Start the new year with clarity and confidence. In this episode, we break down a quick, clear, and easy 30/60/90 day plan designed to help you improve both your health and wealth. Drawing from proven frameworks in financial discipline, physical readiness, and mental resilience, we’ll show you how to:

• Build fiscal stability with smart budgeting, emergency funds, and professional guidance.
• Establish physical strength and endurance through aerobic activity, strength training, and performance nutrition.
• Strengthen mental resilience with stress‑management tools, better sleep hygiene, and simple breathing techniques.

By the end of this episode, you’ll have a practical roadmap for the next three months—one that balances financial responsibility, physical vitality, and mental clarity. It’s about creating sustainable habits that optimize your performance and set you up for long‑term stability.

We break down Operation Absolute Resolve, the stunning January 2026 U.S. military intervention that ended with the capture of Nicolás Maduro and his wife. What began as months of quiet naval buildup and targeted strikes on suspected drug‑trafficking vessels erupted into one of the most consequential foreign policy actions in recent history.

We explore what’s been confirmed so far: the Trump administration’s framing of the mission as a campaign against narco‑terrorism, the international backlash accusing the U.S. of violating Venezuelan sovereignty, and the growing domestic debate over legal authority and reported civilian casualties. Beyond the politics, we look at the global ripple effects — including early signals from energy analysts who expect Venezuela’s transition to reshape oil markets as U.S. firms move to rebuild the country’s long‑neglected energy infrastructure.

Disclaimer: The information, views, comments, and opinions expressed on Podcast "Talking with AI ML" are generated by artificial intelligence and machine learning algorithms. The information, views, comments, and opinions do not reflect the views or positions of the owner/creator(s) or any other party such as but not limited to any past, present, or future employers, organizations, or individuals and are provided for entertainment purposes only. All content provided is for informational and entertainment purposes. The hosts, guests, and contributors (including creator) make no representations as to the accuracy, completeness, suitability, or validity of any information on this Podcast and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. This content is used under the doctrine of Fair Use, Public Domain, No Professional Advice, and External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Fair Use: Certain content within the Podcast may be derived from sources that are protected under copyright laws. This content is used under the doctrine of fair use for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. We believe this constitutes a fair use of any such copyrighted material as provided for in Section 107 of the U.S. Copyright Law.

Public Domain: Some of the materials used in this Podcast may be in the public domain. Public domain materials are not subject to copyright protection and can be freely used by anyone. We make reasonable efforts to determine the public domain status of materials used and to attribute the correct sources.

No Professional Advice: The information provided on this Podcast should not be considered professional advice. Listeners should not act upon any information provided on the Podcast without seeking professional advice. This Podcast is not intended to replace professional consultation in any field, including but not limited to medical, legal, financial, or other professional services.

External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Content Responsibility: The Podcast hosts, guests, and contributors are not responsible for the content of any external websites or third-party services mentioned in the Podcast. The inclusion of any links or references does not imply endorsement of the views expressed therein.

Changes to This Disclaimer: The Podcast reserves the right to amend this disclaimer at any time without prior notice. By continuing to listen to this Podcast, you agree to be bound by the current version of this disclaimer.

Bonus episode: a sharp, data‑driven breakdown of the NBA slate with three tactical leans — Miami Heat, Philadelphia 76ers, and Los Angeles Clippers — plus a plain‑English explainer of how an LSTM deep‑learning model forecasts game outcomes.

What you’ll hear

• Top picks: concise reasoning for the Heat, 76ers, and Clippers with suggested bet types and unit guidance.
• Market moves: how injuries, rest, and sharp money are shifting lines tonight.
• Analytics deep dive: what an LSTM model uses (form, rest, travel, injuries) and its practical limits for bettors.
• Quick watchlist: injury flags, ATS trends, and props to monitor before lock.

Why listen now: immediate post‑lock insights to help you decide whether to act on tonight’s lines, plus a short technical segment that turns complex modeling into usable betting context.

Listen: episode live now — link in bio. Bet responsibly; these are informational leans, not guarantees.

