Sri Santh
880 posts

Sri Santh
@srisanth2004
Founder of @Hipocap | AI Researcher and Consultant

See this is how my graph evoles over time instead of being flat like obsidian... Imagine in obsidian or any other tools... You will create a graph only once spending so much tokens on it. Instead, you can use this which will generate initial graph without LLM needed and evolves over the time based on the question you can to it... Imagine having a Librarian inside the DB who help to identify and arrange things in a optimized manner... Thats what stixDB has... github.com/Pr0fe5s0r/Stix…

See this is how my graph evoles over time instead of being flat like obsidian... Imagine in obsidian or any other tools... You will create a graph only once spending so much tokens on it. Instead, you can use this which will generate initial graph without LLM needed and evolves over the time based on the question you can to it... Imagine having a Librarian inside the DB who help to identify and arrange things in a optimized manner... Thats what stixDB has... github.com/Pr0fe5s0r/Stix…

See this is how my graph evoles over time instead of being flat like obsidian... Imagine in obsidian or any other tools... You will create a graph only once spending so much tokens on it. Instead, you can use this which will generate initial graph without LLM needed and evolves over the time based on the question you can to it... Imagine having a Librarian inside the DB who help to identify and arrange things in a optimized manner... Thats what stixDB has... github.com/Pr0fe5s0r/Stix…










We've raised $6.5M to kill vector databases. Every system today retrieves context the same way: vector search that stores everything as flat embeddings and returns whatever "feels" closest. Similar, sure. Relevant? Almost never. Embeddings can’t tell a Q3 renewal clause from a Q1 termination notice if the language is close enough. A friend of mine asked his AI about a contract last week, and it returned a detailed, perfectly crafted answer pulled from a completely different client’s file. Once you’re dealing with 10M+ documents, these mix-ups happen all the time. VectorDB accuracy goes to shit. We built @hydra_db for exactly this. HydraDB builds an ontology-first context graph over your data, maps relationships between entities, understands the 'why' behind documents, and tracks how information evolves over time. So when you ask about 'Apple,' it knows you mean the company you're serving as a customer. Not the fruit. Even when a vector DB's similarity score says 0.94. More below ⬇️





ok, i got a idea to work on a agentic memory system... Accidentally, created a version of a $6.5 Million dollar idea and made it Open Source - lol😀 github.com/Pr0fe5s0r/Stix… i failed at competitor analysis... my budget is $20 claude subscription anyways 🚶




















