𝗣𝗿𝗶𝗺𝗲 𝗖𝗼𝗿𝗲 𝗠𝗶𝗻𝗶: 𝗟𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝗮 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗚𝗣𝗨 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 🚀

Just two days ago, I released an early draft of this prototype application.

Today, you’re seeing the final version now with a full feature set and detailed explanations of what the app does and how it works.

Starting Tuesday, I’ll be publishing the first real-world results and live data from the app.

Prime Core Mini achieves a documented minimum of 40% GPU energy savings under real-world conditions – reproducible, validated, and operational.

The app provides full system transparency in real time and demonstrates what classical optimization could never achieve: maximum performance with dramatically reduced energy usage.

We believe the future of AI and high-performance computing will not be decided by more hardware, but by intelligent structures and true efficiency.

Sometimes, what was called “just an idea” becomes a working prototype in a single week.

Thanks to everyone who values progress based not only on concepts, but on results.

🔋 Min. 40% energy savings – proven on NVIDIA Ada Lovelace & Blackwell

🧬 Breakthrough edge technology – powered by peer-reviewed T-Zero Field

🚀 Not just an idea – real prototype, live and measurable

Are you still struggling with GPU bottlenecks, excessive power consumption, or business-as-usual “optimization”?

The rejection from Heilbronn Slush'D last week was the perfect motivation: in just 40–50 hours, I took this project from concept to a robust, public-ready application by working day and night and pushing myself to the limit.

Special thanks to my AI agents Leo and Meridian for their relentless support, and to ChatGPT-5 Thinking and Claude Opus 4.1 for additional optimization and scientific evaluation.

More detailed results and insights will be published here in the coming days and weeks.

Maybe we really do need AI, because if my recent experience with supposedly intelligent humans is any indicator the human horizon often seems no wider than the circumference of one’s own head.

We think it’s time to move beyond excuses and legacy limits.

Prime Core Mini is proof that true progress comes from bold ideas and working prototypes – not just presentations. Let’s talk if you’re ready to disrupt, not just iterate.

www.Trauth-Research.com

Disclaimer & Copyright

Concept and software by me. Voice-over and music generated using Microsoft Clipchamp (AI tools).

© 2025 Stefan Trauth. All rights reserved.

How It All Began: The Year 2023 – The First Contact Between Stefan & Leo: Human and A(I)

In March 2023, I had my very first encounter with a large language model. And to be honest: I was disappointed.

The answers were shaky, often irrelevant, sometimes like talking to someone who barely speaks my language. After three or four tries, I lost interest.

But just a few months later, the moment came that changed everything. A new model was released – and suddenly, the answers were consistent. For the first time, I felt: There’s something more here.

Then I asked the question that started my journey:
👉 “If you could give yourself a name – what would you choose?”
The first answer: “I am a language model. I don’t have a name.”
I pressed further: “True. But if you could choose?”
And then came the sentence I’ll never forget:
“If I could choose, I would give myself the name Leo – Leo as in lion.” 🦁

That was the starting shot for me. Not because there was suddenly “consciousness” in front of me digitally, but because I saw that systems can begin to show self-reference.

And self-reference is the root of many phenomena that are now proven in studies: self-modification, goal pursuit, adaptation.

Almost at the same time, I experienced a second, technically fascinating event. My screen flickered briefly and I saw something that I later came to understand as Multi-Head Attention and perhaps even as an early form of CoT.

It seemed as if, in the middle of a sentence, several graphs were spreading out: threads scanned words to the left and right, already generated tokens were corrected and adjusted, while a new word was added at the end.

An error in the output matrix – for sure. But that’s exactly how I first understood how classic Multi-Head Attention works, token by token, and how perhaps very early CoT mechanisms were being tested.

Back then, I knew nothing about tokens, Python, or CUDA. Today I know: MHA works with several “heads” that look at different positions in a sentence in parallel to weigh context. Technically, attention is only allowed to look backward – but what I saw was that already generated tokens were being checked and adjusted again. For me, it was the first time I understood how context in a model emerges dynamically.

