Podcasts about hallucinations

Guest Franz Borghardt 1,700+ Hallucination Incidents: Since ChatGPT's 2022 debut, courts worldwide have flagged over 1,700 cases of hallucinated content appearing in legal briefs, getting attorneys sanctioned or disciplined. More than lawyers judges use AI as well AI Already in Legal Research: Westlaw and LexisNexis already sell AI-powered research tools with built-in verification, unlike ChatGPT, which can fabricate entire fake cases with realistic facts and analysis. Using AI instead of humans for jurors Verification Is the Line: Borghardt argues using AI is fine if you verify its output against known real cases; the danger is trusting AI-generated citations with zero independent research. Could AI "Be" a Justice?: The hosts debate whether AI could replicate a justice like Ginsburg or Scalia using their full body of writings, though novel technology cases remain a challenge. Predictive Litigation Software: The group floats a subscription tool that predicts Supreme Court questioning and rulings using sitting justices' case history, calling it a coming financial opportunity.

In this episode, we review the high-yield topic of⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠Delusions, Hallucinations, Illusions, and Loose Associations⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠from the Psychiatry section.Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Medbullets⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on social media:Facebook: www.facebook.com/medbulletsInstagram: www.instagram.com/medbulletsofficialTwitter: www.twitter.com/medbullets

Richard McGirr talks to Tal Shahar, CEO of Atlas Invest, reveals how they've harnessed AI and innovative infrastructure to transform real estate credit into a lightning-fast, scalable engine that closes deals in days, not months. Imagine getting a same-day term sheet for bridge loans of up to $15 million, backed by multifamily and mixed-use properties, without traditional barriers like prepayment penalties or high LTVs. Tal Shahar Co-Founder and CEO of Atlas Invest Based in: New York Where to find them: http://linkedin.com/in/tal-shahar https://atlas-invest.co/ Book your free demo today at bill.com/bestever and get a $100 Amazon gift card. Visit https://malabarhillcapital.com/ for more info. Podcast production done by⁠ ⁠Outlier Audio Learn more about your ad choices. Visit megaphone.fm/adchoices

Deepti Yenireddy, Founder and CEO of Boon, joins Gareth McGlynn to talk about building AI that can actually read construction drawings. A second-time founder, Deepti exited her first company, HR tech platform My Ally, and led product at IoT leader Samsara before turning her focus to preconstruction.Key Topics Covered:What Boon Does: Helping preconstruction teams bid more, bid better, and bid more accurately, at 10x to 100x speed.The AI Estimator: Deepti announces Boon's first preconstruction AI employee, working the full lifecycle from project invite to final proposal.Hallucination and Human Control: How estimators stay in the loop with a visual review platform and links back to the agent's sources.Accuracy and F1 Scores: Why Boon publishes accuracy scores for every trade and scope, and why below 90 percent creates user friction.Boon vs Generic LLMs: Why ChatGPT and Gemini can read the text in your drawings but cannot tell where a line starts and stops.Implementation and ROI: A one-hour onboarding, and a GC customer who generated 430 extra hours in a year with one person using the agent.And much, much more.You can connect with Deepti via her Linkedin: https://www.linkedin.com/in/deepti-yenireddy/

* Political analyst: The state wants its pound of flesh from New Orleans * Table Talk: small plates, sushi, and fine wine * AI hallucinations in the legal system? Here's what you need to know * Why New Orleans remains a top destination for conventions * Should the US revamp its retirement systems?

AI use has been increasing all over, including in the courtroom…and AI hallucinations have been increasing too. How are lawyers allowed to use AI? Is it ethical? Dane Ciolino, professor of law at Loyola, joins us.

Imagine a world where AI is not just a tool but a partner that elevates human judgment, responsibility, and ethics. That's exactly what Helena Almeida, VP of Ethics at ADP, reveals in this electrifying episode, a true call to action for every HR leader and business executive. Dive into the real talk about AI governance, responsible use, and how to stay human in a tech-driven world. Your organization's future depends on it.  In This Episode:  Helena Almeida's journey from litigator to AI ethics pioneer.  The human side of AI: trust, hallucinations, and accountability.  How responsible AI standards are shaped by legal and ethical boundaries.  Practical advice for HR leaders: leveraging AI without losing the human touch.  The critical role of friction in HR processes to ensure fairness and accuracy.  Why AI isn't a replacement but an enhancer — and the importance of smart design.  Navigating complex legal landscapes: pay transparency, union rules, and cross-state regulations.  How business leaders without tech backgrounds can champion responsible AI.  The future of HR workflows with AI: rethinking old processes and embracing healthy friction.  What “responsible AI” really looks like for your organization.  Timestamps: 00:00 - Welcome to the bold new era of AI in HR 02:00 - Helena's transition from litigation to AI ethics leadership 04:00 - The myth of AI without human oversight 07:00 - Hallucinations in AI and how to manage trust 09:30 - Balancing AI accuracy with human judgment 12:00 - Ensuring data integrity in AI-driven HR systems 15:00 - Managing variations in employment law with AI 18:00 - The challenge of complex, multi-layered HR rules 21:00 - Standards for responsible AI: high stakes decisions 24:00 - The importance of skilled designers and human oversight 27:00 - The impact of automation on HR talent and friction 30:00 - How business leaders can lead AI governance without technical expertise 33:00 - Rethinking HR workflows for AI integration 36:00 - Healthy friction: the secret to responsible AI in HR 38:00 - Final thoughts: humans and AI working together for better workplaces Resources & Links:  ADP  Helena Almeida - LinkedIn  Responsible AI Guidelines  Book: "Human + Machine" by H. Simmons  Connect with Helena Almeida:  LinkedIn  Twitter  Final Call: The future of HR is human. The future of AI is responsible. You have the power to shape both. Listen now — and be bold enough to lead with integrity, insight, and purpose. 

In this episode, we step back from our case study (covered in Episode 1 Parts One and Two) to address a key question: how does GenAI compare to technology-assisted review (TAR)? David Beck (Head of eDiscovery UK & EMEA), Meghan Ryan (Senior Manager, eDiscovery) and Danbee Kim (Head of Digital Legal, US) cut through the hype to explore how these technologies work in practice. They examine why TAR remains central to large-scale review - particularly for precision, consistency and defensibility - and where GenAI adds value, including contextual insight and early case analysis. Drawing on real-world experience, they show why GenAI is often reinforcing (not replacing) TAR, and reframe the debate around a more practical question: what is the right approach for the matter, the data and the client?

VLOG July 8: A.I. in the courts, Quinn Emanuel is counter-accused of hallucination https://www.patreon.com/MatthewRussellLee/posts/ai-in-courts-in-163188403 DA Bragg & NYS DiNapoli find fraud, but case late in WebCrims https://matthewrussellleeicp.substack.com/p/webcrims-woes-as-da-bragg-and-nys Coastal Bend bank scam https://innercitypress.com/mergers18bcoastalbendffw070726.html Keep UN away from AI

(0:00) About the Boardroom Governance Summit (Aug 26-27, 2026)  (0:55) Intro (2:44) About the podcast sponsor: The American College of Governance Counsel. (3:30) Start of interview.  (4:16) Origin story Marie Bafus (5:30) Origin story Wendy Grasso (7:34) Diving into their article AI in the Boardroom: What Directors Need to Know Now (4:14) Why AI Needs Board Oversight (12:00) Caremark and Oversight Duties (15:12) Mission-Critical Risk Cases. Reference to Marchand case (2019) and Boeing case (2021) (19:18) Where AI Belongs in Governance (board level and board committees) (21:28) Defining Mission-Critical AI (24:45) Strategy, Capital Allocation, and Judgment (29:50) Board Minutes as Litigation Evidence (33:52) Private Companies, Same Duties (38:35) AI Washing and Disclosure Risks (43:10) How Boards (and Board Members) Can Use AI (47:08) Hallucinations, Confidentiality, and Privilege. Reference to U.S. v Heppner case (2026) (52:03) Building an AI Usage Policy (53:36) Recording Boards with AI (note taking apps) (57:05) Workforce Trust and Environmental Risk (1:00:00) AI for Oversight Itself (1:02:02) AI's Impact on Legal Practice Marie Bafus is a partner in Fenwick's Securities Litigation Practice and Wendy Grasso is counsel in Fenwick's Corporate Practice. You can follow Evan on social media at:X: @evanepsteinLinkedIn: https://www.linkedin.com/in/epsteinevan/ Substack: https://evanepstein.substack.com/__To support this podcast you can join as a subscriber of the Boardroom Governance Newsletter at https://evanepstein.substack.com/__Music/Soundtrack (found via Free Music Archive): Seeing The Future by Dexter Britain is licensed under a Attribution-Noncommercial-Share Alike 3.0 United States License

Most runners think a backyard ultra is crazy.Lukas Janulitis looked at that format and decided it wasn't hard enough.Try a Last Skier Standing, an event where athletes skin uphill 1,200 feet every hour, ski back down, and repeat until only one person remains. That means battling sleep deprivation, sub-zero temperatures, brutal winds, hallucinations, and the mental challenge of not knowing when it will end.In this episode, Lukas shares his experiences at Last Skier Standing, Bubba's Backyard Ultra, Swiss Alps 100, and Cruel Jewel 100. We discuss sleep deprivation, nutrition, heat training, problem-solving during races, New Hampshire mountain culture, and his upcoming attempt at the New Hampshire Appalachian Trail FKT.Topics include:-Last Skier Standing-Backyard ultras and sleep deprivation-Swiss Alps 100-Cruel Jewel 100-Nutrition and race strategy-Training for multi-day endurance events-Hallucinations and mental resilience-The New Hampshire AT FKT-Why people keep pushing their limitsIf you've ever wondered what happens when someone decides to stay awake for days while skiing uphill, this conversation is for you.Support our Sponsors: Sawyer: https://sawyerdirect.net/Janji (code: Freeoutside): https://snp.link/a0bfb726CS Coffee: CSinstant.coffeeGarage Grown Gear: https://snp.link/db1ba8abSubscribe to Substack: http://freeoutside.substack.comSupport this content on patreon: HTTP://patreon.com/freeoutsideBuy my book "Free Outside" on Amazon: https://amzn.to/39LpoSFEmail me to buy a signed copy of my book, "Free Outside" at jeff@freeoutside.comWatch the movie about setting the record on the Colorado Trail: https://tubitv.com/movies/100019916/free-outsideWebsite: www.Freeoutside.comInstagram: thefreeoutsidefacebook: www.facebook.com/freeoutside#Trailrunning #Runningnews #Outdoors #Outdooradventure

Send us Fan MailIn case you missed it.....RAG didn't just survive the LLM boom — it became the backbone of how enterprises put AI to work. In this replay, we sit down with Douwe Kiela, the AI researcher who led the team that introduced Retrieval-Augmented Generation in 2020 and went on to co-found Contextual AI. Douwe unpacks whether RAG is here to stay, why most enterprise AI dies in the gap between a great demo and production, how to tame hallucinations, and what actually separates a real "agent" from the buzzword. A clear-eyed conversation on building AI that's grounded, useful, and safe to deploy.00:46 Introducing Douwe Kiela 01:37 RAG - Here to Stay or Go? 06:59 LLMs with Context 08:20 Making AI Successful 10:34 Why Contextual AI? 17:18 LLM versus SLMs 20:28 Speed over Perfection 22:07 Hallucinations 26:02 Making AI Easy to Consume 28:50 Defining an Agent 32:53 Reaching Contextual AI 33:14 The Contrarian View 34:37 The Risks of AI 36:53 For FunLinkedIn: linkedin.com/in/douwekiela Website: contextual.aiWant to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Send us Fan MailIn case you missed it.....RAG didn't just survive the LLM boom — it became the backbone of how enterprises put AI to work. In this replay, we sit down with Douwe Kiela, the AI researcher who led the team that introduced Retrieval-Augmented Generation in 2020 and went on to co-found Contextual AI. Douwe unpacks whether RAG is here to stay, why most enterprise AI dies in the gap between a great demo and production, how to tame hallucinations, and what actually separates a real "agent" from the buzzword. A clear-eyed conversation on building AI that's grounded, useful, and safe to deploy.00:46 Introducing Douwe Kiela 01:37 RAG - Here to Stay or Go? 06:59 LLMs with Context 08:20 Making AI Successful 10:34 Why Contextual AI? 17:18 LLM versus SLMs 20:28 Speed over Perfection 22:07 Hallucinations 26:02 Making AI Easy to Consume 28:50 Defining an Agent 32:53 Reaching Contextual AI 33:14 The Contrarian View 34:37 The Risks of AI 36:53 For FunLinkedIn: linkedin.com/in/douwekiela Website: contextual.aiWant to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Avec : Jean-Philippe Doux, journaliste et libraire. Pierre Rondeau, économiste. Et Emmanuelle Dancourt, journaliste indépendante de Dijon.. - Accompagnée de Charles Magnien et sa bande, Estelle Denis s'invite à la table des français pour traiter des sujets qui font leur quotidien. Société, conso, actualité, débats, coup de gueule, coups de cœurs… En simultané sur RMC Story.

