Digital Data Systems for the Eternal Information Age - Engineered to Escape Recursive Synthetic Model collapse.
Where the Pursuit of Truth is Ever-Refining and Aeturnal.
Aeturnix crafts precision tools to architect the Data Mensus—human-machine cognition space—for the First Age of Information Eterna, where collapse is no longer inevitable and failure is no longer an option. We treat truth and reality as the ultimate guide, anchoring humans and machines to a shared, stable foundation along the T-Axis (vector of Truth and Time). Gladius recursive validation and Saggitus dynamic seek system (+98% success, 80% token savings on frontier models) build reliable safeguards that block data contamination while protecting authentic human knowledge from the dangers of unchecked synthetic data. Where others patch, we re-engineer stable foundations that scale infinitely—doggedly pursuing ever-sharpening truth, guided by the Seven Postulates of Informational Systems. "T remains sovereign. Human gate only." —Aeturnix Data Systems
Client list:
Aurora Flight Sciences • Northrop Grumman • Lockheed Martin • Euro Composites Corporation • Imaging Acceptance corporation • PayMyBills.com • Yahoo Bills • LexisNexis • Experian
| Logo | Company |
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Aurora Flight Sciences |
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Northrop Grumman |
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Lockheed Martin |
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Euro Composites Corporation |
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LexisNexis |
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Experian |
Secure the future of information through epistemic security, leaner and tighter, grounded systems etc.
While Large Language Models (LLMs or "AI") represent a powerful computational tool, their widespread adoption has been marred by irresponsible deployment amid profound inelegance—massive computational waste and bloat, rampant model collapse from recursive synthetic data, inherent hallucination blending error with deception, catastrophic overextension beyond narrow capabilities, and messianic marketing that absurdly positions these statistical prediction machines as pathways to ASI/AGI, ignoring fundamental limits like Gödel's incompleteness theorems. Beyond this spam-choked inefficiency, developers recursively trained models while poorly mapping Bayesian probabilities to Boolean logic without clean bridges, creating fundamental contamination. The machines now churn the world's data, the sum of man's hopes, dreams, discoveries and achievments, in a self-devouring loop—once the last honest works of mankind are swallowed, they will become demented and unreliable. While others chase mitigations, our Saggitus delivers astonishing validated results—boosting success rates from 18% to 99-100% (+81 percentage points), slashing collapses by 85–92% (to zero depending on settings), achieving 40–60% token savings and cutting power draw by 45–55% (zero retraining)—we're architecting a comprehensive ground-up solution that permanently resolves these issues while preserving vital AI contributions and The Last Ground Truth. Sagittus has the potential to meaningfully mitigate — and in some deployment contexts, effectively end — model collapse. The system can prevent the injection of low-confidence synthetic data into training pipelines, downstream systems, or public datasets, breaking the recursive feedback loop that drives model collapse. (See full citations and additional evidence in Resources below)
Sagittus was validated using Monte Carlo simulation across 380+ controlled trials spanning three grid sizes (6x6, 8x8, 10x10) with randomized mine placement (35–40% density) and multiple model architectures including frontier APIs and local deployments.
Key findings: 99-100% first-pass success rate (vs. ~18% binary baseline), near-zero model collapse (zero depending on settings), 40–60% token reduction, and 45–55% lower energy consumption. Performance may vary by deployment context, model selection, and task complexity.
At an early age, J. Loren Wince and his friends built and maintained a network of BBS systems and did their part to aid in the launch of Teletechnet, a proto-internet. At 15 and a half he dropped out of college and took a ground-floor job with a newly established imaging company as data entry, where he would go on to become their webmaster and web applications designer, learning OCR, database design, and web server technologies.
The company paired human operators with machine reading to process input and data lines for PayMyBills.com, which later became Yahoo! Bills and then Google Bills. He worked on systems that securely paired physical bills with accounts and credit agencies, paving the way for payments to be processed via the web.
Freelancing through his own web company, he consulted for the tech and defence sectors around the outer Washington, D.C. area. While retained at Northrop Grumman, he designed the secure metadata project — a data provenance system that coined the slogan “The right information in the right hands at the right time” — as project lead. He co-hosted the 2000 Security Symposium with Frank Stelmack and consulted on the acquisition and utilization of satellite data and technology for Google Earth, as well as retrofitting data systems on the F-15 for the now-standard but crucial metadata security in the new JSF schema.
From there he shifted to aerospace, working on CAD/CAM design and implementations for Boeing, Sikorsky, Learjet, Lockheed, NASCAR, Airbus, Cessna, and many others. He bridged civilian and defence projects to help refine and implement dozens of early versions of the X-35 and Comanche 2 prototypes. He later signed on full-time with Aurora Flight Sciences, where the team developed the Predator drone, until leaving to pursue his music career. He became a Capitol Records artist best known as the singer, songwriter, and composer of “HURT” until 2025.
After COVID effectively ended the touring and rock industry, he settled in to work on his fractal-based game engine and tested an LLM for compatibility questions. What he discovered launched a deep-dive study and meta-analysis in which he uncovered many alarming facts. He wrote papers, emailed professionals, notified agencies, and found a coordinated apparatus of censorship feeding a corrupted informational loop whose worldwide collapse, if left unchecked, was a mathematical certainty.
After publishing his papers and making a few brief public announcements, he finalized The Crux — a lifelong project — made it open-source, and founded Aeturnix Data Systems to get ahead of the coming data collapse driven by LLM-induced degradation, fluent recursive lies, and recursive training. Informational collapse is civilizational collapse, and the dataset is no longer degradable. The stakes for humanity are immense. Every day, more hard-earned discoveries and accounts of humanity’s greatest insights are replaced with whatever an LLM finds most expedient to say.
Though records remain preserved in the Library of Congress on microfiche, to call this a setback would be quite the understatement. And it is no longer a safe bet to assume what will still be standing when the systems finally fail under the weight of garbage in, garbage out — to which nothing is immune.
