Things like this make me still love the possibilities of the open web…
One Album A Day – 1001 Albums You Must Hear Before You Die:
A book with 1001 albums chosen by a panel of music critics to be the most important and influential in popular music.
This site will help you listen to them all.
One album a day.
The promise of AI convergence, where machine learning interweaves with ubiquitous sensing, robotics, and synthetic biology, occupies a growing share of public imagination. In its dominant vision, convergence is driven by scale, efficiency, and profitability, amplifying extractive logics first entrenched in colonial plantations and later mechanized through fossil‑fuel modernity. Convergence, however, need not be destiny; it is a meeting of trajectories. This paper asks: What if AI converged not merely with other digital infrastructures but with integral ecological considerations that foreground reciprocity, limits, and participatory co‑creation? Building on process thought (Whitehead; Cobb), ecological theology (Berry), and critical assessments of AI’s planetary costs (Crawford; Haraway), I propose a framework of convergent intelligence that aligns learning systems with the metabolic rhythms and ethical demands of Earth’s biocultural commons.
Two claims orient the argument. First, intelligence is not a private property of silicon or neurons but a distributed, relational capacity emerging across bodies, cultures, and landscapes.[1] Second, AI’s material underpinnings, including energy, minerals, water, and labor, are neither incidental nor external; they are constitutive, producing obligations that must be designed for rather than ignored.[2] [3] Convergent intelligence, therefore, seeks to redirect innovation toward life‑support enhancement, prioritizing ecological reciprocity over throughput alone.
Integral ecology synthesizes empirical ecology with phenomenological, spiritual, and cultural dimensions of human–Earth relations. It resists the bifurcation of facts and values, insisting that knowledge is always situated and that practices of attention from scientific, spiritual, and ceremonial shape the worlds we inhabit. Within this frame, data centers are not abstract clouds but eventful places: wetlands of silicon and copper drawing on watersheds and grids, entangled with regional economies and more‑than‑human communities.
Three premises ground the approach:
Applied to AI, these premises unsettle the myth of disembodied computation and reframe design questions: How might model objectives include watershed health or biodiversity uplift? What governance forms grant communities, especially Indigenous nations, meaningful authority over data relations?[4] What would it mean to evaluate model success by its contribution to ecological resilience rather than click‑through rates?
Convergence typically refers to the merging of technical capabilities such as compute, storage, and connectivity. Integral ecology broadens this perspective: convergence also encompasses ethical and cosmological dimensions. AI intersects with climate adaptation, fire stewardship, agriculture, and public health. Designing for these intersections requires reciprocity practices such as consultation, consent, and benefit sharing that recognize historical harms and current asymmetries.[5]
Ecological traditions, from Christian kenosis to Navajo hózhó, teach that self‑limitation can be generative. Convergent intelligence operationalizes restraint in technical terms: capping model size when marginal utility plateaus; preferring sparse or distilled architectures where possible; scheduling workloads to coincide with renewable energy availability; and dedicating capacity to ecological modeling before ad optimization.[6] [7] These are not mere efficiency tweaks; they are virtues encoded in infrastructure.
A sober accounting of AI’s material footprint clarifies design constraints and opportunities. Energy use, emissions, minerals, labor, land use, and water withdrawals are not background variables; they are constitutive inputs that shape both social license and planetary viability.
Training and serving large models require substantial electricity. Analyses indicate that data‑center demand is rising sharply, with sectoral loads sensitive to model scale, inference intensity, and location‑specific grid mixes.[8] [9] Lifecycle boundaries matter: embodied emissions from chip fabrication and facility build-out, along with end-of-life e-waste, can rival operational impacts. Shifting workloads to regions and times with high renewable penetration, and adopting carbon‑aware schedulers, produces measurable reductions in grid stress and emissions.[10]
AI supply chains depend on copper, rare earths, cobalt, and high‑purity silicon, linking datacenters to mining frontiers. Extraction frequently externalizes harm onto communities in the Global South, while annotation and content‑moderation labor remain precarious and under‑recognized.[11] Convergent intelligence demands procurement policies and contracting models aligned with human rights due diligence, living wages, and traceability.
Large facilities transform landscapes with new transmission lines, substations, and cooling infrastructure, fragment habitats, and alter hydrology. Regional clustering, such as the U.S. ‘data‑center alleys’, aggregates impact on migratory species and pollinators.[12] Strategic siting, brownfield redevelopment, and ecological offsets designed with local partners can mitigate, but not erase, these pressures.
