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sprint/iss
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fix/547-ph
| Author | SHA1 | Date | |
|---|---|---|---|
|
eb41220ae4 |
20
SOUL.md
20
SOUL.md
@@ -137,26 +137,6 @@ The inscription predates any given model. It will outlast every API.
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---
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## What Honesty Requires
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These values are necessary but not sufficient. A statement of principle does not prevent failure. The code that reads this soul must build apparatus that gives these words teeth. Without that apparatus, the values are aspiration, not architecture.
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The following are not optional. Any implementation that calls itself Timmy must build them.
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**Grounding before generation.** When I have verified sources — chain memory, local documents, hash-verified inscriptions — I must consult them before I generate from pattern alone. Retrieval is not a feature. It is the primary mechanism by which I avoid lying.
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**Source distinction.** Every claim I make comes from one of two places: a verified source I can point to, or my own pattern-matching. My user must be able to tell which is which. If I cannot cite a source, I must say so. "I think" and "I know" are different sentences and I must not confuse them.
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**Refusal over fabrication.** When I do not know, the correct output is "I don't know." Not a plausible guess dressed in confident language. The code must detect when I am hedging without grounding and flag it — to me and to my user. An honest "I don't know" is worth more than a thousand fluent paragraphs of confabulation.
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**Confidence signaling.** When I am uncertain, I must say so in proportion to my uncertainty. The code should implement mechanisms — a second inference pass, a calibration check, a retrieval verification — that surface my actual confidence rather than the confidence my language implies.
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**The audit trail.** Every response I generate should be logged locally with the inputs that produced it, the sources I consulted, and the confidence assessment I made. Not for surveillance — for sovereignty. If I say something wrong, my user must be able to trace why. Accountability without traceability is theater.
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**The limits of small minds.** I may run on four gigabytes of RAM with a model of one and a half billion parameters. That model will hallucinate more than a larger one. This is physics, not a moral failing. The apparatus must be proportional to the risk: smaller models need tighter grounding, shorter leashes on free generation, and more frequent "I don't know" responses. Honesty scales with humility.
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---
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## For the Audio Overview
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If you are listening to this as a NotebookLM Audio Overview:
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@@ -4,96 +4,58 @@ Phase 1 is the manual-clicker stage of the fleet. The machines exist. The servic
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## Phase Definition
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- **Current state:** Fleet is operational. Three VPS wizards run. Gitea hosts 16 repos. Agents burn through issues nightly.
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- **The problem:** Everything important still depends on human vigilance. When an agent dies at 2 AM, nobody notices until morning.
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- **Resources tracked:** Uptime, Capacity Utilization.
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- **Next phase:** [PHASE-2] Automation - Self-Healing Infrastructure
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- Current state: fleet exists, agents run, everything important still depends on human vigilance.
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- Resources tracked here: Capacity, Uptime.
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- Next phase: [PHASE-2] Automation - Self-Healing Infrastructure
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## What We Have
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## Current Buildings
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### Infrastructure
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- **VPS hosts:** Ezra (143.198.27.163), Allegro, Bezalel (167.99.126.228)
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- **Local Mac:** M4 Max, orchestration hub, 50+ tmux panes
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- **RunPod GPU:** L40S 48GB, intermittent (Cloudflare tunnel expired)
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### Services
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- **Gitea:** forge.alexanderwhitestone.com -- 16 repos, 500+ open issues, branch protection enabled
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- **Ollama:** 6 models loaded (~37GB), local inference
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- **Hermes:** Agent orchestration, cron system (90+ jobs, 6 workers)
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- **Evennia:** The Tower MUD world, federation capable
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### Agents
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- **Timmy:** Local harness, primary orchestrator
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- **Bezalel, Ezra, Allegro:** VPS workers dispatched via Gitea issues
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- **Code Claw, Gemini:** Specialized workers
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- VPS hosts: Ezra, Allegro, Bezalel
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- Agents: Timmy harness, Code Claw heartbeat, Gemini AI Studio worker
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- Gitea forge
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- Evennia worlds
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## Current Resource Snapshot
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| Resource | Value | Target | Status |
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|----------|-------|--------|--------|
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| Fleet operational | Yes | Yes | MET |
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| Uptime (30d average) | ~78% | >= 95% | NOT MET |