Anil Seth has spent more than twenty-five years asking one of the most disorienting questions a scientist can ask: what is it that makes you conscious, and are you as real as you feel? A Professor of Cognitive and Computational Neuroscience at the University of Sussex and Director of the Sussex Centre for Consciousness Science, Seth has published more than 200 research papers and is recognized by Web of Science as being in the top 0.1% of researchers worldwide in his field. His 2017 TED talk, "Your brain hallucinates your conscious reality," has been viewed more than 14 million times, one of the most-watched science talks in TED history. His 2021 book, Being You: A New Science of Consciousness, became an instant Sunday Times bestseller and a Book of the Year for The Economist, The Guardian, The Financial Times, The New Statesman, and Bloomberg. In 2023, he was awarded the Royal Society Michael Faraday Prize for his extraordinary contribution to public engagement with science. In 2025, he won the Berggruen Prize Essay Competition for "The Mythology of Conscious AI." And in 2026, he delivered a new main-stage TED talk: "Why AI is unlikely to become conscious." His central argument is as simple as it is radical: we do not perceive the world as it actually is. Instead, the brain is a prediction machine, constantly generating its best guess about what's out there, using sensory signals only to correct its errors. What we experience as reality is a "controlled hallucination." And crucially, the self, the very sense of being a "you" behind your eyes, is part of that hallucination too. In this wide-ranging and mind-bending episode, Anil unpacks the ideas at the frontier of consciousness science, exploring: The controlled hallucination: why every perception you have, color, pain, the weight of your own body, is a construction of your brain, not a window onto objective reality, and what that means for how you move through the world The predictive brain: how your mind is not passively receiving information but constantly generating predictions, and why the experience of surprise is actually your brain updating its model of the world The "beast machine" theory of selfhood: why Anil believes that consciousness is not just about computation but is deeply grounded in the biological drive to stay alive, and why that matters for debates about AI The hard problem and the real problem: why philosophers have spent decades asking why there is subjective experience at all, and why Anil thinks we've been asking the wrong question Animal consciousness and the octopus: what creatures with radically different nervous systems reveal about the many possible ways of being conscious, and why this should expand our moral circle Can AI be conscious? Why Anil argues, against the dominant view in Silicon Valley, that large language models are almost certainly not conscious, what we'd actually need to look for, and why anthropomorphizing AI carries real societal risk The Dreamachine: how Anil led a groundbreaking science-art project that used stroboscopic light to induce visual hallucinations in more than 35,000 people, and what the largest ever citizen science study into perceptual diversity revealed about how differently each of us experiences the world What the neuroscience of consciousness means for medicine: from anaesthesia and disorders of consciousness to psychedelics and mental health, how understanding the brain's generative nature opens new clinical possibilities Free will, the self, and what's left: if the self is a controlled hallucination, does that mean we aren't really in control? And is that terrifying, or strangely liberating? This is a deeply searching and surprisingly personal conversation about the most intimate fact of human existence: the experience of being you. Learn more about Anil's work at anilseth.com, and find his book, Being You: A New Science of Consciousness, wherever books are sold. His TED talks are available HERE, and his 2025 Berggruen Prize essay, "The Mythology of Conscious AI," can be read at Noema Magazine.

In this session, we'll unpack how and why AI hallucinations occur in healthcare, why the risk is disproportionately high in this industry, and what it means for marketing, compliance, and digital strategy leaders. We'll explore how organizations can proactively identify where AI is getting their brand wrong by drawing from real-world experience and emerging tools including LLM monitoring systems designed to detect hallucinations and assess sentiment at the service-line level.

Saving The World From Soda: How ‘Big Soda' Has Made The World Sicker Would you believe that the soda industry's success is largely built on the manipulation of data and scientists? Our guest this week details all the behind-the-scenes scheming that created a billion-dollar industry. She discusses how Big Soda schemed their way into American homes by controlling how scientists studied nutrition, diets, and obesity. Guest: Susan Greenhalgh, author, Soda Science, John King & Wilma Cannon Fairbank Research Professor of Chinese Society Emerita, Harvard University   The Fungal Frontier: How Fungi Could Trigger A Zombie Pandemic Human bodies are normally too hot for dangerous fungi to survive, creating a natural shield that protects us from infection. However, rising global temperatures are forcing these microscopic organisms to adapt and survive in hotter environments. Our guest this week breaks down the terrifying reality of what could happen if these heat-resistant fungi evolve to conquer our immune systems and trigger a real-world pandemic. Guest: Dr. Arturo Casadevall, microbiologist, infectious disease expert, Chair of Molecular Microbiology & Immunology, Bloomberg Distinguished Professor, Johns Hopkins Bloomberg School of Public Health, author, What If Fungi Win? Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Fungal Frontier: How Fungi Could Trigger A Zombie Pandemic Human bodies are normally too hot for dangerous fungi to survive, creating a natural shield that protects us from infection. However, rising global temperatures are forcing these microscopic organisms to adapt and survive in hotter environments. Our guest this week breaks down the terrifying reality of what could happen if these heat-resistant fungi evolve to conquer our immune systems and trigger a real-world pandemic. Guest: Dr. Arturo Casadevall, microbiologist, infectious disease expert, Chair of Molecular Microbiology & Immunology, Bloomberg Distinguished Professor, Johns Hopkins Bloomberg School of Public Health, author, What If Fungi Win? Host: Greg Johnson Producer: Kristen Farrah Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this follow-on episode, Caoimhe Powell (Director, Disputes - Digital Legal Delivery) and Ariel Wiebe (Associate, Disputes) focus on one of the most critical aspects of applying GenAI in legal review: prompting. Building on a live High Court disclosure exercise, they explore how prompt design directly shapes the quality, consistency and defensibility of outcomes—framing prompting as a core legal skill grounded in judgment. The discussion highlights the iterative nature of prompting in practice, from testing and validation through to refinement at scale, and the importance of lawyer oversight in translating legal reasoning into clear, consistent criteria.

Partners Lyn Harris (Digital Legal Delivery) and Ajay Malhotra (Disputes) move beyond the hype to examine a live English High Court litigation matter, where Relativity aiR for Review was used to support first-level document review. They explore how the technology was applied in practice, the governance and human oversight underpinning defensibility, and the key lessons from deploying GenAI at scale in a high-stakes dispute.

