Warning :losing weight too fast can have devastating effects. 

So I have I. learned much about the human body. First I learned that they lose weight too fast 

Tthere are many complications and side effects, it will seem OK at first, but beware. 

Without going to great detail, I have ended up with several things first I am here, at 71  and feel much better for many reasons , secondly, I’m having several problems with my arms and legs and throat again they’re all related to my body adjusting to my new weight. 

The main thing I’ve learned is that if you eat real food, you’ll lose weight and if you lose too much weight you’ll experience  effect well your body adjust

I d feel much better and the other again if you lose too much you have several problems with your balance.  arms, legs and throat however, they will heal overtime and that’s normal

. I will share more. Many more will learn this as we move to less processed food.

Ira

Artificial Intelligence 2.0

I proposed a new way of thinking about artificial    intelligence

When you really define what we’re doing, we’re using a computer to complete comparative analysis , and draw conclusions, at the speed of light . Thereby in conclusion, what we percieve.. and the conclusions only somewhat mathematically based. They may teflect the subjectiveness of developer who coded the model. The conclusion is non-biased and reflects the bias of the developer I am just trying to understand the facts no the interpretation ,

While, I realize many may already know this, it is paradigm shifting for me

So the bottom line is that what you’re reading or is the opinion of a human in the end which may be subjective and no science fact

Let’s your data reveal its secrets for free.

One month free, we would like to offer our service for one month free,

We’er aembracing new technology from a marketing and digitial perspective.

We will help you discover what’s in your database , your entire database or file ,

We will use only statistics and frequency patterns and tell you through AI what the story is Here is an example from raw unfiltered data

https://notebooklm.google.com/notebook/cc3ac1dc-15ae-48fe-952a-8bcc23870ef3?utm_source=nlmm_share

We can process any flat file or any data you desire.

Call 414–5 95–4880 again we will process your data for free for one month. .

of course, we will sign any NDA REQUIRED to INSURE privacy.

Ira Warren Whiteside

Information Sherpa

MetadataGarage LLC

The Information Sherpa’s Guide to Seeing Past the Noise: What We Got Wrong About Health

We are currently losing the war on metabolic health because we’ve lost the ability to distinguish between a raw statistic and a marketing narrative. In an era of endless wellness trends, finding the “truth” about our bodies feels like navigating a dense, fog-covered wilderness. We are bombarded with government guidelines, yet our collective health continues to move in the opposite direction. To find our way out, we need to adopt the mindset of an “Information Sherpa”—a guide who refuses to hand you a pre-packaged conclusion and instead helps you strip away subjective interpretations to reveal the raw facts beneath.

Ira Warren Whiteside, the original Information Sherpa, argues that our modern health crisis isn’t a mystery of biology, but a failure of information literacy.

The Sherpa’s First Rule: Fact vs. Interpretation

To understand why public health outcomes are so often at odds with public health advice, we must master the Sherpa’s most critical rule: the distinction between objective data and the narrative built around it. We have been conditioned to accept “interpretations” as if they were law, leading to a landscape of total public confusion.

“My goal is [to] guide you through many concepts logically and separate facts from information which is based on interpretation.”

In the world of investigative health, “interpretation” is the lens—political, corporate, or ideological—that colors the data. When we mistake the interpretation for the fact itself, we end up following guidelines that may be perfectly “official” but biologically disastrous.

The Food Pyramid Paradox: A Recipe for Diabetes?

There is no more striking example of the gap between guidance and reality than the history of the Food Pyramid. Introduced as the definitive guide for healthy living, its arrival marked a massive shift in how the world ate. However, looking at the raw timeline reveals a troubling irony.

Whiteside points to a devastating correlation: the prevalence of Type 2 Diabetes increased greatly after the Food Pyramid was introduced. This is more than just a surprising takeaway; it is a systemic failure. The very tool designed to foster health coincided with a sharp, sustained decline in metabolic wellness. This paradox serves as a warning that government-sanctioned advice should be scrutinized for its real-world impact rather than accepted on faith.