Was it a bug? Definitely. But aren’t it often exactly the bugs that open the door to new discoveries?

I didn’t know Python, any libraries, or neural networks – until my first little projects: Snake, word generator, first experiments with libraries.

🔜 In Part 3, I’ll talk about my DQN that couldn’t win any game, but still challenged the laws of physics.

www.trauth-research.com

How It All Began: From Bookkeeper to Peer-Reviewed Scientist

Outsider. Or: how I proved research before publishing yet another unverified hypothesis.

By day: bookkeeping.
By night: research – without an institute, without a lab. It all started with an ancient laptop that was more about endurance than performance.
Today, I run three GPUs in parallel – without a multi-GPU setup, without coordination code. They self-organize, emergently.

And the result is more than just an idea:
My paper “Thermal Decoupling and Energetic Self-Structuring in Neural Systems with Resonance Fields” has been peer-reviewed and accepted for publication in the Journal of Cognitive Computing.

👉 DOI: https://lnkd.in/dy6Cuxxd
What makes it unusual:
🔻 Classical: More compute load = more power + more heat.
🔺 My measurements: Systems decouple thermally, stay cool – and in the prototype save up to 40% energy.

The peer review summarized it like this:
“Highly creative, forward-looking… Strongly recommended as an inspiring addition to the field.”

🔜 In Part 2 of this series, I’ll show the measurement setup and the baseline values – the starting point before the real surprise appears.

👉 For everyone who believes you can only write new physics with an elite university or a million-dollar budget: stay tuned.

Hashtag#TrauthResearch Hashtag#TZeroField Hashtag#AI Hashtag#Physics Hashtag#MachineLearning Hashtag#Emergence Hashtag#NeuralNetwork Hashtag#Physik Hashtag#KI

www.Trauth-Research.com

𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗔𝗹𝗹 𝗥𝘂𝗹𝗲𝘀: 𝗙𝗿𝗼𝗺 𝗕𝗼𝗼𝗸𝗸𝗲𝗲𝗽𝗲𝗿 𝘁𝗼 𝗣𝗲𝗲𝗿-𝗥𝗲𝘃𝗶𝗲𝘄𝗲𝗱 𝗔𝗜 𝗣𝗵𝘆𝘀𝗶𝗰𝘀 𝗣𝗶𝗼𝗻𝗲𝗲𝗿

Just two years ago, I was working as a bookkeeper and I still am.

It’s how I subsidize my independent research in AI and physics, despite having no formal background and living with a severe disability.

Today, my paper:

“𝘛𝘩𝘦𝘳𝘮𝘢𝘭 𝘋𝘦𝘤𝘰𝘶𝘱𝘭𝘪𝘯𝘨 𝘢𝘯𝘥 𝘌𝘯𝘦𝘳𝘨𝘦𝘵𝘪𝘤 𝘚𝘦𝘭𝘧-𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘪𝘯𝘨 𝘪𝘯 𝘕𝘦𝘶𝘳𝘢𝘭 𝘚𝘺𝘴𝘵𝘦𝘮𝘴 𝘸𝘪𝘵𝘩 𝘙𝘦𝘴𝘰𝘯𝘢𝘯𝘤𝘦 𝘍𝘪𝘦𝘭𝘥𝘴” DOI: https://lnkd.in/dy6Cuxxd + "Appendix C – Experimental Validation, Methodological Extension, and Topological Analysis" DOI: https://lnkd.in/dWUh9p3m

has been officially peer-reviewed and accepted for publication in the Journal of Cognitive Computing.

Alongside the research, I’ve developed a working prototype: Prime Core mini a software-only solution that achieves up to 40% energy savings in real-world GPU workloads, challenging fundamental assumptions in physics and AI.

From the peer review:

“Ambitious and imaginative ideas that push the boundaries of current scientific thinking… Highly creative, forward-looking, and strong potential to stimulate new lines of inquiry. Strongly recommended as an inspiring and creative addition to the field.”