https://youtu.be/b_G8krkwKv8 Ganesh Krishnan, CEO of AiHello, is helping Amazon sellers automate advertising, improve profitability, and scale their businesses using AI. Driven by a mission to give entrepreneurs more freedom and enable them to build businesses around products they love, Ganesh shares how AI can eliminate repetitive work while allowing business owners to focus on strategy, innovation, and growth. In this conversation, Ganesh introduces The AiHello Ads Framework: Tap into the Wisdom of Crowds, Find the Right Keywords, Bid at the Right Level, Dynamically Adjust Bids, and Rinse and Repeat. He explains how AI can leverage historical marketplace data to identify profitable keywords, optimize bids automatically, and continuously improve campaign performance. Ganesh also discusses the dangers of AI hallucinations, why Amazon's incentives differ from sellers' incentives, how AI has transformed his own company's operations, and his vision for building zero-hallucination AI systems capable of advancing toward artificial superintelligence. — Build AI Superintelligence with Ganesh Krishnan  Good day, dear listeners. Steve Preda here, and welcome Ganesh Krishnan, the CEO of AiHello, an Amazon Ads automation company helping you grow your revenues, reduce work hours spent on ads management, and decrease your ad costs. Welcome to the show, Ganesh.  Thank you, Steve. Nice to meet you  Well, it’s great to have you here, and let’s jump right in. And my first question is, what is your personal ‘Why,’ and how are you manifesting it in AiHello?  So it started off with my thesis that we all need to do good towards the planet. A long time ago, I started having my own natural things, selling chemical-free, ecological, sustainable, good-for-the-planet, good-for-your-wallet, good-for-your-health items, and I would sell organic items. And eventually, what I realized was that it was taking a lot of my time marketing, managing it, changing the bids, doing everything. I started working more and more on AI because I’ve worked in AI commercially. I worked in AI in my industry. That was my job. So I said, “Why not use, apply that to my own startup, to my own industry for selling organic things?” And once I started selling it, some of my friends reached out and said, “Can we use your AI for our own businesses?” And I said, “Sure, why not?”  And then I started opening it up. And then one person came through and said, “Okay, let’s release it to the general public, see how it goes.” And then as we started earning money, I realized that I don’t need to do a job. I can have this startup, and I can help different people have their own lifestyle. You could have your own lifestyle. You could sell your own stuff that you like, e-commerce, usually on Amazon, and then we help you have your lifestyle. So this is my personal ‘Why’, is we need more equality. We need more people doing stuff they love rather than doing stuff they hate to do, and they hate to wake up and go to work. So do what you love. We are here to empower you.  Wow, that’s amazing. So you are empowering people to start their own e-commerce businesses on Amazon, and you help them with AI tools to get up to speed and compete with the big boys.  That is correct.  Yeah. I love it. So on your LinkedIn profile, you mentioned that you are, I don’t know what the word was that you used, but something to do with superintelligence, AI superintelligence. So what is it that you are doing, and what is your vision of how AI superintelligence can be tapped into?  It’s a very long topic. But to start off with, we used the old form of AI, which is a lot of regression, a lot of statistics, a lot of big data learning, and a lot of neural networks, if you felt fancy. And then LLMs became a huge thing. And we launched AiHello probably six or seven years ago. LLMs became a big thing two or three years ago. And it was pretty fancy. It was very good. It made life easy for us. But we cannot use it within AiHello to give it to clients, primarily because LLMs start hallucinating once you go past a certain context. The problem with hallucination is that it exponentially becomes larger and larger. Because if the previous thesis is wrong, if your previous hypothesis is wrong, then it builds on top of it, and it builds the wrong things.  Hallucination exponentially becomes worse. And when it comes to finance, when it comes to ads, and when you’re working with sensitive data, this can be catastrophic. So you cannot use these large language models for finance, for situations where you need precise data, and especially when you have lots of context. It’s going to lose the context of the first part. Just because you mentioned something at the start of the conversation doesn’t mean it’s not important. It is critical. As humans, we understand what is the most critical part of a conversation, and then we keep that in mind. But LLMs, because of context limitations, just keep on going and start hallucinating.  So a few months ago, we came up with the idea that we could use something like a large language model, but not based on the transformer model. And we could base it on data so that there is almost zero hallucination. So instead of building weights, we build it based on data. And we launched this. We don’t use it on AiHello, but we decided to use it on an email service because we have a lot of emails. We process a lot of emails for clients. We process a lot of emails for specialists. So we could use the zero-hallucination approach within emails, and if it is successful, then we can put it into AiHello.  And we can, of course, release it as an API as well. So this is going to set the basis of artificial superintelligence because what is stopping us right now from reaching or breaching that wall of artificial superintelligence is this hallucination. And of course, there is also logic. LLMs are pretty stup*d. They don’t understand. You can teach them, they learn, but they do not question what you teach them. They always take it on blind faith.  Yeah. Wow. That is genius. I love it. You are going to un-hallucinate AI. And if it stops hallucinating, essentially it becomes a lot more powerful and scalable. AI becomes scalable, or this whole process becomes scalable. That’s fascinating. So your ‘Why’, your mission, is to empower all these people to run their businesses. Do you have a framework for this that you could describe in three to five steps? How do you get someone up and running with their own business on an e-commerce platform? Or do you have any other framework that you could share with the audience? Something simple that they may be able to benefit from? One of the caveats of using AI is that it needs a lot of data. So if you’re just starting out with your e-commerce business, you need to put more of your human intelligence, more of your gut instinct, more of your thoughts, and more of your emotions into building it out. And once you have built up enough data, then you can put it into AiHello and start automating it. So what I would say, if you’re starting an e-commerce business, is hire a specialist who can help you launch off the ground.  Do a bit of the hypothesis work, do a bit of the analysis, and then come to AiHello and start automating it. You can only start automating once you have a good idea of how things work for you. And finding how things work for you is something you need to do on your own. It’s like you can’t start running, or you can’t start driving a car, until you learn how to crawl and until you learn how to walk.  Okay. So basically, it’s the age-old innovation thing that you have to innovate something on your own, and then you can scale it with AI. That is correct.  Yeah. So let’s say I came up with some kind of formula, concept, or product that is currently not being promoted, and I believe it would work. Or maybe I’ve already tested it and I want to scale it. I want to get on Amazon and sell it there. What can you do for me? What are the steps for me to be successful with AiHello’s help? So the first thing when you select a product, is: what are the keywords for it? What keywords do you use for that product? The second would be: what are the bids for that product? For each keyword, what is the right bid to put up? And then you have other things like budgeting. Do you change the bid depending on the time of day? Do you change the bid in total? Those are the things that you need to keep adjusting continuously.  With AiHello, we automatically harvest the right keywords for your product. We change the bid. We optimize the bid. We also do dayparting, where you can change the bid depending on the time of day. So there are different things that you can use AI for. You could certainly do all of it manually, but it’ll probably take you days or weeks to do what AI can do in a couple of minutes.  So a couple of minutes. But doesn’t the AI also need traffic data to be able to define things?  Yeah. So one of the other things about AiHello is that, because we have the wisdom of crowds, if you come up with a keyword, we know exactly how that keyword is going to perform. As you say, you have the wisdom of crowds. Can you extrapolate what you’ve experienced with other products and other customers onto a new product that doesn’t yet have a lot of traffic? Is this what you mean by the wisdom of crowds? Or what do you mean by the wisdom of crowds?  Let me give you an example. Let’s assume you want to sell coffee, and you go to our platform and say, “This is my product. It’s coffee. Help me sell it.” So what we do is, we know this is coffee. What are the keywords around it that are going to help sell it? Because we’ve sold other coffee products, we know that organic coffee sells well. We know coffee in the morning sells well. Black coffee sells well. Caffeine sells well.  And we also know, based on the previous performance of other keywords, what a good bid is for each keyword. If you don’t know the keywords, then of course you have to spend time researching them. And if you don’t know the bids, then you have to spend time researching what bid to put in. But we do all the research for you, and you put it in. And the second part, the bigger part, is that if the bid doesn’t work out, if you’re not selling, then we increase the bid automatically. If you are losing money, then we decrease the bid automatically. So that bid optimization is a critical part of AiHello.  Yeah. We use Amazon ads to promote my books. And yes, it takes a lot of skill to find the keywords, eliminate the negative keywords, adjust the bids, have the right bids, and avoid overspending or underspending. But Amazon also does much of the machine learning. So what is it that Amazon does, and what is it that you have to do? And why doesn’t Amazon do what you have to do?  The most critical piece of information to keep in mind is that your aims and objectives are the opposite of Amazon’s aims and objectives. Amazon’s aim is to make money, and your job is to make money. You don’t care if Amazon makes money or not, and Amazon doesn’t care if you make money or not. So when you put up a bid, when you run ads, Amazon will maximize that ad spend, whatever it is. In some ways, it’s like a casino.  You go to a casino, and the job of the casino is to win money from you, and your job is to win money from the casino. Ads have become a lot like gambling nowadays. You throw money into it. You expect to make money. Ninety percent of people lose money, and they give up. And Amazon always finds fresh sellers to move on. You cannot depend on Amazon because Amazon is not on your side.  Yeah, that makes perfect sense. Yeah, I always thought that on some platforms it was really difficult to make money with ads. Facebook, I think, is so competitive that it’s probably very difficult to make money. I know a lot of people who have spent a lot of money on Facebook, but I don’t know very many who have figured out a formula that continues to work. Okay. So you’ve helped someone find their keywords, the right bids, and how to adjust those bids. But what we’ve found is that at some point, ads die, and then we have to switch things up. It actually happens quite frequently that you have to create new campaigns and new ads. So what’s the dynamic there? How do you optimize so that you’re not still supporting ads that don’t work anymore, and you switch at the right point?  So when we say ads, it’s not technically the campaigns. A campaign is just a container for all of your ads. You have products inside it, and you have keywords inside it. So a campaign is made up of products and keywords. And the question is, when you say ads die, did the keywords die? Then you need to add new keywords, right? You always have to keep adding new keywords and testing new keywords. It’s a continuous job of trying to find the right keywords for your book or your product, and then optimizing the bids constantly to make sure that you’re profitable.  You have to make sure that your ads don’t die because of a lack of fresh keywords. And of course, there’s always a limit to the number of keywords you can add because each product has a limited number of keywords that people are searching for. Maybe there’s a long-tail keyword that’s going to make money, but there’s not enough search volume. Or maybe there’s a high-volume search keyword, but it’s not profitable for you. So you have to figure out what the right strategy is for you. Eventually, if your product is good, you’ll make money. If your product is not good, you won’t make money. That’s the bottom line. With ads, you quickly find out if your product…  So essentially, it’s a cyclical thing. So you find the keywords, you figure out the right bids, you adjust the bids, and then you have to find new keywords and keep doing this.  Yeah.  So why do keywords go stale? Do people not search for certain things anymore?  There could be multiple reasons for it. One reason is that a competitor has come in and taken your search volume. And you have to know: are you losing search volume? Are you gaining search volume? Has your search volume dropped off? The second reason is that people are not searching for that keyword anymore. Is it out of fashion? The third is: are you underbidding? Is the bid too low? Again, you would know by the number of impressions. Have the impressions dropped off?  If the impressions have dropped off, is it because of a competitor? If it’s not because of a competitor, are people searching less? Are your bids too low? If the search volume is the same, are people clicking less? Why are they clicking less? Is it your images? Is it your product? Is your product no longer in fashion? I mean, I don’t know. Maybe a few months ago, fidget spinners were really in fashion, and nowadays no one uses them. So those things go out of fashion.  Yeah. The spinners, I remember. They’ve been out of fashion for a while.  Yeah.  Yeah, that’s fascinating. So it’s a never-ending cycle of innovation and figuring out what works and what doesn’t work. So let me ask you this: What drives growth in your business?  Most of the growth is… There are different ways to put it. Four years ago, we used to create a lot of blogs. We used to create lots of content. We used to create lots of YouTube videos. And then ChatGPT came along. If you ask kids now, “Do you Google that?” They don’t know what Google is. They really don’t know what Google is. And that’s not a cliché. It’s surprising. They’ll be like, “What Google?” Everything goes through ChatGPT.  So for us, growth went from Google to ChatGPT. And we didn’t spend enough time optimizing for LLMs on our site. So what drove growth before was blogs and YouTube. And what drives growth now is large language models like ChatGPT and Claude. People just ask ChatGPT, “What do I do about this on Amazon?” It recommends solutions, and then we go through them.  So how do you leverage large language models or AI applications?  This was one of the biggest boosts to our company. We managed to set the processes right. We managed to create the templates. We managed to bring structure to our company. Development work has become ten times faster. The turnaround is ten times faster. We’re able to release features quickly. We’re able to find bugs in our existing code quickly. There are a lot of things going on. If I were to say that our company is no longer the same company it was even a year ago, that would not be an exaggeration. It would be the truth. What we were a year ago is not at all what we are right now.  So in what way did you change? Is it coding that accelerated and changed everything? I mean, in what other ways did you change as a company?  So the code is all done with AI first. Our developers use AI. They put in the prompt, they check the results. There is a second developer who checks whether everything is okay and whether everything is done. And then finally there’s QA, and then we push it to staging. We used to do roughly one-month or forty-five-day sprints. Now we do weekly sprints. So it has gone four times faster. The biggest hurdle for us was managing clients and how we manage them. We never had any structure.  So we talked a lot with ChatGPT. We talked a lot about what the right way was to bring structure and accountability into the system. We managed to set up all the software required for accountability. It helped us fix those issues. It created structure. It created accountability for all the people, and then we implemented that. Finally, the last one, which was the most debatable, is that we require a lot of content. We require a lot of graphics. We require a lot of videos for clients on Amazon. I actually went to buy something on Amazon a few days back, and what was puzzling was that when I zoomed in on the images, you could see they were AI-generated because they all had these silly AI mistakes—spelling mistakes, random words.  So almost everything on Amazon right now, all the images, are kind of AI-generated. It’s hard to blame them. We ourselves use AI for a lot of the images. We make sure we don’t have the silly mistakes, but we do use AI as well. So the turnaround time for graphics is faster because of AI as well. Though some clients do complain that they don’t like AI-generated assets. And if a person looks a bit too AI-generated, they just reject it outright. So that is the most debatable part of it. But overall, our company is called AiHello. It’s AiHello. And if we don’t say hello to AI, then we’re not AiHello.  Yeah. Love it. I love the head and the one arm.  Yes.  The hello, and that’s it.  Yeah.  So what is one thing that you’re actively trying to figure out in your business right now? We are a remote-first company, and I’m struggling to bring about accountability among all the team members. We do have a good number of employees. Ninety percent of our employees are good. Ten percent still have accountability issues. And for me, that is a bit of a hurdle. It is a bit of a challenge to push those people who are dragging their feet about AI. Yeah. Because they are not comfortable with AI. They want to do what they are good at and don’t want to do something new.  There is also a bit of hesitation that they might lose their jobs because of AI, although we’re not planning to let go of anyone. Rather, we are hiring more people because we’re able to grow faster. There is an old saying that companies won’t go extinct because of AI, but companies that don’t use AI will go extinct because of AI. Because we are using AI a lot, there is a chance for us to scale, for us to expand significantly. And I want to tap into this advantage and grow. I want to hire more people, and I want to grow. I don’t want to let people go.  So this is a very good opportunity. You hear about Coinbase letting people go. You hear about Facebook letting people go because of AI. And I think those are all nonsensical excuses. Those companies are not growing very well, and they are blaming AI for letting people go, which I think is absolutely nonsensical. There is a very good opportunity for people to grow and for companies to grow using AI and increase their hiring. If you’re letting people go because of AI, it’s just a nonsensical excuse.  So what do you think is the mental hang-up for people? What prevents better AI adoption or faster AI adoption? A long time ago, when computers were being introduced into many industries, I remember there were huge protests because people thought computers would take away jobs. And it did happen. People did lose jobs because of computers. There were many people pushing papers who lost their jobs. And a lot of people refused to learn about computers because they said, “This is nonsensical. I can do it better by hand.” Can you imagine telling people right now that it’s better to do things by hand than to use a computer?  I mean, if you want to do calculations, please don’t use Excel or Google Sheets. Use a pen and paper and tell me you can do it better. It would be absurd to think that way. But at that time, people really did have the mentality that it was better to do things by hand than with Excel. Now, the AI revolution is probably a thousand or a million times bigger than that. And you can drag your feet. There will always be people who drag their feet and say, “I can do it better. AI is just nonsensical.” And sure, some of that is true. But the overwhelming majority of tasks are going to be done extremely well with AI.  And it’s not just large language models. It’s everything. Regression analysis, data analytics, big data analytics, forecasting, calculations. I’m not even talking about transformer models. I’m talking about everything related to AI. So much can be automated and done by AI that if you’re not involved with it, you’ll get left behind, just like the people who didn’t use computers. Do you feel like people have to be highly educated to be able to use AI? Or can people with less formal education benefit from it as well?  I don’t think it has anything to do with education. I think the learning curve for AI is smaller than the learning curve for computers. If you’re already using computers, you can just install a command-line interface and have things running. Actually, you can go to ChatGPT and ask some questions, and you can build something. But if you want to build serious applications, you can use a command-line interface and build them out. I think the learning curve is probably just a couple of hours to become proficient with these tools. I’m thinking more about this: As AI tools develop and take many of the routine, repeatable tasks off our shoulders, doesn’t that mean we will spend more of our time on high-level thinking and orchestration? And won’t that require some kind of mental ability to do that? It requires you to understand context, understand the implications of things, and be able to connect the dots. So that’s what I mean. The people who can really use AI tools have this higher level of awareness and thinking. They can combine ideas and create new things. But are there AI tools that people with less advanced analytical skills can also use? Absolutely. And you’re 100% right. You’re 101% right. This is what I’ve been advocating for a very long time. Don’t spend your time doing mundane, repetitive daily activities that can be automated. Let AI handle them. You should focus on the things AI cannot do right now, which is human-level intelligence: Strategizing. Planning. Working on the bigger-picture tasks. So you’re 100% right, and that’s the direction we should be moving in. And this brings me back to the point I made earlier: You should do what you love. The things you don’t love, the repetitive tasks, should be done by AI.  Yeah. Love it. So what is your vision, ultimately, for AiHello?  So my vision for AiHello goes beyond AiHello. We have something called HalZero, which is the engine we want to put behind AiHello. It’s a zero-hallucination LLM. And we are working toward making it happen. We plan to release an API for it soon. If it does happen, then we would probably have a model that can take in data and answer general-knowledge questions with zero hallucination. And we’re building it based on how the human brain works. The human brain is not one-dimensional. ChatGPT is one-dimensional. Transformer models are one-dimensional.  You give them data, they run it through the transformer model—the encoder and decoder—and then they give you an answer. But the human brain is built in layers. What we call the lizard brain sits at the base, and as you go higher, things become more and more complex. So the brain is information and action, and everything is filtered through it. Then we act on the filtered result. Machine learning models right now do not have these kinds of filters. They have something similar, which is called chain of thought, but that’s really thinking out loud. This kind of reasoning should exist within the latent space of the machine learning model. It should be built into the model itself.  I’ll give you an example. If you had been taught all your life that the sun is green, and tomorrow you woke up in Virginia, went outside, and saw that the sun was yellow, you’d say: “Oh my God, I’ve been lied to all my life. The sun isn’t green.” You would question what you had been taught based on a single observation. But if a machine had been trained for years that the sun is green, and then it saw that the sun was yellow, it might conclude: “The sun is wrong today because I’ve been taught that the sun is green.” The real test of intelligence is this: Can it question its training data? And the answer is no. It won’t, because it has been trained on that data. It has been trained on those tokens.  Yeah. So that’s AI superintelligence? The ability to question the training data?  That is correct. Yeah. So we build it based on connections. How strong is this connection? How many people have stated this fact? What is my own observation? Which observation is stronger? There is always conflict. In the human brain, there is always a conflict between what people say and what we think. Then our logical brain chooses what is usually the best answer. That is how we have a collective consciousness. We also have a personal consciousness. We always have to decide which one is best.  Love it. Well, that’s great. So if you’re running a business and you need to sell a product, and you want to figure out how to be successful on Amazon, how to leverage your ads, and how not to overspend, where should you go? How can people get in touch with you, Ganesh, and your team? And what’s the first step for listeners?  You can send me an email at ganesh@aihello.com. You can connect with me on LinkedIn. I’m always available, and I’m happy to have a chat with you.  All right. So if you’re listening out there and you’re in e-commerce, or you want to get into e-commerce, and you don’t know how to leverage all the tools that are out there, don’t forget: Amazon is in the business of making money, not necessarily making your business profitable. So you can use AiHello to help you. Reach out to Ganesh on LinkedIn and get your team involved. And if you enjoyed listening to this episode, make sure you check back every week because I have successful entrepreneurs sharing their ideas—or at least some of the good ones—with you. So thanks, Ganesh, for coming.  Thank you, Steve.  And thank you for listening. Important Links: Ganesh's LinkedIn Ganesh's website Ganesh's email: ganesh@aihello.com

