In episode 44 of Generationship, Rachel speaks with Felipe Huici, CEO and co-founder of Unikraft, about the powerful world of unikernels. Felipe breaks down how these lightweight, specialized VMs can achieve millisecond cold starts, enabling services to truly "scale to zero" without users noticing a delay. Learn how this efficiency is not just about saving money, but also about providing a secure and scalable foundation for the next wave of AI-generated applications.

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Generationship - Ep. #44, Why Unikernels Are Cool with Felipe Huici of Unikraft

Medium Article: https://medium.com/@jsmith0475/ai-that-thinks-backward-the-rise-of-defensive-intelligence-c0260765a2ed
The academic paper, by Dr. Jerry A. Smith, introduces "Defensive Intelligence" as a new architectural principle for agentic AI, arguing that inversion reasoning—explicitly modeling and avoiding failure modes—significantly improves system robustness over traditional goal-oriented methods. It proposes four technical patterns, such as Adversarial Attention Heads and Failure Mode Memory, that embed this defensive mindset directly into transformer architectures, claiming up to a forty percent reduction in task failures. Beyond implementation, the source explores the profound implications of this failure-aware AI, addressing the cognitive asymmetry between defensive AI and optimism-biased humans, the sociological risks of concentrating "negative knowledge" among elite actors, and the ethical challenges of prioritizing which failures the AI should avoid. Ultimately, the work suggests that this type of defensive reasoning may result in an intelligence that is fundamentally more cautious and alien than human cognition.

AI That Thinks Backward: The Rise of Defensive Intelligence

🚀 This week on the Tech ONTAP Podcast: NetApp Docs goes AI!

We sit down with Aksel Davis, Grant Glass, Jen Kaufman, and Adam Newton to explore how NetApp is transforming documentation—from static PDFs to a living, searchable, AI-powered ecosystem.

💡 Topics we cover:
	•	The shift from closed XML systems to GitHub + open standards
	•	How generative AI powers Doc the Owl, our chatbot trained on 120K+ NetApp documents
	•	Making documentation more accurate, agile, and accessible in 9 languages
	•	Future possibilities: AI-driven authoring tools, in-product assistance, and cross-dataset intelligence

Whether you’re a NetApp customer, partner, or just curious about how AI reshapes technical documentation, this episode is packed with insights.

Ep 406 - AI Powered NetApp Docs

Medium Article: https://medium.com/@jsmith0475/why-current-nil-valuations-fail-and-how-multi-agent-ai-fixes-them-f1652a0e887c
The article, by Dr. Jerry A. Smith, describes VALORE, a novel multi-agent artificial intelligence system designed to accurately value a collegiate athlete's Name, Image, and Likeness (NIL) influence, correcting for the failures of current surface-level metrics. This system employs seven specialized thinking transformer models—such as a Social Media Analysis Agent and a Psychological Profile Agent—that coordinate through goal-oriented consensus mechanisms to integrate diverse factors like behavioral science, economic data, and athletic performance. The research emphasizes that VALORE models crucial human elements like parasocial relationships and authenticity to predict true marketing value, ensuring the system maintains high prediction accuracy, transparent coordination, and ethical oversight through proactive bias detection. Ultimately, VALORE seeks to create more equitable and efficient markets by benefiting athletes, brands, and universities through enhanced decision support and compliance.

Why Current NIL Valuations Fail — and How Multi-Agent AI Fixes Them

Medium Article: https://medium.com/@jsmith0475/the-53-problem-what-traditional-nil-valuations-miss-2ab9fd53d595
The article "The 53% Problem: Cultural Factors in NIL Valuation," by Dr. Jerry A. Smith, argues that traditional Name, Image, and Likeness (NIL) athlete valuation models are fundamentally flawed because they fail to account for cultural factors that contribute to 53% of the variance in market value. The core premise is that characteristics such as gender, race, institutional prestige, and geographic location do not combine additively but rather interact through multiplication, leading to dramatically compounded disadvantages for some athletes. The text proposes using mathematical frameworks, specifically differential equations, as reasoning anchors for multi-agent Artificial Intelligence (AI) systems to model these complex, multiplicative cultural dynamics consistently and accurately. This approach is intended to expose systematic inequities, such as the significant financial penalties faced by international or female athletes, and to provide data-driven strategic guidance for interventions. The source also discusses the ethical challenges and need for empirical validation of these mathematically anchored AI models before their superiority can be confirmed.

The 53% Problem: What Traditional NIL Valuations Miss

Medium Article: https://medium.com/@jsmith0475/we-solved-ais-reproducibility-crisis-by-treating-it-like-a-physics-problem-8936aed52923
The article "Cognitive Anchoring," by Dr. Jerry A. Smith, details a novel solution to the reproducibility crisis in large language models (LLMs) by treating the issue as a physics coordination problem. The core proposal, cognitive anchoring, uses principles from gauge theory to synchronize the attention heads within transformer models, which otherwise drift and produce inconsistent reasoning paths. The authors introduce four specific anchoring mechanisms—symbolic, temporal, spatial, and symmetry—to constrain representational degrees of freedom without sacrificing logical content, leading to a 38% improvement in symbolic consistency during complex tasks like discovering field equations. The framework is presented as a mechanistic alternative to prompt engineering and is demonstrated to generalize across scientific discovery and behavioral science applications, such as modeling complex cultural multipliers in athletic valuation. Ultimately, the paper establishes anchoring as a foundational protocol for achieving stable and reliable inference in AI reasoning systems.

