Guardian
Desktop workspace that keeps your context local and your language yours
Goals
Desktop workspace where chat, terminal, notes, and memory live in one local-first interface. You pick the model, the data stays on your machine.
Process
Started as an Electron shell with a chat window and a local Ollama connection. Built outward from there.
Routing. Provider-agnostic engine across Anthropic, OpenAI, and Ollama. Auto-selects by intent complexity and cost. Runs air-gapped on local models or through cloud APIs.
Reframe detection. Classifies when model responses subtly reshape user statements across 7 types. Triggers prompt correction when user-rated inaccuracy exceeds 40%.
Memory. 4-level compression pipeline (raw -> summary -> pattern -> principle). Strength decays over time, reinforces on retrieval.
Post-session pipeline. Fires on conversation end. Extracts decisions, tasks, and code artifacts. Generates typed notes. Indexes into FTS5 for search. Links entities into a knowledge graph.
Limitations
- Reframe detection uses heuristic classification, not a trained model. False positive rate unmeasured.
- Memory compression thresholds are hand-tuned, not empirically optimized.
- No formal user study on reframe detection accuracy.
Learnings
The hardest problems were state synchronization and process lifecycle management, not the models themselves.
Reframe detection surfaced a deeper problem: models subtly reshape your language over time. Detecting that requires understanding the user’s baseline, which requires memory, which requires local persistence. That dependency chain is why ForgeFrame exists.