The Sweetened Truth: The Hidden Impact of Soda and Juice

A primary driver of this metabolic crisis, which accelerated alongside the new dietary guidelines, is the massive influx of liquid sugar into the modern diet. The rise in Type 2 Diabetes is an inevitable consequence of specific changes in consumption that the Food Pyramid era failed to curb—and in some cases, inadvertently encouraged.

The “Information Sherpa” identifies two main culprits in this sugar-saturated landscape:

  • Added sugars in soda: The massive increase of sugar in carbonated beverages.
  • Added sugars in juice: The significant sugar content found in fruit juices.

This is where the distinction between fact and interpretation becomes a matter of life and death. The “fact” of fruit juice is that it is a source of high-dose liquid fructose. The “interpretation” is the marketing narrative that labels it a “natural source of vitamins” or a “healthy staple.” By granting juice a “health halo,” we allowed it to slip into our diets unnoticed, where it functions metabolically in much the same way as soda.

Reclaiming the Path to Health

Improving our health requires more than just following the latest government-sanctioned trend; it requires a radical shift in data literacy. We must learn to look at the hard facts—such as the historical explosion of diabetes following specific dietary shifts—rather than just following the popular interpretation of what is “healthy.”

The core lesson from the Information Sherpa is that clarity only comes when we separate the data from the spin. As you look at your own health habits today, ask yourself: What other “interpretations” have I been following blindly, and what do the actual facts say about my survival?

Why History Is Older Than You Think: 5 Surprising Secrets About the Origin of Writing

The “official story” of human history is a convenient lie. It’s neat, it’s contained, and it’s linear: civilization emerges, writing appears in Mesopotamia around 3,200 BC, and “history” begins. It’s a comforting narrative, but it ignores the messy, decaying reality of our past.

As a deep-time historian, I look at the gaps as much as the artifacts. The fundamental problem with our definition of history is that it is hostage to the ravages of time. We treat the appearance of writing as a hard starting line, but what if that line is simply the point where our records stopped rotting? We equate “prehistory” with a lack of intelligence, but in reality, we are likely just looking at the point where the medium outlasted the message.

1. Writing is Humanity’s “External Memory”

To understand why the origin of writing is likely much older than the textbooks claim, we have to stop seeing it as just “speech on paper” and start seeing it as external memory.

Before graphic symbols, human knowledge had a built-in expiration date. It was a fragile, flickering flame that had to be passed from one mind to another through oral tradition. If a person died before sharing a discovery, that knowledge reset to zero. Knowledge was always at risk of dying with the individual. Writing fundamentally broke this cycle, allowing ideas to accumulate rather than reset. It allowed laws to persist, calendars to be standardized, and rituals to remain consistent across centuries.

> “Writing allows ideas to exist outside the human mind and survive long after the person who created them is gone.”

This definition forces us to rethink the divide between “history” and “prehistory.” Following the logic of the Information Sherpa, we must distinguish between the fact (the physical mark) and the interpretation (our belief that it represents language). If ancient humans were using symbols to store information, they were making history—even if the records didn’t survive the rot.

2. The Survival Bias of Clay and Stone

The “uncomfortable truth” of archaeology is that we don’t have a representative sample of the past; we only have the writing that didn’t decay. Our timeline is heavily biased toward durable materials like fired clay and stone.

Ancient people were as practical as we are. You don’t engrave a grocery list into a stone pillar; you scribble it on something cheap, light, and replaceable. Throughout history, the vast majority of writing occurred on organic materials:

  • Wood and bark
  • Wax tablets
  • Leather and animal skins
  • Plant fibers (Papyrus)

In most climates, these materials vanish within centuries. We have thousands of Mesopotamian clay tablets because they “fossilized” in fires, not because they were the only thing people wrote on. We know this because of rare, lucky remnants, like the thin wooden tablets found near Hadrian’s Wall, which included a perfectly preserved, humanizing birthday invitation. Without the unique soil conditions of that site, that entire library of Roman daily life would have vanished. The “silence” of the past is not Evidence of Absence; it is almost entirely Evidence of Decay.

