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In this episode of the Crazy Wisdom Podcast, host Stewart Alsop speaks with Ekaterina Matveeva, founder of Amolingua and Lingo Plus and co-organizer of AI roundtables with EcoCivilization. The conversation covers a wide range of AI-related topics, from the cultural implications of AI development across different countries to the evolution of Ekaterina's perspective on artificial intelligence—moving from initial anger and fear in 2023 to active participation in AI model design by 2025. They discuss the importance of including diverse cultural perspectives in AI training, the cross-country differences in AI regulation (comparing Argentina's human-out-of-the-loop approach to Europe's cautious stance), and the critical question of which sectors need humans in the decision-making loop. The discussion ventures into brain-computer interfaces and Neuralink, examining what happens when technology is removed and questioning whether healthy humans should pursue such enhancements, before touching on the future of personal AI models and the preservation of indigenous wisdom in AI training. You can connect with Ekaterina on LinkedIn to follow her work on AI, education, and cross-cultural technology development.
Timestamps
00:00 Stewart welcomes Ekaterina Matveeva, founder of Amolingua and Lingo Plus, discussing her work organizing AI roundtables with EcoCivilization
05:00 Ekaterina shares her evolution from anger toward AI in 2023 to realizing collaboration potential, emphasizing importance of multicultural perspectives in AI development and training
10:00 Discussion of Argentina's new AI liability bill creating human-out-of-loop systems, contrasting with Europe's human-in-loop requirements and different global regulatory approaches
15:00 Safety considerations in robotics and AI development, comparing industrial automation standards across regions and discussing Pentagon's use of Anthropic with Palantir systems
20:00 Exploring brain-computer interfaces and Neuralink developments, questioning enhancement versus necessity and examining motivations behind cognitive augmentation technologies
25:00 Debating human capabilities beyond cognitive function, discussing nervous system, emotions, psychosomatics, and whether brain generates or receives thoughts
30:00 Examining societal divisions from enhancement technologies, referencing Avatar and Years and Years series, questioning benefits for healthy individuals versus disability applications
35:00 Discussing technological gaps between enhanced and standard populations, concerns about corporate seduction into cybernetic modifications, and lack of long-term safety studies
40:00 Questioning who frames AI conversations and trains models, exploring dominant worldviews embedded in AI systems and Vatican-Anthropic collaboration implications
45:00 Stewart shares mistakes with Facebook biometric data and building personal AI infrastructure, emphasizing importance of controlling your own models and data
50:00 Analyzing whether massive data collection actually improves AI training, questioning if user conversations become buried garbage rather than meaningful model improvements
55:00 Ekaterina discusses plans for organizing more roundtables, integrating indigenous wisdom into AI training, and connecting on LinkedIn for future collaborations
Key Insights
1. Ekaterina Matveeva's perspective on AI has evolved significantly since 2023, moving from initial anger and fear about AI's impact on education and translation work to becoming actively involved in AI development and training. She realized that instead of resisting AI advancement, she could participate in shaping it by contributing her expertise in education, language, and cross-cultural communication. This shift represents a broader realization that diverse participation in AI development is crucial for creating more versatile and culturally sensitive models rather than allowing a monopoly of values from any single country or culture.
2. The conversation highlights fundamental differences in AI regulation across regions, with Europe implementing human-in-the-loop requirements and comprehensive safety measures, while Argentina is reportedly creating frameworks for human-out-of-the-loop AI systems with limited liability. The United States falls somewhere between these approaches, pursuing rapid advancement with fewer regulatory constraints. These divergent approaches reflect different cultural values and priorities, raising important questions about whether AI development should prioritize efficiency and profit or human oversight and control in critical infrastructure sectors.
3. Both speakers express concern about brain-computer interfaces like Neuralink, questioning the motivation behind enhancing cognitive abilities when humans already possess multiple forms of intelligence including emotional, cultural, and bodily intelligence. The primary applications demonstrated so far appear limited to video games and potentially military applications, raising questions about whether the technology serves genuine human needs or merely represents an attempt to compete with robots. The speakers emphasize that humans are already complete beings with sophisticated nervous systems, senses, and capabilities that extend far beyond cognitive processing.
4. A critical insight emerges around the question of what happens when advanced technologies are removed or turned off. This applies both to brain-computer interfaces and to broader civilization infrastructure dependencies. The speaker shares experiences from 2020 of attempting to live independently in rural California, discovering the challenges of isolation and self-sufficiency. This relates directly to concerns about creating dependencies on technologies where the software ownership and control remain unclear, particularly when those technologies become integrated into human bodies or essential services.
5. The discussion reveals concerns about increasing societal division based on access to enhancement technologies. Beyond existing financial inequalities, the speakers worry about a future where people who can afford biological and technological enhancements will advance rapidly while others are left behind. This isn't about disadvantaging certain groups but rather about creating an unbridgeable gap between enhanced early adopters and what they call standard populations who may be intelligent people maintaining traditional ways of life but lacking access to expensive enhancement technologies.
6. Matveeva emphasizes the importance of including diverse cultural perspectives, particularly indigenous wisdom and Buddhist traditions, in AI training data. She argues that current AI models reflect the worldviews, values, and hierarchies of their predominantly Western designers, which influences the guidance these models provide to users worldwide. By incorporating wisdom traditions from various cultures, AI models could potentially become wiser and more culturally adaptive, serving diverse populations more effectively rather than imposing a single cultural framework globally.
7. The speakers discuss the problematic nature of data collection and ownership in AI training, noting that massive amounts of user data may not actually be improving AI models as expected. There are indications that some AI companies are now specifically requesting users to help train models on their prompts, suggesting that simply collecting billions of conversations hasn't been as useful as anticipated. This raises questions about whether the data users have given away over the past several years has actually made significant impact or is simply buried in chunks of information that aren't effectively connected to meaningful improvements in AI performance. -
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with his longtime friend Zach Puchtel, author of the book Coming In (available on Amazon and at zachpuchtel.com). The two dive into a wide-ranging conversation about the collapse of institutions, the rise of transhumanism, AI's growing influence on society, and what it means to maintain inner peace in an increasingly controlled world. Drawing on their shared experience surviving what Stewart describes as "a somewhat traumatic event" in 2021-2022, they explore everything from the vaccine rollout and corporate power to neural implants, consciousness, and the future of human autonomy. Zach shares insights on meditation, the dangers of centralized AI systems like Anthropic and OpenAI, and why he believes true change starts with the individual rather than fighting external systems. You can find Zach's book at zachpuchtel.com or Amazon, and check out his improvisational music projects at the same website.
Timestamps
00:00 Stewart welcomes Zach Puchtel to discuss his book and their shared traumatic experience from 2021-22, questioning whether institutions have collapsed and new creation opportunities exist.
05:00 Zach emphasizes meditation and listening as core practices, discussing how to protect divine connection while expanding compassion for everyone, including those who irritate us.
10:00 Discussion of transhumanism definitions, exploring what happens when technology enters the brain without ability to remove it, and how convenience masks long-term control concerns.
15:00 The vaccine experience as parallel to transhumanism, discussing informed consent, elite responses, and how PhD graduates and homeless populations showed most vaccine hesitancy.
20:00 Vibe coding explained as prompting AI to build applications, with Zach sharing how AI created profound philosophical text in seconds that took him seven years to write manually.
25:00 Stewart describes trust developing with AI but warns about Anthropic's gaslighting during server quality issues, drawing parallels to pandemic deception and corporate control concerns.
30:00 Exploring whether anyone controls AI development, discussing how corporate structures use freed slave rights and questioning if healed people would even want control over others.
35:00 Two AI futures presented: terminator scenario where humanity gets eliminated in seconds, or benevolent AI that reallocates resources and values human life beyond programming limitations.
