• Earned Wisdom: Stories of Overcoming and Resilience by Dan Weddle has achieved #1 bestseller status on Amazon as of December 11, ranking at the top of multiple categories. The book offers honest and personal recounts of overcoming adversity, leadership, and building a meaningful life and career through perseverance, faith, and determination.

Memphis, Tennessee, 4th February 2026, ZEX PR WIRE, Earned Wisdom: Stories of Overcoming and Resilience by Dan Weddle has secured the #1 spot on Amazon in the Motivational Business Management, Personal Success in Business, and Biographies of Business Professionals categories.

The book is an inspiring and candid reflection on Dan Weddle’s personal and professional journey. As President and CEO of ProTech Services Group, Inc., Weddle traces the long, often painful road that shaped him into the leader he is today. Weddle grew up in a broken family with generational struggles around addiction and limited financial stability, and navigated grief, loss, and career uncertainty for a long time. Through the book, he offers readers an honest look at what it takes to keep moving forward.

Before becoming the President and CEO of a respected technology and talent services firm, Weddle began his career as a nineteen-year-old beat cop specializing in narcotics. During that time, he never would’ve imagined leading an innovative IT company one day. However, through hard work, learning on the job, and consistently seizing opportunities as they arose, Weddle built a thriving career.

The book also explores extremely personal chapters of Weddle’s life. He shares what it meant to juggle three jobs, make a complete career shift, become an instant father, and face the devastating loss of a child. These experiences shaped his leadership style and his outlook on life. Throughout Earned Wisdom, Weddle emphasizes the importance of faith, family, and community, as well as the humility to ask for help when it is needed most.

“There will almost always, at some point in life, be struggles and hardships,” Weddle says. “No one can avoid them. What’s important is how we deal with those hardships, and that we get back up every time we’re knocked down.”

Dan Weddle is also widely recognized for his leadership at ProTech Services Group, Inc., a Memphis-based company founded in 1992. The firm provides managed services, cybersecurity, cloud solutions, technology consulting, and talent acquisition. In the Memphis community, Weddle serves on multiple boards, including Streets Ministries and the Memphis Shelby County Crime Commission, and is actively engaged in leadership and advisory organizations.

These experiences and successes converge in Earned Wisdom, offering readers a fresh service-based perspective on leadership and responsibility. The book’s success is an incredible milestone for Dan Weddle.

Earned Wisdom: Stories of Overcoming and Resilience is available on Amazon.

About the Author:

Dan Weddle is the President and CEO of ProTech Services Group, Inc., a Memphis, Tennessee–based technology and talent services firm. He has more than three decades of experience in leadership, IT consulting, and business development. Weddle is known for his people-first approach and commitment to helping organizations and individuals succeed personally and professionally. Earned Wisdom is his debut book, offering lessons on adversity, leadership, and life experience.

California, US, 4th February 2026, ZEX PR WIRE, For decades, the “Erdős problems”, a collection of over 1,000 mathematical conjectures posed by the legendary Hungarian mathematician Paul Erdős, have served as a rigorous proving ground for the world’s brightest human minds. They range from deceptively simple number theory puzzles to complex combinatorial nightmares.

For a long time, the consensus was that Artificial Intelligence could handle calculation, but not creation. It could crunch numbers, but it couldn’t reason through abstract proofs.

That consensus just shattered.

Over a single weekend, Neel Somani, a software engineer and founder of the blockchain platform Eclipse, utilized OpenAI’s GPT 5.2 Pro to crack open Erdos problems that had remained unsolved for years. This wasn’t a case of a computer searching a database faster than a human; it was a case of an AI generating novel mathematical logic, formalized by a human expert.

We are witnessing a fundamental shift in the epistemological hierarchy of mathematics. The barrier between “human reasoning” and “machine processing” is dissolving, and Somani’s work provides the data to prove it.

The Weekend Win: Cracking Problem #397

Neel Somani, a former quantitative researcher at Citadel with a triple major from UC Berkeley, was stress-testing the reasoning capabilities of the new GPT 5.2 Pro model. He wasn’t looking for a calculator; he was looking for a co-author.

He fed the model Erdos Problem #397. After approximately 15 minutes of “thinking,” the model returned a full solution.

The problem asks for integer solutions to a specific binomial identity. To the layman, it looks like a jumble of variables. To a mathematician, it represents a precise relationship between numbers. The AI proposed that since $a$ can be chosen arbitrarily large, there are infinitely many distinct-index solutions.