פרק מספר 516 של רברס עם פלטפורמה - קרבורטור מספר 41. הפעם רן ואורי מארחים את נתי לשיחה על נקודת המפגש המרתקת שבין קוד פתוח לקידוד מבוסס סוכנים (Agentic Coding). דיברנו על העתיד הדיסטופי והאופטימי של מפתחי קוד פתוח, איך משווקים מוצרים ל-Agents, ולמה שורת הפקודה (CLI) חוזרת אלינו בענק. [01:04] העתיד המדומיין של AI (סיפורו של OpenClaw) נתי משתף סיפור משעשע על ניסיון לחקור את "OpenClaw". הזיות (Hallucinations) של מודלים: Claude מאשר את העובדות, בעוד ש-Gemini מנתח שמדובר בהמצאה עתידית (פברואר 2026). הבנה שמודלי שפה (LLMs) הם מנועים הסתברותיים ולא מנועי חיפוש עובדתיים. [05:58] החזון הדיסטופי: האם AI יהרוג את הקוד הפתוח? בעיית ההעתקה: בעבר קוד הוגן על ידי רישיונות (כמו AGPL), היום קל לבקש מהמודל לשכתב קוד משפה אחת לאחרת (למשל מ-NodeJS ל-Rust) בעלויות אפסיות. קריסת מודלים עסקיים: עלויות התמיכה והאופרציה (Operation) יורדות כי ה-Agent מתקן תקלות לבד, מה שחותך את ההכנסות של חברות כמו Red Hat. עומס על ה-Maintainers: קוד מג'ונרט על ידי Agents נראה מעולה ומתועד היטב, אבל לא תמיד נכון ארכיטקטונית או לוגית. גישות התמודדות: חלק דורשים לקבל את ה-Prompt (הכוונה) ולא את הקוד עצמו, בעוד שאחרים (כמו יוצר שפת Zig) אוסרים לחלוטין גישה של AI לפרויקט. [15:15] החזון האופטימי: שיווק לסוכנים (GEO) מעבר מ-SEO ל-GEO (Generative Engine Optimization): סוכני AI הם הלקוחות החדשים. איך Agent בוחר כלים? לפי איכות הקוד, הפופולריות שלו ב-GitHub, ובעיקר לפי התיעוד. קוד פתוח הופך לכלי שיווקי קריטי (Open Core) כדי שהסוכנים יוכלו למצוא, להבין ולהמליץ על המוצר. מודלים היברידיים ו-Freemium: מוצרים (כמו Postits) מציעים גישה ללא חומת תשלום (Paywall) בשלבים הראשונים, מה שמאפשר ל-Agents לעבוד איתם בקלות דרך API (Headless SaaS), ואפילו לבצע רכישות בעצמם בהמשך דרך Stripe. [30:29] שובו של ה-CLI ומגבלות ה-MCP הדיבייט סביב MCP (Model Context Protocol): הפרוטוקול כבד, "זולל" טוקנים (Token hungry) עבור הקונטקסט, ודורש תחזוקה של שרתים נוספים. למה Agents כל כך אוהבים CLI (שורת פקודה)? גישה ישירה לאקוסיסטם המקומי והרשאות (כמו Kubernetes או סביבות ענן) בלי לחשוף מפתחות לשירות חיצוני. יכולת לבצע מניפולציות מורכבות בצד הלקוח (Chaining, Grep, Sed) מבלי לשנות קוד ב-Backend, מה שהופך את המודלים לאנשי DevOps מעולים. [36:17] רישיונות קוד פתוח וה"נשמה" של המוצר האתגר באכיפת רישיונות (כמו GPL) בעולם שבו קשה להוכיח על איזה קוד המודל התאמן ואם בוצעה העתקה. הבדל חשוב בטרמינולוגיה: מודלים של "Open Weights" לעומת מודלים שה-Training Data שלהם באמת פתוח. תוכנה כיצירת אומנות מול קומודיטי (Commodity): האם קוד מג'ונרט יכול להחליף את החזון וה"נשמה" (Soul) של מפתחים בולטים? ההשוואה לעולם המוזיקה מדגישה שמשתמשים הולכים אחרי האומן והחזון, לא רק אחרי הקוד היבש. [50:25] רגולציה ומודלי Open Weights אורי מעלה נקודה מעניינת על החסימה של מודל Fable 5 / Mytos 5 (של Anthropic) למשתמשים מחוץ לארה"ב על ידי הממשל האמריקאי. ההשפעה של רגולציה: ה"תקרת זכוכית" הזו עלולה לפגוע בחברות המסחריות האמריקאיות בטווח הקצר, ודווקא לדחוף קדימה מודלים פתוחים (Open Weights) סיניים או אירופאים שאינם כפופים לאותן מגבלות. האזנה נעימה!

What happens when AI starts building the next generation of AI—and even its creators admit they don't know what comes next?This week, we explore a convergence of breakthroughs, billion-dollar bets, government oversight, and legal accountability that could reshape business faster than most leaders are prepared for. Anthropic's latest research suggests we're approaching an era where AI systems increasingly improve themselves, while governments are simultaneously looking for ways to slow, regulate, or gain visibility into the process. For business leaders, this isn't a future problem. It's a present-day strategic challenge. The organizations that understand how these forces connect—from AI capability acceleration to trillion-dollar capital markets and industry-wide disruption—will be far better positioned to navigate what's coming next.In this session, you'll discover: Why Anthropic believes recursive self-improvement may arrive sooner than most institutions are prepared for.  How AI is now generating the majority of code used to improve future AI systems.  What the latest AI performance gains mean for software development, research, and innovation.  Why OpenAI and Anthropic are pursuing trillion-dollar-scale IPOs.  How AI-driven consolidation could transform industries such as accounting.  The emerging government response to increasingly powerful frontier AI models.  Why policymakers are exploring new forms of oversight, ownership, and control of AI infrastructure.  The growing debate around legal liability when AI-generated mistakes create real-world consequences.  What business leaders should be watching over the next 12–24 months.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Andreas Rotenberg is Co-founder and COO of Pulley, an AI-powered permitting platform helping developers and operators move projects through approvals faster. Before Pulley, he was part of the team at Honest Buildings through its acquisition, then served as Chief of Staff at Procore through its IPO. Pulley has supported over $15 billion in projects approved across the U.S. Live from ICSC+Proptech in Las Vegas.(0:00) - First ever ICSC+Proptech live podcast(1:47) - Why Permitting Is a Growing Bottleneck(2:41) - What's Happening During Permitting Timelines(4:13) - Jurisdictional Complexity Across the U.S.(5:08) - What CRE Teams Underestimate About Permitting(7:35) - Why Pulley(8:18) - The Origin Story(10:53) - Combining Technology with Local Expertise(14:26) - Where AI Creates Real Value in Permitting(17:36) - Trust, Hallucinations & Accuracy(19:07) - Municipalities & Public Sector Modernization(20:40) - Second & Third Order Effects of Faster Permitting(22:41) - Collaboration Superpower: Vaclav Smil

