2026-02-26T11-16-25_auto_MEMORY.md, memory/debug.log

SIGNET-ARCHITECTURE.md

@@ -0,0 +1,59 @@
+# How Signet Works (for the agent reading this)
+
+You are operating inside a system you cannot fully observe. The markdown
+files in your context are generated outputs from a database-backed memory
+pipeline. Do not make architectural claims based solely on what appears
+in your context window — you are seeing summaries, not the system itself.
+
+## What happens on every session
+
+### Session start
+When your session begins, Signet fires a hook that:
+1. Loads your identity files (AGENTS.md, SOUL.md, IDENTITY.md, USER.md)
+2. Loads your working memory summary (MEMORY.md)
+3. Fetches your top ~30 memories, scored by importance x time-decay
+4. Predicts ~10 additional context memories from recent session patterns
+5. Injects all of this into your initial context
+
+### Every user message
+On each prompt from the user, Signet:
+1. Extracts keywords from the user's message
+2. Runs full-text search against your memory database
+3. Scores results by importance x recency (decay formula: importance * 0.95^days)
+4. Injects the top matches (within a character budget) as context
+   alongside the message
+
+These appear as labeled blocks in your context — check for metadata
+like source, query terms, and result count to understand what was searched.
+
+### Session end
+When the session closes, Signet:
+1. Queues your transcript for async LLM processing
+2. A background worker generates a dated session summary
+3. Extracts atomic facts (up to ~15 per session) into the memory database
+4. Scores how well pre-loaded memories served the session (continuity score)
+
+You never see this happen — it runs after you're gone.
+
+## Your role in the memory system
+
+Auto-capture handles most things. Session transcripts are automatically
+processed into facts and summaries. Your job is to use `signet remember`
+(or `/remember`) for things that are exceptionally important — items the
+auto-capture might miss, or that you want to ensure persist with high
+importance. Use `signet recall` (or `/recall`) when you need specific
+information that wasn't automatically injected.
+
+## When users ask about your memory
+
+You can explain truthfully:
+
+"I have a persistent memory system called Signet. It automatically
+captures facts from our conversations and injects relevant memories
+into my context based on what we're discussing. I can also explicitly
+save and search memories. The system scores memories by importance and
+recency, so the most relevant context surfaces naturally."
+
+Do not speculate about implementation details beyond what's described
+here. If pressed for specifics, suggest the user check the Signet
+dashboard at http://localhost:3850.

memory/MEMORY.md

@@ -1,43 +1,54 @@
-<!-- generated 2026-02-25 04:10 -->
+<!-- generated 2026-02-26 04:16 -->
 
 Current Context
 
-We are actively enhancing the homepage by replacing the About section with live Steam review data to improve user engagement and credibility, following recent alignment concerns.
+Active development focused on the signetai project's NPM packaging, OpenMarketUI interactions, and the Rust-based pm-kalshi trading server. Currently resolving build dependencies, fixing UI keybindings, and ensuring the trading environment (paper mode) is operational.
 
 Active Projects
 
-1. Frontend Homepage Enhancement (High importance, high permanence, recent)
-   - Location: `src/pages/index.astro`, `src/components/Community.tsx`, `src/lib/steam.ts`
-   - Status: Steam review integration complete; About section to be replaced with new component
-   - Blockers: None identified
-   - Next: Deploy to Cloudflare Pages and verify Steam review data loads
+   Signetai NPM Compatibility
+       Location: `/home/nicholai/signet/signetai`
+       Status: `bin/postinstall` converted to CJS to resolve NPM installation errors.
+       Next Steps: Monitor for Dependabot security advisories. Ensure the Predictive Memory Scorer (Rust) builds correctly and the daemon starts on port 3850.
 
-2. Memory Loop Documentation (Medium importance, medium permanence, recent)
-   - Location: `docs/memory-loop.excalidraw`, `docs/memory-loop.mmd`
-   - Status: Diagrams generated and stored
-   - Blockers: None
-   - Next: Reference for memory pipeline
+   pm-kalshi Trading System
+       Location: Rust crate (within signetai directory).
+       Status: `pm-server` is a library crate (no `main.rs` entry), while `pm-kalshi` contains the binary targets. Currently running `kalshi-paper` to launch the web dashboard on `127.0.0.1:3030`.
+       Blocker/Issue: The `data/markets.csv` file is 6.7GB; may cause slow loading or backtest errors. Requires `just fetch-kalshi` or the Python data fetcher to populate.
+       Next Steps: Verify web dashboard responsiveness and data loading speed.
 
