Crazy Wisdom

Larry Diamond, co-founder of Healing with the Diamonds, shares his inspiring journey from severe metabolic illness to vibrant health. He discusses heart-brain coherence and the alchemical journey of self-discovery. Insightfully, he critiques modern dietary misconceptions and emphasizes the importance of ancestral eating. Larry introduces concepts like insulin resistance and the metaphorical 'parasites' feeding on fear, linking personal wellness to deeper spiritual truths and the wisdom of the divine feminine. His insights encourage a revolution in our approach to health.

Join Rory Aronson, CEO of FarmBot, as he shares his journey in revolutionizing home gardening with open-source technology. He explains how FarmBot operates like a CNC machine for gardens, detailing its evolution from basic electronics to sophisticated custom boards. Rory discusses the importance of closed-loop feedback systems for accuracy and the intricate nature of integrating hardware and software. They envision a solarpunk future, highlighting the role of local food systems and the ease of growing your own food, making sustainability accessible for everyone.

Thamir Ali Al-Rahedi, host of the From First Principles podcast, dives deep into the art of questioning and its vital role in entrepreneurship. He discusses how effective questions can drive value while answers merely capture it, challenging the traditional views influenced by scientism. Thamir reflects on Steve Jobs' innovative mindset and spiritual practices as key to Apple’s success. The conversation also touches on the complexities of using AI in education and the generalist's struggle in balancing creativity and discernment.

On this episode of Crazy Wisdom, I, Stewart Alsop, talk with Sarah Boisvert, founder of New Collar AI, about the future of work in manufacturing, the rise of “new collar” jobs, and how technologies like 3D printing and AI are transforming skills training. We cover her experience with Fab Labs, creating a closed-loop AI tutor for workforce development, and the challenges of capturing implicit knowledge from retiring experts. Sarah also shares insights from her books The New Collar Workforce and People of the New Collar Workforce, which feature augmented reality to bring stories to life. You can connect with Sarah through LinkedIn.Check out this GPT we trained on the conversationTimestamps00:00 Sarah introduces New Collar jobs and how digital skills are transforming blue collar roles, discussing FedEx robotics and augmented workers.05:00 Stewart asks about 3D printing challenges; Sarah explains advances in printer automation and the ongoing difficulty of CAD design.10:00 They discuss Generation Z as digital natives, instant gratification, and workforce engagement, highlighting Lean manufacturing principles.15:00 Sarah reflects on how technology speeds life up, her experiences with management training, and the importance of communication on factory floors.20:00 They explore text-to-CAD possibilities, Sarah’s closed-loop AI tutor for manufacturing, and the creation of a proprietary technical database.25:00 Sarah describes the scale of open jobs in 3D printing, challenges of filling them, and shifting perceptions of manufacturing work.30:00 Discussion of robotics safety, small business adoption barriers, and the need for human oversight in automation.35:00 Sarah talks about capturing implicit knowledge from retiring experts, using LLMs for factory floor solutions, and military applications.40:00 Knowledge management, boutique data sets, and AI’s role in preserving technical expertise are explored.45:00 Sarah shares insights on product design, her AR-enabled book, and empowering workers through accessible technical training.Key InsightsSarah Boisvert introduces the concept of “new collar” jobs, emphasizing that modern manufacturing roles now require digital skills traditionally associated with white-collar work. She highlights how roles like CNC machinists and 3D printing operators blend hands-on work with advanced tech, making them both in-demand and engaging for a younger, tech-savvy workforce.The conversation explores the rise of Fab Labs worldwide and their role in democratizing access to manufacturing tools. Boisvert shares her experience founding a Fab Lab in Santa Fe, enabling students and adults to gain practical, project-based experience in CAD design, 3D printing, and repair skills critical for today’s manufacturing environment.Boisvert underscores the persistent skills gap in manufacturing, noting that 600,000 U.S. manufacturing jobs remain unfilled. She attributes part of this to outdated perceptions of manufacturing as “dirty and unsafe,” a narrative she’s actively working to change through her books and training programs that show how modern factories are highly technical and collaborative.She reveals her team’s development of a closed-loop large language model for workforce training. Unlike ChatGPT, this system draws from a proprietary database of technical manuals and expert knowledge, offering precise, context-specific answers for students and workers without relying on the open internet.The episode dives into generational differences in the workplace. Boisvert describes how Gen Z workers are motivated by purpose and efficiency, often asking “why” to understand the impact of their work. She sees Lean principles as a key to managing and empowering this generation to innovate and stay engaged.On automation, Boisvert stresses that robots are not replacing humans in manufacturing but filling labor shortages. She notes that while robots improve efficiency, they require humans to program, monitor, and repair them—skills that new collar workers are being trained to master.Finally, she shares her innovative approach to storytelling in her book People of the New Collar Workforce, which uses augmented reality to bring worker stories to life. Readers can scan photos to hear directly from individuals about their experiences transitioning into high-tech manufacturing careers.