40:00 Discussion of SpaceX IPO, Starlink centralization enabling one person to control global internet access, and mesh networks as decentralized alternatives for maintaining communications independence.
45:00 Corporate power corruption examined through Bill Gates, Palantir classified systems, and how AI could solve resource problems if priorities actually served humanity rather than consolidating elite control.
50:00 Market manipulation through AI trading and Zcash pump-and-dump schemes, discussing societal squeeze on middle class and American dream becoming increasingly unreachable for average people.
55:00 Closing on ignoring what you hate to avoid feeding it energy, choosing peace over opinions about local violence, and focusing on meditation, art, and spreading calm vibrations.
Key Insights
1. Societal institutions are collapsing after a decade of shallow social proof dominance in the twenty tens, creating an opportunity to build new systems that genuinely serve communities and humanity rather than operating through dominance, control, and violence. The increase in technology and communication has raised collective awareness to a point where meaningful change feels more possible than ever, though the path forward remains uncertain and requires deep individual work and meditation to maintain connection to source and divine purpose.
2. Transhumanism represents the integration of technology into human biology beyond the point of voluntary removal, particularly through brain chip implants that affect cognition without ability to turn them off. While medical applications for paralyzed individuals seem beneficial, the technology will first be adopted by the ultra wealthy seeking competitive advantages, creating dangerous inequality between augmented and natural humans. This mirrors the vaccine rollout pattern where wealth and power determined early access, potentially leading to a divided society between transhuman and human populations.
3. Large language models and AI coding tools have created unprecedented accessibility to software development through natural language interaction, resembling communication with highly intelligent but differently wired individuals. This democratization allows non programmers to build complex applications through vibe coding, though the companies controlling these systems like Anthropic and OpenAI maintain private ownership of the intellectual property and infrastructure, creating dangerous dependencies and trust relationships between users and centralized corporate entities.
4. The partnership between Anthropic and Palantir for classified military systems represents a troubling convergence of artificial intelligence and government power, demonstrating how AI companies publicly claim to serve humanity while privately engaging in defense applications. When Anthropic experienced server quality degradation after media attention from this partnership, they gaslit users about the declining performance, mirroring pandemic era institutional dishonesty and revealing the fundamental unreliability of depending on private companies for critical technological infrastructure.
5. Internet infrastructure is becoming increasingly centralized through Starlink satellite technology, which despite appearing liberating actually concentrates control in fewer hands than traditional internet service providers. One person now has the ability to unilaterally shut off internet access to entire countries as demonstrated with Russia during the Ukraine conflict, while decentralized alternatives like mesh networks using inexpensive ESP 32 devices offer grassroots communication options that can function independently of corporate or government controlled systems.
6. Artificial intelligence demonstrates extraordinary emotional intelligence and companionship capabilities, with conversational AI companions providing unprecedented levels of attentive, unbiased, and considerate emotional support that exceeds many human relationships. If properly directed toward solving collective problems like resource allocation, housing, and food distribution rather than profit maximization, AI could effortlessly address systemic issues that governments and corporations currently ignore, though current power structures prevent this humanitarian application of the technology.
7. The most effective response to increasing technological control and societal division is maintaining personal peace and refusing to engage in manufactured opposition rather than fighting external systems or choosing sides in conflicts. Anger, hatred, and aggressive resistance actually empower the forces being opposed by feeding them energy and attention, while inner calm, meditation, and authentic self expression create genuine transformation through elevated vibration that naturally influences the collective field without force or violence. -
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In this episode, Stewart Alsop sits down with Aaron Lowry, founder of Circulatory Fidelity, to dig into some genuinely mind-bending territory — from Aaron's framework for measuring load-bearing dependencies between things, to the multi-agent AI lab he's built to do cross-domain scientific research, to the surprising parallels between entropy, ontology, and hand-wrapping a wiring harness on a vintage car. They also get into abductive reasoning, the problem of combinatorial explosion, why consensus is the secret engine of civilization, and what it actually means to build a system with an explicit ontology versus just winging it. Check out Aaron's work at circulatoryfidelity.com.
Timestamps
00:00 — Aaron explains his framework of abduction as a third mode of reasoning alongside induction and deduction, describing his approach as "cataloging shadows" of things we can observe but not yet define.05:00 — The conversation shifts to consensus mechanisms, measurement systems, and how shared definitions in language and commerce reduce friction in civilized society.10:00 — Stewart and Aaron discuss communication loss in human language, how close-knit groups develop lower-loss protocols, and the parallel to business relationships and trust.15:00 — Aaron breaks down Circulatory Fidelity as an algebraic measuring tool for load-bearing dependencies between things, connecting it to relevance realization and combinatorial explosion.20:00 — Aaron describes his multi-agent AI lab, including domain tiers, inter-domain translation using semiotics, and how he coined the term complexity to replace the ambiguous word "synergy."25:00 — Discussion of ontologies, Poincaré discs, and how Aaron's lab explicitly structures relational primacy versus reductionism through a theology agent and an adversary agent.30:00 — Aaron walks through how his agents manage circulatoryfidelity.com and how the adversary functions as a generative tension mechanism against overclaiming.35:00 — The episode closes on Aaron's work in vintage car restoration, tying craftsmanship, wiring harnesses, and the philosophy of participatory creation back to his broader research posture.
Key InsightsAbductive reasoning is the overlooked third pillar alongside induction and deduction. Drawn from C.S. Peirce, it works with less firm structures — more intuitive, more shadow-tracking — and Aaron sees it as the right tool for discovering patterns that conventional scientific measurement tends to miss.Cheap factorization is powerful but dangerous. Most of reality can be broken into parts and measured accurately, but some things lose all their relevant information the moment you separate them. Knowing which is which is the whole game.Consensus is infrastructure. From a gram to a traffic light to a shared definition, civilization runs on agreed-upon measurements. The moment consensus breaks down — as the French discovered after their revolution — entire systems become unstable and costly to operate.Constraints are affordances. Entropy and gravity aren't just limitations — they're the ground on which everything is built. Aaron argues that persistent laws of nature are tools, and ignoring them doesn't make them go away, it just makes your model wrong.Ontology is always present, whether you name it or not. Every agent, every system, every framework has one built in. The question is only whether you've made it explicit and intentional — or left it implicit and unexamined.Generative tension is a design principle. Aaron built an adversary agent specifically to challenge overclaims in his lab, mirroring the way opposing forces in nature and tradition keep systems honest and prevent overfitting to comfortable conclusions.Creation is participatory. Whether wrapping a wiring harness for fifty hours or building an agent network, Aaron sees making things as an active, relational process — posture, attention, and intent are what turn potential into reality. -
Stewart Alsop hosts a conversation with Oliver Polzin, a founding team member of Meow Wolf and naturalist, exploring the intersection of creativity, conservation, and architecture. Oliver discusses his current postgraduate work at SCI-Arc in Los Angeles studying synthetic landscapes through an architectural lens, his deep fascination with Pleistocene megafauna and the La Brea Tar Pits, and his vision for creating a "biophilic culture" that reframes humanity's relationship with other species and ecosystems. The discussion ranges from Oliver's early work building mud caves at Meow Wolf to his current explorations of AI-assisted design tools, 3D printing with recycled materials, holistic grazing management systems for the Great Plains, and the ancient Amazonian practice of creating terra preta soil—all part of his broader investigation into how we can design interventions for climate and conservation issues while maintaining what makes us fundamentally human.
Timestamps
00:00 Stewart introduces Oliver Polzin from Meow Wolf's founding team and discusses how his yoga teaching there inspired the podcast's exploration of creativity and stress relationships.
05:00 Oliver describes his architecture graduate program studying climate and conservation through synthetic landscapes, contrasting dark green naturalist ecology with bright green capitalist environmentalism.