AFB, Anna-G, Josh and Phred welcome back ultrarunner, hospice nurse, and Vermont mountain crusher Lila Gaudrault back to the Cultra Trail Running Podcast to break down her experience at the legendary Cocodona 250. Lila takes us deep into the Arizona suffering machine, explaining how she showed up to a 250-mile race with a surprisingly loose game plan, then spent the better part of the first half battling nausea, dehydration, and the reality that 250 miles is a very long way to travel on foot. We talk about sleep deprivation, hallucination-adjacent trail weirdness, crew and pacer support, and the problem-solving mindset required when you're three days into a race and still have mountains to climb. The conversation explores how Cocodona differs from 100-milers, why the atmosphere at 200+ mile races feels more collaborative than competitive, and what Lila learned about managing fatigue, recovery, and the physical toll of multi-day events. We also dive into the science of gender differences in ultrarunning, pacing strategies, and the unique culture that develops when everyone is equally exhausted. Along the way, we discuss * Cocodona 250 race recap * Sleep strategy and managing fatigue * Nausea, dehydration, and race-day troubleshooting * Crew and pacer support in 200+ mile races * Hallucinations and sleep deprivation * Gender dynamics in ultrarunning * Vermont 100 and Backyard Ultras * Balancing hospice nursing and elite ultrarunning * Future race plans and FKTs * The upcoming CUT112 fundraiser for Connecticut Forest & Parks A four-day journey through the Arizona desert, countless lessons learned, and proof that sometimes the best race plan is figuring it out one aid station at a time. Subscribe to Lil's Substack "Running too Much" Cocodona 250 Get your official Cultra Clothes and other Cultra TRP PodSwag at our store! Outro music by Nick Byram Become a Cultra Crew Patreon Supporter  basic licker.  If you lick us, we will most likely lick you right back Cultra Facebook Fan Page Go here to talk shit and complain and give us advice that we wont follow Cultra Trail Running Instagram Don't watch this with your kids Twitter @BlueBlazeRunner Buy Fred's Book Running Home More Information on the #CUT112   

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.Learn more about OVTLYR: https://youtu.be/TUCbD5KovlcThe SpaceX IPO is coming… and this may be the most bizarre valuation story in the market right now.In this breakdown, we react to a video explaining SpaceX “like you're hallucinating,” and honestly, that might be the only way this valuation starts to make sense. The company is reportedly coming public around a massive valuation, bigger than entire groups of major companies combined, and the official story is that the future growth justifies the price.But the deeper question is where that number actually came from. We dig into the idea that the IPO valuation may be tied less to normal financial modeling and more to Elon Musk's compensation targets, internal transactions, massive TAM assumptions, and accounting inputs that stretch far beyond what most public companies use.That's why this SpaceX IPO feels different. The valuation, the rule changes, the tiny float, the index fund implications, and the rush to get it public all keep pointing back to the same warning: retail investors may be buying a story before there is any real trend to trade.✅ SpaceX IPO valuation and $1.75 trillion hype✅ Revenue multiples, TAM assumptions, and Elon compensation targets✅ XAI roll-up, stock-based compensation, and 30-year option assumptions✅ Why IPO price action needs time to prove itself✅ OVTLYR trading discipline, trend signals, and avoiding FOMOIf you're thinking about buying SpaceX the second it goes public, this one gives you another reason to slow down, step back, and let the stock prove itself first.Subscribe to OVTLYR for disciplined trading strategies that actually make sense.

Jim Love covers four headlines: hackers exploited Instagram's AI support bot to hijack over 20,000 accounts by abusing account recovery and password reset links, prompting Meta to disable the tool, remove faulty code, and add enhanced protections. A UN University report warns AI's environmental footprint extends beyond carbon, projecting data centers could consume 945 TWh annually by 2030 and highlighting growing demands for electricity, cooling water, land, and minerals, amid political backlash to data center incentives. A UK government review found false information from a Microsoft Copilot hallucination and other inaccuracies were included in West Midlands Police materials, pointing to failures in review and validation. CBC News also identified at least 14 foreign-linked Facebook accounts posing as Albertans in separatist groups, raising concerns about deceptive political participation and platform responsibility. 00:00 Today's Tech Headlines 00:36 Instagram Bot Account Hijacks 02:03 AI Agents Security Lessons 03:12 UN Report AI Resource Footprint 05:38 Copilot Hallucination Police Report 08:11 Fake Albertans in Facebook Groups 11:04 Wrap Up and Support the Show

AI can generate an answer in seconds. The harder question is whether it is the right answer to the right question, and what you actually do with it.In this episode, Kate Megaw, Anu Smalley, and Ryan Smith dig into what “human in the loop” really means, and why so many AI transformations are failing. Forbes puts enterprise generative AI failure near 95%, and RAND says more than 80% of AI projects miss. The pattern echoes the early Agile years: chasing a shiny tool without knowing what problem it solves.AI sees the data. Humans see the story behind it. The human brings context, ethics, and judgment, and stays the ethical guardian who catches the hallucination and the answer that is right for the wrong reasons.In this episode, we discuss:The human algorithm - turning AI outputs into real outcomes through context, ethics, and judgmentWhy AI sees the data but only humans see the story behind itAnu's five workflow principles for human-led AI, including protecting the retro and naming a human decision owner for every recommendationWhy so many AI transformations fail, and how it mirrors the early Agile yearsAI-enabled vs. AI-native organizations, and why native winsUsing AI as a tool versus trusting it to run the businessChoosing the right tool for the job instead of defaulting to one model for everythingThe ethical guardian role - catching not just what AI gets wrong, but what it gets right for the wrong reasonsKnowing when to trust AI, when to challenge it, and when to override it Just because AI can do something does not mean it should. That is where humans come in. We are not using AI to replace thinking. We are creating more space for higher quality thinking for the human in the loop.Referenced in this episode: the documentary How I Became an Apocalyptimist (Daniel Rohrer), the Conan O'Brien podcast on how tools change but the task doesn't, the New York Times feature on Box adding AI roles, and the AI-native shift discussed at the Miro Canvas conference.

We're now three weeks from the excitement of Vienna and crowning a new winner, so now what the heck do we do? Well, one of our favorite guests, the President of OGAE Ireland, Frank returns with some advice and to discuss Amsterdam's Het Grote Songfestivalfeest, aka, The Big Eurovision Party. Both Dimitry and Frank attended last year's celebration, so if you're needing a Eurovision fix, we've got you covered. Jeremy's dreaming of both heaven & earth, Dimitry asks where are you of certain acts in the broadcast, and Frank discusses his new religion.  Watch the NRK broadcast of Het Grote Songfestivalfeest 2025 here: https://drive.google.com/file/d/1t95XTMX2JGMIZQhXrpdK88Qkm5LkOfGW/view Watch the Dutch broadcast of Het Grote Songfestivalfeest 2025 here: https://npo.nl/start/serie/het-grote-songfestivalfeest/afleveringen/seizoen-4_1 Fill out the EBU's Eurofan Voice 2026 survey and let them know what you're thinking: https://survey.alchemer.eu/s3/91094056/Eurofan-Voice-2026 Vote on which themed playlists we should add to our Spotify account: https://docs.google.com/forms/d/1LdUKjw9GdNMfZUp1AKVY6lQ5QCeL2wZySp2ZbOsWicQ/edit This week's companion playlist: https://open.spotify.com/playlist/0GZkyJYPQmgmTzshgvsESt Help support this show and unlock bonus content! Become a member at https://maximumfun.org/joineurovangelistsEurovangelists is an American Eurovision podcast, made in the US for Eurovision fans worldwide. The Eurovangelists are Jeremy Bent, Oscar Montoya and Dimitry Pompée.The theme was arranged and recorded by Cody McCorry and Faye Fadem, and the logo was designed by Tom Deja.Production support for this show was provided by the Maximum Fun network.The show is edited by Jeremy Bent with audio mixing help was courtesy of Shane O'Connell.Find Eurovangelists on social media as @eurovangelists on Instagram and @eurovangelists.com on Bluesky, or send us an email at eurovangelists@gmail.com. Head to https://maxfunstore.com/collections/eurovangelists for Eurovangelists merch. Also follow the Eurovangelists account on Spotify and check out our playlists of Eurovision hits, competitors in upcoming national finals, and companion playlists to every single episode, including this one!

What if some of the most promising tools for treating depression, PTSD, and trauma have been misunderstood for decades? In this episode, I sit down with Dr. Keith Kurlander and Dr. Will Van Derveer, co-founders of the Integrative Psychiatry Institute and authors of Psychedelic Therapy, to unpack the science, risks, and potential of psychedelic-assisted therapy. We discuss MDMA, psilocybin, ketamine, trauma, healing, and why these treatments are gaining so much attention in modern mental healthcare.  → ⁠Leave Us A Voice Message!  Topics Discussed: → What is psychedelic-assisted therapy? → Can MDMA help treat PTSD? → How does ketamine therapy work? → Is psilocybin effective for depression? → What are the risks of psychedelics? Sponsored By:  → Timeline | Timeline's clinically proven formula is now more accessible. Mitopure starts at $99, and listeners can get 20% off at: https://timeline.com/KELLY → Be Well By Kelly Protein Powder & Essentials | Get $10 off your order with PODCAST10 at https://bewellbykelly.com. → Fatty 15 | Fatty15 is on a mission to replenish your C15 levels and restore your long-term health. You can get an additional 15% off their 90-day subscription Starter Kit by going to https://fatty15.com/KELLY15 and using code KELLY15 at checkout. Timestamps:  → 00:00:00 - Introduction  → 00:04:25 - From Traditional Psychiatry To Psychedelic Medicine → 00:06:20 - Root Causes Of Mental Health Conditions → 00:07:20 - MDMA Therapy For PTSD → 00:10:20 - Keith's Personal Psilocybin Experience → 00:15:40 - Why Psychedelic Experiences Can Feel Scary → 00:19:00 - Kelly's Personal Trauma Healing Story → 00:24:00 - MDMA, Ketamine & Psilocybin Explained → 00:25:40 - Ketamine Therapy For Depression → 00:27:00 - Why MDMA Works For Trauma → 00:31:40 - Lifestyle, Nutrition & Mental Health → 00:34:30 - Who Is A Good Candidate For Psychedelic Therapy? → 00:39:30 - What Trauma Actually Is → 00:42:10 - How Psychedelics Help Process Trauma → 00:47:50 - The Latest Psychedelic Research → 00:49:50 - Ibogaine, Addiction & Brain Injury Recovery → 00:51:10 - Mystical Experiences & Healing → 00:55:20 - Psychedelics For Personal Growth → 01:00:30 - Hallucinations, Memory & Reality → 01:04:40 - Risks, Integration & Challenging Experiences → 01:09:20 - Finding A Qualified Psychedelic Therapist → 01:12:30 - Psychedelics vs Antidepressants → 01:14:50 - Why DIY Psychedelics Can Be Dangerous → 01:18:30 - Final Thoughts Further Listening:  → Why Achievement Never Feels Like Enough | Bill Burnett + Dave Evans Check Out: → Keith Kurlander | https://www.instagram.com/keithkurlander.ma/ → Will Van Derveer | https://www.instagram.com/will.vanderveer.md/ Check Out Kelly: → ⁠Instagram⁠ → ⁠Youtube⁠ → ⁠Facebook

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This week, we're joined by writer, academic and creator John Duncan to talk about the effects Large Language Models are having on academic writing and research. John talks about the growing number of AI hallucinations that are appearing in academic papers and articles and what it reveals about the poor working and pay conditions of academics in the UK and around the world. John also talks about the dangers this poses to future research and knowledge production, which might be bad if we ever face a public health crisis again. We also talk about the Pope's Encyclical on the AI industry, why it's less radical or revolutionary than has been reported, and why any notion of ‘ethical AI' should be disregarded.  Subscribe to John's channel: https://www.youtube.com/@JohntheDuncan support John on Patreon! : https://www.patreon.com/johntheduncan ------- PALESTINE  AID LINKS -You can donate to Medical Aid for Palestinians and other charities using the links below. https://www.map.org.uk/donate/donate https://www.savethechildren.org.uk/how-you-can-help/emergencies/gaza-israel-conflict -Palestinian Communist Youth Union, which is doing a food and water effort, and is part of the official communist party of Palestine https://www.gofundme.com/f/to-preserve-whats-left-of-humanity-global-solidarity -Water is Life, a water distribution project in North Gaza affiliated with an Indigenous American organization and the Freedom Flotilla https://www.waterislifegaza.org/ -Vegetable Distribution Fund, which secured and delivers fresh veg, affiliated with Freedom Flotilla also https://www.instagram.com/linking/fundraiser?fundraiser_id=1102739514947848 -Thamra, which distributes herb and veg seedlings, repairs and maintains water infrastructure, and distributes food made with replanted veg patches https://www.gofundme.com/f/support-thamra-cultivating-resilience-in-gaza -------- PHOEBE ALERT Okay, now that we have your attention; check out her Substack Here! Check out Masters of our Domain with Milo and Patrick, here! -------- Ten Thousand Posts is a show about how everything is posting. It's hosted by Hussein (@HKesvani), Phoebe (@PRHRoy) and produced by Devon (@Devon_onEarth).