In this episode of the Crazy Wisdom Podcast, I, Stewart Alsop, sit down with returning guest Brian Ahuja to explore a thought-provoking idea he’s been stewing on—could we one day build a robot capable of true partner dancing? From the biomechanics of salsa to the possibilities of AI embodiment, we unpack what it would take to engineer fluid, responsive movement and how that intersects with everything from artificial muscles to the intimacy of tactile feedback. We also touch on Brian’s long-term vision for a potential lab or foundation to tackle this challenge. You can follow Brian and future developments on Twitter @brianahuja.Check out this GPT we trained on the conversationTimestamps00:00 – Brian Ahuja returns to discuss AI embodiment, sparked by his experience in ballroom dance and curiosity about translating physical intelligence into robotics.05:00 – They explore robotics in partner dancing, touching on the difference between choreographed motion and improvisational, responsive movement.10:00 – Brian breaks down human biomechanics, emphasizing that hip motion in dances like salsa originates from knees and feet—not the hips directly.15:00 – The conversation shifts to balance, proprioception, and ocular reflexes, linking them to movement stability in dance.20:00 – They compare robot vs. human movement, noting robots’ jerky motions and the absence of muscle-based initiation.25:00 – The need for haptic feedback is discussed, with Brian detailing how partner dancing depends on tactile signals and real-time response.30:00 – They touch on robotic form factors, questioning whether humanoid robots are the best approach and pondering the design of artificial muscles.35:00 – Brian proposes the idea of the Ahuja Test, gauging if a robot can move so fluidly it's indistinguishable from a human, using dance as the standard.Key InsightsPartner Dancing as a Frontier for Robotics: Brian Ahuja proposes that partner dancing could be a benchmark for robotic embodiment, where success would indicate a robot’s ability to replicate fluid, responsive human movement. This task is far more complex than solo choreography—it requires real-time tactile feedback, improvisation, and nuanced physical communication.Movement Origin in Humans vs. Robots: A critical difference lies in how movement is generated. Human motion begins with muscle contraction, not at the joints. Robots, however, typically initiate movement at joint points, missing the layered interplay of muscles, tendons, and fascia that create smooth, lifelike motion.Haptic Feedback and Improvisation: Real partner dancing involves subtle cues, like pressure through fingertips, to signal direction and timing. For a robot to follow or lead a dance, it would need a highly sensitive haptic feedback system capable of interpreting and responding to these nonverbal signals in real time.The Limits of Current Robotics: Even with advanced robots like the Tesla bot, current movement still appears jerky and lacks the fluidity needed for partner dancing. The mechanical design—especially the lack of artificial musculature—may impose fundamental limits on how closely robots can mimic human motion.Applications Beyond Dance: The implications of this inquiry stretch beyond dance into fields like physical therapy, elder care, and domestic robotics. A robot that could move like a human could handle tasks requiring adaptability, precision, and physical sensitivity.Vision and Systems Thinking: Brian frames the challenge as a systems problem that might start with a lab or foundation. He emphasizes not needing to do everything alone, recognizing the value of building knowledge iteratively through conversations, research, and community.The Ahuja Test: Inspired by the Turing Test, Brian coins the idea of the “Ahuja Test”—a way to measure if a robot can move indistinguishably from a human. He suggests partner dancing could serve as the ultimate proving ground for such a test, given its demand for embodied intelligence and nuanced coordination.