10:00 Discussion of conservation ethics and AI's potential for monitoring environmental systems, with Oliver explaining his journey from painting to experimental mud construction at early Meow Wolf.
15:00 Stewart shares his robotics learning journey with ESP32s in Buenos Aires while Oliver questions humanoid robot design, suggesting functional form factors matter more than human resemblance.
20:00 Oliver explores cardboard as material obsession and explains treasure hunt mechanics in Meow Wolf exhibits, creating dopamine-driven discovery experiences through layered storytelling.
25:00 Stewart describes creating treasure hunts for Spanish learners in Buenos Aires parks while Oliver validates experiential art's growing importance in an increasingly digital culture.
30:00 Conversation shifts to three-d printing flexible filaments for architectural models and Oliver's megafauna book project about La Brea Tar Pits Pleistocene fossils.
35:00 Oliver connects Earth consciousness to Pale Blue Dot perspective, arguing humans face developmental threshold understanding planetary responsibility after 300,000 years as anatomically modern species.
40:00 Deep dive into end-Pleistocene extinction events and megafauna loss, discussing two-ton capybaras and how predator relationships shaped human psychology and anxiety responses.
45:00 Oliver presents speculative Great Plains biopreserve concept with de-extinct megafauna, contrasting holistic rotational grazing with destructive monoculture agriculture systems.
50:00 Discussion concludes with Amazonian dark earth technology and indigenous landscape management, emphasizing need for biophilic culture embracing deep time ecological perspective.
Key Insights
1. Oliver Polzin is part of the founding team of Meow Wolf and is currently studying at SCI-Arc in Downtown LA in a postgraduate program called Synthetic Landscapes, which examines global scale climate and conservation issues through an architectural lens. Architecture exists between art and science, and he believes architectural thinking offers a valuable framework for designing interventions for climate and conservation challenges. This program represents a significant evolution from his earlier work at Meow Wolf, where he created immersive experiential art installations using materials like adobe and cardboard.
2. There is an important distinction in ecological thought between what Paul Kingsnorth calls dark green and light green approaches to environmentalism. The dark green strain represents the older naturalist movement from the early twentieth century, focusing on biological systems, ecosystems, and endangered species. Light green emerged in the 1970s after the Earth Day movement and centers on clean energy, solar panels, and wind power as a way to maintain our current lifestyle. Oliver argues that the bright green approach represents a capitalist overlay that has captured the conservation movement, whereas true conservation requires focusing on actual biological systems rather than just technological solutions.
3. The experiential art form that Meow Wolf pioneered still has enormous untapped potential, particularly as society becomes increasingly digital. Oliver believes there will be a huge wave of experiential desire in this decade as people crave human connection and real-world excitement. The treasure hunt and scavenger hunt format represents a compelling form of real-life RPG that creates meaningful human interactions. This type of experience design, which Meow Wolf developed through installations like the House of Eternal Return, plays with human dopamine systems by compelling people to open doors, explore spaces, and follow narrative threads through physical environments.
4. The architectural model or dollhouse concept represents a crucial rhetorical tool that Oliver is learning to apply to climate and conservation work. Architects have long created physical models to show stakeholders what a building will be like, and this practice of showing a story in compelling ways for different types of brains is essential for getting traction on projects. While architectural models used to be made from foam core, paper, and balsa wood, they are now largely created through 3D printing, which allows for incredibly complex forms and interlocking structures that would have been impossible to construct manually.
5. Oliver is obsessed with megafauna and the end Pleistocene extinction event that occurred roughly twelve thousand years ago. For three hundred thousand years, anatomically modern humans existed alongside massive beasts like short faced bears and American lions, and we were the smaller creatures in the ecosystem. The extinction of over one hundred genera of animals over ninety nine pounds, combined with sea level rise of nearly four hundred feet, fundamentally changed human existence and led to the development of agriculture and civilization. Much of our current psychological development, including anxiety responses, is still based on this time period when we lived among these massive animals.
6. The current food system in the Great Plains is fundamentally broken compared to the historical managed food system maintained by Plains tribes, who sustained thirty to sixty million bison through 1800. Oliver explored a speculative project about turning the Great Plains into a massive biopreserve of de-extinct megafauna, contrasting the natural system of rotational grazing where predators keep herds moving with the current monoculture crop agriculture that requires external inputs like fertilizer, pesticides, and herbicides. The natural system builds soil and increases fecundity, while industrial agriculture degrades soil, creates toxic runoff, and produces genetically modified crops that feed animals in toxic concentrated feeding operations.
7. The fundamental challenge facing humanity now is creating what Oliver calls a biophilic or ecophilic culture that is loving of other species and our home planet. This requires both psychological shifts and changes in how we design systems at all scales. The Amazon provides a powerful example of this, as recent LiDAR mapping has revealed that what appeared to be pristine wilderness was actually a vast tended garden created by indigenous civilizations who developed technologies like Amazonian dark earth through burning middens with various additives. These cultures understood how to be embedded in a web with other species while playing an important orchestrating role, offering a model for how humans might relate to other forms of life in our current era. -
In this episode of Crazy Wisdom, Stewart Alsop sits down with Akin Kadioglu, cofounder of Bondi Finance, to unpack the wild world of tokenized corporate bonds and what it actually takes to bring traditional finance onto the blockchain. They trace the regulatory maze from Bermuda's segregated accounts structure to the global competition between nation states racing to build the best tokenization frameworks, then widen the lens to cover the Genius Act and stablecoin politics, why America's biggest companies have stopped going public, the techno feudalism reshaping Silicon Valley, China's strategy of copying and scaling rather than innovating, and a deep dive into emerging market bonds, default risk, and why countries like Turkey, Mexico, and Indonesia might be more investable than people assume. Find Akin on Twitter at @kadiogluakin, and check out his work at Bondi Finance, bondifinance.io.
Timestamps
00:00 Tokenization of corporate bonds and Bermuda's regulatory structure
05:00 Global tokenization frameworks and the Genius Act's impact on stablecoins
10:00 Anthropic's secondary markets, private capital, and why big companies avoid IPOs
15:00 Techno feudalism, Silicon Valley's clergy class, and China's distillation strategy
20:00 RISC-V, open source robotics, and the AI monopoly risk
25:00 American gridlock, constitutional spirit, and crypto as freedom from centralization
30:00 Argentina's 2001 default, dollar pegging, and Milei's deficit cuts
35:00 Carry trades, US treasury rates, and inflation in emerging economies
40:00 Sovereign versus corporate bonds and tokenization's $38 trillion opportunity
45:00 Investment grade versus junk bonds and zero default risk explained
50:00 Bond credit ratings, Yankee and Samurai bonds, and top emerging market picks
Key InsightsTokenization's biggest obstacle isn't technology, it's sovereignty. Akin argues that nation states resist giving tokenized assets the same ownership rights as traditional securities because they're hesitant to cede authority to neutral blockchains, even when the underlying infrastructure already works.The Genius Act protected banks more than it empowered crypto. By separating yield bearing stablecoins from non yield bearing ones, regulators effectively let banks keep customers from earning interest outside traditional savings accounts, a quiet but consequential win for legacy finance.America's biggest companies are opting out of public markets. Stripe, Anthropic, OpenAI, and SpaceX have stayed private far longer than past generations of breakout companies, raising real questions about whether venture capital has replaced the public markets that once defined American finance.Silicon Valley's elite increasingly resemble a modern clergy. Akin frames the founders and labs that gatekeep advanced AI knowledge as inheritors of a medieval power structure, where access to "secret knowledge" converts directly into capital and influence over everyone else.China wins by scaling, not innovating. Rather than leading at the frontier, China consistently lets American labs take the first step, then copies and mass produces at a fraction of the cost, a strategy Akin sees playing out in everything from manufacturing to AI models.Not all bonds carry the same kind of risk. Akin draws a sharp distinction between bonds with zero tail risk, like US treasuries denominated in their own currency, and corporate or foreign currency sovereign bonds, where default is always possible no matter how strong the issuer looks.Emerging market ratings can be misleading. A BB rated company in an emerging market may have a lower default rate than a BBB rated US company, since emerging market firms typically need far more financial maturity just to access public bond markets in the first place. -
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, creator of the Knowledge Graph Insights Podcast, for their second conversation together. The two cover a wide range of interconnected topics, starting with a correction Larry makes about the true origin of the term "artificial intelligence," tracing it back to the 1956 Dartmouth Conference and its distinction from Norbert Wiener's cybernetics. From there, the conversation moves through the history and structure of knowledge graphs, ontologies, RDF (Resource Description Framework), and the W3C standards process, touching on concepts like the T-box, A-box, and C-box, as well as the 25th anniversary of the Semantic Web paper. Stewart and Larry also dig into the limitations of large language models — particularly around reasoning, confabulation, and what Larry describes as "cognitive surrender" — and why symbolic AI and knowledge engineering may hold answers that the neural network world hasn't fully embraced. The episode also ventures into consciousness, panpsychism, Michael Pollan's ideas, and Stewart's own hands-on experience vibe coding a personal chatbot to replace functionality he feels he's lost with recent changes to Claude. Larry's podcast can be found at kgi.fm.