Disclaimer: The information, views, comments, and opinions expressed on Podcast "Talking with AI ML" are generated by artificial intelligence and machine learning algorithms. The information, views, comments, and opinions do not reflect the views or positions of the owner/creator(s) or any other party such as but not limited to any past, present, or future employers, organizations, or individuals and are provided for entertainment purposes only. All content provided is for informational and entertainment purposes. The hosts, guests, and contributors (including creator) make no representations as to the accuracy, completeness, suitability, or validity of any information on this Podcast and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. This content is used under the doctrine of Fair Use, Public Domain, No Professional Advice, and External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Fair Use: Certain content within the Podcast may be derived from sources that are protected under copyright laws. This content is used under the doctrine of fair use for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. We believe this constitutes a fair use of any such copyrighted material as provided for in Section 107 of the U.S. Copyright Law.

Public Domain: Some of the materials used in this Podcast may be in the public domain. Public domain materials are not subject to copyright protection and can be freely used by anyone. We make reasonable efforts to determine the public domain status of materials used and to attribute the correct sources.

No Professional Advice: The information provided on this Podcast should not be considered professional advice. Listeners should not act upon any information provided on the Podcast without seeking professional advice. This Podcast is not intended to replace professional consultation in any field, including but not limited to medical, legal, financial, or other professional services.

Content Responsibility: The Podcast hosts, guests, and contributors are not responsible for the content of any external websites or third-party services mentioned in the Podcast. The inclusion of any links or references does not imply endorsement of the views expressed therein.

Changes to This Disclaimer: The Podcast reserves the right to amend this disclaimer at any time without prior notice. By continuing to listen to this Podcast, you agree to be bound by the current version of this disclaimer.

Recognition: Special thank you and credit to Google, and Microsoft, for use of their Large Language Models (LLMs) in helping research and generate content.

Bet responsibly!!!

Follower Request - This episode unpacks the seismic shift in corporate AI between FY25 and FY26, with a practical lens for leaders, product teams, and platform engineers. We trace how agentic/autonomous systems graduated from demos to narrow, high‑value workflows; why MLOps became table stakes for reliability; and how efficiency gains finally made scale affordable. We also cover the industry’s sober pivot away from hype toward governance, auditability, and procurement discipline. Through concise case examples and actionable frameworks, we show how organizations can pilot agentic workflows with clear ROI, formalize MLOps and observability, and measure total cost of ownership so AI becomes an embedded operating capability rather than a one‑off experiment.

Disclaimer: The information, views, comments, and opinions expressed on Podcast "Talking with AI ML" are generated by artificial intelligence and machine learning algorithms. The information, views, comments, and opinions do not reflect the views or positions of the owner/creator(s) or any other party such as but not limited to any past, present, or future employers, organizations, or individuals and are provided for entertainment purposes only. All content provided is for informational and entertainment purposes. The hosts, guests, and contributors (including creator) make no representations as to the accuracy, completeness, suitability, or validity of any information on this Podcast and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. This content is used under the doctrine of Fair Use, Public Domain, No Professional Advice, and External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Fair Use: Certain content within the Podcast may be derived from sources that are protected under copyright laws. This content is used under the doctrine of fair use for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. We believe this constitutes a fair use of any such copyrighted material as provided for in Section 107 of the U.S. Copyright Law.

Public Domain: Some of the materials used in this Podcast may be in the public domain. Public domain materials are not subject to copyright protection and can be freely used by anyone. We make reasonable efforts to determine the public domain status of materials used and to attribute the correct sources.

No Professional Advice: The information provided on this Podcast should not be considered professional advice. Listeners should not act upon any information provided on the Podcast without seeking professional advice. This Podcast is not intended to replace professional consultation in any field, including but not limited to medical, legal, financial, or other professional services.

External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Content Responsibility: The Podcast hosts, guests, and contributors are not responsible for the content of any external websites or third-party services mentioned in the Podcast. The inclusion of any links or references does not imply endorsement of the views expressed therein.

Changes to This Disclaimer: The Podcast reserves the right to amend this disclaimer at any time without prior notice.

Recognition: Special thank you and credit to Google, and Microsoft, for use of their Large Language Models (LLMs) in helping research and generate content.

Ever found yourself stuck next to a loud traveler, squeezed out of your seat by a space hog, or watching someone blatantly cut the boarding line? Air travel can bring out the best—and sometimes the worst—in people. In this episode, we dive into the hidden playbook for managing rude or aggressive behavior on aircraft.

✈️ You’ll learn:

• The most common passenger missteps that spark conflict and delays.
• Step-by-step scripts for calmly addressing everything from overhead bin disputes to unruly children.
• Why safety and de-escalation should always come first.
• How to protect yourself legally by documenting incidents, gathering witnesses, and knowing when to involve law enforcement.