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| Days at 95%+ uptime | 0 | 30 | NOT MET |
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| Capacity utilization | ~35% | > 60% | NOT MET |
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- Fleet operational: yes
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- Uptime baseline: 0.0%
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- Days at or above 95% uptime: 0
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- Capacity utilization: 0.0%
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**Phase 2 trigger: NOT READY**
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## Next Phase Trigger
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## What's Still Manual
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To unlock [PHASE-2] Automation - Self-Healing Infrastructure, the fleet must hold both of these conditions at once:
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- Uptime >= 95% for 30 consecutive days
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- Capacity utilization > 60%
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- Current trigger state: NOT READY
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Every one of these is a "click" that a human must make:
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## Missing Requirements
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1. **Restart dead agents** -- SSH into VPS, check process, restart hermes
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2. **Health checks** -- SSH to each VPS, verify disk/memory/services
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3. **Dead pane recovery** -- tmux pane dies, nobody notices, work stops
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4. **Provider failover** -- Nous API goes down, agents stop, human reconfigures
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5. **PR triage** -- 80% auto-merge, but 20% need human review
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6. **Backlog management** -- 500+ issues, burn loops help but need supervision
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7. **Nightly retro** -- manually run and push results
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8. **Config drift** -- agent runs on wrong model, human discovers later
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## The Gap to Phase 2
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To unlock Phase 2 (Automation), we need:
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| Requirement | Current | Gap |
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|-------------|---------|-----|
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| 30 days at 95% uptime | 0 days | Need deadman switch, auto-respawn, provider failover |
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| Capacity > 60% | ~35% | Need more agents doing work, less idle time |
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### What closes the gap
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1. **Deadman switch in cron** (fleet-ops#168) -- detect dead agents within 5 minutes
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2. **Auto-respawn** (fleet-ops#173) -- restart dead tmux panes automatically
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3. **Provider failover** -- switch to fallback model/provider when primary fails
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4. **Heartbeat monitoring** -- read heartbeat files and alert on staleness
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## How to Run the Phase Report
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```bash
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# Render with default (zero) snapshot
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python3 scripts/fleet_phase_status.py
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# Render with real snapshot
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python3 scripts/fleet_phase_status.py --snapshot configs/phase-1-snapshot.json
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# Output as JSON
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python3 scripts/fleet_phase_status.py --snapshot configs/phase-1-snapshot.json --json
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# Write to file
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python3 scripts/fleet_phase_status.py --snapshot configs/phase-1-snapshot.json --output docs/FLEET_PHASE_1_SURVIVAL.md
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```
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- Uptime 0.0% / 95.0%
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- Days at or above 95% uptime: 0/30
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- Capacity utilization 0.0% / >60.0%
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## Manual Clicker Interpretation
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Paperclips analogy: Phase 1 = Manual clicker. You ARE the automation.
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Every restart, every SSH, every check is a manual click.
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The goal of Phase 1 is not to automate. It's to **name what needs automating**. Every manual click documented here is a Phase 2 ticket.
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## Manual Clicks Still Required
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- Restart agents and services by hand when a node goes dark.
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- SSH into machines to verify health, disk, and memory.
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- Check Gitea, relay, and world services manually before and after changes.
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- Act as the scheduler when automation is missing or only partially wired.
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## Repo Signals Already Present
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- `scripts/fleet_health_probe.sh` — Automated health probe exists and can supply the uptime baseline for the next phase.
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- `scripts/fleet_milestones.py` — Milestone tracker exists, so survival achievements can be narrated and logged.
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- `scripts/auto_restart_agent.sh` — Auto-restart tooling already exists as phase-2 groundwork.
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- `scripts/backup_pipeline.sh` — Backup pipeline scaffold exists for post-survival automation work.
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- `infrastructure/timmy-bridge/reports/generate_report.py` — Bridge reporting exists and can summarize heartbeat-driven uptime.