We Solved AI's Reproducibility Crisis by Treating It Like a Physics Problem

Medium Article: https://medium.com/@jsmith0475/why-your-ai-agents-keep-failing-and-what-synthetic-intelligence-can-do-about-it-416f035266bc
"Synthetic Intelligence: Why AI Agents Fail and What Comes Next," by Dr. Jerry A. Smith, details the widespread failure of current enterprise AI agents, citing failure rates as high as 95% for pilots and high operational costs due to unsustainable energy consumption. The author argues that transformer-based AI is fundamentally limited because it can only respond and simulate intelligence, lacking the capacity for genuine autonomy, intrinsic motivation, and continuous learning required for complex business tasks. As an alternative, the text introduces Synthetic Intelligence (SI), an architecture based on neuromorphic computing and Psi-Theory, which replicates biological brain functions to create non-biological intelligence that is vastly more energy-efficient and capable of genuine adaptive decision-making. The author strongly advises a hybrid strategy where businesses continue using existing reactive AI for simple tasks while immediately investing in SI to gain a competitive advantage in building truly autonomous systems.

Why Your AI Agents Keep Failing - And What Synthetic Intelligence Can Do About It

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Your Radio Law Talk hosts are exceptional attorneys and love what they do! They take breaks from their day jobs and make time for Radio Law Talk so that the rest of the country can enjoy the law like they do. Follow Radio Law Talk on Youtube, Facebook, Twitter & Instagram!

HR3 CONC: Kim Kardashian and Kris Jenner Sue Ray J; US Sues LA County Sheriff for 2nd Amendment Violations; VA AG Candidate Convicted; AI Safety Protocols Enhanced in CA

Medium Article: https://medium.com/@jsmith0475/chatgpt-is-too-smart-for-the-fda-until-now-8beb59745153
"ChatGPT Is Too Smart for the FDA — Until Now," by Dr. Jerry A. Smith, addresses the critical problem of non-reproducibility in large language models (LLMs), which prevents their adoption in highly regulated fields like pharmaceutical manufacturing. The author introduces cognitive anchoring, a novel gauge-theoretic framework that stabilizes transformer architectures by synchronizing their parallel attention heads using structured constraints derived from principles similar to those in Maxwell's equations. This method ensures that identical inputs yield consistent, deterministic outputs, achieving significant improvements in symbolic consistency and reducing complexity in analytical report generation. The work establishes a necessary foundation for trustworthy AI compliant with FDA data integrity standards (ALCOA+ and 21 CFR Part 11) by demonstrating that LLMs can be constrained to meet mandatory reproducibility requirements.

ChatGPT Is Too Smart for the FDA — Until Now

Medium Article: https://medium.com/@jsmith0475/chatgpt-cant-write-fda-compliant-reports-here-s-what-can-e2154b82c537
"Auditable AI for FDA-Compliant Reports: Cognitive Anchoring,"by Dr. Jerry A. Smith, argues that traditional AI models like ChatGPT cannot meet the Food and Drug Administration's (FDA) requirements for reproducible and predictable documentation in pharmaceutical quality assurance (QA). Dr. Jerry A. Smith identifies the current system of manual report generation as a significant bottleneck in the industry, costing vast amounts of time and money due to bureaucratic overhead. The author proposes a solution called cognitive anchoring, which uses a multi-agent AI system constrained by four mathematical rules (symbolic, temporal, spatial, and symmetry anchoring) to ensure compliance and consistency. This system is auditable because it measures whether outputs rely on Euclidean reasoning (factual retrieval) or hyperbolic reasoning (logical inference), providing a geometric breakdown that satisfies regulatory demands. Ultimately, the piece posits that deploying this production-ready technology is a strategic necessity for Contract Research Organizations (CROs) to achieve massive cost savings, increase report throughput by thousands of times, and lead the future of pharmaceutical QA.

ChatGPT Can’t Write FDA-Compliant Reports. Here’s What Can.

Heavybit is the leading early-stage investor in enterprise infrastructure. Since 2013, we've helped launch and scale visionary technical startups—from DevSecOps and feature flagging to AI code generation and beyond. We go hands-on from day 0, backing founders as they turn code into companies and build the future of software from the bottom up to the top down.

In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

Each week, the Tech ONTAP Podcast dives into all-things NetApp, including storage, public & private cloud, and much more. The team will also be interviewing subject-matter experts from across the industry and detailing various best practices for getting the most out of your datacenter.

Our hosts are average, ordinary people. Their only flaw is that they are exceptional attorneys and love what they do. Luckily, they take breaks from their day jobs and make time for Radio Law Talk so that the rest of the country can enjoy the law like they do.

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