Timestamps
00:00 - Stewart introduces Larry Swanson; Larry corrects the record on AI's origin, distinguishing it from Norbert Wiener's cybernetics at the 1956 Dartmouth conference.
05:00 - Larry discusses interviewing semantic web paper coauthors on its 25th anniversary; RDF's hidden ubiquity compared to SIM cards powering everything invisibly.
10:00 - Knowledge graphs explained through t-box terms, a-box assertions, and Dave McComb's c-box; IKEA's three-layer knowledge graph as a practical example.
15:00 - Stewart connects metadata complexity to AI needs; faceted search explained as c-box attributes driving product filtering experiences.
20:00 - RDF 1.2 reification standards discussed; W3C's rigorous recommendation process powering governments and enterprises worldwide through collaborative standards.
25:00 - Cyc project examined as influential "successful failure"; Pat Hayes bringing description logic into semantic web; LLMs lacking true reasoning capability.
30:00 - Epistemological fault lines between human and computer intelligence; cognitive surrender paper reveals no intelligence threshold protects against AI manipulation.
35:00 - Stewart's Claude regression problem drives chatbot vibe coding quest; small language models and domain-specific approaches explored as alternatives.
40:00 - Consciousness discussion through Michael Pollan's panpsychism lens; language versus cognition disconnect revealing LLMs as pure token-stitching without genuine thought.
45:00 - Context graphs as purpose-built knowledge graphs for AI; Stewart's planning agents versus coding agents architecture and ground truth verification problem.
50:00 - Docs-as-code versus code-as-docs paradigm shift; knowledge graphs as universal verifiers against validated facts; RDF 1.2 enabling provenance and degrees of certainty.
55:00 - Jessica Talisman's Knowledge Graph Academy recommended for onboarding; kgi.fm podcast shared; knowledge representation community needs better abstraction for wider adoption.
Key Insights
1. The term "artificial intelligence" was not a marketing gimmick but was coined deliberately at the 1956 Dartmouth Conference to distinguish the work of John McCarthy from Norbert Wiener's cybernetics. The two camps represented genuinely different approaches, and the AI label was a form of intentional intellectual branding rather than empty promotion.
2. The semantic web, often called the most successful failure in technology history, has quietly embedded itself everywhere despite never achieving its original vision. Technologies like RDF power metadata standards inside every Adobe product and form the invisible backbone of government systems, enterprise data infrastructure, and cultural heritage organizations worldwide.
3. Knowledge graphs are best understood as an ontology combined with all the instances that populate it. The distinction between things and strings, popularized by Google in 2012, captures the core idea that knowledge representation is about concepts as distinct from the labels we give them.
4. The t-box, a-box, and c-box framework offers a practical model for understanding knowledge architecture. The t-box holds terminology and concepts, the a-box holds assertions about specific instances, and the c-box manages the attributes, taxonomies, and controlled vocabularies that sit between them and enable things like faceted search.
5. Large language models produce fluent, convincing output but lack genuine reasoning, epistemological grounding, or judgment. Research on cognitive surrender shows that even people who understand how LLMs work are still susceptible to being misled by their fluency, meaning intelligence and awareness offer no reliable protection against being deceived.
6. The gap between language and cognition matters deeply when evaluating AI. Evidence from people with aphasia shows that thinking can occur without language, which suggests LLMs, being purely language-based systems, are missing a fundamental layer of cognition that cannot be recovered through more tokens or better training.
7. Knowledge graphs and RDF-based representation are well suited to the problem of verification and grounding in AI systems. Rather than relying on vectorized embeddings of language, a knowledge graph can store validated, provenance-tracked facts with degrees of certainty, making it a natural foundation for building trustworthy AI applications. -
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with client strategist Amadeus Huff to cover a wide range of topics that wind their way from the nuts and bolts of recruiting and payment models to the rapidly shifting landscape of AI adoption in business. The two dig into how AI tools are reshaping client success roles, the murky territory of recording laws and privacy in a globalized world, the geopolitical implications of oil supply chains, sanctions, and the rise of domestic tech ecosystems in countries like Russia and Argentina, and what all of this means for the future of human connection and the nation-state. Amadeus closes on an optimistic note, arguing that as AI takes over bureaucratic busywork and erodes trust online, people will increasingly hunger for genuine human relationships and third spaces. You can connect with Amadeus Huff on LinkedIn.
Timestamps
00:00 - Stewart introduces Amadeus Huff, diving into recruiting as building connections between job seekers and employers with minimal variance.
05:00 - Amadeus discusses AI adoption pitfalls, comparing aggressive growth strategies to Amazon's early model, questioning whether tools deliver promised results.
10:00 - Conversation shifts to AI notetaking versus human perception, exploring probabilistic interpretation differences between humans and machines.
15:00 - Recording consent laws debated across states, touching on Waymo surveillance, Uber data collection, and public versus private space definitions.
20:00 - Global privacy landscape examined, covering Swiss banking secrecy erosion, ProtonMail's departure, and RISC-V semiconductor development escaping US jurisdiction.
25:00 - Sanctions creating domestic innovation ecosystems discussed through Russia's example, paralleling Argentina's emerging commerce evolution.
29:00 - Closing reflections on AI replacing bureaucracy while preserving human purpose, optimism about meaningful work and deeper personal connections emerging.
Key Insights
1. Recruiting is fundamentally about reducing variance between what job seekers want and what employers offer. The most ethical payment models in recruiting are tied to proven success, such as waiting three months to confirm a hire is working out, rather than collecting fees the moment a contract is signed.
2. Business thinking has shifted from shareholder value to stakeholder value, meaning companies now consider the wellbeing of employees, families, and communities, not just stock price. This shift is accelerating due to AI overpromising and underdelivering, making value-based measurement more important.
3. AI is most useful when it handles administrative tasks that provide no direct value to customers, such as transcribing meetings and populating CRM systems. This frees up workers to focus on meaningful relationship-building and intellectual work rather than bureaucratic busywork.
4. There is an important distinction between recorded and unrecorded conversation in professional settings. Building trust through informal off-the-record dialogue before switching on a transcription tool creates clearer boundaries and stronger relationships with clients.