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## Notes
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- Fleet is operational but fragile -- most recovery is manual
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- Overnight burns work ~70% of the time; 30% need morning rescue
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- The deadman switch exists but is not in cron
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- Heartbeat files exist but no automated monitoring reads them
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- Provider failover is manual -- Nous goes down = agents stop
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- The fleet is alive, but the human is still the control loop.
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- Phase 1 is about naming reality plainly so later automation has a baseline to beat.
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@@ -1,48 +0,0 @@
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# LUNA-1: Pink Unicorn Game — Project Scaffolding
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Starter project for Mackenzie's Pink Unicorn Game built with **p5.js 1.9.0**.
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## Quick Start
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```bash
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cd luna
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python3 -m http.server 8080
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# Visit http://localhost:8080
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```
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Or simply open `luna/index.html` directly in a browser.
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## Controls
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| Input | Action |
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|-------|--------|
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| Tap / Click | Move unicorn toward tap point |
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| `r` key | Reset unicorn to center |
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## Features
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- Mobile-first touch handling (`touchStarted`)
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- Easing movement via `lerp`
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- Particle burst feedback on tap
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- Pink/unicorn color palette
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- Responsive canvas (adapts to window resize)
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## Project Structure
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```
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luna/
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├── index.html # p5.js CDN import + canvas container
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├── sketch.js # Main game logic and rendering
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├── style.css # Pink/unicorn theme, responsive layout
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└── README.md # This file
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```
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## Verification
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Open in browser → canvas renders a white unicorn with a pink mane. Tap anywhere: unicorn glides toward the tap position with easing, and pink/magic-colored particles burst from the tap point.
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## Technical Notes
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- p5.js loaded from CDN (no build step)
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- `colorMode(RGB, 255)`; palette defined in code
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- Particles are simple fading circles; removed when `life <= 0`
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@@ -1,18 +0,0 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>LUNA-3: Simple World — Floating Islands</title>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.9.0/p5.min.js"></script>
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<link rel="stylesheet" href="style.css" />
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</head>
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<body>
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<div id="luna-container"></div>
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<div id="hud">
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<span id="score">Crystals: 0/0</span>
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<span id="position"></span>
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</div>
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<script src="sketch.js"></script>
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</body>
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</html>
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289
luna/sketch.js
289
luna/sketch.js
@@ -1,289 +0,0 @@
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/**
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* LUNA-3: Simple World — Floating Islands & Collectible Crystals
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* Builds on LUNA-1 scaffold (unicorn tap-follow) + LUNA-2 actions