In this episode of Crazy Wisdom, I, Stewart Alsop, speak with Andrew Einhorn, CEO and founder of Level Fields, a platform using AI to help people navigate financial markets through the lens of repeatable, data-driven events. We explore how structured patterns in market news—like CEO departures or earnings surprises—can inform trading strategies, how Level Fields filters noise from financial data, and the emotional nuance of user experience design in fintech. Andrew also shares insights on knowledge graphs, machine learning in finance, and the evolving role of narrative in markets. Stock tips from Level Fields are available on their YouTube channel at Level Fields AI and their website levelfields.ai.Check out this GPT we trained on the conversationTimestamps00:00 – Andrew introduces Level Fields and explains how it identifies event-driven stock movements using AI.05:00 – Discussion of LLMs vs. custom models, and how Level Fields prioritized financial specificity over general AI.10:00 – Stewart asks about ontologies and knowledge graphs; Andrew describes early experiences building rule-based systems.15:00 – They explore the founder’s role in translating problems, UX challenges, and how user expectations shape product design.20:00 – Insight into feedback collection, including a unique refund policy aimed at improving user understanding.25:00 – Andrew breaks down the complexities of user segmentation, churn, and adapting the product for different investor types.30:00 – A look into event types in the market, especially crypto-related announcements and their impact on equities.35:00 – Philosophical turn on narrative vs. fundamentals in finance; how news and groupthink drive large-scale moves.40:00 – Reflection on crypto parallels to dot-com era, and the long-term potential of blockchain infrastructure.45:00 – Deep dive into machine persuasion, LLM training risks, and the influence of opinionated data in financial AI.50:00 – Final thoughts on momentum algos, market manipulation, and the need for transparent, structured data.Key InsightsEvent-Based Investing as Market Forecasting: Andrew Einhorn describes Level Fields as a system for interpreting the market’s weather—detecting recurring events like CEO departures or earnings beats to predict price movements. This approach reframes volatility as something intelligible, giving investors a clearer sense of timing and direction.Building Custom AI for Finance: Rejecting generic large language models, Einhorn’s team developed proprietary AI trained exclusively on financial documents. By narrowing the scope, they increased precision and reduced noise, enabling the platform to focus only on events that truly impact share price behavior.Teaching Through Signals, Not Just Showing: Stewart Alsop notes how Level Fields does more than surface opportunities—it educates. By linking cause and effect in financial movements, the platform helps users build intuition, transforming confusion into understanding through repeated exposure to clear, data-backed patterns.User Expectation vs. Product Vision: Initially, Level Fields emphasized an event-centric UX, but users sought more familiar tools like ticker searches and watchlists. This tension revealed that even innovative technologies must accommodate habitual user flows before inviting them into new ways of thinking.Friction as a Path to Clarity: To elicit meaningful feedback, Level Fields implemented a refund policy that required users to explain what didn’t work. The result wasn’t just better UX insights—it also surfaced emotional blockages around investing and design, sharpening the team’s understanding of what users truly needed.Narrative as a Volatile Market Force: Einhorn points out that groupthink in finance stems from shared academic training, creating reflexive investment patterns tied to economic narratives. These surface-level cycles obscure the deeper, steadier signals that Level Fields seeks to highlight through its data model.AI’s Risk of Amplifying Noise: Alsop and Einhorn explore the darker corners of machine persuasion and LLM-generated content. Since models are trained on public data, including biased and speculative sources, they risk reinforcing distortions. In response, Level Fields emphasizes curated, high-integrity inputs grounded in financial fact.