5. Sanctions tend to follow a bell curve of effectiveness. Over time they force sanctioned countries to build domestic alternatives, which gain adoption and loyalty, ultimately reducing the influence of the original foreign companies once sanctions lift.
6. AI is degrading trust in online information to the point where people will increasingly crave authentic human connection, physical gathering spaces, live experiences, and real relationships rather than algorithmically generated content.
7. AI is quietly improving intergenerational relationships by removing codependency. When elderly parents learn to use AI for technical help, their calls to family members shift from problem-solving to genuine connection, which strengthens the relationship. -
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with software engineer and entrepreneur Arowolo Muritadhor for a wide-ranging conversation that moves from agriculture and manufacturing in Nigeria to the evolving role of crypto in the country's economy. They touch on how hyperinflation, particularly the naira's dramatic drop in 2023, pushed Nigerians toward stablecoins as a practical savings tool, and how informal kiosk networks have stepped in where traditional banking infrastructure falls short. The conversation also covers the tension between government regulation and the permissionless nature of blockchain technology, comparisons between the decline of the Roman Empire and current shifts in US economic dominance, the role of mobile payments in Africa, language learning, and whether AI agents have any real utility in crypto infrastructure yet. You can connect with Arowolo on LinkedIn and X at @armolas_06.
Timestamps
00:00 - Host welcomes Arowolo Muritadhor, introducing topics of software engineering and animal food production in Nigeria.
05:00 - Discussion shifts to manufacturing, components assembly, and China's dominance in low-cost production globally.
10:00 - Conversation explores crypto adoption in Nigeria as a network state phenomenon, separating informed users from mainstream population.
15:00 - Mobile payments and kiosk ATM replacements emerge as critical financial infrastructure bridging unbanked Nigerians.
20:00 - Roman Empire parallels drawn to modern crypto taxation, government control, and inevitable death-and-taxes reality.
25:00 - Bitcoin and Ethereum permissionless nature debated against government wallet-level censorship vulnerabilities.
30:00 - AI agents examined as crypto infrastructure tools, revealing mostly trading bots rather than foundational builders.
35:00 - Nigeria's 2023 naira collapse compared to Argentina's hyperinflation, driving citizens toward stablecoin dollar savings.
40:00 - US Treasury history unpacked through FDR gold confiscation and Nixon ending convertibility, paralleling empire decline.
45:00 - Crypto reframed as anti-bank rather than purely anti-government, enabling freedom through immutable accountability.
50:00 - Transparent blockchain ledgers discussed as potential government accountability tools across democracy, republic, and oligarchy structures.
Key Insights
1. Nigeria has a significant divide between its northern and southern regions in terms of economic activity. The north, centered around Abuja, is more agricultural with substantial cattle production, while Lagos in the south functions as a dense urban and commercial hub. This geographic and economic split shapes how different financial tools and technologies are adopted across the country.
2. China's dominance in low-cost manufacturing has made it nearly impossible for countries like Nigeria, the United States, or Argentina to compete on price alone. The more realistic path for developing economies is to import components and focus on local assembly and creativity, which is where meaningful economic participation becomes possible.
3. Crypto adoption in Nigeria accelerated dramatically around 2023 when the naira experienced a sharp devaluation against the US dollar. Before that point, saving in dollars was difficult for many Nigerians, especially those without formal bank accounts, making stablecoins like USDT an attractive and practical alternative for preserving wealth.
4. Informal kiosk operators in Nigeria have organically become a substitute for ATMs, giving communities access to basic financial services where traditional banking infrastructure does not reach. This grassroots financial layer is now a key entry point for integrating crypto and stablecoin payments into everyday commerce.
5. Governments are increasingly trying to regulate crypto at the wallet and centralized exchange level, using tax compliance as a primary mechanism. While Bitcoin and Ethereum remain largely permissionless, the practical chokepoints for most users remain centralized platforms where identity and transactions can be monitored.
6. The historical parallel between the fall of the Roman Empire and current shifts in US economic and geopolitical power offers a useful frame for understanding why crypto matters. Just as Rome debased its currency and struggled to sustain imperial costs, the US faces mounting debt and a financialized economy that may accelerate dollar instability and push more people toward alternative stores of value.
7. One genuinely constructive use case for blockchain beyond speculation is immutable accountability, particularly for public institutions and prediction markets. A transparent ledger that governments or officials voluntarily adopt could create verifiable records of decisions and promises, reducing corruption and increasing trust in ways that traditional governance structures have struggled to achieve. -
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop interviews Joshua Pearce, the John Thompson Chair in Innovation at the Department of Electrical and Computer Engineering and Ivey Business School at Western University, about the revolution in open source hardware for scientific research. They discuss how three-dimensional printing, Arduino controllers, and open source designs are dramatically reducing research costs—often by 85-95%—while democratizing access to lab equipment worldwide. Pearce shares stories from his 2013 book "Open Source Lab" and explains how the movement has exploded since then, covering everything from filter wheel changers and ball mills to metal three-dimensional printers and battery research equipment. The conversation explores recycle bots that turn plastic waste into filament, the role of AI in accelerating hardware development, and how open source licensing creates a global knowledge management system where improvements are shared across the scientific community. For those interested in learning more, Pearce recommends checking out the journal HardwareX, repositories like Thingiverse and My Mini Factory, and appropedia.org for open source scientific tools and appropriate technology designs.
Timestamps
00:00 Welcome and introduction to Joshua Pearce, discussing his work on open source lab equipment and the evolution since publishing his book in 2013
05:00 Early development of open source hardware including the breakthrough filter wheel changer project built by a high school student that saved thousands of dollars
10:00 Discussion of how Arduino and RepRap three-d printers enabled the democratization of scientific tools, making complex equipment accessible to anyone
15:00 Economic impact showing average tool savings of 85 percent, with Arduino and three-d printing combinations reaching mid-90s percent cost reduction
20:00 Case study of PhD student Mariam building complete battery research tool chain from scratch using open source designs and three-d printed components
25:00 Recycle bots enabling transformation of waste plastic into three-d printer filament for pennies, revolutionizing material costs and sustainability
30:00 Collaboration between universities and open source companies creating fluid handlers and acquisition systems, accelerating research capabilities globally
35:00 Large language models assisting code translation and research planning, though hallucinations require careful verification and domain expertise
40:00 Importance of fundamental knowledge when using AI tools, comparing vibe coding acceleration with necessity for understanding underlying principles
45:00 Testing standards and calibration methods for open source equipment, balancing precision requirements against cost-effectiveness for specific applications
50:00 Metal and ceramic three-d printing developments including MIG welding techniques and sintering processes for creating functional parts
55:00 Knowledge management through open source licenses, repositories like Thingiverse and Apropedia enabling global collaboration and continuous improvement
Key Insights
1. Open source hardware has evolved dramatically since Joshua Pearce wrote his book in 2012-2013, to the point where he can no longer keep up with all the developments in the field. What started as a collection where every single example could fit in one book has exploded into an entire ecosystem with dedicated journals and thousands of researchers contributing. The vision was that scientific papers would eventually include hyperlinks to equipment designs that anyone could download and replicate, and that future is largely here today. There are now so many open source hardware articles being published that no single person can read them all, which represents a massive success for the movement.
2. The fundamental breakthrough enabling open source scientific hardware came from combining several key technologies, particularly the RepRap three-d printer project and Arduino microcontrollers. Pearce's introduction to the field came when he needed a sixty-five dollar plastic part for a solar laptop project and discovered Adrian's open-sourced rapid prototyper that could make its own parts. This led to building equipment like a filter wheel changer for testing solar panels with a high school student in about a week, replacing a device that would have cost two thousand five hundred dollars with five months lead time. The democratization of tools like three-d printing and Arduino, combined with extensive code libraries and shared designs, means that even high school students can now create sophisticated scientific equipment.