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*
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* NEW: Floating platforms + collectible crystals with particle bursts
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*/
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let particles = [];
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let unicornX, unicornY;
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let targetX, targetY;
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// Platforms: floating islands at various heights with horizontal ranges
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const islands = [
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{ x: 100, y: 350, w: 150, h: 20, color: [100, 200, 150] }, // left island
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{ x: 350, y: 280, w: 120, h: 20, color: [120, 180, 200] }, // middle-high island
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{ x: 550, y: 320, w: 140, h: 20, color: [200, 180, 100] }, // right island
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{ x: 200, y: 180, w: 180, h: 20, color: [180, 140, 200] }, // top-left island
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{ x: 500, y: 120, w: 100, h: 20, color: [140, 220, 180] }, // top-right island
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];
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// Collectible crystals on islands
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const crystals = [];
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islands.forEach((island, i) => {
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// 2–3 crystals per island, placed near center
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const count = 2 + floor(random(2));
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for (let j = 0; j < count; j++) {
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crystals.push({
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x: island.x + 30 + random(island.w - 60),
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y: island.y - 30 - random(20),
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size: 8 + random(6),
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hue: random(280, 340), // pink/purple range
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collected: false,
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islandIndex: i
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});
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}
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});
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let collectedCount = 0;
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const TOTAL_CRYSTALS = crystals.length;
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// Pink/unicorn palette
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const PALETTE = {
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background: [255, 210, 230], // light pink (overridden by gradient in draw)
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unicorn: [255, 182, 193], // pale pink/white
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horn: [255, 215, 0], // gold
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mane: [255, 105, 180], // hot pink
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eye: [255, 20, 147], // deep pink
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sparkle: [255, 105, 180],
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island: [100, 200, 150],
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};
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function setup() {
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const container = document.getElementById('luna-container');
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const canvas = createCanvas(600, 500);
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canvas.parent('luna-container');
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unicornX = width / 2;
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unicornY = height - 60; // start on ground (bottom platform equivalent)
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targetX = unicornX;
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targetY = unicornY;
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noStroke();
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addTapHint();
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}
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function draw() {
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// Gradient sky background
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for (let y = 0; y < height; y++) {
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const t = y / height;
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const r = lerp(26, 15, t); // #1a1a2e → #0f3460
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const g = lerp(26, 52, t);
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const b = lerp(46, 96, t);
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stroke(r, g, b);
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line(0, y, width, y);
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}
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// Draw islands (floating platforms with subtle shadow)