In this episode of Crazy Wisdom, host Stewart Alsop speaks with Moritz Bierling, community lead at Daylight Computer, about reimagining our relationship to technology through intentional hardware and software design. The conversation traverses the roots of Daylight Computer—born from a desire to mitigate the mental and physiological toll of blue light and digital distraction—into explorations of AI integration, environmental design, open-source ethos, and alternative models for startup funding. Moritz discusses the vision behind Daylight’s “Outdoor Computing Club,” a movement to reclaim nature as a workspace, and the broader philosophical inquiry into a “third timeline” that balances techno-optimism and primitivism. You can explore more about the project at daylightcomputer.com and connect through their primary social channels on X (Twitter) and Instagram.Check out this GPT we trained on the conversationTimestamps00:00 – Introduction to Daylight Computer, critique of mainstream tech as a distraction machine, and inspiration from Apple’s software limitations.05:00 – Origin story of Daylight, impact of blue light, and how display technology influences wellbeing.10:00 – Exploration of e-ink vs. RLCD, Kindle as a sanctuary, and Anjan’s experiments with the Remarkable tablet.15:00 – Development of Solo OS, the role of spaces in digital environments, and distinctions between hardware and software.20:00 – Vision for AI-assisted computing, voice interaction, and creating a context-aware interface.25:00 – Emphasis on environmental design, using devices outdoors, and the evolutionary mismatch of current computing.30:00 – Reflections on solar punk, right relationship with technology, and rejecting accelerationism.35:00 – Introduction of the third timeline, rhizomatic organizational structure, and critique of VC funding models.40:00 – Discussions on alternative economics, open-source dynamics, and long-term sustainability.45:00 – Outdoor Computing Club, future launches, on-device AI, and the ambition to reclaim embodied computing.Key InsightsTechnology as Both Lifeline and HindranceMoritz Bierling frames modern computing as a paradox: it connects us to society and productivity while simultaneously compromising our well-being through overstimulation and poor design. The Daylight Computer aims to resolve this by introducing hardware that reduces digital fatigue and invites outdoor use.Inspiration from E-Ink and Purposeful ToolsThe initial concept for Daylight Computer was inspired by the calm, focused experience of using a Kindle. Its reflective screen and limited functionality helped Anjan, the founder, realize the power of devices built for singular, meaningful purposes rather than general distraction.Designing for Contextual IntentWith the introduction of Sol OS, Daylight enables users to define digital “spaces” aligned with different modes of being—such as waking, deep work, or relaxation. This modular approach supports intentional interaction and reduces the friction of context-switching common in modern OS designs.Respectful Integration of AIRather than chasing full automation, the Daylight team is exploring AI in a measured way. They’re developing features like screen-aware AI queries through physical buttons, creating a contextual assistant that enhances cognition without overpowering it or promoting dependency.Alternative Economic ModelsRejecting venture capital and the short-term incentives of traditional tech funding, Daylight pursues a community-backed model similar to Costco’s membership. This aligns financial sustainability with shared values, rather than extracting maximum profit.Third Timeline VisionMoritz discusses a conceptual “third timeline”—a balanced future distinct from both primitivism and techno-solutionism. This alternative future integrates technology into life harmoniously, fostering right relationship between humans, nature, and machines.Environmental Computing and Cultural RegenerationDaylight is not just a hardware company but a movement in environmental design. Through initiatives like the Outdoor Computing Club, they aim to restore sunlight as a central influence in human life and work, hinting at a cultural shift toward solar punk aesthetics and embodied digital living.

In this episode of Crazy Wisdom, host Stewart Alsop speaks with futurist Richard Yonck about the profound implications of our accelerating relationship with technology. Together, they explore the emergence of emotionally intelligent machines, the nuances of anticipatory systems, and how narrative frameworks help societies prepare for possible futures. Richard unpacks the role of emotion in AI and why cultivating foresight is essential in an age of rapid disruption.Check out this GPT we trained on the conversationTimestamps00:00 – The episode opens with Richard Yonck introducing the concept of artificial emotional intelligence and why it matters for the future of human-machine interaction.05:00 – The discussion moves to anticipatory systems, exploring how technologies can be designed to predict and respond to future conditions.10:00 – Richard explains how narrative foresight helps individuals and societies prepare for possible futures, emphasizing the power of storytelling in shaping collective imagination.15:00 – A deeper