptively, right? And that's what these agentic systems are fundamentally about.”

“The agent is not good at looking at time series data, so that's, that is what statistics needs to take care of. But statistics doesn't understand what is the relationship between latency and memory usage and disk utilization. So that is the LLM part.”