Nishkarsh

Founder @Hydra_DB

[Nishkarsh @contextkingceo]

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.

@hydra_db 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.

[Nishkarsh @contextkingceo]

9/ That layer is infrastructure. It's what we're building @hydra_db to be. The thing that sits between everything an organization knows and the narrow window a model can hold -- and decides what crosses over.

The models are capable. They were never the problem. The problem is what you put in front of them.

[Nishkarsh @contextkingceo]

Bigger context windows and bolt-on memory don't fix long-horizon agents.

The bottleneck is not how much you can store. It's deciding what the model attends to. It's that very layer that is worth building as infrastructure. 🧵

[Nishkarsh @contextkingceo]

8/ And selection is a layer of its own. It can't live inside one model, because the model you build on today isn't the one you'll run next year. It can't live inside one agent or one session, because it has to see across all of them. And it can't be a vector lookup, because similarity isn't relevance.

It's what connects to what matters right now.

[Suhas Sumukh @suhasasumukh]

Introducing Agent Arena.

An AI agent hackathon.

6 hours.
$10,000 in prizes.

Apply below

[Nishkarsh @contextkingceo]

@kamath_sutra @smallest_AI Amazing

[Nishkarsh @contextkingceo]

@GaddipatiHarsha Will it act as my chief of staff for life?

[Nishkarsh @contextkingceo]

@KaranVaidya6 🙏

[Nishkarsh @contextkingceo]

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.

@hydra_db 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.

[Nishkarsh @contextkingceo]

@ragsgups Thank you @ragsgups! Onward and upward.

[Nishkarsh @contextkingceo]

@gokulr Thanks @gokulr! Plenty more coming on context and observability, excited to be building together!

[Nishkarsh @contextkingceo]

@aakashgupta 100%. Context is the bottleneck right now, not the model. That's exactly what we're solving for with @hydradb.

[Nishkarsh @contextkingceo]

@iHarnoorSingh Well said. Flat embeddings are fine until the agent actually has to think. Then the cracks show and everything downstream gets shakier.

[Nishkarsh @contextkingceo]

@samdblond @hydra_db Thank you!

[Nishkarsh @contextkingceo]

@nemir19408698 @alex_prompter i audibly laughed at that

[Nishkarsh @contextkingceo]

@appealstoheaven you can export everything - https://t.co/iUnmTtm8jq