look into affective computing, with examples of how machines are learning to detect and simulate emotional states to improve user experience.20:00 – The conversation touches on the role of emotion in intelligence, challenging the misconception that emotion is the opposite of logic.25:00 – Richard outlines how technological disruption can mirror societal values and blind spots, urging more thoughtful design.30:00 – The focus shifts to long-term thinking, highlighting how future-oriented education and leadership are vital in an age of rapid change.35:00 – Closing thoughts center around the evolution of human-technology partnerships, stressing the need for ethical, emotionally aware systems to support a thriving future.Key InsightsEmotion as a Computational Frontier: Richard Yonck highlights that as we push the boundaries of artificial intelligence, the next significant frontier involves enabling machines to understand, interpret, and possibly simulate emotions. This capacity isn’t just a novelty—it plays a crucial role in how machines and humans interact, influencing trust, empathy, and cooperation in increasingly digital environments.The Importance of Anticipatory Systems: One of the core ideas explored is the concept of anticipatory systems—those that can predict and react to future conditions. Richard emphasizes how building such foresight into our technologies, and even into our societal structures, is vital in managing the complexity and volatility of the modern world. It’s not just about responding to the future, but actively shaping it.Narrative as a Tool for Foresight: The discussion underscores that storytelling isn’t just entertainment—it’s a powerful instrument for exploring and communicating possible futures. By framing future scenarios as narratives, we can emotionally and cognitively engage with potential outcomes, fostering a deeper understanding and preparedness across different segments of society.Emotions as Integral to Intelligence: Contrary to the view that emotion impairs rationality, Richard points out that emotions are essential to decision-making and intelligence. They help prioritize actions and signal what matters. Bringing this understanding into AI development could result in systems that more effectively collaborate with humans, particularly in roles requiring empathy and nuanced social judgment.Technology as a Mirror of Humanity: A recurring insight is that the technologies we create ultimately reflect our values, assumptions, and blind spots. Emotionally intelligent machines won’t just serve us—they’ll embody our understanding of ourselves. This raises profound ethical questions about what we choose to model and how these choices shape future interactions.Urgency of Long-Term Thinking: The conversation brings to light how short-termism is a critical vulnerability in current systems—economic, political, and technological. Richard advocates for integrating long-term thinking into how we design and deploy innovations, suggesting that futures literacy should be a core skill in education and leadership.Evolutionary Partnership Between Humans and Machines: Lastly, Richard describes the trajectory of human-technology interaction not as domination or subservience, but as an evolving partnership. This partnership will require emotional nuance, foresight, and ethical maturity if we’re to co-evolve in ways that support human flourishing and planetary stability.Contact InformationRichard Yonck's LinkedIn

I, Stewart Alsop, am thrilled to welcome Xathil of Poliebotics to this episode of Crazy Wisdom, for what is actually our second take, this time with a visual surprise involving a fascinating 3D-printed Bauta mask. Xathil is doing some truly groundbreaking work at the intersection of physical reality, cryptography, and AI, which we dive deep into, exploring everything from the philosophical implications of anonymity to the technical wizardry behind his "Truth Beam."Check out this GPT we trained on the conversationTimestamps01:35 Xathil explains the 3D-printed Bauta Mask, its Venetian origins, and its role in enabling truth through anonymity via his project, Poliepals.04:50 The crucial distinction between public identity and "real" identity, and how pseudonyms can foster truth-telling rather than just conceal.10:15 Addressing the serious risks faced by crypto influencers due to public displays of wealth and the broader implications for online identity.15:05 Xathil details the core Poliebotics technology: the "Truth Beam," a projector-camera system for cryptographically timestamping physical reality.18:50 Clarifying the concept of "proof of aliveness"—verifying a person is currently live in a video call—versus the more complex "proof of liveness."21:45 How the speed of light provides a fundamental advantage for Poliebotics in outmaneuvering AI-generated deepfakes.32:10 The concern of an "inversion," where machine learning systems could become dominant over physical reality by using humans as their actuators.45:00 Xathil’s ambitious project to use Poliebotics for creating cryptographically verifiable records of biodiversity, beginning with an enhanced Meles trap.Key InsightsAnonymity as a Truth Catalyst: Drawing from Oscar Wilde, the Bauta mask symbolizes how anonymity or pseudonyms can empower