Rethinking Continual Learning: Learn the Pattern, Not the Correction
How Perpetual turns corrections into tested hypotheses before changing memory, harness, or weights.
Ideas, experiments, and technical notes on agents that learn from business outcomes.
How Perpetual turns corrections into tested hypotheses before changing memory, harness, or weights.
How Perpetual agents turn long-horizon work into private learning loops that improve small open-source models.
Building intelligent AI is becoming less about model size and more about what it learns from in real work.
The idea underneath everything we're building.