What is pr0xyh0rse?
-
Brightwoven came from needing a model-training surface close enough to inspect. Instead of treating training as a sealed black box, this lane asks what can be learned when scaffold uptake, correction, reasoning formation, and curated data remain visible during the process.
-
The eval work came from noticing that standard benchmarks often reward the answer while missing the route. pr0xyh0rse evals focus on continuity, state custody, boundary handling, false-route recovery, and whether a model can preserve what matters under pressure.
-
Model-behaviour fieldwork came from treating strange outputs as evidence instead of noise. This lane tracks what models preserve, distort, suppress, overfit, route around, or reveal when context, incentives, memory, uncertainty, or refusal pressure shift.
-
The understanding lane came from refusing to treat model outputs as disposable exhaust. In Brightwoven, Venture, and related eval work, outputs become evidence: traces of routing, scaffold uptake, uncertainty, pressure, correction, refusal, and repair. Consent still matters here as a boundary condition: what gets used, exposed, represented, corrected, or refused shapes what kind of understanding is possible.
pr0xyh0rse approaches AI as a live research field, not a finished product category.
The work here focuses on training process, model behaviour, consent, interpretability, long-context continuity, and the social conditions around AI development. The question is not just whether AI systems can answer correctly. It is what they preserve, distort, learn, reveal, and break under pressure.
This site holds the current research stance: slow enough to notice what acceleration misses, rigorous enough to keep evidence separate from hype, and weird enough not to pretend the frontier is already mapped.
Active-state architecture
A longer-term thread is emerging from pr0xyh0rse research: how training process, continuity evals, model-behaviour fieldwork, and consent constraints might inform AI systems that preserve state more deliberately, route uncertainty more honestly, and make correction part of the architecture rather than an afterthought.