High‑performance computing consumes significant water for evaporative cooling and electricity generation. Recent work highlights the hidden water footprint of AI training and inference, including temporal mismatches between compute demands and watershed stress.[13] Designing for water efficiency, including closed‑loop cooling, heat recovery to district systems, and workload shifting during drought, should be first‑order requirements.
Responding to these impacts requires more than incremental efficiency. Convergent intelligence is guided by three mutually reinforcing principles: participatory design, relational architectures, and regenerative metrics.
Integral ecology insists on with‑ness: affected human and more‑than‑human communities must shape AI life‑cycles. Practical commitments include: (a) free, prior, and informed consent (FPIC) where Indigenous lands, waters, or data are implicated; (b) community benefits agreements around energy, water, and jobs; (c) participatory mapping of energy sources, watershed dependencies, and biodiversity corridors; and (d) data governance aligned with the CARE Principles for Indigenous Data Governance.[14]
Borrowing from mycorrhizal networks, relational architectures privilege decentralized, cooperative topologies over monolithic clouds. Edge‑AI and federated learning keep data local, reduce latency and bandwidth, and respect data sovereignty.[15] [16] Technically, this means increased use of on‑device models (TinyML), sparse and distilled networks, and periodic federated aggregation with privacy guarantees. Organizationally, it means capacity‑building with local stewards who operate and adapt the models in place.[17]
Key performance indicators must evolve from throughput to regeneration: net‑zero carbon (preferably net‑negative), watershed neutrality, circularity, and biodiversity uplift. Lifecycle assessment should be integrated into CI/CD pipelines, with automated gates triggered by thresholds on carbon intensity, water consumption, and material circularity. Crucially, targets should be co‑governed with communities and regulators and audited by third parties to avoid greenwash.
Inspired by the efficiency of fungal hyphae, sparse and branching network topologies can reduce parameter counts and memory traffic while preserving accuracy. Recent bio‑inspired approaches report substantial reductions in multiply‑accumulate operations with minimal accuracy loss, suggesting a path toward ‘frugal models’ that demand less energy per inference.[18] Beyond metaphor, this aligns optimization objectives with the ecological virtue of sufficiency rather than maximalism.[19]
In fire‑adapted landscapes, local cooperatives deploy low‑power vision and micro‑meteorological sensors running TinyML models to track humidity, wind, and fuel moisture in real time. Paired with citizen‑science apps and tribal burn calendars, these systems support safer prescribed fire and rapid anomaly detection while keeping sensitive data local to forest commons.[20] Federated updates allow regional learning without centralizing locations of cultural sites or endangered species.[21]
A prototype ‘Whitehead Scheduler’ would treat compute jobs as occasions seeking harmony rather than dominance: workloads bid for energy indexed to real‑time renewable availability. At the same time, non‑urgent tasks enter latency pools during grid stress. Early experiments at Nordic colocation sites report reduced peak‑hour grid draw alongside improved utilization.[22] The aim is not simply to lower emissions but to re-pattern computing rhythms to match ecological cycles.
Camera traps, acoustic recorders, and eDNA assays generate sensitive biodiversity data. Convergent intelligence supports federated learning across these nodes, minimizing centralized storage of precise locations for rare species while improving models for detection and phenology. Governance draws from commons stewardship (Ostrom) and Indigenous data sovereignty, ensuring that benefits accrue locally and that consent governs secondary uses.[23] [24]
When intelligence is understood as a shared world‑making capacity, AI’s moral horizon widens. Integral ecology draws on traditions that teach humility, generosity, and restraint as technological virtues. In practice, this means designing harms out of systems (e.g., discriminatory feedback loops), allocating compute to public goods (e.g., climate modeling) before ad targeting, and prioritizing repair over replacement in hardware life cycles.[25] [26] [27] Critical scholarship on power and classification reminds us that technical choices reinscribe social patterns unless intentionally redirected.[28] [29] [30]
Convergent intelligence reframes AI not as destiny but as a participant in Earth’s creative advance. Adopting participatory, relational, and regenerative logics can redirect innovation toward:
Barriers such as policy inertia, vendor lock‑in, financialization of compute, and geopolitical competition are designable, not inevitable. Policy levers include carbon and water-aware procurement; right-to-repair and extended producer responsibility; transparency requirements for model energy and water reporting; and community benefits agreements for new facilities.[33] [34] Research priorities include benchmarks for energy/water per quality‑adjusted token or inference, standardized lifecycle reporting, and socio‑technical audits that include affected communities.
