I'm a B.Tech Computer Science (Data Science) student at B V Raju Institute Of Technology, Narsapur. Outside of coursework and internships, I run an independent research track focused on making large-model architectures -- attention, memory, inference -- work under real hardware constraints instead of just on paper.
Most of what is here falls into four buckets:
- Memory and attention architectures for foundation models -- designing systems that scale sub-quadratically without giving up retrieval quality
- Consumer-hardware inference engineering -- squeezing large-model workloads onto a single 4GB-VRAM GPU through quantization, caching, and prediction tricks
- AI-native systems -- building operating system substrates where the AI model is the kernel, not an application running on top of one
- Developer tooling -- small, sharp tools (compilers, package managers, UI libraries) that solve one problem cleanly
I evaluate my own work adversarially before anyone else gets to. If a result looks too good, I go find out why before I publish it.
|
HGDM -- Hierarchical Gated Delta Memory 100% attention-free, byte-level sequence architecture with Hierarchical Temporal Decimation, Factored Bilinear State Highways, and a custom Triton fused scan engine (Nitro). Trained at 1B parameters on 1.46B tokens. Achieves O(1) inference memory -- VRAM footprint stays constant regardless of context length, verified empirically across 20x prompt scaling.
|
NCM -- Native Cognitive Memory Tensor-based episodic memory with state-conditioned retrieval for AI systems. Uses a four-field distance function -- semantic, emotional, state-conditioned, and temporal -- as the native memory subsystem for agent-style architectures. Ported to
|
Active research (not yet public):
- ZS-ISAB -- a method for scaling TabPFN's attention from quadratic to linear complexity via seeded anchor selection, an online softmax accumulator, and a zero-shot attention mask -- enabling 500K+ row inference on 4GB VRAM. Currently in iteration toward TMLR submission.
- PAS-Offload -- a consumer-GPU LLM inference engine combining CPU-side low-rank activation prediction, transposed column-major weight caching, and dynamic 2-bit weight slicing, benchmarked on an RTX 3050.
|
NOUS -- Cognitive Operating System A bare-metal AI-native OS in Rust for
|
A small, rule-based, deterministic compiler that takes a plain app description and generates native Web, Android, and iOS UI code without an LLM in the loop.
A Tkinter GUI for managing Python virtual environments and pip packages -- for when you do not want to remember venv flags.
|
|
Streamlit app powered by Gemini 2.5 Flash Image that turns one uploaded photo into platform-ready assets for Amazon, Flipkart, Instagram, Spotify, and more -- with in-place AI re-editing via chat instructions.
|
Lightweight React library for cinematic scroll-driven image sequences -- eager/lazy frame loading, TypeScript support, SSR-safe, and built-in accessibility (respects
|
Other repositories
- Windows-MCP -- contributing to an MCP server for computer-use automation on Windows
- ailang-py -- Python project for AI-native language utilities
- VoxMimic -- voice cloning and mimicry system
- storyboarding -- AI-assisted storyboard generation
- synthetic-life-system -- emergent behavior and synthetic agent simulation
- Event-Ticket-Booking-System, Buzzer, SmartFarm, Chaff -- earlier web and coursework projects
Core
ML and Infra
Cloud and Tools
- Iterating on ZS-ISAB toward TMLR submission -- resolving TabICL baseline verification and regenerating figures against the current seeded-anchor architecture
- Running a 91-dataset TabZilla benchmark against tree-based baselines
- Prototyping row-ordering strategies for fusing MiniMax Sparse Attention into a TabPFN-style tabular foundation model
- Continuing development on NOUS -- next milestone is a persistent NVMe driver replacing the simulated cold storage tier
- Open to research collaborations on efficient attention, memory-augmented architectures, and consumer-hardware ML systems