Aadil Garg

Building AI & Search Systems that think fast and scale smarter.
ML & DevOps Engineer | RAG • LLMOps • Infra • Retrieval

[Soham Ratnaparkhi @SohamR_7113]

public static void main(String[] args) is an emotion

[Soham Ratnaparkhi @SohamR_7113]

One of the best lines I have read on X in recent times 🫡

"The more u learn, the more u realize nothing is too complex to be proud of."

[Aadil Garg @Aadil_5122]

@neppy Me in Indonesia (as an Indian)

[Samay @Samaytwt]

Forget FAANG
Forget MANGO

The whole world is run by ONLYFANS now.

[Aadil Garg @Aadil_5122]

@rimjhimitttal 💯💯

[Aadil Garg @Aadil_5122]

@rguldener Also to run away from maintenance bridge :)

[Aadil Garg @Aadil_5122]

@contextkingceo One thing building @hydra_db taught us: memory isn't about storing more.

It's about preserving what remains relevant as everything else changes.

[Aadil Garg @Aadil_5122]

Spent a long time thinking we were building retrieval.

The problem turned out to be continuity.

[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.

[Soham Ratnaparkhi @SohamR_7113]

An OS with persistent memory - an OS that knows you!

[Harnoor Singh @iHarnoorSingh]

the hackathon SF deserves!

/ build matcha
/ build code

best matcha wins
best code wins

upto $1000 to be won lots of fun creating matcha art!!

[Aadil Garg @Aadil_5122]

@iHarnoorSingh @BudgetSF @hydra_db @contextkingceo LFG 💯🚀

[Harnoor Singh @iHarnoorSingh]

I really didn't get Silicon Valley show part 10 years back when I watched, now this is relatable af..

[Aadil Garg @Aadil_5122]

@MannGarima This makes a lot of sense. The gap between demo and prod is real. Congrats @MannGarima

[HydraDB @hydra_db]

It's a wrap for the greatest Total Agent Recap Hackathon.

Partners:
@gmi_cloud
@Kimi_Moonshot
@photonhq
@dify_ai
@Sky9Capital

[Aadil Garg @Aadil_5122]

@contextkingceo Step 1: Build agent
Step 2: Add users
Step 3: Discover distributed systems the hard way

[Aadil Garg @Aadil_5122]

Turns out “just prompt it better” wasn’t a production strategy.

[Nishkarsh @contextkingceo]

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.

[Harnoor Singh @iHarnoorSingh]

One of my fav Hackathon I have ever organized!

Partnering: @gmi_cloud @Kimi_Moonshot @photonhq @dify_ai @Sky9Capital

Thanks @contextkingceo and @hydra_db for the opportunity!!

[Harnoor Singh @iHarnoorSingh]

Your AI Agents deserve something More Powerful Than Vector DB

Every developer building AI agents right now is using a vector database. Embed your data, cosine similarity, retrieve, done. And it works... until it doesn't.

@hydra_db is trying to solve the problem, so I am making a series of why it's important over the course of next few videos.

[Aadil Garg @Aadil_5122]

@contextkingceo Scaling pattern matching and calling it intelligence.
No wonder it breaks the moment context is even slightly off.

[Aadil Garg @Aadil_5122]

Nothing is broken — we just confused similarity with understanding.

[Nishkarsh @contextkingceo]

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 ⬇️