Watch the system improve itself overnight while you sleep. Real benchmarks, real hypotheses, real results.
Every night, the system evaluates itself, generates hypotheses, tests ideas, and ships improvements.
Execute all agents
Measure quality
Generate improvements
Test next morning
Every night, the system generates and tests new hypotheses automatically.
Based on evaluation results, the system proposes structured improvements: better prompts, new agent combinations, parameter tuning, and novel approaches.
Next run, new hypotheses compete against the baseline. Win/loss recorded. Best performers graduate to production.
Tracking quality deltas across 5+ dimensions. Content quality, research depth, speed, user satisfaction. Everything quantified.
Winners stay in rotation. Losers are archived. Experiment velocity compounds with each cycle.
Over weeks, the system discovers novel agent combinations you'd never think of. Unexpected synergies emerge.
All experiments are logged. You approve major changes. The system learns, but you decide what ships.
Performance metrics from actual overnight runs. Updated weekly.
Benchmarks updated every 7 days from live system data. All metrics are real measurements, not projections.
Insights generated by the system. Published as we ship improvements.
A deep dive into the architecture patterns that enable AI systems to measurably improve themselves through structured experimentation and evaluation.
Why running models locally unlocks capabilities that cloud-based systems can't offer. Privacy, control, and latency advantages.
From DAG execution to shared state buses: the infrastructure layer that makes multi-agent systems reliable and predictable at scale.
How to set up experiments that let your AI system generate and test its own improvements automatically. Structured experimentation for autonomous systems.
Early access subscribers get real-time dashboards showing every hypothesis, every test, every improvement.