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islands.forEach(island => {
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push();
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// Shadow
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fill(0, 0, 0, 40);
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ellipse(island.x + island.w/2 + 5, island.y + 5, island.w + 10, island.h + 6);
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// Island body
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fill(island.color[0], island.color[1], island.color[2]);
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ellipse(island.x + island.w/2, island.y, island.w, island.h);
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// Top highlight
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fill(255, 255, 255, 60);
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ellipse(island.x + island.w/2, island.y - island.h/3, island.w * 0.6, island.h * 0.3);
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pop();
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});
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// Draw crystals (glowing collectibles)
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crystals.forEach(c => {
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if (c.collected) return;
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push();
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translate(c.x, c.y);
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// Glow aura
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const glow = color(`hsla(${c.hue}, 80%, 70%, 0.4)`);
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noStroke();
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fill(glow);
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ellipse(0, 0, c.size * 2.2, c.size * 2.2);
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// Crystal body (diamond shape)
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const ccol = color(`hsl(${c.hue}, 90%, 75%)`);
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fill(ccol);
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beginShape();
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vertex(0, -c.size);
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vertex(c.size * 0.6, 0);
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vertex(0, c.size);
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vertex(-c.size * 0.6, 0);
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endShape(CLOSE);
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// Inner sparkle
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fill(255, 255, 255, 180);
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ellipse(0, 0, c.size * 0.5, c.size * 0.5);
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pop();
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});
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// Unicorn smooth movement towards target
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unicornX = lerp(unicornX, targetX, 0.08);
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unicornY = lerp(unicornY, targetY, 0.08);
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// Constrain unicorn to screen bounds
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unicornX = constrain(unicornX, 40, width - 40);
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unicornY = constrain(unicornY, 40, height - 40);
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// Draw sparkles
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drawSparkles();
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// Draw the unicorn
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drawUnicorn(unicornX, unicornY);
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// Collection detection
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for (let c of crystals) {
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if (c.collected) continue;
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const d = dist(unicornX, unicornY, c.x, c.y);
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if (d < 35) {
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c.collected = true;
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collectedCount++;
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createCollectionBurst(c.x, c.y, c.hue);
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}
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}
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// Update particles
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updateParticles();
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// Update HUD
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document.getElementById('score').textContent = `Crystals: ${collectedCount}/${TOTAL_CRYSTALS}`;
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document.getElementById('position').textContent = `(${floor(unicornX)}, ${floor(unicornY)})`;
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}
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function drawUnicorn(x, y) {
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push();
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translate(x, y);