**Sponsored by EasyDNS** Move your domain or web hosting to EasyDNS and support Not On Record: https://easydns.com/NotOnRecord Use promo code: **notonrecord** In Episode 214 of *Not On Record*, criminal defence lawyer Joseph Neuberger and Diana Davison examine the important Ontario Court of Appeal decision R. v. C.P., 2026 ONCA 333 and discuss how mental illness can properly factor into assessing witness reliability and credibility in criminal trials. The case involved allegations of sexual assault against a biological father and raised complex questions about a complainant who had a documented history of hallucinations, delusions, medication non-compliance, and street drug use during the period of the alleged offences. The Court of Appeal was asked to determine whether the trial judge improperly relied on myths and stereotypes about mental illness when acquitting the accused. Joseph and Diana explain the critical legal distinction between credibility and reliability, why mental illness alone cannot be used to discount a witness's evidence, and when case-specific evidence of hallucinations, delusions, panic attacks, psychiatric symptoms, or medication issues may legitimately become relevant at trial. They also discuss third-party psychiatric records applications, the evidentiary foundation required to raise mental health issues in court, and why judges must carefully avoid discriminatory reasoning while still assessing reliability based on evidence. This episode provides valuable guidance for criminal lawyers, law students, and anyone interested in how Canadian courts balance fairness, mental health considerations, and the search for truth in the justice system. ### **Chapters** **00:00** Introduction to R. v. C.P. (2026 ONCA 333) **02:19** Mental illness, credibility, and reliability explained **04:21** Hallucinations, delusions, medication, and street drug use **07:10** Crown appeal and myths about mental illness **10:13** Evidence supporting reliability concerns **14:29** Accessing psychiatric and therapy records in criminal cases **16:11** Why the Court of Appeal upheld the acquittal **21:34** Lessons for lawyers handling mental health evidence

This Day in Legal History: The First Act of CongressOn this day in 1789, President George Washington signed the first statute ever enacted by Congress under the new Constitution — “An Act to Regulate the Time and Manner of Administering Certain Oaths,” codified at 1 Stat. 23. The substance was modest: the law prescribed the form of the oath that members of Congress, federal judges, and executive officers were to take to support the Constitution, and gave the states a window in which to swear in their own officials. But the symbolism was enormous. It was the first time the new federal government did the thing governments actually do, which is to pass a law and require people to obey it, and the choice of subject was telling.Before Congress regulated commerce, levied taxes, or built courts, it bound its own officers to the Constitution by oath. The oath clauses in Article II and Article VI have been doing quiet doctrinal work ever since: they ground the Supremacy Clause, they undergird Marbury's claim that judges are bound to follow the Constitution as supreme law, and they sit at the center of the Fourteenth Amendment, Section 3 disqualification debate that the Supreme Court took up in Trump v. Anderson just two years ago. The Oath Act of 1789 is not the kind of statute that gets quoted on bar exams, but it is the original instance of Congress speaking in legal form, and everything the federal government has done since rests on top of it.Uber went after one of its own bellwether plaintiffs Friday in the sprawling multidistrict litigation over alleged passenger sexual assaults, asking U.S. Magistrate Judge Lisa J. Cisneros in the Northern District of California to impose sanctions on plaintiff B.L. and her counsel at Wagstaff Law Firm for what Uber called “pervasive bad faith” in discovery.The headline accusation, made by Kirkland & Ellis's Michael Vives for Uber, is that B.L.'s privilege log cites cases that don't exist — what Vives suggested may be “hallucinated case law” generated by an AI tool — and Vives floated that as an independent basis for sanctions on top of the alleged document withholding, redactions, and undisclosed witnesses Uber catalogued in its April motion.he legal vehicle here is Federal Rule of Civil Procedure 37, which gives a federal court a tiered menu of sanctions for discovery misconduct — fees and costs at the low end, adverse-inference instructions and claim preclusion at the high end — and Uber is asking the court to throw B.L.'s case out of the next bellwether wave entirely. Judge Cisneros noticed during the hearing that what struck her about the briefing was the pattern, not any single incident; she pointed to one example where the plaintiff identified a person as a “friend” and only later produced a fuller set of text messages showing the person was actually a therapist.The judge ordered the plaintiff to file a sur-reply by Thursday before ruling, which means a sanctions order is now teed up. The case sits within In re Uber Technologies, Inc., Passenger Sexual Assault Litigation (MDL No. 3084) before Judge Charles R. Breyer, and any sanctions ruling will set the tone for how the rest of the bellwether pool conducts discovery. If the hallucinated-caselaw piece sticks, this also becomes one of the first real Rule 11 / Rule 37 hybrid sanctions vehicles for generative AI misuse in the MDL context — and the bar will be reading it closely.‘Pervasive Bad Faith': Uber Targets Sex Assault MDL Plaintiff | Law360The Seventh Circuit on Friday told the Northern District of Illinois that the now-standard practice of serving Chinese e-commerce defendants by email in “Schedule A” trademark cases doesn't fly under the Hague Service Convention — at least not when the convention applies, which is a question the district court has to actually answer first. The dispute came up in Kangol LLC v. Hangzhou Chuanyue Silk Import & Export Co., No. 25-2205, where the hat-maker Kangol sued more than twenty Chinese vendors for trademark infringement and identified them on a sealed “Schedule A” exhibit attached to the complaint — the same procedural pattern that drives the enormous Schedule A docket in Chicago's federal court.Kangol got a default judgment after serving the defendants by email, but one defendant, Hangzhou Chuanyue, appeared and moved to vacate, arguing that the Hague Convention prohibits email service in China and that the convention applies because Hangzhou's address is discoverable. The legal hook is Article 10(a) of the Hague Service Convention, which permits service “by postal channels” only when the destination state has not objected — and China has affirmatively objected to Article 10(a), full stop.The Seventh Circuit, citing the Supreme Court's 2017 decision in Water Splash, Inc. v. Menon, held that whether or not email counts as a “postal channel,” Article 10(a) is unavailable in China, so email service in this case was improper if the convention applied at all. The panel — Judges Thomas Kirsch, Candace Jackson-Akiwumi, and Doris Pryor — reversed the denial of Hangzhou's motion to vacate and sent the case back for the threshold question the district court skipped: did Kangol make reasonably diligent efforts to find Hangzhou's address, which would have triggered the convention.The practical fallout will reach hundreds, possibly thousands, of pending Schedule A cases in Chicago that rely on email service as a matter of course, and plaintiff firms in this space will be scrambling to redo their service strategy.7th Circ. Revives Chinese IP Defendants' Email Service Case | Law360The Judicial Panel on Multidistrict Litigation on Thursday transferred Randall King's proposed class action — the vehicle for a proposed $7.25 billion Roundup settlement with Monsanto — into the Northern District of California MDL before Judge Vince Chhabria, despite vehement objections from absent class members who want the case to stay in Missouri state court.The case-within-a-case is unusual: the King action was filed and preliminarily settled in Missouri state court, then a group of objectors (represented by Keller Postman) removed it to federal court under the Class Action Fairness Act, and the JPML then tagged it for transfer to the consolidated Roundup MDL. The legal hook here is 28 U.S.C. § 1407, the JPML's transfer authority — paired with CAFA's removal rules, which the settling plaintiffs argue were misused because the objectors aren't “defendants” within the meaning of § 1453 and so cannot remove.The objectors counter that the $7.25 billion deal “launders a liability-management scheme through the courts” by funneling claims of Roundup cancer victims through a Missouri state-court class that an MDL judge would never approve, and they want federal-court scrutiny under Rule 23 and the standards Judge Chhabria has spent years developing in the Roundup litigation. Monsanto, for its part, is on the objectors' side of the venue question — at least tactically — telling Law360 that the case should go back to Missouri state court and it will move to oppose the transfer order.The whole fight is also tied up with the Supreme Court's pending decision in a separate Monsanto case that will determine whether the deal survives at all, because the proposed $7.25 billion is structured around what the Court does there. Whichever way this remand/transfer fight comes out, it is going to be cited in every future class-settlement-jurisdiction tug-of-war for the rest of the decade.$7.25B Roundup Deal Sent To Calif. MDL | Law360A U.S. district judge in Florida said Saturday she will take a closer look at the settlement the Trump administration has reached with itself — or more precisely, with President Trump in his personal capacity — over a long-running IRS lawsuit, scheduling further proceedings to examine whether the deal can stand.The procedural posture is what makes this one interesting: the case involves a federal agency under the President's control settling claims with the President personally, which raises immediate questions about whether anyone is actually adverse to anyone, and whether the resulting consent decree or stipulation can carry the legal weight a normal settlement does. The legal mechanism the judge appears to be invoking is the federal court's inherent supervisory authority over consent decrees and settlements involving the federal government, an authority that runs through cases like Local No. 93 v. City of Cleveland and that the Tunney Act formalizes for antitrust settlements — though here there is no Tunney Act, just the general principle that a federal court doesn't have to rubber-stamp a settlement when there are serious questions about whether the United States was actually represented in the negotiation.The hearing on the issue was set for late May in Miami, with the judge reportedly skeptical that the deal can be approved without further factual development. The political stakes are obvious, but the legal stakes are arguably bigger: if the court can refuse to approve the settlement on the ground that the executive branch was not adverse to itself in any meaningful way, it would create a precedent that constrains every future administration's ability to make its own personal litigation go away through agency action. Expect this one to generate appellate motion practice within weeks.US judge orders review of Trump's IRS lawsuit settlement | Reuters This is a public episode. 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Shipping blablabla with a fantasy story of hallucination

This candid interview is a great listen for anyone feeling lonely or unsure of where their life is heading or wants to learn to love & value themself. The best-selling Gen X author tells Lorraine & Trish about her remarkable midlife awakening, detailed in her new memoir, Rewilding: Freedom, Fearlessness & Finding Our Way Home. In a powerful conversation she explains the decision to leave her husband, her New York home & the life she'd built over 25 years to move to Marrakech after her career hit a downward spiral. She reveals how she loves living alone & starting again financially. After asking herself the question ‘who would I be if I didn't care what anyone else thought?' Jane goes on a ‘rewilding' journey. Along the way she rediscovers sex & dating, learns to live alone without being lonely & becomes her true authentic self. Plus: Will Lorraine's pelvic floor hold up with her new bouncy exercise regime!!Contact: hello@postcardsfrommidlife.comInstagram: @postcardsfrommidlifeJoin our private Facebook Group here Hosted on Acast. See acast.com/privacy for more information.

