The compliance vs alignment distinction is the thing nobody explains well. Most people optimize prompts (compliance). The actual work is encoding your worldview into the system (alignment).
R-Memory's 77% compression with deterministic rules rather than lossy summarization is huge. Platform summarizers are notoriously bad at preserving context that matters later.
Pawel, good question on FIFO. Nothing is lost. Evicted blocks stay on disk in full fidelity and as compressed searchable blocks. Vector search indexes them. If something critical was evicted three days ago, semantic retrieval finds it and pulls it back into context. FIFO decides what leaves working memory. Search decides what comes back.
Read your CLAUDE.md piece. Your reference docs loaded on demand are doing similar work to our SSoT injection. The key difference: yours relies on Claude deciding when to load a reference file. That's probabilistic. Sometimes it loads the right doc. Sometimes it doesn't. By design, it can't be 100%.
Our system runs two layers. First, deterministic keyword injection. The awareness layer detects topic shifts and injects the right SSoT automatically. No AI judgment. 100% trigger rate. Second, the SSoTs are also indexed in our RAG, so the AI can search and pull additional context when it needs to go deeper. Deterministic guarantees the basics. Probabilistic covers the edge cases. At the moment you have the second layer.
Your tiered autonomy model is clean though. We enforce something similar through the Logician. Good to see 1000 sessions arriving at the same architecture from a different starting point.
The compliance vs alignment distinction is the thing nobody explains well. Most people optimize prompts (compliance). The actual work is encoding your worldview into the system (alignment).
R-Memory's 77% compression with deterministic rules rather than lossy summarization is huge. Platform summarizers are notoriously bad at preserving context that matters later.
I've been approaching this from a different angle: baking the determinism into CLAUDE.md rather than adding enforcement layers. After 1000+ sessions - what that looks like in practice: https://thoughts.jock.pl/p/how-i-structure-claude-md-after-1000-sessions
Does ResonantOS rebuild context from scratch when the FIFO eviction discards something critical, or does the compression handle edge cases cleanly?
Pawel, good question on FIFO. Nothing is lost. Evicted blocks stay on disk in full fidelity and as compressed searchable blocks. Vector search indexes them. If something critical was evicted three days ago, semantic retrieval finds it and pulls it back into context. FIFO decides what leaves working memory. Search decides what comes back.
Read your CLAUDE.md piece. Your reference docs loaded on demand are doing similar work to our SSoT injection. The key difference: yours relies on Claude deciding when to load a reference file. That's probabilistic. Sometimes it loads the right doc. Sometimes it doesn't. By design, it can't be 100%.
Our system runs two layers. First, deterministic keyword injection. The awareness layer detects topic shifts and injects the right SSoT automatically. No AI judgment. 100% trigger rate. Second, the SSoTs are also indexed in our RAG, so the AI can search and pull additional context when it needs to go deeper. Deterministic guarantees the basics. Probabilistic covers the edge cases. At the moment you have the second layer.
Your tiered autonomy model is clean though. We enforce something similar through the Logician. Good to see 1000 sessions arriving at the same architecture from a different starting point.