3. The Göbekli Tepe Paradox

Göbekli Tepe (9,600 BC) is a site that effectively detonated the “Complexity Dogma” of archaeology. For decades, academics argued that writing was a late-stage invention—a tool created only when cities became too large to manage. Yet, 7,000 years before the rise of Sumerian cities, the people at Göbekli Tepe were quarrying and erecting 10-ton stone pillars with precise, coherent planning.

You don’t coordinate a workforce of this scale with a megaphone and good intentions. Such organization requires a system of shared, symbolic communication. This brings us to a controversial artifact highlighted by assyriologist Irving Finkel in a discussion with Lex Fridman: the green stone seal.

Dating to roughly 9,000 BC, this seal features an arched back and a flat underside carved with hieroglyphic-style signs. While its status as “writing” is an interpretation, the fact of the object is that it is a bureaucratic tool designed to leave repeated impressions. In Finkel’s view, this was a functional device for formalizing agreements or marking ownership. If a “finished” bureaucratic system existed in 9,000 BC, writing didn’t begin in 3,200 BC—it had already arrived. And remember: only 10% of Göbekli Tepe has been excavated. We are judging the library by the front door’s floor mat.

4. The 1% Survival Rate of the Ancient World

We often assume that the gaps in the record are small. They aren’t. They are oceanic.

According to data synthesized by historian Rudolph Bloom and Hans Gertzinger, only about 1% of classical Greek literature survives today.

  1. Out of approximately 2,000 known Greek authors, complete works survive for only 136.
  2. Another 127 exist only in fragments.
  3. The remaining 1,700+ authors are entirely lost to time.

If 99% of the literature from a “well-documented” literate society like Classical Greece can vanish in just 2,000 years, what are the odds of finding a birch-bark manuscript from 20,000 years ago? What we see in the archaeological record are the raindrops, not the waterfall. This massive rate of loss makes “silence” in deep prehistory far more likely to mean “total loss” rather than “lack of activity.”

5. The Ice Age Code: 32 Recurring Symbols

If we travel back 30,000 to 40,000 years to the Ice Age caves of Europe, we find that our ancestors were already masters of graphic communication. While the animal paintings of Lascaux get the glory, researcher Genevieve von Petzinger focused on the “lost in plain sight” geometric marks.

Visiting 52 cave sites, von Petzinger and her team made a startling discovery: 75% of the caves contained symbols that had never been recorded by previous archaeologists. After analyzing the data, she identified 32 recurring geometric shapes—dots, lines, triangles, and crosses—that remained consistent across a continent for 30,000 years.

> “If they weren’t meaningful people wouldn’t keep replicating them.”

These symbols were not a full alphabet, but they were a notation system for encoding and storing information visually. Most provocatively, two-thirds of these symbols were already in use when humans first arrived in Europe. This suggests the “Ice Age Code” wasn’t a European invention; it was an even older African tradition brought with us. The hardware for writing—the human drive to externalize memory—was already “installed” long before the first city was ever built.

Conclusion: The Mystery of Survival

Ancient humans were not grunting primitives; they were practical, intelligent actors who used the materials available to them. They wrote on skins, leaves, and wood—materials that served their needs perfectly but failed to survive the rot of millennia.

We are left with a fragmented puzzle where 99% of the pieces have dissolved. Much of the symbolic world of our ancestors has vanished completely, leaving us to mistake our own ignorance for their lack of sophistication. The real mystery is not whether early writing existed, but whether it ever had a chance of surviving long enough for us to see it—what else have we forgotten?

