“The story of humanity is not one of war or trade. It is the story of discovery.”
We discovered fire and we were never the same again.
We discovered atoms, electricity, DNA, semiconductors.
We crossed oceans, decoded the genome, and built machines that dream.
And at every step every turning point—we proved the same thing:
When we understand more, we become more.
But today, discovery is broken.
The Scientific Bottleneck
- Billions of compute hours sit idle globally.
- Researchers are bottlenecked by grant access, closed systems, and institutional politics.
- Promising models die in Jupyter Notebooks.
- Massive datasets sit untouched because no one can process them at scale.
We don't lack imagination.
We lack the infrastructure to test our ideas at global scale.
That’s why I built NEXAPod.
NEXAPod: The Discovery Engine
NEXAPod is a decentralized, cryptographically-validated scientific compute mesh for AI-driven science.
I’m not building another toy infra stack.
This is a generalist, global engine for solving civilization-scale scientific problems—starting with proteins, ending with DreamMS, and expanding to whatever problem humanity dares to throw at it.
Mythos: Why I’m Doing This
"The story of humanity is the story of discovery."
Not commerce.
Not conquest.
But our ability to understand the world and shape it with that understanding.
Science is how we make sense of the chaos.
Technology is how we turn that knowledge into progress.
And yet, science itself has become gated, slow, elite.
But what if we could flip that?
What if every person with a CPU or GPU could run scientific inference?
What if we scaled collective scientific compute the way we scaled Bitcoin?
What if we aligned incentives, compute, and curiosity to accelerate science itself?
That’s the core idea behind NEXAPod.
“I am just one dude with a Docker container and a dream.”
But if this works?
We’re talking about the scientific equivalent of LLaMA.cpp or Folding@Home, but applied to frontier inference: proteins, quantum, climate, materials, disease.
What We’ve Built (and Where We’re Going)
Alpha – Now Live
- Inference: Secondary structure protein prediction via NexaBio-1
- Containerized client with cryptographic hash logging
- Coordinator server that mirrors the DB and assigns tasks
- Integration-tested across multiple simulated nodes
This is the bootstrapping phase—testing the Core Scheduling Engine (CSE), gathering contributors, laying groundwork.
Beta – Tertiary Structure & Scaling
- Inference: Tertiary 3D structure prediction (NexaBio-2)
- Adds: Redundant job validation, reputation system, Nexa Credits, dashboards
- Hardened scheduler, fuzzed job queue
- Wider contributor pool, robust volunteer mesh
Omega – DreamMS: The 201M Molecule Challenge
- Goal: Process 201 million MS-MS spectra
- Adds: ZK-proofs, P2P coordination, tokenized incentives
- Scientific intelligence models trained on verified compute runs
- The first public mesh LLMs for molecular science
What Is DreamMS?
It’s a real dataset.
A massive, underutilized reservoir of over 201 million unannotated MS-MS (mass spectrometry) spectra.
It represents:
- New drugs
- New materials
- New chemistry
- New unknowns waiting to be discovered
No one has run it at scale because no one has built the mesh to do it.
NEXAPod will.
Link to more reading on the omega: https://github.com/pluskal-lab/DreaMS
Architecture Summary
System Roles
- Client Node: Runs the job, logs results, signs output hash
- Coordinator (VPS): Assigns jobs, validates hashes, updates credits
- Result Hashing: Cryptographically signed, ready for future ZK-rollup integration
- Incentive Engine: In design phase—Nexa Credits now, tokens later
Security Plan
- Redundant job execution (N > M match model)
- Light reputation tracking for nodes
- Early fuzz testing for input validation
- ZK-proof system coming in Omega
The system isn't perfectly decentralized yet. But it's moving toward a verifiable, incentivized, and trustless compute mesh.
How You Can Help
Run the Node
- Open source client ready now
- Join the Alpha by running jobs from your machine
- Instructions on GitHub: > https://github.com/DarkStarStrix/NexaPod
Contribute Code
- Fork the repo
- Submit PRs
- First Issues and CONTRIBUTING.md coming soon
Support the Project
- GitHub Sponsors: https://github.com/sponsors/DarkStarStrix
- Patreon: https://patreon.com/user?u=12454131&utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink
Every dollar goes into scaling the infrastructure, paying for compute, and helping build the future of decentralized science.
For Investors & Companies
If you’re an angel, a philanthropic funder, or a visionary organization:
You're not investing in a product.
You're investing in scientific freedom at scale.
Let’s talk.
Why This Matters
Because we are squandering potential.
Because too many ideas die in notebooks.
Because the tools of science belong to all of us.
Because problems like protein folding, quantum simulation, and molecular modeling shouldn’t be gatekept by capital.
We already proved collective compute works:
- Folding@Home
- BOINC
- LLaMA.cpp
NEXAPod is next.
This is AI for science, run by the people, for the future.
Final Rallying Cry
“Do not go gentle into that good night…”
We won't.
We will build.
We will train.
We will simulate proteins, solve spectra, model atoms, and engineer new futures—because we believe.
If you've ever wanted to push back against stagnation—this is it.
If you've ever wanted to help humanity solve real problems—start here.
NEXAPod is not just a mesh.
It’s a movement.
Join the Revolution
- GitHub: https://github.com/DarkStarStrix/NexaPod
- Contribute
- Run the software
- Sponsor the vision
- Amplify the Mythos
Those who take action shape the future. Be one of them.
NEXAPod: The Discovery Engine.
Link to full paper: https://github.com/DarkStarStrix/CSE-Repo-of-Advanced-Computation-ML-and-Systems-Engineering/blob/main/Papers/Engineering/NexaPod_full.pdf
Top comments (1)
wow keep going