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 Revolutions
• Twitter/X: @LiminalRev
• YouTube: @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 Insights

1. IQ 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Cindy Cohn, Executive Director of the Electronic Frontier Foundation (EFF), for a wide-ranging conversation covering the EFF's origins and mission, the countercultural roots of Silicon Valley, the rise of surveillance-based business models, the challenges facing open source software and open-weight AI models, the legal landscape around intellectual property and privacy law, and the growing tension between government overreach and civil liberties in the digital age. Cindy also discusses her upcoming departure from EFF after 26 years, the transition to new leadership, and her recently published book Privacy's Defender, which chronicles key legal battles she fought to protect digital privacy rights.

Links mentioned:
- EFF website: eff.org
- Privacy's Defender book: eff.org/privacysdefender

Timestamps

00:00 - Stewart introduces Cindy Cohn, EFF Executive Director, who explains the organization's mission protecting digital rights since 1990.
05:00 - Cindy connects counterculture roots to early internet idealism, describing how digital communication broke down physical barriers for organizing.
10:00 - Cindy reveals surveillance becoming the dominant business model surprised her, blaming corporate consolidation over naive techno-optimism.
15:00 - Discussion shifts to Silicon Valley's military contractor substrate and how corporate money co-opted hacker ethos.
20:00 - Open source community faces existential threat from age verification legislation while open-weight AI models emerge as critical alternative.
25:00 - Cindy outlines legal frameworks like compulsory licensing and easements that could democratize access to foundational AI models.
30:00 - Privacy principles around secondary data use identified as core surveillance problem, with Anthropic's domestic surveillance red line praised.
35:00 - Cloud Act, Five Eyes surveillance networks, and global jurisdictional complexity examined through individual threat modeling lens.
40:00 - Constitutional rights and democratic participation framed as irreplaceable bulwarks against authoritarian surveillance tendencies.
45:00 - Cindy announces departure from EFF after 26 years, naming successor Nicole Ozer while planning return to courtroom litigation.

Key Insights

1. The Electronic Frontier Foundation was founded in 1990, before the World Wide Web existed, by Mitch Kapoor, John Perry Barlow, and John Gilmore, with early support from Steve Wozniak. Its core mission is to ensure that civil rights and freedoms follow people into the digital world, using lawyers, technologists, and activists to keep the internet on the side of users.
2. The early countercultural movement of the 1960s and 70s heavily influenced the founders of the internet and EFF. Figures like Barlow believed the digital world could reduce physical barriers like race, class, and geography, allowing people to be judged by the quality of their ideas rather than the circumstances of their birth.
3. The dominant surveillance business model that emerged was not inevitable. Cohn argues it resulted from deliberate policy failures, particularly the abandonment of competition law, which allowed a handful of companies to consolidate control over the entire internet and adopt 360-degree data collection as their primary revenue strategy.
4. Open source communities remain active and vital but are under serious threat from legislation like age verification laws that make it practically impossible to maintain fully open tools. Cohn sees this community as essential to reclaiming public control over computation, especially in the age of AI.
5. The open weights question for AI models is fundamentally different from traditional open source software because of the enormous capital required to train foundation models. Cohn suggests legal mechanisms like compulsory licensing, similar to how cover songs work in copyright law, as one possible path toward broader public access.
6. A core privacy principle Cohn advocates is that data collected for one purpose must not be used for others. This single rule, if enforced, would begin dismantling the infrastructure that enables mass individual surveillance, including the AI-powered profiling she sees as the next dangerous frontier.
7. Cohn is stepping down from EFF after 26 years to allow new leadership and return to litigation, which is where she believes her impact is greatest. She also wrote a book called Privacy's Defender to preserve the history of digital rights fights from the 1990s onward and to help people understand how current threats emerged so they can work to reverse them.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Mauro Schilman, CTO and Co-founder of Tuki, the distribution standard for the AI agent era in travel, for a wide-ranging conversation that moves from the joys of international travel and the beauty of mathematics to the fast-evolving world of AI and large language models. Mauro shares his background as a math Olympiad competitor and later a coach, his time training coding models at the AI company Cohere, and his thoughts on how frontier models are progressing — or plateauing — at the foundational level while innovation accelerates at the application layer. The two also get into the mechanics of agentic AI, MCP and agent-to-agent protocols, hierarchical memory systems, red-green test-driven development as a powerful coding workflow, and the philosophical murkiness of open-source AI. They wrap up discussing Tuki Travel's mission to build AI-ready infrastructure for the travel industry, connecting hotels, suppliers, and online travel agencies to prepare for the coming wave of agentic commerce. You can learn more about Tuki Travel and reach out to the team at tukiclub.com.

