6e992cc0c5
- Rename 'three-pronged' folder to 'knowledge-layers' — prong metaphor
was misleading (implied parallel tines), replaced with epistemic layers
(deductive base, empirical middle, probabilistic oracle — vertical stack)
- Collapse 11 overlapping files into 3 coherent documents:
- knowledge-layers/_index.org: core framework (two engines + one store,
World Model formula, 0-14 layer table, provenance store design,
conflict resolution, cold-start, stage mapping)
- knowledge-layers/practical-implications.org: design-world-aware-of-
physics, 10 powers, Schafmeister existence proof, epistemic transparency
- knowledge-layers/neurological-empirical.org: neural networks in
provenance framework (kept intact)
- Relocate wolfram/mathematica and Schafmeister docs to ideas/viability/
- Integrate into main architecture _index.org:
- Gate: expanded from two vectors (ACL2+LLM) to three (deductive,
provenance/empirical, LLM oracle)
- Autodidactic loop: split into Track 1 (deductive hardening, fast)
and Track 2 (empirical validation, slow, experimental-feedback-driven)
- See also: added Knowledge Layers cross-reference
- Add all-lisp geometry engine note (ideas/lisp-geometry-engine.org) as
concrete illustration of the empirical layer's effect on design work
- Rebuild site: 148 files, 0 errors
1.7 KiB
Hermes Agent
The agent running this conversation. Python, ~17K core lines, MIT.
Architecture: Synchronous conversation loop with OpenAI-format messages. 60+ built-in tools. 109+ providers via pluggable transport layer. 15+ messaging platforms via gateway. MCP client (native, not bridge). Ink/React TUI as Node.js subprocess. Cron jobs, Kanban board, subagent delegation.
Safety model: Multi-layer but NOT a deterministic gate stack: message sanitization, Tirith binary scanner, command approval system, memory injection detection, secret/PII redaction, tool call guardrails, MCP security, context fencing. All heuristic or prompt-based — no structural type-level gates.
Data model: SQLite session DB (FTS5 full-text search). File-based memory (MEMORY.md + USER.md). YAML config. No knowledge graph. No Org-mode.
Self-modification: Skill system writes SKILL.md files. Memory tool edits MEMORY.md/USER.md. Core Python code is read-only in execution but no gate specifically prevents the LLM from requesting source modifications.
Verification: None.
Key gap vs Passepartout: No deterministic gate stack (heuristic layers, not structural/typed), no knowledge graph, no Org-mode, no neurosymbolic architecture, no self-verification, no proof system. Hermes's strength is breadth — 109 providers, 15 platforms, MCP ecosystem. But it has no depth in safety, knowledge representation, or reasoning architecture.
See the full competitive analysis for the landscape view and comparison.