Your AI Has the Memory of a Goldfish. HydraDB Wants to Fix That.

Harshitha Vajja — Mon, 05 Oct 2026 05:13:17 +0000

Most AI chatbots have the memory of a goldfish. 🐠

You tell them something today, and by the next conversation, it's gone. You explain your project again. You repeat your preferences again. It feels like meeting a stranger every time.

That's the problem HydraDB is trying to solve, and it recently went open source. 🔓

What is HydraDB?

HydraDB is a graph database built to be the memory and context layer for AI agents.

Most AI memory today saves information as loose chunks of text. HydraDB saves relationships instead: who knows whom, what happened, and how everything connects.

Why does that matter?

Think about how you remember a friend. You don't just remember sentences they said. You remember who they are, who they're connected to, and what you've been through together.

That's the difference between the two approaches:

Vector search finds text that looks similar to your question.
A graph remembers how things are connected.

For an AI assistant, that's the difference between recalling a sentence and actually understanding the context behind it.

A simple example

Imagine you told an AI assistant these three things, in three different chats:

"My manager is Stella."
"Stella is leading Project Atlas."
"The Project Atlas deadline moved to Friday."

A week later, you ask:

"Is anything coming up this week that my manager cares about?"

🔍 Chatbot with vector memory

It looks for saved text that sounds like your question. "Manager" matches the first note, so it finds "My manager is Stella."

But the deadline note never mentions "manager," so it may never find it.

Answer: "Your manager is Stella." Correct, but not helpful.

🕸️ Chatbot with graph memory

It doesn't just search for similar words. It follows the connections:

You → manager → Stella → leads → Project Atlas → deadline → Friday

Answer: "Yes. Stella is leading Project Atlas, and its deadline moved to Friday."

Same three notes. The difference is that the graph remembers how they connect.

What makes it interesting

☁️ Low-cost storage: it keeps data in S3-style object storage, which is much cheaper than keeping everything on fast servers.
🔌 Works with Neo4j tools: it connects through Neo4j drivers and supports the core Cypher queries. If you've used Neo4j, it will feel familiar.
🦀 Written in Rust: a fast, safe programming language.
🔓 Open source: released under the AGPL-3.0 license.
🖥️ Self-hostable: you can run it on your own setup.

What you could build with it

Chatbots that remember users across sessions
Company knowledge bases that understand how teams, documents and projects relate
Personal AI assistants with real long-term memory

My take

I'm exploring HydraDB myself right now, and it changed how I think about AI memory. Good memory isn't just about storing information. It's about connecting it.

Have you worked with graph databases or agent memory yet? Tell me in the comments. 👇

🔗 Want to explore HydraDB yourself?

GitHub repo: github.com/hydra-db/hydradb
Official site: hydradb.com

Originally published on Compiled Thoughts.
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Hello, World: Why I'm Compiling My Thoughts Here

Harshitha Vajja — Sat, 03 Oct 2026 14:30:07 +0000

👋 New here on DEV! This is my first post, a quick intro to who I am and what I'll be writing about.

Every programmer's first program prints two words: Hello, World. It doesn't do anything useful. It just proves that everything is set up and working. This post is my version of that.

I'm Harshitha Vajja, though most people call me Hani. I just finished my B.Tech in Computer Science with a focus on AI and Machine Learning. Over the past year, I've spent a lot of time building things: data pipelines, dashboards, RAG systems, multi-agent experiments, cloud networking projects. Along the way, I noticed something. I learned the most not when things worked, but when I had to explain why they worked.

That's what this series is for.

Why "Compiled Thoughts"?

When you compile code, you take scattered, human-written instructions and turn them into something a machine can actually run. My notes about AI and tech are scattered too: bookmarks, half-finished docs, ideas at 2 a.m. Writing here is how I compile them into something clear and useful, for me and for you.

What you can expect

How AI models actually work: what's happening inside LLMs, explained without heavy math or jargon.
Model breakdowns and comparisons: when a new model comes out, what really changed and whether it matters.
Big tech events: what's happening across tech, and why it's worth your attention.
Lessons from building: honest write-ups from my own projects, including the parts that broke.

Who this is for

Students, fresh graduates, early-career developers, and anyone curious about AI who wants to understand it, not just use it. If you've ever read an AI announcement and thought, "Okay, but what does that actually mean?", you're in the right place.

A promise

I'm still learning, and I'll say so when I'm unsure. I'd rather be clear and honest than sound impressive.

Tell me in the comments what AI topic confuses you most. That might become the next post.

Hello, world. Let's compile some thoughts.

— Harshitha Vajja

Originally published on Compiled Thoughts. Subscribe there to get new posts every week.

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