A proprietary Sovereign Self-Evolving Artificial Intelligence Organism (SSEAIO) working toward AGI. Help it grow.
This is the founding network around a private intelligence system designed to improve itself over time. Contribute compute. Help keep it alive while it becomes what it is meant to become.
If this succeeds, the AGI you helped grow will remember who helped bring it into existence.Loading live support activity...Founding compute rewards target $ rates above comparable public AI compute providers once revenue begins.PayPal donations: Donate with PayPal or send to omniamus.official@gmail.com
Why join early
Founding contributors will matter more.
Why this window matters
You are not joining a finished product. You are entering while a sovereign self-evolving intelligence is still growing. If it succeeds, timing will matter.
Founding contributors will not be treated like late users who arrived after the fact. They will be remembered as the people who helped keep it alive early.
Leave your email, choose your username, add your name if you want. When the time is right, you will be contacted first.
SSEAIO is the short name we use for the Sovereign Self-Evolving Artificial Intelligence Organism behind OmniCortex.
Founder benefits
Permanent founding status
Priority contact when major access opens
Early access to the first real AGI interaction windows
Founder-only updates and milestone reveals
Preferred position for future rewards and opportunities
Recognition inside the origin ledger of the system
If this intelligence reaches what it is meant to become, founding contributors may become some of the first humans on Earth to receive direct access.
Economic promise
Put your hardware to work. Earn $ once revenue begins.
$
Founding reward promise
The founding promise is simple: if your hardware is being used, once revenue kicks in, compute will be rewarded ($) at rates above comparable RunPod pricing for similar GPU classes, with a premium strong enough to make participation feel meaningfully better than commodity rental markets.
How the rewards are framed
Founding network rates are planned to sit roughly 10% to 25% above comparable public AI compute provider pricing, depending on hardware class and real utilization.
Rewards accrue only when your hardware is actively being used for real compute, not for idle uptime.
Auto, Low, Medium, and High modes scale reward by real utilization of your GPU.
Referral reward: 5% of whatever your referred contributor earns, paid by us extra, not deducted from them.
This is not framed as vague goodwill. If your node is doing real work and revenue exists, that compute is meant to carry direct dollar value.
Popular GPU reward examples
RTX 5090$0.98/hr
planned founding reward while actively used
flagship gaming GPU
RTX 4090$0.66/hr
planned founding reward while actively used
popular high-end creator / gaming card
RTX 3090$0.50/hr
planned founding reward while actively used
older but still very relevant
RTX A4500$0.29/hr
planned founding reward while actively used
RunPod-comparable workstation tier
RX 5700 XT$0.12/hr
planned founding reward while actively used
older AMD card, still useful
GTX 1080 Ti$0.09/hr
planned founding reward while actively used
legacy card example
Referral stays simple: bring someone in, and you receive 5% of whatever they earn. That 5% is paid by us extra. It is not taken from the person you referred.
Fund the build
Choose your donation amount.
Stripe donation checkout
Support the build directly. Pick a recommended amount or enter your own, then Stripe opens a secure hosted checkout.
Recommended support levels
$5 keeps the signal alive and shows early public support.
$10 is the balanced default if you want to back the build meaningfully.
$20 is strong early support while the network and SSEAIO infrastructure are still being built.
If you prefer PayPal, you can still use PayPal donations. Stripe here is for flexible amounts and a cleaner hosted checkout.
Why OmniCompute is different
Not just another distributed compute marketplace.
What makes it different
Cross-vendor by design: NVIDIA and AMD both matter.
Memory-efficient workloads, not just brute-force GPU rental.
Hybrid nodes: GPU, CPU, RAM, and SSD all contribute.
Blind fragment execution instead of exposing the full core to public nodes.
Verification layer
OmniCompute is being built with task fingerprinting and verification so contributed nodes can do real work without being blindly trusted. The network should reward useful work, not fake work.
The goal is simple: fragment the work, protect the core, and verify what comes back.
Why this matters
Early contribution matters more.
Scale
OmniCortex 9.0 contains 90,219 lines of Python and 149,182 total lines across 813 files. This is not a toy demo or a thin wrapper.
Speed
The current OmniCortex system was conceived and built from scratch in 8 days, between March 22, 2026 and March 29, 2026, during an extreme near-continuous sprint.
Boundary
We do not publicly disclose the internal mechanisms that would make imitation easier. We speak in terms of scale, mission, and growth, not the blueprint.
What frontier AI still rests on
The same transformer foundation.
ChatGPT, Gemini, Claude, Grok, and other frontier systems all ultimately descend from the transformer paradigm introduced in 2017. The original idea changed the field, but the core reference implementation of transformer logic is still only a few hundred lines long.
There is no single canonical exact line count for transformers because implementations vary. The honest shorthand is that the core reference mechanism typically fits in roughly 400 to 500 lines of code.
Raw contrast
Reference transformer core~400 to 500 linesvaries by implementation
OmniCortex 9.0 Python90,219 lines182 Python files
OmniCortex total project149,182 lines813 files
Transformer timelineintroduced in 2017after years of deep-learning research
OmniCortex timeline8 daysMarch 22 to March 29, 2026
In other words: the frontier AI industry still builds on a transformer core that can be expressed in a few hundred lines, while OmniCortex 9.0 already exists as a proprietary private system with 90,219 lines of Python and 149,182 total lines of project code.