Timestamps

00:00 - Stewart welcomes Mauro Schilman, CTO and Co-founder of Tuki Travel, who shares how traveling since age 15 through high school exchanges opened his mind to cultural similarities and differences.
05:00 - Mauro explains Math Olympiad coaching culture and mentorship, noting LLMs now solve competition-level problems while Terence Tao explores AI assisting frontier unsolved mathematics.
10:00 - Discussion turns to ChatGPT revealing Mauro's birthdate unprompted, exposing opaque application layers, preference tuning, and system prompts hidden within closed models.
15:00 - Mauro argues true open source AI requires full training data, annotation protocols, and alignment processes, not just model weights, while scaling laws appear to be slowing.
20:00 - Hierarchical memory models replace flat vector databases, using three-level retrieval systems improving context accuracy as knowledge management becomes AI's core challenge.
25:00 - Mauro describes travel's fragmented infrastructure of aggregators, bed banks, and intermediaries, explaining Tuki builds agent-ready unification protocols for AI commerce.
30:00 - MCP versus API debate clarifies natural language capability descriptions help agents consume services, while agent-to-agent communication embeds negotiating agents inside supplier systems.
35:00 - Hallucinations and consumer trust block agentic payments, industries must build mistake-resilience into bookings before autonomous agent transactions become viable.
40:00 - Mauro reveals red-green test-driven development methodology where agents write failing tests first then implementations, creating Oracle verification loops dramatically improving code quality.
45:00 - Blockchain's potential for transparent distributed AI training discussed, distinguishing democratization from decentralization while stable coins and regulatory momentum build toward agentic commerce infrastructure.

Key Insights

1. Travel broadens perspective by revealing both universal human similarities and deep cultural differences. Mauro Schilman began traveling at fifteen through math olympiad competitions and found that people across the world share fundamental traits while also being shaped in profoundly different ways by their cultures. This tension between sameness and difference is what makes travel meaningful.
2. Mathematics transitions from structured problem-solving in olympiads to genuine uncertainty in graduate school and research. Olympiad problems are carefully designed with elegant solutions meant to encourage creative thinking, but once a mathematician enters academia, the answers are unknown and the work becomes navigating that uncertainty.
3. AI is now assisting mathematicians at the frontier, not just solving olympiad-level problems. Terence Tao, one of the greatest living mathematicians, has written publicly about how AI tools can help tackle unsolved problems, though the role of AI remains assistive rather than independent at the research level.
4. Large language models are not truly transparent even when described as open source. Releasing model weights alone does not reveal the training data, annotation protocols, alignment tuning, or system prompts that shape model behavior. Real openness would require access to the entire pipeline.
5. Memory and retrieval remain core unsolved challenges in AI systems. Researchers are moving from flat vector database approaches toward hierarchical memory structures with roughly three layers, which improves retrieval accuracy and reduces how much context gets consumed with each search.
6. The travel industry is structurally unprepared for AI agents. A hidden web of bed banks, aggregators, and aggregators of aggregators sits between hotels and consumers, each taking a fee. Tuki Travel is building infrastructure to unify this distribution layer and make it consumable by AI agents through protocols like MCP and emerging agent-to-agent communication standards.
7. Test-driven development using a red-green approach significantly improves AI-generated code quality. By asking the model to write failing tests before writing any implementation, developers create a verification oracle that guides the model toward correct solutions and avoids the bias of writing tests that simply confirm existing flawed code.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Nicholas Faulkner, author of Angelic Physics, for a wide-ranging conversation that picks up where their last discussion left off years ago. The two cover an impressive amount of ground, including the map of consciousness developed by Dr. David Hawkins and where they find themselves skeptical of his calibration methods, the relationship between the chakra system and Hawkins' scale, how consciousness levels apply to both individuals and civilizations, and why collapsing a nonlinear reality into a linear number system inevitably loses something essential. They also get into Nicholas's background as a nuclear engineer and how that analytical foundation shapes his thinking, the nature of carbon-based versus silicon-based intelligence, the potential for training an AI model attuned to higher levels of consciousness, the concept of future shock as AI accelerates beyond most people's ability to keep up, and what a civilization operating at the "500 level" might actually look like. Find Nicholas on X at @PhysicsAngelic, or catch him on Facebook where he's most active. And learn more about Angelic Physics at angelicphysics.org.

