Project summary

LambdaZero, an independently developed scientific AI ecosystem. Built entirely by me, a solo researcher and engineer, LambdaZero is an end-to-end, open-core framework that accelerates scientific discovery through foundational machine learning models, automated hypothesis generation, dataset synthesis, and robust infrastructure — all deployable via a simple API. This is not a startup. This is a personal research lab and engineering system. I built it alone from scratch, not for profit, but to move science forward — faster, cheaper, and with more creativity. This grant would fund compute, infrastructure, and outreach to bring LambdaZero to real-world researchers.

What are this project's goals? How will you achieve them?

The goal of this project — LambdaZero — is to accelerate scientific discovery by building an open, modular, and efficient AI ecosystem that allows researchers to generate hypotheses, analyze data, and simulate experiments across fields like biology, physics, materials science, and quantum systems.

I will achieve this by:

• Continuing to refine and scale my suite of NEXA domain-specific foundational models

• Serving these models through a clean, low-latency inference + validation API

• Providing researchers with a scientific LLM (110M parameter MoE) for hypothesis/method generation

• Open-sourcing my research methodology, models, and results through Obsidian graphs, GitHub repos, and structured datasets

• Creating accessible documentation, tutorials, and a lightweight GUI for usage
  

How will this funding be used?

Compute Infrastructure Build dedicated AI workstation clusters (A100/H100), cloud GPU spend for large-scale model runs and serving $300,000

Software Platform & APIs Expand full-stack infrastructure (FastAPI, Redis, Docker, CI/CD, DevOps); UI/UX for researcher dashboard and GUI for model interaction $100,000

R&D & Model Development Expand and improve NEXA models, scientific LLMs, and inference tuning (biology, QST, particle physics, CFD) $250,000

Scientific Validation Collaborate with independent labs and researchers to validate predictions in real-world settings; build tooling for transparency, XAI, and peer-review $100,000

Open Research & Publishing Publish papers, datasets, model weights, research graph; maintain GitHub and Zenodo presence with documentation $50,000

Outreach & Ecosystem Growth Organize workshops, documentation, developer onboarding, community science competitions, and outreach to universities $50,000

Operational Costs (solo dev)Living stipend, compute maintenance, legal, server admin, health, software licenses (for one year full-time independent R&D) $150,000

Who is on your team? What's your track record on similar projects?

Team:
Just me — a 21-year-old independent ML engineer and scientific researcher. I am a one-man research lab.

Track Record:

• Trained and benchmarked six domain-specific models across fields like protein prediction, astrophysics, and quantum physics

• Built a 110M parameter MoE scientific LLM from scratch using optimized CPU/GPU distributed methods

• Created a full-stack inference API + validation engine using Docker, FastAPI, Redis, and Python toolchain

• Authored multiple research papers and documented methodologies with XAI interpretability included

• Built a custom compiler toolchain (PYC) in C for model acceleration and quantization

• Maintained a live Obsidian research graph interlinking my methods, models, notes, and theories

• All of this was done independently — with no team, no funding, and free-tier compute
  

What are the most likely causes and outcomes if this project fails?

Causes of failure:

• Inability to scale infrastructure due to lack of compute/funding

• Difficulty reaching researchers or academic partners for adoption

• Burnout or operational bottlenecks as a solo developer

Likely outcomes if failed:

• The models, research, and code will remain open-source and usable by others

• I will continue working on it at a slower pace without infrastructure support

• Core insights will be published and might be picked up or extended by others

Even in failure, the knowledge and artefacts produced will endure because everything is versioned and openly documented.

How much money have you raised in the last 12 months, and from where?

$0.00 raised.
I have received no grants, funding, or institutional support. All work has been done entirely independently, on free cloud GPUs (e.g., Kaggle T4s), my personal laptop, and open-access datasets.

This proposal marks my first formal request for funding.