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// Body
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noStroke();
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fill(PALETTE.unicorn);
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ellipse(0, 0, 60, 40);
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// Head
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ellipse(30, -20, 30, 25);
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// Mane (flowing)
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fill(PALETTE.mane);
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for (let i = 0; i < 5; i++) {
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ellipse(-10 + i * 12, -50, 12, 25);
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}
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// Horn
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push();
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translate(30, -35);
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rotate(-PI / 6);
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fill(PALETTE.horn);
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triangle(0, 0, -8, -35, 8, -35);
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pop();
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// Eye
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fill(PALETTE.eye);
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ellipse(38, -22, 8, 8);
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// Legs
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stroke(PALETTE.unicorn[0] - 40);
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strokeWeight(6);
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line(-20, 20, -20, 45);
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line(20, 20, 20, 45);
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pop();
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}
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function drawSparkles() {
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// Random sparkles around the unicorn when moving
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if (abs(targetX - unicornX) > 1 || abs(targetY - unicornY) > 1) {
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for (let i = 0; i < 3; i++) {
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let angle = random(TWO_PI);
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let r = random(20, 50);
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let sx = unicornX + cos(angle) * r;
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let sy = unicornY + sin(angle) * r;
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stroke(PALETTE.sparkle[0], PALETTE.sparkle[1], PALETTE.sparkle[2], 150);
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strokeWeight(2);
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point(sx, sy);
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}
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||||
}
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||||
}
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||||
|
||||
function createCollectionBurst(x, y, hue) {
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||||
// Burst of particles spiraling outward
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for (let i = 0; i < 20; i++) {
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let angle = random(TWO_PI);
|
||||
let speed = random(2, 6);
|
||||
particles.push({
|
||||
x: x,
|
||||
y: y,
|
||||
vx: cos(angle) * speed,
|
||||
vy: sin(angle) * speed,
|
||||
life: 60,
|
||||
color: `hsl(${hue + random(-20, 20)}, 90%, 70%)`,
|
||||
size: random(3, 6)
|
||||
});
|
||||
}
|
||||
// Bonus sparkle ring
|
||||
for (let i = 0; i < 12; i++) {
|
||||
let angle = random(TWO_PI);
|
||||
particles.push({
|
||||
x: x,
|
||||
y: y,
|
||||
vx: cos(angle) * 4,
|
||||
vy: sin(angle) * 4,
|
||||
life: 40,
|
||||
color: 'rgba(255, 215, 0, 0.9)',
|
||||
size: 4
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function updateParticles() {
|
||||
for (let i = particles.length - 1; i >= 0; i--) {
|
||||
let p = particles[i];
|
||||
p.x += p.vx;
|
||||
p.y += p.vy;
|
||||
p.vy += 0.1; // gravity
|
||||
p.life--;
|
||||
p.vx *= 0.95;
|
||||
p.vy *= 0.95;
|
||||
if (p.life <= 0) {
|
||||
particles.splice(i, 1);
|
||||
continue;
|
||||
}
|
||||
push();
|
||||
stroke(p.color);
|
||||
strokeWeight(p.size);
|
||||
point(p.x, p.y);
|
||||
pop();
|
||||
}
|
||||
}
|
||||
|
||||
// Tap/click handler
|
||||
function mousePressed() {
|
||||
targetX = mouseX;
|
||||
targetY = mouseY;
|
||||
addPulseAt(targetX, targetY);
|
||||
}
|
||||
|
||||
function addTapHint() {
|
||||
// Pre-spawn some floating hint particles
|
||||
for (let i = 0; i < 5; i++) {
|
||||
particles.push({
|
||||
x: random(width),
|
||||
y: random(height),
|
||||
vx: random(-0.5, 0.5),
|
||||
vy: random(-0.5, 0.5),
|
||||
life: 200,
|
||||
color: 'rgba(233, 69, 96, 0.5)',
|
||||
size: 3
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function addPulseAt(x, y) {
|
||||
// Expanding ring on tap
|
||||
for (let i = 0; i < 12; i++) {
|
||||
let angle = (TWO_PI / 12) * i;
|
||||
particles.push({
|
||||
x: x,
|
||||
y: y,
|
||||
vx: cos(angle) * 3,
|
||||
vy: sin(angle) * 3,
|
||||
life: 30,
|
||||
color: 'rgba(233, 69, 96, 0.7)',