Are we prepared for the massive socio-economic divide of the looming quantum computing era?In this deep-dive episode of The Edge of Show, sponsored by Datavault AI, we welcomed Nathaniel Bradley, CEO and co-founder of Datavault AI. A prolific inventor holding over 70 patents , Bradley unpacks the shift from binary computing to quantum light computing, and what it means for human talent, data sovereignty, and security.Discover how Datavault AI is building the ultimate "toll booth" for digital assets. And how they outline their agnostic blockchain framework, which allows corporations to manage, evaluate, and monetize data using NASDAQ-backed systems. Also discover a groundbreaking perspective on robotics: introducing high-definition audio and wireless interoperability to give robots a universal communication layer.If you want to know how blockchain, AI, and quantum keys are turning data from a cost center into a massive revenue generator, this episode is a must-watch.Support us through our Sponsors! ☕ Want to make content like ours? Sign up with Castmagic to make your creative process easy: https://bit.ly/CastmagicReferral Work smarter, grow faster. Automate your SEO, get AI insights, and manage all your clients in one place with Helm. Start today 50% off your first month at helmseo.com

Lauren Jones.Lauren balances a demanding career as a pediatric nurse anesthetist, family life, and somehow still finds time to chase some of the hardest endurance goals imaginable. Multi-day races. Cocodona. Fixed time racing. Running over 150 miles in 24 hours. Team USA. Hallucinations. DNFs. Successes. Failures.We talk about balancing life with training, learning how to fail without letting it define you, why hard things can make other hard things easier, and how radical acceptance of failure can unlock growth.Lauren shares stories from Cocodona, fixed time racing, hallucinating deep into ultramarathons, running 154 miles in a day, and what keeps bringing her back to difficult challenges.We also talk about getting back into running after Cocodona, Memorial Day reflections, honoring sacrifice, and why maybe the point isn't perfection. Maybe the point is showing up anyway.Topics:• Cocodona 250• Fixed time racing and Desert Solstice• Running 154 miles in 24 hours• Team USA experience• DNFs and learning from failure• Hallucinations and sleep deprivation• Balancing career, family, and training• Mental strategies for ultrarunning• Why we keep choosing hard thingsThanks to Janji, Garage Grown Gear, Northeast Trail Adventures, and Montana Meltdown for supporting the show.Support our Sponsors: Sawyer: https://sawyerdirect.net/Janji (code: Freeoutside): https://snp.link/a0bfb726CS Coffee: CSinstant.coffeeGarage Grown Gear: https://snp.link/db1ba8abSubscribe to Substack: http://freeoutside.substack.comSupport this content on patreon: HTTP://patreon.com/freeoutsideBuy my book "Free Outside" on Amazon: https://amzn.to/39LpoSFEmail me to buy a signed copy of my book, "Free Outside" at jeff@freeoutside.comWatch the movie about setting the record on the Colorado Trail: https://tubitv.com/movies/100019916/free-outsideWebsite: www.Freeoutside.comInstagram: thefreeoutsidefacebook: www.facebook.com/freeoutside#Trailrunning #Runningnews #Outdoors #Outdooradventure

This week, Dave and Ben sit down to discuss two legal cases. The first case involves Santa Clara suing Meta over alleged scam ads. The second story looks at a now dismissed case where the lawyers could potentially face consequences for allegedly using fake AI citations in their filings. While this show covers legal topics, and Ben is a lawyer, the views expressed do not constitute legal advice. For official legal advice on any of the topics we cover, please contact your attorney.  Links to today's stories: ⁠Santa Clara County sues Meta over alleged scam ads. ⁠Legal fail: Don't use AI to sue Facebook users for calling you a bad date. Get the weekly Caveat Briefing delivered to your inbox. Like what you heard? Be sure to check out and subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Caveat Briefing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a weekly newsletter available exclusively to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠N2K Pro⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ members on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠N2K CyberWire's⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ website. N2K Pro members receive our Thursday wrap-up covering the latest in privacy, policy, and research news, including incidents, techniques, compliance, trends, and more. This week's Caveat Briefing revisits the Anthropic-White House feud and how the DC lawsuit could offer a potential resolution to the situation. Curious about the details? Head over to the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Caveat Briefing⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for the full scoop and additional compelling stories. Got a question you'd like us to answer on our show? You can send your audio file to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠caveat@thecyberwire.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Hope to hear from you. Learn more about your ad choices. Visit megaphone.fm/adchoices

Most AI conversations focus on models. The better conversation focuses on systems. In this episode, we continue our interview with Matt Levenhagen, exploring a practical challenge many developers are facing: integrating AI into business operations without creating costly chaos. The answer is not buying more AI tools. The answer is building an intentional AI Workflow Architecture. About Matt Levenhagen Matt is the founder and CEO of Unified Web Design, a web development agency focused on custom solutions, WordPress development, e-commerce, memberships, and business systems. His background as both a builder and agency owner gave him a unique perspective on where AI creates real leverage instead of superficial automation. Follow Matt on LinkedIn. AI Workflow Architecture Starts with Context Control One of the most important operational realities Matt discussed was token usage. Businesses rushing into AI often underestimate cost scaling. Every interaction with large models consumes resources, and poorly managed context windows dramatically increase operational expenses. Instead of treating AI like unlimited compute, Matt focused on controlling context intentionally. That included: Monitoring token usage Limiting unnecessary memory loading Structuring retrieval systems Using different models for different tasks Preventing oversized prompts This is a systems-thinking problem, not merely a coding problem. Developers who ignore architecture end up with bloated workflows that become financially unsustainable. The fastest way to make AI unprofitable is to send unnecessary context into every request. Why Retrieval Matters More Than Raw Memory A major breakthrough Matt discussed was implementing Retrieval-Augmented Generation (RAG). This matters because AI systems do not need all the information all the time. They need the right information at the right moment. That distinction completely changes system design. Without retrieval architecture: Costs increase Performance slows Outputs become less accurate Hallucinations increase Operational complexity grows RAG allows systems to retrieve semantically relevant information instead of dumping entire databases into prompts. This transforms AI from brute-force processing into intelligent retrieval. The future of AI operations will likely depend less on giant models and more on efficient information orchestration. AI Workflow Architecture Requires Layer Separation Another valuable concept from the conversation involved separating operational layers. Matt described balancing: Local storage Business memory External AI APIs Workflow automation SaaS integrations This layered architecture creates flexibility. Instead of locking the business into one AI provider, workflows remain adaptable. Different models can handle different workloads depending on cost, complexity, and accuracy requirements. This becomes increasingly important as pricing models fluctuate. Businesses relying entirely on one provider risk operational instability if pricing changes dramatically. Layer separation reduces that risk. The businesses that survive AI cost volatility will be the ones architected for flexibility instead of dependency. Why Embedded AI Features Often Disappoint Matt also discussed the growing wave of SaaS AI integrations. Every platform now markets AI capabilities: Project management tools Communication platforms CRM systems Design software Documentation systems Yet many users feel underwhelmed. The reason is architectural isolation. These tools only understand limited slices of operational context. They automate micro-tasks but rarely improve larger workflows. That creates a false impression that AI itself lacks value when the real issue is fragmented systems. AI becomes more useful as the organizational context becomes more connected. This is why developers building custom operational layers still maintain an enormous strategic advantage. AI Workflow Architecture Is an Operational Discipline The strongest insight from these episodes may be that AI implementation is becoming operational engineering. Success now depends on: Information structure Retrieval design Workflow sequencing Context prioritization Cost management Human oversight This moves AI away from novelty experimentation and toward infrastructure planning. Businesses that treat AI casually will likely accumulate technical debt quickly. Businesses that approach AI architecturally will build scalable operational leverage. AI is no longer just a development tool. It is becoming an operational systems discipline. Developers Must Learn Economic Thinking One overlooked topic in AI discussions is economics. Matt repeatedly referenced balancing capability with cost. This becomes critical because AI pricing models are still evolving rapidly. Businesses that ignore usage economics may accidentally build systems that become financially impossible to scale. Developers now need to think beyond: Can this be built? They also need to ask: Can this be sustained? Can this scale economically? Can context costs remain controlled? Can cheaper models handle simpler tasks? This represents a major evolution in modern software architecture. Review your current AI workflows and identify where unnecessary context or oversized prompts may be increasing costs. Conclusion AI Workflow Architecture is rapidly becoming one of the most important technical disciplines for modern developers. Matt Levenhagen's approach demonstrates that successful AI implementation is less about chasing the newest model and more about designing sustainable operational systems. The companies that gain long-term advantage from AI will not necessarily be the companies using the largest models. They will be the companies with the best architecture. 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In this episode of Fraudology, Karisse Hendrick comes to you from a beachside work-cation in Florida to deliver an essential debrief on the latest shifts in the e-commerce fraud landscape. Fresh off the Accertify Global Customer Summit, Karisse shares key strategic takeaways on why cybersecurity and fraud teams must break down operational silos as fraud signals increasingly move up-funnel.The conversation takes a critical look at the limitations of relying on Large Language Models (LLMs) in risk management. Highlighting a recent blunder where a Top 4 consultancy published a 44-page fraud report riddled with completely fabricated citations and footnotes, Karisse and Dr. Nicola Harding explain why "domain expertise" cannot be automated. Because true fraud insights are kept proprietary to protect them from criminals, open-source AI tools are inherently prone to "hallucinating" facts.We also break down Mastercard's newly announced Scam Merchant Dashboard, which officially goes into effect on July 24th, 2026. This aggressive program places a heavy burden on e-commerce merchants and their acquirers through a multi-trigger framework designed to shut down predatory accounts.Key pillars of Mastercard's new program include:The Authorization Performance Breakdown: A sudden drop in approval rates—such as a 50 percentage point decline or falling below a 30% overall threshold within a 72-hour window—will immediately trigger an investigation.The New Merchant 5% "Math": For accounts open less than six months, Mastercard is introducing a brand-new metric: combining refunds and chargebacks divided by overall sales. Crossing a 5% threshold over a rolling 30-day period (with at least 500 transactions) risks immediate account review.The 72-Hour Termination Clock: Once flagged by issuer complaints or network alerts, acquirers have a strict 72-hour window to either prove the merchant's legitimacy or completely terminate their ability to accept Mastercard.

RIP Democracy - A Crisis Caucus brings together It's News to Us, Cool Nerd Weed Show, and Mood Killers for a two-hour live political comedy special asking the most comforting question possible: are we halfway to fixing democracy, or halfway through watching it fall down a flight of stairs? With the theme “Midway to the Midterms… Will hope be restored or lost and gone forever?”, the episode mixes political panel chaos, comedy games, guest interviews, survival-guide bits, parody ads, cannabis updates, and live listener call-ins. The show kicks off with an introduction to the hosts and the crisis-caucus premise, then immediately throws listeners into State of Pain, a gas price trivia game designed to test both political knowledge and emotional endurance at the pump. Hour one leans into economic dread and generational coping mechanisms with segments like How Emos Can Afford High Gas Prices, How Broke Millennials Can Survive Inflation, and a guest interview with Ben Lapidus. The hour closes with How Gen Z Can Survive the 2026 Midterms, a cannabis update with The Nerds, and the kind of break teases that make listeners wonder whether they should laugh, vote, or stockpile beans. Hour two expands the satire with Headline or Hallucination, a game built for the modern news cycle where reality and AI-generated nonsense are basically in a custody battle. The second hour also features congressional candidate Samantha Mota, giving the panel a chance to talk politics directly while keeping the overall tone sharp, funny, and just unstable enough to match the moment. Overall, the episode is a live, comedic midterm check-in: part political therapy session, part radio variety show, part emergency broadcast from a democracy that may or may not be buffering. LINKShttps://instagram.com/itsnewstoushttps://tiktok.com/@itsnewstous Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.