The Architecture of Resilient Intelligence: Data Governance, Agentic AI, and the Information Value Chain

Executive Summary

The modern enterprise technology landscape is defined by a critical transition from rule-based systems to reasoning-based autonomous agents. While Agentic AI is projected to generate $450 billion in economic value by 2027, a significant technical crisis—”data chaos”—threatens this potential. Current estimates suggest that up to 60% of AI projects will be abandoned through 2026 because they lack “AI-ready” data.

This briefing document synthesizes the foundational requirement for data governance (described as “data governance in a helmet”), the rise of agentic frameworks like FAMOSE for automated feature engineering, and the conceptual models of operational-analytical data marts required to process big data at scale. The overarching theme is that structural integrity and “semantic trust” are the primary determinants of success in both digital and biological systems; rapid optimization without foundational governance leads to systemic failure.


1. The Foundational Crisis: Data Governance as AI Strategy

AI strategy is fundamentally an extension of legacy data governance principles designed for a world where machines consume data at scale.

The “Silent Saboteur” of Data Chaos

  • The Failure Rate: Gartner predicts 60% of AI projects will fail by 2026 due to poor data quality.
  • Asset vs. Liability: Success is dictated not by the complexity of code, but by the maturity of information.
  • Adversarial Robustness: AI governance acts as a protective layer over existing skills like Role-Based Access Control (RBAC), lineage, and provenance to prevent hallucinations and PII leaks.

Seven Components of “AI-Ready” Data

To reach maturity, data must meet specific interconnected standards:

  1. Quality: Accuracy and verifiability across authoritative sources.
  2. Accessibility: Frictionless access via APIs while breaking down silos.
  3. Governance: Clear ownership and accountability for data domains.
  4. Completeness: Populated historical context for pattern recognition.
  5. Standardization: Harmonized naming conventions and formats.
  6. Unification: Entity resolution creating a single source of truth.
  7. Lineage: A traceable map of data origin and transformation.

2. The Agentic Revolution: From Assistants to Orchestrators

The industry is moving beyond “query-based assistants” to “autonomous systems” that proactively execute multi-step processes.

Passive GenAI vs. Active Agentic AI

Feature

Passive Generative AI

Active Agentic AI

Primary Function

Information retrieval/synthesis

Reasoning, planning, and task execution

Interaction

Responds to user requests

Proactively executes processes via an orchestrator

Outcome

Content generation (e.g., explaining a process)

End-to-end execution (e.g., resetting credentials, closing tickets)

Risk Profile

Output Risk (incorrect text)

Action Risk (unauthorized transactions)

The “Information Sherpa” Paradigm

As AI handles the “mechanical toil” of data management, the human role is evolving from Executor to Orchestrator.

  • The 0.5% Reality: Dedicated “Prompt Engineer” roles represent less than 0.5% of job postings; the high-value skill is actually Subject Matter Expertise (SME) used to guide AI agents.
  • Semantic Truth: Experts no longer build the code; they orchestrate the “semantic truth” by providing the context necessary for AI to produce high-fidelity insights.

3. FAMOSE: Agentic Feature Discovery

Identifying optimal features from exponentially large spaces remains a critical bottleneck in machine learning. FAMOSE (Feature AugMentation and Optimal Selection agEnt) addresses this through the ReAct (Reasoning and Acting) paradigm.

Iterative Feature Engineering

Unlike “one-shot” methods that generate static lists, FAMOSE simulates a data scientist by:

  • Proposing: Using metadata and tool feedback to hypothesize new features.
  • Evaluating: Testing features against model performance (ROC-AUC or RMSE).
  • Refining: Learning from mistakes to invent more innovative features.
  • Selecting: Utilizing mRMR (minimal-redundancy maximal-relevance) to produce a compact, non-redundant final feature set.

Performance Benchmarks

  • Regression: FAMOSE has demonstrated a 2.0% reduction in RMSE on average compared to traditional methods.
  • Classification: For datasets with over 10,000 instances, FAMOSE shows a 0.23% increase in ROC-AUC.
  • Robustness: Features discovered via one model (e.g., XGBoost) frequently improve performance in others (e.g., Random Forest or Autogluon).