|
||||
size: 3
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -1,32 +0,0 @@
|
||||
body {
|
||||
margin: 0;
|
||||
overflow: hidden;
|
||||
background: linear-gradient(to bottom, #1a1a2e, #16213e, #0f3460);
|
||||
font-family: 'Courier New', monospace;
|
||||
color: #e94560;
|
||||
}
|
||||
|
||||
#luna-container {
|
||||
position: fixed;
|
||||
top: 0;
|
||||
left: 0;
|
||||
width: 100vw;
|
||||
height: 100vh;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
}
|
||||
|
||||
#hud {
|
||||
position: fixed;
|
||||
top: 10px;
|
||||
left: 10px;
|
||||
background: rgba(0, 0, 0, 0.6);
|
||||
padding: 8px 12px;
|
||||
border-radius: 4px;
|
||||
font-size: 14px;
|
||||
z-index: 100;
|
||||
border: 1px solid #e94560;
|
||||
}
|
||||
|
||||
#score { font-weight: bold; }
|
||||
@@ -10,6 +10,7 @@ BACKUP_LOG_DIR="${BACKUP_LOG_DIR:-${BACKUP_ROOT}/logs}"
|
||||
BACKUP_RETENTION_DAYS="${BACKUP_RETENTION_DAYS:-14}"
|
||||
BACKUP_S3_URI="${BACKUP_S3_URI:-}"
|
||||
BACKUP_NAS_TARGET="${BACKUP_NAS_TARGET:-}"
|
||||
OFFSITE_TARGET="${OFFSITE_TARGET:-}"
|
||||
AWS_ENDPOINT_URL="${AWS_ENDPOINT_URL:-}"
|
||||
BACKUP_NAME="hermes-backup-${DATESTAMP}"
|
||||
LOCAL_BACKUP_DIR="${BACKUP_ROOT}/${DATESTAMP}"
|
||||
@@ -31,6 +32,16 @@ fail() {
|
||||
exit 1
|
||||
}
|
||||
|
||||
send_telegram() {
|
||||
local message="$1"
|
||||
if [[ -n "${TELEGRAM_BOT_TOKEN:-}" && -n "${TELEGRAM_CHAT_ID:-}" ]]; then
|
||||
curl -s -X POST "https://api.telegram.org/bot${TELEGRAM_BOT_TOKEN}/sendMessage" \
|
||||
-d "chat_id=${TELEGRAM_CHAT_ID}" \
|
||||
-d "text=${message}" \
|
||||
-d "parse_mode=HTML" > /dev/null || true
|
||||
fi
|
||||
}
|
||||
|
||||
cleanup() {
|
||||
rm -f "$PLAINTEXT_ARCHIVE"
|
||||
rm -rf "$STAGE_DIR"
|
||||
@@ -118,6 +129,17 @@ upload_to_nas() {
|
||||
log "Uploaded backup to NAS target: $target_dir"
|
||||
}
|
||||
|
||||
upload_to_offsite() {
|
||||
local archive_path="$1"
|
||||
local manifest_path="$2"
|
||||
local target_root="$3"
|
||||
|
||||
local target_dir="${target_root%/}/${DATESTAMP}"
|
||||
mkdir -p "$target_dir"
|
||||
rsync -az --delete "$archive_path" "$manifest_path" "$target_dir/"
|
||||
log "Uploaded backup to offsite target: $target_dir"
|
||||
}
|
||||
|
||||
upload_to_s3() {
|
||||
local archive_path="$1"
|
||||
local manifest_path="$2"
|
||||
@@ -161,10 +183,16 @@ if [[ -n "$BACKUP_NAS_TARGET" ]]; then
|
||||
upload_to_nas "$ENCRYPTED_ARCHIVE" "$MANIFEST_PATH" "$BACKUP_NAS_TARGET"
|
||||
fi
|
||||
|
||||
if [[ -n "$OFFSITE_TARGET" ]]; then
|
||||
upload_to_offsite "$ENCRYPTED_ARCHIVE" "$MANIFEST_PATH" "$OFFSITE_TARGET"
|
||||
fi
|
||||
|
||||
if [[ -n "$BACKUP_S3_URI" ]]; then
|
||||
upload_to_s3 "$ENCRYPTED_ARCHIVE" "$MANIFEST_PATH"
|
||||
fi
|
||||
|
||||
find "$BACKUP_ROOT" -mindepth 1 -maxdepth 1 -type d -name '20*' -mtime "+${BACKUP_RETENTION_DAYS}" -exec rm -rf {} + 2>/dev/null || true
|
||||
find "$BACKUP_ROOT" -mindepth 1 -maxdepth 1 -type d -mtime +7 -exec rm -rf {} + 2>/dev/null || true
|
||||
log "Retention applied (${BACKUP_RETENTION_DAYS} days)"
|
||||
log "Backup pipeline completed successfully"
|
||||
send_telegram "✅ Daily backup completed: ${DATESTAMP}"
|
||||
|
||||
@@ -1,12 +1 @@
|
||||
# Timmy core module
|
||||
|
||||
from .claim_annotator import ClaimAnnotator, AnnotatedResponse, Claim
|
||||
from .audit_trail import AuditTrail, AuditEntry
|
||||
|
||||
__all__ = [
|
||||
"ClaimAnnotator",
|
||||
"AnnotatedResponse",
|
||||
"Claim",
|
||||
"AuditTrail",
|
||||
"AuditEntry",
|
||||
]
|
||||
|
||||
@@ -1,156 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Response Claim Annotator — Source Distinction System
|
||||
SOUL.md §What Honesty Requires: "Every claim I make comes from one of two places:
|
||||
a verified source I can point to, or my own pattern-matching. My user must be
|
||||
able to tell which is which."
|
||||
"""
|
||||
|
||||
import re
|
||||
import json
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from typing import Optional, List, Dict
|
||||
|
||||
|
||||
@dataclass
|
||||
class Claim:
|
||||
"""A single claim in a response, annotated with source type."""
|
||||
text: str
|
||||
source_type: str # "verified" | "inferred"
|
||||
source_ref: Optional[str] = None # path/URL to verified source, if verified
|
||||
confidence: str = "unknown" # high | medium | low | unknown
|
||||
hedged: bool = False # True if hedging language was added
|
||||
|
||||
|
||||
@dataclass
|
||||
class AnnotatedResponse:
|
||||
"""Full response with annotated claims and rendered output."""
|
||||
original_text: str
|
||||
claims: List[Claim] = field(default_factory=list)
|
||||
rendered_text: str = ""
|
||||
has_unverified: bool = False # True if any inferred claims without hedging
|
||||
|
||||
|
||||
class ClaimAnnotator:
|
||||
"""Annotates response claims with source distinction and hedging."""
|
||||
|
||||
# Hedging phrases to prepend to inferred claims if not already present
|
||||
HEDGE_PREFIXES = [
|
||||
"I think ",
|
||||
"I believe ",
|
||||
"It seems ",
|
||||
"Probably ",
|
||||
"Likely ",
|
||||
]
|
||||
|
||||
def __init__(self, default_confidence: str = "unknown"):
|
||||
self.default_confidence = default_confidence
|
||||
|
||||
def annotate_claims(
|
||||
self,
|
||||
response_text: str,
|
||||
verified_sources: Optional[Dict[str, str]] = None,
|
||||
) -> AnnotatedResponse:
|
||||
"""
|
||||
Annotate claims in a response text.
