Nishkarsh
@contextkingceo • 7,918 subscribers
Founder @Hydra_DB
Videos
Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now. HydraDB combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.
Nishkarsh2,244,668 次观看 • 2 天前
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 HydraDB 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 ⬇️
Nishkarsh3,854,468 次观看 • 2 个月前
AI agents are failing in production...not a surprise. As you scale your knowledge base, embeddings start creating noise. It’s called ‘semantic collapse’ - when conversations run too long, you have hundreds of PDFs, millions of data points to give to your AI. Your AI can’t flag it because it doesn’t know it’s hallucinating. Similarity gets passed off as relevance. Fix your context. Make your agents work. Build intelligent AI. If your AI is plateauing at 50% accuracy and hallucinations are still a problem, let's talk. Book a 20 minute demo with the link in the next thread. We'll dig into your setup and find out how we can help.
Nishkarsh1,297,035 次观看 • 2 个月前
Is AI being designed to fail? Everyone talks about reasoning. But when given a task, the AI isn't reasoning the way you might expect. It looks at your input, finds the closest match it's seen before, and predicts the most likely next action. That process is called vector similarity search. It's genuinely powerful. It's also not the same thing as understanding what you actually meant. Think of a plumber who hears the word "leak" and starts pulling up floorboards before you've finished the sentence. He's not being careless. He's pattern-matching - that's exactly how he was trained. Your AI agent is doing the same thing. Context is the one thing that gets deprioritized when teams are racing to ship. But without it, you don't have an intelligent agent. You have a very fast guesser. Similarity ≠ relevance. How? Find out with the link in the comments ⬇️
Nishkarsh822,425 次观看 • 2 个月前
Memory isn’t just a feature in AI, it’s the difference between a system that responds and one that truly understands. In the first episode of The Long Walk, Sudarshan Kamath and I dive into how memory is shaping the next generation of voice agents, and why it’s becoming critical for real enterprise workflows.
Nishkarsh30,135 次观看 • 3 个月前
Introducing Findr - pinterest for your mind. Save ANYTHING that makes your mind go 🤯 Search, organise, and utilise all your relevant stuff from one place. your (ai) second brain helps you remember anything. Save links, notes, emails, webpages, articles, tweets, PDFs, anything that you don't want to forget.
Nishkarsh11,474 次观看 • 1 年前
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