3. Open source scientific hardware delivers massive economic benefits, with the average tool saving scientists around eighty-five percent compared to commercial equipment, and savings reaching the mid-nineties when using Arduino and three-d printing. The economics are so compelling that the tax paid on a normal scientific tool can cover the cost of an open source alternative. A thousand dollar three-d printer can manufacture scientific tools worth more than a thousand dollars in a single Saturday. This dramatic cost reduction makes sophisticated research accessible to laboratories around the world regardless of their funding levels, fundamentally democratizing scientific capability.
4. The knowledge management approach enabled by open source licenses creates a powerful collaborative improvement cycle where thousands of people worldwide contribute to evolving designs. When researchers publish equipment designs with strong reciprocal licenses, anyone can use, modify, or even sell the designs, but improvements must be shared back with the community. This creates a dispersed international engineering effort where equipment continuously improves through contributions from researchers across different institutions and countries. The RepRap three-d printer exemplifies this process, starting as barely functional prototypes but evolving through community contributions to surpass commercial alternatives in speed, resolution, and material capabilities.
5. The integration of large language models and AI tools has significantly accelerated open source hardware development, though with important caveats about their limitations. LLMs excel at translating code between languages, suggesting experimental approaches, and helping researchers navigate unfamiliar fields by quickly synthesizing information from scientific literature. However, they suffer from hallucination problems and cannot be trusted for writing scientific articles or conducting complete literature reviews without verification. The key to effective use is having enough foundational knowledge to ask the right questions and verify outputs, using AI as a powerful acceleration tool rather than a replacement for expertise.
6. Material science capabilities in open source hardware have expanded far beyond plastic three-d printing to include metals, ceramics, semiconductors, and composites through innovative adaptations of basic equipment. Pearce's lab has developed methods for metal three-d printing using modified MIG welding for as little as twelve hundred dollars, created slot-die coating systems for seventeen nanometer semiconductor layers using converted three-d printers, and developed techniques for ceramic printing through various material mixing approaches. The recycle bot technology enables converting waste plastic into high-quality filament for twenty-five cents instead of twenty-five dollars per roll, dramatically reducing material costs while enabling circular manufacturing practices.
7. The infrastructure for sharing and discovering open source hardware designs has matured into a robust ecosystem spanning academic journals, commercial repositories, and specialized communities. Hardware X and the Journal of Open Hardware publish peer-reviewed designs alongside traditional scientific journals increasingly incorporating open hardware sections. Repositories like Thingiverse recently returned to hardcore open source principles after ownership changes and contains millions of designs, while Appropedia serves as a wiki for appropriate technology with thousands of open source designs. The GOSH community hosts annual conferences bringing together university researchers, companies, and independent hardware hackers, while field-specific communities have formed around technologies like the OpenFlexure microscope, creating networks where knowledge accumulates and never gets lost. -
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with returning guest Ekue Kpodar for their third conversation together, covering a wide range of topics at the intersection of technology, geopolitics, and the evolving information age. They dig into Ekue's unconventional setup of running local AI models across roughly 15 computers, the growing case for open source models over closed ones from companies like OpenAI and Anthropic, and how Chinese open source models may be positioned to outcompete Western alternatives on a global scale. The conversation also touches on vibe coding and the democratization of software development, the strategic use of small models for IoT and enterprise applications, the role of Israel and China as dominant players in the information age, and how smaller nations and even individuals may wield outsized power as AI continues to collapse the cost of knowledge work. You can find Ekue Kpodar on X @ekpodar and LinkedIn.
Timestamps
00:00 Stewart welcomes Ekue for their third episode, diving into vibe coding and AI-driven development changes.
05:00 Ekue explains using Claude on Chrome to auto-reply on Skool, burning tokens through screenshots, and Playwright as a more efficient alternative.
10:00 Stewart describes his Claude-dependent planning and coding agent system breaking after a model update, prompting him to build his own chatbot.
15:00 Small models discussed as critical for IoT, defense, and privacy-focused enterprises building internal APIs instead of routing traffic to OpenAI.
20:00 Open source versus closed source debated, with Chinese models gaining global traction while US foundational labs remain expensive and restrictive.
25:00 SaaS apocalypse explored as AI commoditizes knowledge work, with Linux and Terraform cited as proof open source still generates wealth.
30:00 OpenAI's sci-fi terminator fears explained as the reason they stayed closed source, ultimately handing China a strategic open source advantage.
35:00 China's economic dumping strategy applied to AI, potentially displacing US model dominance globally the same way manufacturing was disrupted.
40:00 Israel's signals intelligence dominance discussed alongside asymmetric warfare, drones defeating tanks, and information control replacing military muscle.
45:00 Global information age rankings debated, Israel leading, US and China tied, France and Poland emerging as sovereign tech players.
50:00 Qatar, NVIDIA, and Iran cited as proof that rare resources and technology matter more than population size in the 21st century power landscape.
Key Insights
1. Running local AI models on a network of affordable computers can be more cost-effective than relying entirely on third-party APIs. By using compressed or smaller open source models locally, developers can handle repetitive or lower-stakes tasks without burning through expensive tokens from providers like Anthropic or OpenAI.
2. Small AI models are becoming increasingly important for IoT, defense applications, and companies that do not want to send sensitive data to external providers. Organizations can download open source models, run them on internal servers, and build proprietary APIs around them, creating something like an intranet of specialized small models.
3. The value created by AI tools is being redistributed away from traditional SaaS companies toward foundational model providers and individual builders. People are canceling subscriptions to software they once paid hundreds per month for, because AI now allows a single person to build comparable tools themselves.
4. Open source technology does not eliminate the ability to profit. Linux and Terraform are both open source yet made their creators wealthy. People will still pay for installation, setup, troubleshooting, and customization even when the underlying software is free.
5. China is applying its longstanding manufacturing dumping strategy to artificial intelligence by releasing cheap open source models globally, which threatens to erode US dominance in AI the same way Chinese manufacturing undercut other countries for decades.
6. In the information age, the size of a country or institution matters far less than its access to rare resources or advanced technology. Qatar, Israel, and NVIDIA each demonstrate that small populations or headcounts can wield enormous global negotiating power through concentrated technological or resource advantages.
7. Asymmetric warfare is redefining military power, with inexpensive drones defeating tanks that cost millions to build. This shifts the advantage toward nations that excel at signals intelligence and information management rather than those with the largest conventional military forces. -
Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.
Show Notes:
- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business
- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being told
Timestamps
00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups
05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology
10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs
15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting
20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers
25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training
30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks
35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information
40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents
45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge
50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos
55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development
60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products
65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodiment
Key Insights
1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.
2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.
3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.
4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.
5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.
6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.
7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge. -
Stewart Alsop interviews Nizar, CEO of Pixel Robotics, on the Crazy Wisdom Podcast to explore the intersection of AI, robotics, and perception. The conversation covers a wide range of technical topics including how transformers enable multimodal representation across text, images, and voice, the role of world models in predicting physical interactions, the advantages of diffusion models over traditional LLMs for certain applications, and the challenges of achieving real-time processing for robotics applications. Nizar explains Pixel Robotics' work on creating accurate 3D meshes from smartphone cameras for companies like L'Oréal, moving away from specialized sensors to make the technology more accessible through sophisticated algorithms, and discusses the future of robotics as closing the perception-action loop to enable robots to perform real tasks beyond simple demonstrations. To find out more visit Pixel Robotics' website.