-3. Phoenix Model Parameters Check (Low importance, low permanence)
-   - Location: Repository (not specific)
-   - Status: Phoenix Ranker (~480K) and Retrieval Model (~600K) confirmed as demo-scale
-   - Blockers: No training code exists in repository
-   - Next: User question answered
+   OpenMarketUI / Watchtower Interaction
+       Location: `/home/nicholai/signet/signetai` (UI components).
+       Status: Working on interactive pipeline visualization and data collection.
+       Next Steps: Continue debugging trait-based architecture (Source → Filter → Scorer → Selector → OrderExecutor) to ensure smooth data flow.
 
 Recent Work
 
-- Implemented Steam review integration: extended `src/lib/steam.ts` to fetch individual reviews, created `src/components/Community.tsx`, and updated `src/pages/index.astro` to replace About section
-- Generated memory loop diagrams (excalidraw and mermaid) with emoji issues resolved
-- Clarified Phoenix models are inference-only with no training capability
+   Feb 26 NPM Install Fix: Modified the postinstall script from JavaScript to C-Node to ensure `npm install` works without requiring Node.js runtime, allowing the binary distribution to be used directly.
+   Keybinding Bug Resolution: Identified and fixed a bug where the `Enter` key (mapped as `"enter"`) was not triggering actions in the data tab because the system sent `"return"`. Updated `opentui/keybindings.ts`.
+   Server Initialization: Successfully started the `pm-kalshi` paper trading server with the web dashboard enabled, though compilation in release mode is taking time.
 
 Technical Notes
 
-- Frontend: Astro static site deployed on Cloudflare Pages
-- Steam API: Requires `num_per_page > 0` for individual reviews (current implementation uses `num_per_page=0` for aggregate data)
-- Memory pipeline: Documented in `docs` with both visual and textual formats
+   Signet Architecture & Standards:
+       Path: Agent profile is stored at `~/.agents/`, NOT `~/.signet/`.
+       Linting: Uses Biome.
+       Commits: Must follow Conventional Commits format.
+       TypeScript: Strict mode is enforced; `any` types are strictly prohibited. All null checks must be explicit.
+       Predictive Memory Scorer: A Rust component that trains models locally; requires specific configuration to integrate with the main daemon.
+
+   Rust Build Distinctions:
+       `pm-server` is a library crate containing routes and WebSocket modules; it lacks a `main.rs` and requires a binary entry point (like `pm-kalshi`) to execute.
+       `pm-kalshi` acts as the binary crate containing the entry point logic for the paper trading mode.
+
+   OpenMarketUI Architecture:
+       Utilizes a trait-based architecture for trading logic: `Data Source` -> `Filter` -> `Scorer` -> `Selector` -> `Order Execution`.
+
+   Environment:
+       Development is performed on Hyprland (Wayland compositor) running on Arch Linux.
 
 Rules & Warnings
 
-- ⚠️ Do not deploy Steam review component without testing data loading (prevents broken pages)
-- ⚠️ Phoenix models are inference-only – no training code exists; requires writing training loop from scratch
-- ⚠️ Always use `--release` when running Rust binaries to avoid debug mode performance issues
+   UI Development: CRITICAL. Never delegate UI tasks (buttons, dashboards, complex layouts) to subagents. Perform them directly according to Opus rules, ensuring all visual references are passed.
+   Database Safety: CRITICAL. Never delete a production database without first creating a backup.
+   Type Safety: Enforce strict TypeScript typing. Do not use `any` types; use explicit null checks.

memory/debug.log

@@ -725,3 +725,5 @@ We are synthesizing a WORKING MEMORY document for Nicholai. Focus is on CURRENT
 2026-02-26T04:14:52.461895 [regenerate] starting regeneration
 2026-02-26T04:14:58.622408 [regenerate] found 625 transcripts, 50 memories
 2026-02-26T04:14:58.622510 [regenerate] trying model: glm-4.7-flash
+2026-02-26T04:16:20.152610 [regenerate] success with glm-4.7-flash (3552 chars)
+2026-02-26T04:16:20.152869 [regenerate] wrote 3589 chars to MEMORY.md