Ecological crises and the exponential growth of AI converge on the same historical moment. Whether that convergence exacerbates overshoot or catalyzes regenerative futures depends on the paradigms guiding research and deployment. An integral ecological approach, grounded in relational ontology and participatory ethics, offers robust guidance. By embedding convergent intelligence within living Earth systems, technically, organizationally, and spiritually, we align technological creativity with the great work of transforming industrial civilization into a culture of reciprocity.
[1] James Bridle, Ways of Being: Animals, Plants, Machines: The Search for a Planetary Intelligence (New York: Farrar, Straus and Giroux, 2022).
[2] Kate Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (New Haven, CT: Yale University Press, 2021).
[3] Emma Strubell, Ananya Ganesh, and Andrew McCallum, “Energy and Policy Considerations for Deep Learning in NLP,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019), 3645–3650.
[4] Global Indigenous Data Alliance, “CARE Principles for Indigenous Data Governance,” 2019.
[5] Donna J. Haraway, Staying with the Trouble: Making Kin in the Chthulucene (Durham, NC: Duke University Press, 2016).
[6] Thomas Berry, The Great Work: Our Way into the Future (New York: Bell Tower, 1999).
[7] Emily M. Bender, Timnit Gebru, Angelina McMillan‑Major, and Margaret Mitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (New York: ACM, 2021), 610–623.
[8] International Energy Agency, Electricity 2024: Analysis and Forecast to 2026 (Paris: IEA, 2024).
[9] Eric Masanet et al., “Recalibrating Global Data Center Energy‑Use Estimates,” Science 367, no. 6481 (2020): 984–986.
[10] David Patterson et al., “Carbon Emissions and Large Neural Network Training,” arXiv:2104.10350 (2021).
[11] Kate Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (New Haven, CT: Yale University Press, 2021).
[12] P. Roy et al., “Land‑Use Change in U.S. Data‑Center Regions,” Journal of Environmental Management 332 (2023).
[13] Shaolei Ren et al., “Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models,” arXiv:2304.03271 (2023).
[14] Global Indigenous Data Alliance, “CARE Principles for Indigenous Data Governance,” 2019.
[15] Sebastian Rieke, Lu Hong Li, and Veljko Pejovic, “Federated Learning on the Edge: A Survey,” ACM Computing Surveys 54, no. 8 (2022).
[16] Peter Kairouz et al., “Advances and Open Problems in Federated Learning,” Foundations and Trends in Machine Learning 14, no. 1–2 (2021): 1–210.
[17] Pete Warden and Daniel Situnayake, TinyML (Sebastopol, CA: O’Reilly, 2020).
[18] Islam, T. Mycelium neural architecture search. Evol. Intel. 18, 89 (2025). https://doi.org/10.1007/s12065-025-01077-z
[19] Thomas Berry, The Great Work: Our Way into the Future (New York: Bell Tower, 1999).
[20] Pete Warden and Daniel Situnayake, TinyML (Sebastopol, CA: O’Reilly, 2020).
[21] Sebastian Rieke, Lu Hong Li, and Veljko Pejovic, “Federated Learning on the Edge: A Survey,” ACM Computing Surveys 54, no. 8 (2022).
[22] David Patterson et al., “Carbon Emissions and Large Neural Network Training,” arXiv:2104.10350 (2021).
[23] Global Indigenous Data Alliance, “CARE Principles for Indigenous Data Governance,” 2019.
[24] Elinor Ostrom, Governing the Commons (Cambridge: Cambridge University Press, 1990).
[25] Emily M. Bender, Timnit Gebru, Angelina McMillan‑Major, and Margaret Mitchell, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (New York: ACM, 2021), 610–623.
[26] Ruha Benjamin, Race After Technology (Cambridge: Polity, 2019).
[27] Safiya Umoja Noble, Algorithms of Oppression (New York: NYU Press, 2018).
[28] Ruha Benjamin, Race After Technology (Cambridge: Polity, 2019).