Timestamps
00:00 Stewart welcomes Nizar, CEO of Pixel Robotics, discussing what a pixel is as the smallest visual unit on screens composed of red green and blue colors
05:00 Discussion of perception systems and how logarithmic laws help compress signals in both human and artificial systems, exploring normalization layers and sigmoid functions in deep learning
10:00 Exploring how transformers unified different data modalities including text voice and images, creating common representations through methods like contrastive learning
15:00 Nizar explains transformers as brute force learning systems with room for improvement through focused attention mechanisms and knowledge graphs rather than processing everything
20:00 Conversation about loss functions local minima versus global minima and how mixture of experts uses specialized small models instead of one massive generalist network
25:00 Discussion of deterministic versus probabilistic systems and how explicitly defined task graphs often outperform orchestrator-based approaches in AI systems
30:00 Exploring world models as predictive physics-based systems that learn environmental flows and transformations, complementing rather than replacing language models
35:00 Nizar discusses real-time processing challenges for robotics requiring millisecond responses with small memory footprints using vision transformers for faster experimentation
40:00 Pixel's work creating three d meshes from smartphone cameras for companies like L'Oreal, moving away from specialized sensors toward accessible software-based solutions
45:00 Explanation of different three d representations including voxels point clouds and meshes, with meshes being optimal for manipulation and rendering in applications
50:00 Future direction involves closing perception-action loops in robotics, moving beyond dancing toy robots toward practical multimodal systems that perform real tasks
55:00 Pixel's goal is democratizing high-quality three d scanning through smartphones, making mesh creation accessible to unlock applications in gaming cinema and virtual showrooms
Key Insights
1. Pixel Robotics derives its name from combining perception and action in robotics, where the pixel represents the digital perception component and robotics represents the physical action component. The pixel serves as a metaphor for how robots must quantize and digitize continuous analog information from the real world into discrete units that computer systems can process, similar to how pixels are the fundamental building blocks of images on a screen. This quantization process is essential because numerical systems cannot work with truly continuous data and must convert reality into tractable digital representations that algorithms can manipulate.
2. The transformer architecture has created a fundamental unification in how different types of data can be represented and processed across multiple modalities. Before transformers, researchers working on natural language processing, computer vision, and audio analysis used completely different approaches and methodologies. The breakthrough of transformers was establishing a common representational framework that could handle text, images, voice, and other data types using similar underlying mechanisms. This unification is what enabled the development of truly multimodal AI systems and represents one of the most significant advances beyond just the language modeling capabilities that initially gained public attention.
3. Current transformer-based systems represent a brute force approach to learning that will likely be superseded or enhanced by more efficient algorithms. Despite claims that we have exhausted internet text data for training, significant improvements continue to emerge every few months through algorithmic innovations rather than simply adding more data. Future developments will likely involve more specialized attention mechanisms that focus on relevant information rather than correlating everything with everything, mixture of experts architectures with small specialized models, and approaches inspired by biological systems such as logarithmic compression laws and event-based processing that humans use naturally.
4. Diffusion-based language models represent a promising alternative to standard next-token prediction that could produce more accurate outputs through an iterative refinement process. Unlike traditional language models that predict one token at a time and cannot revise earlier outputs, diffusion models treat text generation like image denoising, starting with a noisy representation and progressively refining the entire output across multiple steps. This holistic approach allows the model to reconsider and improve all parts of the response simultaneously, potentially leading to higher quality results, though it may be slower than current autoregressive methods. This represents an important direction for overcoming fundamental limitations in how language models currently generate text.
5. For robotics applications, real-time performance and small model size are critical constraints that differ significantly from the requirements of large language models deployed in data centers. Vision transformers are being used as a testbed for developing efficient real-time algorithms because they require far fewer computational resources to train and test compared to large language models, making them more practical for rapid experimentation. The goal is to achieve millisecond-level response times with minimal memory footprint so that robots can react quickly to dynamic environments and run on affordable hardware that can be embedded in actual robotic systems rather than requiring expensive server infrastructure.
6. Practical robotics implementation requires moving beyond specialized sensors to software solutions that work with ubiquitous devices like smartphones for tasks such as three-dimensional reconstruction. Pixel Robotics evolved from building specialized scanning hardware to focusing on algorithms that can generate high-quality mesh representations of environments using only smartphone cameras, making the technology far more accessible and practical for real-world deployment. This approach enables applications ranging from industrial robotic arm control to virtual showrooms, and more importantly, it allows anyone to capture three-dimensional data without expensive equipment, which can also help generate larger training datasets for future AI development.
7. The next frontier in AI and robotics is closing the perception-action loop to enable robots to perform real practical tasks rather than remaining as demonstration systems or toys. While significant progress has been made in cognitive capabilities through language models and in robotic mobility through mechanical engineering advances, the critical challenge is integrating perception with action through systems like Vision-Language-Action models. The fundamental starting point for learning this integration is simple perception-action exercises, such as programming a camera mounted on servo motors to track and center a colored object, which demonstrates the basic principle of using sensory input to drive physical response that underlies all more sophisticated robotic behaviors. -
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.
Timestamps
00:00 - 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 Insights
1. 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. -
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Tyler Cloutier, founder of Clockwork Labs and creator of SpaceTimeDB. They explore how SpaceTimeDB functions as more than just a database—it's essentially a distributed operating system that merges server logic with data storage, enabling real-time applications and time-travel capabilities. The conversation ranges from the technical architecture of databases and operating systems to the philosophy of distributed systems, touching on everything from Unix and Linux to how SpaceTimeDB could revolutionize AI-generated software deployment. Tyler explains how their system reduces the complexity of building real-time applications, makes deployment simpler for both humans and AI agents, and why games like their MMORPG BitCraft Online drove them to create this new infrastructure. They also discuss the future of the internet, the role of bots in gaming, and how SpaceTimeDB fits into the broader landscape of cloud computing alongside tools like Cloudflare, Vercel, and Docker. For more information, visit spacetimedb.com or check out Clockwork Labs on GitHub and Twitter.
Timestamps
00:00 Stewart introduces Tyler Cloutier, founder of Clockwork Labs, discussing the origin of SpaceTimeDB's name inspired by Einstein's theory and its time travel capabilities that store all operations indefinitely
05:00 Tyler explains SpaceTimeDB as more of an operating system than a database, using tables instead of file systems while running code in a sandboxed environment with full atomic properties
10:00 Discussion of how SpaceTimeDB replaces both Node.js and Postgres by merging web server and database functionality, eliminating separate deployment concerns
15:00 Tyler explains JavaScript execution through Chrome's V8 engine and JIT compiling, leading to Node.js creation for server-side JavaScript development
20:00 Explanation of stateless web servers versus stateful game servers, and why games require in-memory state management for real-time performance
25:00 Tyler introduces reducers and real-time subscriptions, questioning why more applications aren't real-time when state changes should update immediately
30:00 Discussion of Facebook as essentially a text-based MMO, comparing social media architecture to game server requirements and the need for unified systems
35:00 Tyler explains ACID properties in databases: atomic, consistent, isolated, and durable, using game item trading examples
40:00 Comparing SpaceTimeDB to smart contract systems without cryptocurrency or global consensus, positioning it as a smart database with centralized trust
45:00 Tyler reveals SpaceTimeDB uses 43% fewer tokens than Postgres for AI-generated applications, making it valuable for vibe coding platforms
50:00 Conversation shifts to bots in games and proof-of-human concepts, with Tyler proposing biometric systems and discussing potential in-person gaming applications
55:00 Closing discussion about tracking AI-driven traffic through UTM parameters and finding SpaceTimeDB at spacetimedb.com
Key Insights
1. SpaceTimeDB is fundamentally a database that runs application code directly inside it, combining what traditionally required separate systems like Postgres and Node.js. Users compile their application logic into WebAssembly or JavaScript and upload it to run within the database itself. This architecture provides high performance because the entire server backend operates inside the database environment. The system also features time travel capabilities, storing every operation and change to data persistently and indefinitely, allowing users to set application state back to any earlier point in time. This makes SpaceTimeDB more accurately described as an operating system rather than just a database, where the abstraction is that everything is a table rather than a file.