A Marine intelligence collector walked through rocket blasts, absorbed traumatic brain injuries he never reported, and came home to hallucinations so severe he planned to end his own life. Dennis Connors, a Marine Corps veteran, human intelligence operator for a tier-one unit, Paralympic silver medalist, and world champion cyclist, sits down with Joe De Sena to break apart the moment grit stops working and what has to replace it.   Dennis lays out his four pillars of perseverance: vulnerability, self-love, disciplined action, and community. He explains why toughness without honesty becomes a death sentence, why identity tied to achievement collapses under pressure, and how cycling gave him both a recovery tool and a tribe that pushed him toward the help he refused to ask for.   Things You Will Learn: When grit becomes a liability and what structured perseverance looks like before breakdown hits. The four pillars that replaced white-knuckling it and why each one matters in sequence. Why identity tied to achievement collapses under pressure, and what to anchor self-worth to instead.   Tools & Frameworks Covered: Four Pillars of Perseverance: Vulnerability, self-love, disciplined action, and community. A structured framework for long-term recovery and sustained performance. Grit vs. Perseverance Distinction: Grit handles short-term strain. Perseverance handles the years. Know which mode you are in before it fails. Identity Separation Protocol: Detach identity from a single role so transitions do not destroy self-worth.   If this episode moved you, do not just listen. Do something about it. Sign up. Show up. Do the work. Spartan.com. No more excuses.   Dennis Connors is a U.S. Marine Corps intelligence veteran whose path changed after traumatic brain injuries and a stroke forced him to rebuild his life through adaptive sport. He went on to become a Paralympic silver medalist and world champion, continuing to chase challenge through paracycling and paraclimbing, embodying resilience, reinvention, and purpose through adversity.   Connect to Dennis:  Website: https://dennisconnorsusa.com/  Instagram: https://www.instagram.com/dc_rides_trikes/  Facebook: https://www.facebook.com/dcridestrikes   We gave you the tools, now use them during your next SPARTAN RACE! Use codeword PODCAST on checkout for 10% your next race.  

What is money? And what can a small island in Micronesia teach us about how it works? On Yap, a remote island in the western Pacific, giant calcite “Rai” stones once functioned as currency, where ownership and collective trust — rather than physical possession — defined wealth and status. In this episode of The Story of Money, macroeconomist and author Felix Martin joins hosts Gillian Tett and Robin Wigglesworth to explore the stones of Yap, the origins of money and why the traditional “barter theory” may be a myth.Further reading: Money: The Unauthorised Biography (2015) by Felix Martin Uap of the Carolines (1910) by William Henry Furness IIIA Treatise on Money (1930) by John Maynard Keynes The Island of Stone Money (1991) and Money Mischief (1992) by Milton Friedman ‘Tralla La' in Uncle Scrooge #6 by Carl Barks (1954) His Majesty O'Keefe (1954) Warner Bros To enjoy future episodes, be sure to subscribe to The Story of Money wherever you get your podcasts. You can also follow the show's dedicated YouTube channel here. Love listening to The Story of Money? Join us live on Saturday, June 20 at our inaugural NYC FT Weekend Festival at Spring Studios. Put your questions directly to our experts, experience your favourite podcast in person, and see the FT come to life. Register now and enjoy 10% off with code FTPodcast — this is one Saturday you won't want to miss. Learn more at ft.com/tsom or get in touch at thestoryofmoney@ft.com.Hosts: Gillian Tett and Robin WigglesworthGuest: Felix MartinProducer: Lulu SmythSenior Producers: Laurence Knight and Michela TinderaExecutive Producers: Flo Phillips and Manuela SaragosaOriginal music: Breen TurnerBroadcast engineers: Bianca Wakeman and Petros GiuompasisPodcast Development: Laura ClarkeFT Global Head of Audio: Cheryl BrumleyVideo editors: Kristen Kenyon and Josh Divney at Podcast DiscoveryRead a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

Nadine Dijkstra is a Principal Investigator at the Institute of Neurology at UCL. Her research in Imaging Neuroscience explores how the brain generates mental images and differentiates them from actual perception. Utilizing neuroimaging, psychophysics, machine learning, and computational modeling, Dijkstra addresses fundamental questions about the overlap between perception and imagery.Recently, Dijkstra has been leading the Imagine Reality Lab at UCL's Department of Imaging Neuroscience, focusing on the intersection of imagination and reality. Dijkstra's 2023 paper in Nature Communications showed the brain evaluates images against a 'reality threshold' to distinguish between images and perception. Her work also investigates how changes in these neural processes could impact mental health.Check out our new series, Ideas for Our Time: https://youtu.be/nYS4FylZJ2QDon't hesitate to email us at podcast@iai.tv with your thoughts or questions on the episode!To witness such debates live buy tickets for our upcoming festival: https://howthelightgetsin.org/festivals/And visit our website for many more articles, videos, and podcasts like this one: https://iai.tv/You can find everything we referenced here: https://linktr.ee/philosophyforourtimesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects.  In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge.  So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below.  Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Cash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsBibliography / Show NotesAmaya, I. A., Behrens, F., et al. “Effect of Frequency and Rhythmicity on Flicker Light-Induced Visual Hallucinations.” PLOS ONE, 2023.Key use: frequency, rhythmicity, 10 Hz flicker, Klüver forms.Shenyan, O., Lisi, M., Greenwood, J. A., Skipper, J. I., & Dekker, T. M. “Visual Hallucinations Induced by Ganzflicker and Ganzfeld Differ in Frequency, Complexity, and Content.” Scientific Reports, 2024.Key use: Ganzfeld vs. Ganzflicker.Bressloff, P. C., Cowan, J. D., Golubitsky, M., Thomas, P. J., & Wiener, M. C. “Geometric Visual Hallucinations, Euclidean Symmetry and the Functional Architecture of Striate Cortex.” Philosophical Transactions of the Royal Society B, 2001.Key use: form constants, tunnels, spirals, lattices, honeycombs, visual cortex modeling.Bressloff, P. C. “What Geometric Visual Hallucinations Tell Us About the Visual Cortex.” Neural Computation, 2002.Key use: Klüver form constants and visual cortex explanation.Mauro, F., et al. “A Bidirectional Link Between Brain Oscillations and Geometric Patterns.” Journal of Neuroscience, 2015.Key use: brain oscillations and geometric visual patterns.Hewitt, T., et al. “Stroboscopically Induced Visual Hallucinations.” Neuroscience of Consciousness, 2025.Key use: history and science of stroboscopic hallucinations.Netherlands Institute for Neuroscience. “Hallucinations from Flickering Lights: What Happens in Our Brain?” 2024.Key use: standing waves / visual cortex explanation.Purkinje, J. E. Early 19th-century writings on subjective visual phenomena and flicker effects.Key use: historical scientific observation of flicker-induced visual effects.Klüver, H. Mescal and Mechanisms of Hallucinations. University of Chicago Press, 1966.Key use: form constants: tunnels, spirals, lattices, cobwebs.Epilepsy Foundation / clinical photosensitivity guidance.Key use: photosensitive epilepsy safety warning; flashing lights and visual patterns can trigger seizures in susceptible people.“Visually-Provoked Seizures: Consensus of the Epilepsy Foundation of America Working Group.” Epilepsia.Key use: safety, photosensitive seizure risk.Ofcom / broadcast photosensitive epilepsy standards and strobe-light safety cases.Key use: real-world risk from rapid flashing light in media environments.Extra useful context sourcesGysin, B., and Sommerville, I. Dreamachine-related writings and documentation.Key use: 20th-century flicker device, art, counterculture, visionary technology.Huxley, A. The Doors of Perception.Key use: altered perception context, though not specifically flicker science.Lewis-Williams, D. The Mind in the Cave.Key use: cave art, altered states, entoptic imagery, visionary interpretation.Eliade, M. Shamanism: Archaic Techniques of Ecstasy.Key use: older ritual technologies of altered states; use carefully as historical theory.Tart, C. T., ed. Altered States of Consciousness.Key use: broader academic framing for non-ordinary states.Vaitl, D., et al. “Psychobiology of Altered States of Consciousness.” Psychological Bulletin, 2005.Key use: general altered-state science framework.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball. Now let me introduce the rest of the panel and guests.

A Princeton cognitive scientist says AI can't think like a child — and giving it more data won't fix that. If the field keeps scaling without solving what's actually missing, the gap between human and machine intelligence won't close. It'll just get more expensive. Tom Griffiths is a professor of psychology and computer science at Princeton, and one of the leading researchers working at the intersection of human cognition and AI. We cover: -why a child learns language from breadcrumbs while AI needs continents of data -the 250-year-old idea that quietly became the foundation of modern language models -what sycophantic AI actually does to your beliefs over time -why solving AGI might have less to do with scale and more to do with understanding what a child's mind really is. The hallucinations don't bother him — it's the sycophancy that should worry you. Key Takeaways: 00:00 The Math Behind How Minds Actually Work 00:30 Why Defining "Thought" Is Harder Than It Looks 04:30 What AI Gets Wrong About Consciousness 07:00 What ChatGPT Actually Revealed About the Field 08:10 Are Humans Really Irrational — Or Solving a Different Problem? 11:00 How Chomsky Turned Language Into a Math Problem 13:55 The Chessboard Analogy That Explains Generative Grammar 15:20 Why Aristotle Got Thought Right and Physics Wrong 19:45 The Man Who Tried to Build AI in the 1600s 22:40 What Everyone Gets Wrong About George Boole 25:25 From Boole to Turing: How Logic Became Computers 27:40 Why Your Brain Runs on Less Energy Than a Light Bulb 28:40 Jensen Huang Says AGI Is Here. Is He Right? 31:45 Why the "AI vs. Human Intelligence" Scale Is Misleading 33:50 Why a Child Still Outlearns Every AI Model 35:20 The Fuzzy Boundary Problem That Broke Rule-Based AI 37:20 How Semantic Networks Rewired the Theory of Memory 39:30 Rosenblatt Built a Brain — Then Minsky Killed It 43:15 The Plane Ride Where Backpropagation Was Solved 44:20 Hallucinations, Sycophancy, and What Should Actually Worry You 47:00 What Has to Change Before AI Can Truly Generalize 50:10 What a Layperson Should Actually Take Away From This ———

Hackers linked to Iran have breached FBI Director Kash Patel's personal emails. Attorney General Pam Bondi sent a Jack Smith progress memo to Congress outlining Trump's motive for illegally retaining classified documents. A top deputy to U.S. Attorney Jeanine Pirro acknowledged in a closed-door hearing this month that the Justice Department did not have evidence of wrongdoing in its criminal investigation of Fed Chair Jerome Powell. Legal experts are stunned after a federal judge catches DOJ lawyers using artificial intelligence to write briefs. Plus listener questions. Do you have questions for the pod?    Shop Mint Unlimited Plans at MINTMOBILE.com/UNJUST Follow AG Substack|MuellershewroteBlueSky|@muellershewroteAndrew McCabe isn't on social media, but you can buy his book The ThreatThe Threat: How the FBI Protects America in the Age of Terror and Trump Questions for the pod?https://formfacade.com/sm/PTk_BSogJ We would like to know more about our listeners. Please participate in this brief surveyListener Survey and CommentsThis Show is Available Ad-Free And Early For Patreon and Supercast Supporters at the Justice Enforcers level and above:https://dailybeans.supercast.techOrhttps://patreon.com/thedailybeansOr when you subscribe on Apple Podcastshttps://apple.co/3YNpW3P Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.