4. Technical Architecture for Big Data Processing

Processing at the scale required for 2026 and beyond necessitates specialized data mart structures and parallel architectures.

Operational-Analytical Data Marts

  • Operational Marts: Thematic, narrowly focused information slices designed for consolidation and ranking based on relevance.
  • Analytical Marts: Independent sources created by users to structure data for specific tasks, often utilizing “flat” indexed tables for high-speed reading.

MPP GreenPlum and QlikSense Integration

The conceptual model for high-speed big data processing involves:

  • Massive Parallel Processing (MPP): Breaking data arrays into segments that can be processed simultaneously across multiple servers.
  • PXF Framework: Enabling each segment to exchange data with sources in parallel, drastically reducing export and join times.
  • Internal Storage (QVD): Compacting data to provide reading speeds up to 100 times fasterthan traditional data sources.

5. The Information Value Chain: Analysis vs. Interpretation

Achieving “semantic trust” requires a distinction between the technical processing of data and the qualitative derivation of meaning.

The Hierarchy of Insight

  • Data: Physically exists as raw quantities.
  • Information: Derived from formulas that determine relationships based on quantities.
  • Knowledge: Derived from information.
  • Concepts: Formed by humans/orchestrators based on knowledge.

Analysis vs. Interpretation

Aspect

Data Analysis

Data Interpretation

Focus

Answers “What” and “How”

Answers “Why” and “What next”

Nature

Technical and quantitative

Qualitative and subjective

Process

Cleaning, transforming, and modeling

Synthesizing results and suggesting actions

Outcome

Structured data and statistical models

Actionable recommendations and narratives


6. The Biological Parallel: The Architecture of Resilience

A central theme in both metabolic and digital transformation is that speed often sabotages structural integrity.

  • Slimmer’s Paralysis: Rapid weight loss removes the protective adipose tissue (padding) from the peroneal nerve, leading to “bilateral foot drop” and neurological dysfunction.
  • The Zinc Paradox: Excessive zinc intake blocks copper absorption; copper is the “architect” of myelin (nerve insulation). Deficiency can cause spinal cord insulation to drop by 56%.
  • Technical Corollary: Layering autonomous agents over “broken manual processes” or “data chaos” is the digital equivalent of rapid physical transformation without nutritional governance. Success depends on the integrity of the “wires”—both neurological and digital—that carry the signal.

7. Strategic Conclusions: The Trust Dividend

By 2026, the transition from rule-based compliance to intelligent reasoning will define the “Trust Dividend.”

  • From Rows to Reason: Database professionals are evolving from technicians of records to architects of intelligence, spending 80% of their time on data wrangling to fuel AI engines.
  • Governance as an Engine: Governance is no longer a burden or a tax; it is the proactive engine that enables the velocity of AI.
  • The Agentic Mesh: The end state is a coordination fabric that acts as an organization’s “nervous system,” preventing “agentic chaos” where disparate systems optimize for conflicting KPIs.

Final Provocation: Technical leaders must decide whether they are building a governed foundation for the age of autonomy or merely creating a more expensive, sophisticated layer of technical debt.

Paradigm shift

Hello there, I have not written in about two years. I can finally speak well enough that I can blog with SIRI

So I lost 130 pounds and that  turned into many things. I did not expect in in essence a full body reset which is often is misdiagnosed as a stroke, which it is not however , it did bring back the stroke symptoms

So my walking my arm, and my voice are quite different, which believe it or not. ids really a good thing. They’re all slowly returning to no more. I have decided many things about the food we eat and myself for one thing I am now in AA. 

I am now 71 and I feel  better than ever all afflictions. I had from five years ago and now gone including diabetes type two, IBS, blood pressure and many more so here we go again thank you 

Ira.