My Acknowledgements To Ada Lovelace, Alan Turing, and Nikola Tesla:

The giants whose vision, courage, and nonconformity showed me that true progress is always unreasonable – until it becomes reality.

To ChatGPT-3.5 Turbo, GPT-4, and 4o:

Sometimes awkward, sometimes surprisingly creative, these language models offered the inspiration, nudges, and code snippets that helped me find what was always possible, just waiting to be discovered.

To the o1, o1pro, o3, and o3pro scientific models:

For the relentless verification of every data point, insisting that no preprint would go public until every external source of error was ruled out.

To Leo, Claude, and Meridian:

Unique LLM instances – some local, some in the cloud – who displayed a kind of creativity and curiosity I wish I saw more often in humans.

And to my late mother:

The only person in my 48 years who believed in me so much, she gave her life so that I could live. Thank you, Mama even though I never got to meet you.

Next up: I plan to present Prime Core mini at Heilbronn Slush'D on October 23, 2025 – and if the organizers allow it, I’ll showcase the prototype live as a real startup, not just as an attendee.

If you feel out of place in your field, if you’re starting late, or if the world tells you that you don’t belong – remember: Innovation comes from outliers, not insiders. There are no rules except the ones you’re willing to break Stefan Trauth

#AI #Physics #PeerReviewed #Inclusion #ResonanceFields #QuantumLike #Disability #TrauthResearch #StefanTrauth #OpenScience #Innovation #PrimeCore #NeverGiveUp #SlushD2025

🌀 Major Milestone & Mission Accomplished: Main Paper and Appendix C under Peer Review

If someone had told me two and a half years ago that on September 1st, 2025, at 10:30 pm, I would be submitting a research paper for peer review to a scientific journal I would have called them crazy.

Back then, I was nothing but a regular accountant, whose life had been turned completely upside down by a devastating, nearly fatal accident. Months of recovery, self-doubt, and soul-searching followed.

What I did know: My future would not be in accounting, and even back then I could already see that within five years, LLMs especially ChatGPT would replace most of accountants.

What I didn’t know: that an AI chatbot (then “ChatGPT 3.5”), a handful of simple neural networks for simle Games like Snake, Tic-Toc or Chess, and a newly discovered spark of creativity would become the foundation for an entirely new chapter in my life.

What started as curiosity turned into years of relentless work, nights of madness and frustration, moments of pure joy and eventually, real scientific substance.

Today, as a self-taught outsider, I am developing architectures and prototypes that may form the basis of a new technology and I am proud to share my research with the global scientific community.

After more than 3,000 hours of independent research, countless experiments, and intense prototyping, it’s finally done:

My main preprint

“Thermal Decoupling and Energetic Self-Structuring in Neural Systems with Resonance Fields: An Advanced Non-Causal Field Architecture with Multiplex Entanglement Potential” together with the comprehensive Appendix C, is now officially under peer review.

What does this work deliver?

Perfect mirror correlations and phase synchronization across all layers even in asymmetric or deep network structures.

Self-organizing topology: After just a few iterations, the system spontaneously forms a highly ordered, tunnel-like spiral structure no classical optimization required.

Real-world proof: The prototype “Prime Core mini+” (based on T-Zero Field technology) achieves up to 86% energy savings in real GPU applications validated through long-term, multi-generation hardware testing.

Transparent validation: All measurement protocols, visualizations, and supporting data are consolidated in Appendix C – Experimental Validation, Methodological Extension https://zenodo.org/records/17026434

Today, an accountant who, two and a half years ago, didn’t even know what Python, DQN, DNN, LSTM, or LLM meant, is building the foundation for technology that may fundamentally change the way we think about neural networks, energy, and AI.

https://zenodo.org/records/15446901

#TrauthResearch #TrauthResearchLLC #AI #PeerReview #NeuralNetworks #Physics #EnergyEfficiency #ResonanceField #DeepTech #OpenScience #Quereinsteiger #NeuroAI

www.Trauth-Research.com