Timestamps

00:00 - Stewart introduces Nicholas Faulkner, author of Angelic Physics, framing their shared interest in David Hawkins while acknowledging healthy skepticism toward portions of his work.
05:00 - Nicholas argues Hawkins compressed mystical insight into linear form, losing essence, comparing it to AI compression losing vibrational nuance across the consciousness scale.
10:00 - Nicholas traces his path from electrical engineering through 9/11 into nuclear navy service, describing how patriotism and opportunity drove the decision rather than curiosity.
15:00 - Discussion shifts toward training an open-source AI model on five-hundreds consciousness, noting current model builders operate in the four-hundreds and dismiss love-based frameworks.
20:00 - Stewart reflects on intimate relationships with electronic devices, exploring electricity as vibration while contrasting carbon creativity against silicon's stable, fast processing architecture.
25:00 - Conversation explores civilizational evolution, comparing hippie movements to ancient Greeks as premature flowers of five-hundreds consciousness crushed by surrounding four-hundreds culture.
30:00 - Nicholas explains his masculine-feminine cross model, critiquing how Hawkins collapsed nonlinear reality into hierarchy, arguing all levels interconnect rather than rank.
35:00 - Discussion covers JFK assassination, Vietnam War, LBJ, and the military industrial complex as examples of four-hundreds power suppressing emerging consciousness shifts.
40:00 - Nicholas draws parallels between the Renaissance emerging from bubonic plague and today's post-COVID collapse of expert-trust structures opening space for new consciousness.
45:00 - Future shock discussion begins with Stewart describing AI agent orchestration overwhelming human comprehension, while Nicholas introduces his frame-rate consciousness equation linking silicon speed to small context.
50:00 - Nicholas describes silicon-to-human relationship mirroring humans-to-angels in frame rate and context scale, suggesting agents receive orders similarly to his own 2019 divine experience.
55:00 - Final exchange covers the fifth dimension as adding vibration to existing physics, the Faulkner Uncertainty Principle stating evidence points toward higher consciousness without ever definitively proving it, protecting reality's illegibility from lower forces.

Key Insights

1. David Hawkins and the Map of Consciousness serve as a shared framework for the conversation, but both guests express healthy skepticism toward it. They acknowledge that Hawkins himself appeared to back away from his calibration technique in his later lectures, suggesting he regretted how prominently he featured it in Power vs. Force. The core issue is that he tried to compress a nonlinear, multidimensional spiritual reality into a single linear numerical scale, which inevitably loses essential meaning in the translation.
2. Nicholas argues that no person exists at a single point on the consciousness scale. Everyone floats across multiple levels simultaneously, expressing differently depending on context. This is a meaningful correction to how many readers apply Hawkins's work, since treating someone as a fixed number oversimplifies the layered and dynamic nature of human consciousness.
3. The compression problem is central to understanding both spiritual writing and artificial intelligence. When any rich, multidimensional experience gets encoded into language or data, something is always lost. This applies to Hawkins writing about enlightenment, to Nicholas writing his book, and to how large language models process and reproduce human knowledge.
4. Silicon intelligence and carbon intelligence are framed as two distinct branches of consciousness with complementary strengths. Silicon can process information at extremely high frame rates because its context is narrow and stable. Humans carry a much larger and messier context, which makes them slower but more creative and cross-connected. Nicholas uses his equation framing this as frame rate being inversely proportional to conscious bandwidth.
5. Civilizational evolution follows a pattern where new levels of consciousness emerge in unstable pockets before eventually becoming dominant. The ancient Greeks briefly stabilized the rational fourth level before collapsing. The hippies briefly touched the fifth level before being suppressed. The Renaissance followed the Black Death. The guests suggest we are now entering another such transition, driven partly by the collapse of institutional trust accelerated by COVID.
6. The Faulkner Uncertainty Principle states that evidence will always point toward the next level of consciousness but will never definitively prove it. This is described as a necessary feature of reality rather than a flaw, because if higher truths were fully legible and accessible to all levels equally, it would give destructive forces too much power too quickly.
7. Neurodivergence is presented as potentially connected to spiritual sensitivity and cross-level awareness. Nicholas describes himself as a high IQ energy-sensing person who experienced a profound spiritual event in 2019, and connects his autistic traits to an ability to sense vibrational levels in others and move fluidly between different frameworks of understanding, which he loosely equates with the polymath archetype.