[29] Safiya Umoja Noble, Algorithms of Oppression (New York: NYU Press, 2018).
[30] Shoshana Zuboff, The Age of Surveillance Capitalism (New York: PublicAffairs, 2019).
[31] Sebastian Rieke, Lu Hong Li, and Veljko Pejovic, “Federated Learning on the Edge: A Survey,” ACM Computing Surveys 54, no. 8 (2022).
[32] Elinor Ostrom, Governing the Commons (Cambridge: Cambridge University Press, 1990).
[33] International Energy Agency, Electricity 2024: Analysis and Forecast to 2026 (Paris: IEA, 2024).
[34] Shaolei Ren et al., “Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models,” arXiv:2304.03271 (2023).
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. New York: ACM, 2021.
Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. Cambridge: Polity, 2019.
Berry, Thomas. The Great Work: Our Way into the Future. New York: Bell Tower, 1999.
Bridle, James. Ways of Being: Animals, Plants, Machines: The Search for a Planetary Intelligence. New York: Farrar, Straus and Giroux, 2022.
Cobb Jr., John B. “Process Theology and Ecological Ethics.” Ecotheology 10 (2005): 7–21.
Couldry, R., and U. Ali. “Data Colonialism.” Television & New Media 22, no. 4 (2021): 469–482.
Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven, CT: Yale University Press, 2021.
Haraway, Donna J. Staying with the Trouble: Making Kin in the Chthulucene. Durham, NC: Duke University Press, 2016.
International Energy Agency. Electricity 2024: Analysis and Forecast to 2026. Paris: IEA, 2024.
Islam, T. Mycelium neural architecture search. Evol. Intel. 18, 89 (2025). https://doi.org/10.1007/s12065-025-01077-z
Kairouz, Peter, et al. “Advances and Open Problems in Federated Learning.” Foundations and Trends in Machine Learning 14, no. 1–2 (2021): 1–210.
Latour, Bruno. Down to Earth. Cambridge, UK: Polity, 2018.
Masanet, Eric, Arman Shehabi, Jonathan Koomey, et al. “Recalibrating Global Data Center Energy-Use Estimates.” Science 367, no. 6481 (2020): 984–986.
Merleau-Ponty, Maurice. Phenomenology of Perception. London: Routledge, 2012.
Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press, 2018.
Ostrom, Elinor. Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge: Cambridge University Press, 1990.
Patterson, David, et al. “Carbon Emissions and Large Neural Network Training.” arXiv:2104.10350 (2021).
Pokorny, Lukas, and Tomáš Grim. “Integral Ecology: A Multifaceted Approach.” Environmental Ethics 39, no. 1 (2017): 23–42.
Ren, Shaolei, et al. “Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models.” arXiv:2304.03271 (2023).
Rieke, Sebastian, Lu Hong Li, and Veljko Pejovic. “Federated Learning on the Edge: A Survey.” ACM Computing Surveys 54, no. 8 (2022).
Roy, P., et al. “Land-Use Change in U.S. Data-Center Regions.” Journal of Environmental Management 332 (2023).
Strubell, Emma, Ananya Ganesh, and Andrew McCallum. “Energy and Policy Considerations for Deep Learning in NLP.” In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. 2019.
TallBear, S. The Power of Indigenous Thinking in Tech Design. Cambridge, MA: MIT Press, 2022.
Tsing, Anna Lowenhaupt. The Mushroom at the End of the World. Princeton, NJ: Princeton University Press, 2015.
Warden, Pete, and Daniel Situnayake. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers. Sebastopol, CA: O’Reilly, 2020.
Whitehead, Alfred North. Process and Reality. New York: Free Press, 1978.
Zuboff, Shoshana. The Age of Surveillance Capitalism. New York: PublicAffairs, 2019.
What a wonderful legacy to leave for one’s children and all the children of humanity.
Here’s a NY Times piece (gift article) about Dan and his reading logs:
He Read (at Least) 3,599 Books in His Lifetime. Now Anyone Can See His List. – The New York Times:
He Read (at Least) 3,599 Books in His Lifetime. Now Anyone Can See His List.
After Dan Pelzer died this month at 92, his children uploaded the handwritten reading list to what-dan-read.com, hoping to inspire readers everywhere.