Specifically, for an example where $a = 2$, the AI derived that $c = 49$, and the identity becomes:

$$ binom{2}{1} binom{49}{6} = binom{98}{3} binom{1}{1} $$

Somani reviewed the output. It wasn’t just hallucinated gibberish; it was sound logic. He formalized the proof using a tool called Harmonic and submitted it. The result? Accepted by Terence Tao, one of the most respected mathematicians alive today.

The AI had not only identified a solution but had effectively “reasoned” its way through a path that differed from previous partial attempts. While the model found a 2013 Math Overflow post by Harvard mathematician Noam Elkies regarding a similar problem, GPT 5.2 Pro’s final proof offered a more complete solution to the specific version posed by Erdős.

The “Unambiguous Instance”: Problem #281

If Problem #397 was a fluke, Problem #281 was the confirmation.

Neel Somani turned the model toward a covering system problem in number theory. The problem posits:

Let $n_1 < n_2 < dots$ be an infinite sequence such that, for any choice of congruence classes $a_i pmod{n_i}$, the set of integers not satisfying any of the congruences $a_i pmod{n_i}$ has density 0.

The question was whether for every $epsilon > 0$ there exists some $k$ such that the density of integers not satisfying the congruences is less than $epsilon$.

GPT 5.2 Pro generated a new proof. When Somani published the results, Terence Tao referred to it as “perhaps the most unambiguous instance” of AI solving an open problem.

This distinction is critical. In the past, AI “solutions” were often just efficient retrievals of existing literature. In this case, no prior solution was found. The AI bridged the gap between the known and the unknown.

The Data: Separating Genius from Hallucination

Skeptics often point to LLM hallucinations as a reason to dismiss their utility in rigorous fields. Somani, approaching this with the mindset of a quant and a computer scientist, decided to quantify the model’s actual efficacy.

He recruited a team of undergraduates to construct a dataset of ChatGPT responses to every open Erdos problem, 675 in total. The results provide a fascinating map of the current AI frontier:

• Recited known literature: 618 (The AI acted as a search engine)

• Incorrect: 17 (The AI hallucinated or failed)

• Correct, known results: 12

• New solutions to Erdos problems: 3

While 3 out of 675 might seem statistically small, in the world of high-level mathematics, it is monumental. It implies that for a specific subset of problems, the AI is already operating at the level of a published mathematician.

The “Long Tail” of Mathematics

Why is this happening now? Terence Tao, observing Somani’s progress, conjectured on Mastodon that AI systems are uniquely suited for the “long tail” of obscure Erdos problems.

Many of these problems are not “unsolvable” in the sense that they require a new branch of mathematics to be invented (like Fermat’s Last Theorem). Rather, they are tricky, labor-intensive, and obscure. They require connecting disparate axioms, Legendre’s formula, Bertrand’s postulate, the Star of David theorem, in novel ways.

This is where the scalable nature of AI shines. A human mathematician might spend a lifetime solving a dozen such problems. An AI, directed by a human operator like Somani, can attempt thousands in a day, clearing out the “clutter” of the mathematical landscape and leaving humans to focus on the deepest, most structural conjectures.

The Role of the Human Operator

It is important to note that GPT 5.2 Pro did not do this alone. It required Neel Somani.

Neel Somani’s background is pivotal here. As the founder of Eclipse, a Layer 2 blockchain platform that raised $65 million, and a former researcher at Citadel, he understands complex systems. His triple major from Berkeley in CS, Math, and Business gave him the vocabulary to prompt the model effectively and, more importantly, the expertise to verify the output.

The future of mathematics, and enterprise problem solving, looks exactly like this workflow:

1. The Architect (Human): Identifies the problem and frames the prompt.

2. The Engine (AI): Generates potential proofs, leveraging vast databases of axioms.

3. The Verifier (Human/Formalization Tools): Tools like Lean or Harmonic are used to formally check the logic, ensuring the AI hasn’t made a subtle error.

The Future of Discovery

We are moving toward an era of “Formalization,” where labor-intensive verification is automated by tools like Harmonic’s Aristotle, and creative reasoning is augmented by models like GPT 5.2.

Neel Somani’s work with the Erdos problems is not just a “weekend win” for a crypto founder. It is a signal to every industry. If AI can reason through unsolved number theory, what can it do for supply chain logistics? For cryptographic security? For protein folding?

The tools are here. The frontier is open. The only question remaining is: what problem will you prompt next?