individuals to reveal deeper, more authentic truths. This challenges the notion that masks only serve to hide, suggesting they can be tools for genuine self-expression.The Bifurcation of Identity: In our digital age, distinguishing between one's core "real" identity and various public-facing personas is increasingly vital. This separation isn't merely about concealment but offers a space for truthful expression while navigating public life.The Truth Beam: Anchoring Reality: Poliebotics' "Truth Beam" technology employs a projector-camera system to cast cryptographic hashes onto physical scenes, recording them and anchoring them to a blockchain. This aims to create immutable, verifiable records of reality to combat the rise of sophisticated deepfakes.Harnessing Light Speed Against Deepfakes: The fundamental defense Poliebotics offers against AI-generated fakes is the speed of light. Real-world light reflection for capturing projected hashes is virtually instantaneous, whereas an AI must simulate this complex process, a task too slow to keep up with real-time verification.The Specter of Humans as AI Actuators: A significant future concern is the "inversion," where AI systems might utilize humans as unwitting agents to achieve their objectives in the physical world. By manipulating incentives, AIs could effectively direct human actions, raising profound questions about agency.Towards AI Symbiosis: The ideal future isn't a human-AI war or complete technological asceticism, but a cooperative coexistence between nature, humanity, and artificial systems. This involves developing AI responsibly, instilling human values, and creating systems that are non-threatening and beneficial.Contact Information*   Polybotics' GitHub*   Poliepals*   Xathil: Xathil@ProtonMail.com

I, Stewart Alsop, had a fascinating conversation on this episode of Crazy Wisdom with Mallory McGee, the founder of Chroma, who is doing some really interesting work at the intersection of AI and crypto. We dove deep into how these two powerful technologies might reshape the internet and our interactions with it, moving beyond the hype cycles to what’s truly foundational.Check out this GPT we trained on the conversationTimestamps00:00 The Intersection of AI and Crypto01:28 Bitcoin's Origins and Austrian Economics04:35 AI's Centralization Problem and the New Gatekeepers09:58 Agent Interactions and Decentralized Databases for Trustless Transactions11:11 AI as a Prosthetic Mind and the Interpretability Challenge15:12 Deterministic Blockchains vs. Non-Deterministic AI Intents18:44 The Demise of Traditional Apps in an Agent-Driven World35:07 Property Rights, Agent Registries, and Blockchains as BackendsKey InsightsCrypto's Enduring Fundamentals: Mallory emphasized that while crypto prices are often noise, the underlying fundamentals point to a new, long-term cycle for the Internet itself. It's about decentralizing control, a core principle stemming from Bitcoin's original blend of economics and technology.AI's Centralization Dilemma: We discussed the concerning trend of AI development consolidating power within a few major players. This, as Mallory pointed out, ironically mirrors the very centralization crypto aims to dismantle, potentially shifting control from governments to a new set of tech monopolies.Agents are the Future of Interaction: Mallory envisions a future where most digital interactions aren't human-to-LLM, but agent-to-agent. These autonomous agents will require decentralized, trustless platforms like blockchains to transact, hold assets, and communicate confidentially.Bridging Non-Deterministic AI with Deterministic Blockchains: A fascinating challenge Mallory highlighted is translating the non-deterministic "intents" of AI (e.g., an agent's goal to "get me a good return on spare cash") into the deterministic transactions required by blockchains. This translation layer is crucial for agents to operate effectively on-chain.The Decline of Traditional Apps: Mallory made a bold claim that traditional apps and web interfaces are on their way out. As AI agents become capable of generating personalized interfaces on the fly, the need for standardized, pre-built apps will diminish, leading to a world where software is hyper-personalized and often ephemeral.Blockchains as Agent Backbones: We explored the intriguing idea that blockchains might be inherently better suited for AI agents than for direct human use. Their deterministic nature, ability to handle assets, and potential for trustless reputation systems make them ideal backends for an agent-centric internet.Trust and Reputation for Agents: In a world teeming with AI agents, establishing trust is paramount. Mallory suggested that on-chain mechanisms like reward and slashing systems can be used to build verifiable reputation scores for agents, helping us discern trustworthy actors from malicious ones without central oversight.The Battle for an Open AI Future: The age-old battle between open and closed source is playing out again in the AI sphere. While centralized players currently seem to dominate, Mallory sees hope in the open-source AI movement, which could provide a crucial alternative to a future controlled by a few large entities.Contact Information*   Twitter: @McGee_noodle*   Company: Chroma