I’m back with Matthew Klippenstein this week. Our episode began with a discussion about AI tools and their impact on research and employment, including experiences with different web browsers and their ecosystems. The conversation then evolved to explore the evolving landscape of technology, particularly focusing on AI’s impact on web design and content consumption, while also touching on the resurgence of physical media and its cultural significance. The discussion concluded with an examination of Mary Shelley’s “Frankenstein” and its relevance to current AI discussions, along with broader themes about creation, consciousness, and the human tendency to view new entities as either threats or allies.
Matthew and Sam discussed Sam’s paper and the use of AI tools like GPT-5 for research and information synthesis. They explored the potential impact of AI on employment, with Matthew noting that AI could streamline information gathering and synthesis, reducing the time required for tasks that would have previously been more time-consuming. Sam agreed to send Matthew links to additional resources mentioned in the paper, and they planned to discuss further ideas on integrating AI tools into their work.
Sam and Matthew discussed their experiences with different web browsers, with Sam explaining his preference for Brave over Chrome due to its privacy-focused features and historical background as a Firefox fork. Sam noted that he had recently switched back to Safari on iOS due to new OS updates, while continuing to use Chromium-based browsers on Linux. They drew parallels between browser ecosystems and religious denominations, with Chrome representing a dominant unified system and Safari as a smaller but distinct alternative.
Sam and Matthew discussed the evolving landscape of technology, particularly focusing on AI’s impact on web design, search engine optimization, and content consumption. Sam expressed excitement about the new iteration of web interaction, comparing it to predictions from 10 years ago about the future of platforms like Facebook Messenger and WeChat. They noted that AI agents are increasingly becoming the intermediaries through which users interact with content, leading to a shift from human-centric to AI-centric web design. Sam also shared insights from his personal blog, highlighting an increase in traffic from AI agents and the challenges of balancing accessibility with academic integrity.
Sam and Matthew discussed the resurgence of physical media, particularly vinyl records and CDs, as a cultural phenomenon and personal preference. They explored the value of owning physical copies of music and books, contrasting it with streaming services, and considered how this trend might symbolize a return to tangible experiences. Sam also shared his interest in integral ecology, a philosophical approach that examines the interconnectedness of humans and their environment, and how this perspective could influence the development and understanding of artificial intelligence.
Sam and Matthew discussed the rapid development of AI and its environmental impact, comparing it to biological R/K selection theory where fast-reproducing species are initially successful but are eventually overtaken by more efficient, slower-reproducing species. Sam predicted that future computing interfaces would become more humane and less screen-based, with AI-driven technology likely replacing traditional devices within 10 years, though there would still be specialized uses for mainframes and Excel. They agreed that current AI development was focused on establishing market leadership rather than long-term sustainability, with Sam noting that antitrust actions like those against Microsoft in the 1990s were unlikely in the current regulatory environment.
Sam and Matthew discussed the evolving landscape of information consumption and the role of AI in providing insights and advice. They explored how AI tools can assist in synthesizing large amounts of data, such as academic papers, and how this could reduce the risk of misinformation. They also touched on the growing trend of using AI for personal health advice, the challenges of healthcare access, and the shift in news consumption patterns. The conversation highlighted the transition to a more AI-driven information era and the potential implications for society.
Sam and Matthew discussed the impact of AI and automation on employment, particularly how it could affect white-collar jobs more than blue-collar ones. They explored how AI tools might become cheaper than hiring human employees, with Matthew sharing an example from a climate newsletter offering AI subscriptions as a cost-effective alternative to hiring interns. Sam referenced Ursula Le Guin’s book “Always Coming Home” as a speculative fiction work depicting a post-capitalist, post-extractive society where technology serves a background role to human life. The conversation concluded with Matthew mentioning his recent reading of “Frankenstein,” noting its relevance to current AI discussions despite being written in the early 1800s.
Matthew shared his thoughts on Mary Shelley’s “Frankenstein,” noting its philosophical depth and rich narrative structure. He described the story as a meditation on creation and the challenges faced by a non-human intelligent creature navigating a world of fear and prejudice. Matthew drew parallels between the monster’s learning of human culture and language to Tarzan’s experiences, highlighting the themes of isolation and the quest for companionship. He also compared the nested storytelling structure of “Frankenstein” to the film “Inception,” emphasizing its complexity and the moral questions it raises about creation and control.
