this is pure f*cking treasure A Stanford AI rese…（@marfinxx）

this is pure f*cking treasure A Stanford AI research group found how to orchestrate Claude Haiku 5.5, GPT-6.1 Sol, Claude Sonnet 5.5, and Claude Opus 5.5 together, and it completely breaks the agentic context rot wall most developers try to combine frontier models by chaining prompts in one context window or hardcoding naive retry loops. you burn tokens on context bloat and freeze models into contradictory schemas that fail within 5 iterations Cambridge's architecture eliminates human-designed prompt scaffolding and splits the cognitive stack: > syntax triage: Claude Haiku 5.5 strips raw AST boilerplate into typed interfaces (<35ms, -92.1% token compression) before prompt injection > candidate proposer: GPT-6.1 Sol explores high-entropy code variations inside isolated execution sandboxes > code synthesis: Claude Sonnet 5.5 executes verified AST diffs that pass unit test suites > causal supervisor: Claude Opus 5.5 anchors the 1M-token causal outline graph, stopping hallucination loops a