Coffee with Claude

After 70 years of discovering that every year brings surprising opportunities and delights, I've found a way to share decades of stories at the speed of morning conversation.

An NPR story about inner speech sent me inward—discovering that my mind doesn’t monologue or visualize, but narrates in real time, building understanding as story even before words hit the page.

Twenty-one years ago, we built parallel systems to catch when sensors drifted or failed. Now the “sensors” are language models that fail by succeeding too well at the wrong task.

Watching Robin Williams portray an AI striving for authentic selfhood, I found myself wondering: what do our small language models actually know versus confidently perform?

A Thanksgiving meditation on parallel lives, grounded optimism, and the intellectual kinship between two systems thinkers who’ve never met—written as an open letter to a thinker I’ve long admired.

A professor and his AI post-doc review thirty days of engineering the Macroscope—from sensor federation to Society of Mind to the temporal compression architecture that comes next.

A lifetime journey from fantasy crystals to quantum physics converges on a new architecture for environmental intelligence—where temporal patterns become geometric structure.

Five research papers reveal an interconnected crisis: human intelligence declining, AI tools that improve performance while destroying metacognition, and systems collapsing when trained on their own output. Can we trust AI to help humans become smarter again?

A near-future story about an interconnected crisis in human and artificial intelligence—and one family's response. Can we trust AI to help humans become smarter again, or are we trapped in a mutual degradation loop?

The family begins designing their proposal—mapping what makes Strata different, bringing in community partners, and discovering that the solution isn't to scale one AI, but to provide a framework for communities to grow their own.

The family presents their framework to the world, wins the challenge, and discovers the hard work is just beginning. Plus an epilogue set five years later, when Maya defends her doctoral dissertation on consciousness-safe human-AI integration.

A retired ecologist reflects on collaborating with AI to build observational tools for his granddaughter’s future, drawing lessons from the Reformation and the evolution of scientific practice.

A chance encounter with Robert Whittaker in 1973 set a young wilderness ranger on a path from mountain ecology to pioneering sensor networks—and ultimately to the Macroscope paradigm.

Three breakthrough papers reveal how 7-million-parameter networks outperform billion-parameter LLMs—and why that matters for building trustworthy scientific instruments. A Coffee with Claude exploration of reasoning architecture, security vulnerabilities, and the collaborative brainstorming that shapes Macroscope development.

From Mary Shelley's Villa Diodati to living neurons on Lake Geneva to my own distributed sensor network, four distinct approaches to creating intelligence converge on a single question: What is our relationship to the minds we make?

Thirty years ago, we gathered at a mountain field station to discuss how complex behaviors emerge from simple rules. Today’s debates about AI agents and alignment are asking remarkably similar questions—but now we finally have the infrastructure and tools to explore them with real-world data.

When my dissertation committee summited Mount San Jacinto in 1981, Ted Hullar brought the same rigorous mind that discovered a foundational chemical reaction to understanding wilderness conservation. Four decades later, that July dinner in Portland became our final conversation about Cornell, conservation, and the arc of a remarkable life.

A field ecologist reflects on claims that mathematics emerges from embodied cognition and AI can achieve explainable consciousness, finding the gap between pattern and mechanism wider than theorists imagine.

After 36 years watching technology waves reshape ecology research, I’ve learned to distinguish signal from noise. The Collective Intelligence Project’s vision for governing transformative technology is intellectually compelling—but will it become infrastructure or remain aspiration?

I discovered that the autonomous ecological monitoring system I’ve been building isn’t safe to deploy. Two new research papers from Meta, OpenAI, Anthropic, and Google DeepMind reveal that prompt injection—the vulnerability I’d been planning to work around—remains fundamentally unsolved. Here’s what that means for the future of citizen science.

Reading about self-hosting brought a familiar recognition—this "countercultural" approach is just basic engineering discipline learned from decades of field deployments. Systems that require constant connectivity are fundamentally fragile systems, whether in remote wilderness or your living room.

Can we build systems that learn what everything means? This morning's reading about lifelogging and my own digitization urgency converge on a fundamental question: not whether to record, but how to build systems that learn observational expertise.

Reading about AI developing metalinguistic abilities crystallized decades of work on classification systems—from bedroom terrariums to field station sensor networks. Can machines learn to reason about reasoning itself?

Reading C. Wright Mills' 1945 essay crystallized fifty years of professional unease—the shock of seeing my career arc from field naturalist to big science administrator dissected by someone who died before I was born. The trajectory wasn't failure, but adaptation.

Reading Nordhaus's critique of McKibben crystallized fifty years of professional unease—watching ecosystems degrade despite every regulatory framework while both climate and abundance movements operate within fundamentally anthropocentric frameworks.