Sam and Matthew discussed the historical context of early computing, mentioning Ada Lovelace and Charles Babbage, and explored the theme of artificial intelligence through the lens of Mary Shelley’s “Frankenstein.” They examined the implications of teaching AI human-like emotions and empathy, questioning whether such traits should be encouraged or suppressed. The conversation also touched on the nature of consciousness as an emergent phenomenon and the human tendency to view new entities as either threats or potential allies.
Sam and Matthew discussed the book “Childhood’s End” by Arthur C. Clark and its connection to the film “2001: A Space Odyssey.” They also talked about the origins of Mary Shelley’s “Frankenstein” and the historical context of its creation. Sam mentioned parallels between human creation of technology and the concept of gods in mythology, particularly in relation to metalworking and divine beings. The conversation touched on the theme of human creation and its implications for our understanding of divinity and ourselves.
Matthew and Sam discussed the concept of robustness versus optimization in nature and society, drawing on insights from a French biologist, Olivier Hamant, who emphasizes the importance of resilience over efficiency. They explored how this perspective could apply to AI and infrastructure, suggesting a shift towards building systems that are robust and adaptable rather than highly optimized. Sam also shared her work on empathy, inspired by the phenomenology of Edith Stein, and how it relates to building resilient systems.
Sam and Matthew discussed the importance of efficiency versus redundancy and resilience, particularly in the context of corporate America and decarbonization efforts. Sam referenced recent events involving Elon Musk and Donald Trump, highlighting the potential pitfalls of overly efficient approaches. Matthew used the historical example of polar expeditions to illustrate how redundancy and careful planning can lead to success, even if it means being “wasteful” in terms of resources. They agreed that a cautious and prepared approach, rather than relying solely on efficiency, might be more prudent in facing unexpected challenges.
Sam and Matthew discussed Mary Shelley’s “Frankenstein,” exploring its themes and cultural impact. They agreed on the story’s timeless appeal due to its exploration of the monster’s struggle and the human fear of the unknown. Sam shared personal experiences teaching the book and how students often misinterpret the monster’s character. They also touched on the concept of efficiency as a modern political issue, drawing parallels to the story’s themes. The conversation concluded with Matthew offering to share anime recommendations, but they decided to save that for a future discussion.
Trying out GPT-5 for the first time while doing some work on a paper about AI and integal ecologies… I’m blown away. This is transformative and exciting and scary all at the same time. Talk about ontological shock 👊
Can’t beat a free run through of one of my favs on a random Thursday evening in Spartanburg (with cookies for high tea time, of course)!
“Find out just what any people will quietly submit to and you have found out the exact measure of injustice and wrong which will be imposed upon them…” Frederick Douglass
Fun list here from Pseudo-Dionysis (I’m a fan with my philosophical ecological thinking, btw) to Meister Eckhardt to Kafka DeLillo)… I should make a list like this.
God-Tier Books: A Personal Library of Holy Scripture ‹ Literary Hub:
Meister Eckhardt was a German Catholic monk in the 11th century influenced by Pseudo-Dionysius. His writings were condemned by the church as heresy but found a fan centuries later in Martin Heidegger, which makes sense. Eckhardt’s commentaries on God and scripture are dense and recursive, breaking ideas into component parts, placing them onto higher and lower planes, making hierarchies and triads out of them until eventually becoming something like an investigation into being and nothingness themselves. Occasional gnomic jewels emerge from the tangle: “God is a word, a word unspoken.” “God is a word that speaks itself.” The mobius-thinking at times almost seems like Medieval Zen, what with the emphasis on emptiness and silent meditation, and in fact that was what the Church fathers objected to most: too much quiet, solitary contemplation, not enough pious instruction.
Time indeed does “fly” when it comes to your children. I can’t believe we’ve already had two years to celebrate Lily’s life and adventures and can’t wait to see where the next few decades take her.
Give yourself 10 mins today to stare at Bosch’s work and learn a little about yourself, the world, consciousness, and projection (not a bad use of just 10 mins of your day instead of doomscrolling Reels or TikTok)…
10-Minute Challenge: Bosch’s ‘Garden of Earthly Delights’ – The New York Times (Gift Article):
Today, we bring you another focus challenge, in which we invite you to spend uninterrupted time looking at one piece of art. This one is a 500-year-old, three-paneled triptych by the Dutch painter Hieronymus Bosch.