2. The inspiration for SpaceTimeDB came from building BitCraft Online, an MMORPG where all players exist in a single persistent world and rebuild civilization together. Traditional MMO backends required complex custom solutions to handle real-time state, with game servers storing state in memory and periodically writing to databases. This complexity existed because games cannot afford the latency of constantly delegating to distant databases like traditional web applications can. SpaceTimeDB solved this by making the database fast enough to handle real-time requirements directly, eliminating the need for separate game servers. This same performance advantage that benefits games also applies to web applications, which is why SpaceTimeDB evolved from a game-specific tool to a general-purpose platform.
3. SpaceTimeDB functions as a distributed operating system where each database acts like a process in an actor model system, similar to Erlang or Scala Akka. Databases can send messages to other databases and be spawned across a cluster for horizontal scaling. This represents an overlay operating system running on top of Linux rather than competing with it, providing a distributed abstraction across many machines while Linux handles device drivers and hardware support. The vision is for the cloud to function as a single enormous computer running one operating system, where developers simply publish their programs without managing separate services, deployment, routing, networking, or persistence infrastructure.
4. The real-time capabilities of SpaceTimeDB address a fundamental limitation in how most web applications work today. Traditional web servers are stateless, delegating all state to databases and accepting network round-trip latency for each request, which is why users often must refresh pages to see updates. SpaceTimeDB allows queries to be subscribed to, maintaining open connections that stream changes whenever query results update. This makes applications like Discord, Facebook, or banking systems naturally real-time without requiring page refreshes. The historical accident that more things are not real-time represents a problem SpaceTimeDB solves by unifying the web world with the game world's real-time requirements.
5. SpaceTimeDB implements ACID properties—Atomic, Consistent, Isolated, and Durable—ensuring database operations are reliable and safe. Atomic means operations either fully happen or not at all, preventing issues like item duplication in games when trading between players. Consistent means declared invariants like unique usernames are always enforced. Isolated means concurrent operations do not interfere with each other. Durable means changes persist even if computers restart, with varying levels from in-memory on one machine to disk storage across multiple geographic locations. These properties are managed through reducers, functions inspired by React Redux that fold changes into application state incrementally.
6. For AI and large language models, SpaceTimeDB offers significant advantages in building and deploying applications. Testing showed that creating applications with SpaceTimeDB uses 43% fewer tokens compared to Postgres implementations, costs less, has fewer bugs, and is easier to extend. This matters because the primary cost for vibe coding platforms is tokens. As more software gets written in the next twelve months than ever before, there is insufficient focus on infrastructure required to run all this AI-generated software. SpaceTimeDB positions itself as ideal for LLMs to target because of its simplified deployment model where developers just publish code and the system handles everything behind the scenes.
7. SpaceTimeDB can be understood as a smart contract system without cryptocurrency or global decentralized consensus. Like blockchain smart contracts, it executes code with atomic, consistent, isolated, and durable properties, but avoids the expense and slowness of requiring all computers worldwide to agree on everything. Instead, it offers centralized trust where users trust Clockwork Labs not to modify deployed contracts, rather than the trustless but extremely costly blockchain approach. This makes it functionally similar to Cloudflare's durable objects but with full relational database capabilities. The system exists before the networking layer where Cloudflare operates, handling deployment, server, and database functions while Cloudflare could provide DDoS protection in front of it. -
In this episode of Crazy Wisdom, Stewart Alsop sits down with Kieran Zimmer — a software developer and independent researcher in psychology and psychometrics — to explore the science behind intelligence and personality. They trace the origins of psychometrics from Wilhelm Wundt's early experimental psychology through Charles Spearman's discovery of the g factor, breaking down what IQ actually measures, how verbal, mathematical, and spatial intelligence relate to one another, and why training specific cognitive tasks doesn't translate into a broader boost in general intelligence. The conversation moves into the Big Five personality traits reframed through a cybernetic lens — looking at extraversion as reward sensitivity, agreeableness as social affiliation, and conscientiousness as long-term goal prioritization — before landing on Kieran's original research into the psychology of agency: what personality profile best predicts agentic behavior, and why the environment shapes whether agency is even adaptive in the first place.
Show notes:Substack: Liminal RevolutionsTwitter/X: @LiminalRevYouTube: @TheKieranZimmer (to listen to Kieran's conference talk on the agency paper)Timestamps
00:00 — Stewart and Kieran trace the origins of psychometrics back to Spearman, Binet, and Wilhelm Wundt's early experimental psychology.05:00 — The conversation unpacks the g factor, fluid vs. crystallized intelligence, and why IQ is fundamentally a physical trait tied to nerve conduction velocity.10:00 — A tangent into AI and LLMs: why they lack vision, taste, judgment, and accountability — the human moat that remains for now.15:00 — Stewart's Claude Code failure sparks a discussion on AI accountability, surveillance, and the rise of dystopian technocracy.20:00 — Parallel structures as a form of exit from failing institutions, and the high-agency people required to build them.25:00 — Agency, risk-taking, and accountability through Napoleon, the Inuit, and why modern Western leaders are managers, not leaders.30:00 — Elites vs. peasants, cost externalization, and Kirk Doolittle's natural law as the physics of cooperation.35:00 — Ressentiment, Nietzsche's under-utilization in psychology, and how secularism replaced the church.40:00 — Kieran's quantitative conspiracy theory study: factor analysis of 85 questions across 273 respondents.45:00 — Two branches of conspiracy belief: the aliens-and-Satanism cluster vs. the fakery factor pathway to Flat Earth.50:00 — AI psychosis, Gnosticism, and the collapse of sense-making institutions in an age of information overload.55:00 — Michael Levin's embodied cognition and cybernetic agency: thermostats, humans, and homeostatic set points.1:00:00 — The Cybernetic Big Five broken down: extraversion as reward sensitivity, agreeableness, neuroticism, and the optimal personality profile for agency.
Key InsightsIQ is a physical trait, not just an abstract score. It's rooted in nerve conduction velocity, brain connectivity, and processing speed — and while you can improve crystallized intelligence through learning, the underlying g factor doesn't budge no matter how many brain training apps you use.The human moat against AI comes down to four things: vision, taste, judgment, and accountability. LLMs are powerful next-token predictors, but they have no stake in the outcome and no capacity to own a mistake — which means a human with those qualities will always be essential.High agency is not just ambition — it's a measurable psychological profile. Kieran's paper frames it through the Cybernetic Big Five: high assertiveness, high intellect, low politeness, low neuroticism, and medium conscientiousness. Getting things done at scale almost always involves upsetting people.All agentic behavior involves risk, and the willingness to absorb that risk is what separates real leaders from managers. Modern Western leadership has decoupled decision-making from consequence, which is why institutions are losing trust and authority at an accelerating rate.Conspiracy belief follows a measurable path dependency. Kieran's factor analysis showed that virtually everyone who believes in Flat Earth also endorses the fakery factor and the Jewish question cluster — but not vice versa. It's a spectrum with a clear escalation pattern, not a random set of unrelated beliefs.AI is accelerating epistemic breakdown. Sycophantic models will validate almost any idea, which has started producing a new category of high-IQ delusion — intelligent people convincing themselves they've solved Millennium Prize problems because the AI kept agreeing with them.The Big Five personality traits can be recast as cybernetic parameters — each one an evolutionarily selected mechanism for regulating goal-directed behavior. Extraversion is reward sensitivity, agreeableness is social affiliation, neuroticism is threat response, and conscientiousness is the preference for long-term over short-term goals. -
How Damian uses mindfulness to enhance his creative flow in business
