Nature runs on sunlight.
Useful AI should not require industrial-scale computation. Build intelligence small enough to run locally—even on a device powered by a solar battery.
An ecology of intelligence
Use less. Know more in context.
Useful intelligence does not require one enormous model to contain the world. It emerges from small, transparent systems—fitted to purpose, grounded in local knowledge, and working with the human who knows why the answer matters.
Life's operating instructions
Nine principles. One engineering practice.
Janine M. Benyus described how living systems endure. Bast translates those patterns into decisions about compute, architecture, data, and the relationship between people and machines.
Nature uses only the energy it needs.
Use the fewest useful tokens, the smallest capable model, and an architecture that can do independent work in parallel. More computation is not automatically more intelligence.
Nature fits form to function.
Use the right tool for the right reason: graphs for explicit relationships, language models for language, rules where rules are known, and people where judgment matters.
Nature recycles everything.
Retrieve a verified answer before generating another one. Reuse evidence, provenance, feedback, and hard-won knowledge instead of paying to rediscover them.
Nature rewards cooperation.
AI augments human intelligence. The more effective outcome comes from a person and a machine cooperating—each contributing what the other cannot.
Nature banks on diversity.
Nature resists monoculture. Wider variation—in models, sources, perspectives, and examples—creates a more resilient picture of what is typical.
Nature demands local expertise.
Hyper-personalization starts with the person and place where knowledge will be used. You know what you need to know when you need it; context determines meaning.
Nature curbs excesses from within.
Confidence, provenance, scope, budgets, and refusal belong inside the system. Feedback should govern behavior before excess becomes harm.
Nature taps the power of limits.
A trustworthy system knows what it cannot support. Limits focus attention, make behavior legible, and create the conditions for useful generalization.
The kaleidoscope hypothesis
Small structures. Recombined into remarkable range.
“The intrinsic structure-containing universe is very small, but it is repeated in all kinds of ways.”
François Chollet
Chollet's point is not that experience is unnecessary. It is that intelligence depends on finding reusable structure within experience—then recombining it to meet something new.
Fit form to function
No single technique is the system.
Bast combines methods according to the work: neuro-symbolic graphs, language models, retrieval, deterministic rules, local models, and human judgment. Transparency keeps the relationships visible. Context keeps the information alive.
01Begin with a local need
A real person, decision, place, and moment.
02Assemble the right forms
Graph, model, retrieval, rule, interface, human.
03Preserve relationships
Sources, provenance, uncertainty, and context stay visible.
So thatKnowledge emerges in use.
The system augments the person with the information they need, when they need it, without pretending to replace what they know.
This approach resonates with Nora Bateson's relational, transcontextual “Warm Data”; Dave Snowden's insistence that action must fit the nature of the situation; and François Chollet's pursuit of generalization from reusable structure.
Build with us
Build the smart local augmentation people need now.
Small enough to belong in its environment. Transparent enough to trust. Connected enough to become part of a larger intelligent system.
Talk to a human