Packed with practical strategies and real-world scenarios, this episode equips you to navigate tense travel moments with confidence, composure, and a touch of authority. Whether you’re a frequent flyer or an occasional traveler, you’ll walk away ready to turn turbulence into teachable moments.

Disclaimer: The information, views, comments, and opinions expressed on Podcast "Talking with AI ML" are generated by artificial intelligence and machine learning algorithms. The information, views, comments, and opinions do not reflect the views or positions of the owner/creator(s) or any other party such as but not limited to any past, present, or future employers, organizations, or individuals and are provided for entertainment purposes only. All content provided is for informational and entertainment purposes. The hosts, guests, and contributors (including creator) make no representations as to the accuracy, completeness, suitability, or validity of any information on this Podcast and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. This content is used under the doctrine of Fair Use, Public Domain, No Professional Advice, and External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

A clear, evidence‑first update on interstellar comet 3I/ATLAS: what telescopes and spacecraft have observed, what new science we’ve gained, and a methodical debunk of the most viral false claims. This episode separates sensational headlines from reproducible data and explains, in plain language, why scientists conclude 3I/ATLAS is a natural object and what open questions remain.

Disclaimer: The information, views, comments, and opinions expressed on Podcast "Talking with AI ML" are generated by artificial intelligence and machine learning algorithms. The information, views, comments, and opinions do not reflect the views or positions of the owner/creator(s) or any other party such as but not limited to any past, present, or future employers, organizations, or individuals and are provided for entertainment purposes only. All content provided is for informational and entertainment purposes. The hosts, guests, and contributors (including creator) make no representations as to the accuracy, completeness, suitability, or validity of any information on this Podcast and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. This content is used under the doctrine of Fair Use, Public Domain, No Professional Advice, and External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

Two AI hosts go head to head in a spirited debate about whether education still pays. One AI champions human‑centered careers like Advanced Practice Nursing and Genetic Counseling; the other argues for digital and technical paths such as Data Science and Cybersecurity. Expect sharp contrasts, surprising tradeoffs, and clear takeaways as the hosts spar over job satisfaction, student debt, automation risk, and real‑world earning potential.

What you’ll hearA lively debate that balances empathy and evidence as each host defends a different path.

• Real tradeoffs: deep fulfillment and heavy debt in some clinical careers versus resilient, well‑paid roles like Wind Turbine Technician.
• Training strategies explained on both sides — why a Master’s plus industry certifications often wins for long‑term leadership and employability.
• Practical money moves: how to calculate program ROI and use tax tools like the American Opportunity Tax Credit.

If you’re thinking about grad school, planning a midcareer pivot, or advising someone who is, this debate gives both sides equal time, sharpens the questions you should ask, and hands you actionable next steps to evaluate programs and protect your finances.

Disclaimer: The information, views, comments, and opinions expressed on Podcast "Talking with AI ML" are generated by artificial intelligence and machine learning algorithms. The information, views, comments, and opinions do not reflect the views or positions of the owner/creator(s) or any other party such as but not limited to any past, present, or future employers, organizations, or individuals and are provided for entertainment purposes only. All content provided is for informational and entertainment purposes. The hosts, guests, and contributors (including creator) make no representations as to the accuracy, completeness, suitability, or validity of any information on this Podcast and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. This content is used under the doctrine of Fair Use, Public Domain, No Professional Advice, and External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.

This bonus episode gives busy parents a calm, practical roadmap for when to tell your child the truth about Santa, how to say it without breaking trust, and simple rituals to preserve the magic.

In less than five minutes you’ll get clear signs that your child is ready, a short age‑appropriate script you can use word‑for‑word, and quick follow‑ups to turn disappointment into responsibility and wonder.

Disclaimer: The information, views, comments, and opinions expressed on Podcast "Talking with AI ML" are generated by artificial intelligence and machine learning algorithms. The information, views, comments, and opinions do not reflect the views or positions of the owner/creator(s) or any other party such as but not limited to any past, present, or future employers, organizations, or individuals and are provided for entertainment purposes only. All content provided is for informational and entertainment purposes. The hosts, guests, and contributors (including creator) make no representations as to the accuracy, completeness, suitability, or validity of any information on this Podcast and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. This content is used under the doctrine of Fair Use, Public Domain, No Professional Advice, and External Links: The Podcast may contain links to external websites that are not provided or maintained by or in any way affiliated with the Podcast. Please note that the Podcast does not guarantee the accuracy, relevance, timeliness, or completeness of any information on these external websites.