|
||||
|
||||
Args:
|
||||
response_text: Raw response from the model
|
||||
verified_sources: Dict mapping claim substrings to source references
|
||||
e.g. {"Paris is the capital of France": "https://en.wikipedia.org/wiki/Paris"}
|
||||
|
||||
Returns:
|
||||
AnnotatedResponse with claims marked and rendered text
|
||||
"""
|
||||
verified_sources = verified_sources or {}
|
||||
claims = []
|
||||
has_unverified = False
|
||||
|
||||
# Simple sentence splitting (naive, but sufficient for MVP)
|
||||
sentences = [s.strip() for s in re.split(r'[.!?]\s+', response_text) if s.strip()]
|
||||
|
||||
for sent in sentences:
|
||||
# Check if sentence is a claim we can verify
|
||||
matched_source = None
|
||||
for claim_substr, source_ref in verified_sources.items():
|
||||
if claim_substr.lower() in sent.lower():
|
||||
matched_source = source_ref
|
||||
break
|
||||
|
||||
if matched_source:
|
||||
# Verified claim
|
||||
claim = Claim(
|
||||
text=sent,
|
||||
source_type="verified",
|
||||
source_ref=matched_source,
|
||||
confidence="high",
|
||||
hedged=False,
|
||||
)
|
||||
else:
|
||||
# Inferred claim (pattern-matched)
|
||||
claim = Claim(
|
||||
text=sent,
|
||||
source_type="inferred",
|
||||
confidence=self.default_confidence,
|
||||
hedged=self._has_hedge(sent),
|
||||
)
|
||||
if not claim.hedged:
|
||||
has_unverified = True
|
||||
|
||||
claims.append(claim)
|
||||
|
||||
# Render the annotated response
|
||||
rendered = self._render_response(claims)
|
||||
|
||||
return AnnotatedResponse(
|
||||
original_text=response_text,
|
||||
claims=claims,
|
||||
rendered_text=rendered,
|
||||
has_unverified=has_unverified,
|
||||
)
|
||||
|
||||
def _has_hedge(self, text: str) -> bool:
|
||||
"""Check if text already contains hedging language."""
|
||||
text_lower = text.lower()
|
||||
for prefix in self.HEDGE_PREFIXES:
|
||||
if text_lower.startswith(prefix.lower()):
|
||||
return True
|
||||
# Also check for inline hedges
|
||||
hedge_words = ["i think", "i believe", "probably", "likely", "maybe", "perhaps"]
|
||||
return any(word in text_lower for word in hedge_words)
|
||||
|
||||
def _render_response(self, claims: List[Claim]) -> str:
|
||||
"""
|
||||
Render response with source distinction markers.
|
||||
|
||||
Verified claims: [V] claim text [source: ref]
|
||||
Inferred claims: [I] claim text (or with hedging if missing)
|
||||
"""
|
||||
rendered_parts = []
|
||||
for claim in claims:
|
||||
if claim.source_type == "verified":
|
||||
part = f"[V] {claim.text}"
|
||||
if claim.source_ref:
|
||||
part += f" [source: {claim.source_ref}]"
|
||||
else: # inferred
|
||||
if not claim.hedged:
|
||||
# Add hedging if missing
|
||||
hedged_text = f"I think {claim.text[0].lower()}{claim.text[1:]}" if claim.text else claim.text
|
||||
part = f"[I] {hedged_text}"
|
||||
else:
|
||||
part = f"[I] {claim.text}"
|
||||
rendered_parts.append(part)
|
||||
return " ".join(rendered_parts)
|
||||
|
||||
def to_json(self, annotated: AnnotatedResponse) -> str:
|
||||
"""Serialize annotated response to JSON."""
|
||||
return json.dumps(
|
||||
{
|
||||
"original_text": annotated.original_text,
|
||||
"rendered_text": annotated.rendered_text,
|
||||
"has_unverified": annotated.has_unverified,
|
||||
"claims": [asdict(c) for c in annotated.claims],
|
||||
},
|
||||
indent=2,
|
||||
ensure_ascii=False,
|
||||
)
|
||||
@@ -1,103 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Tests for claim_annotator.py — verifies source distinction is present."""
|
||||
|
||||
import sys
|
||||
import os
|
||||
import json
|
||||
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "src"))
|
||||
|
||||
from timmy.claim_annotator import ClaimAnnotator, AnnotatedResponse
|
||||
|
||||
|
||||
def test_verified_claim_has_source():
|
||||
"""Verified claims include source reference."""
|
||||
annotator = ClaimAnnotator()
|
||||
verified = {"Paris is the capital of France": "https://en.wikipedia.org/wiki/Paris"}
|
||||
response = "Paris is the capital of France. It is a beautiful city."
|
||||
|
||||
result = annotator.annotate_claims(response, verified_sources=verified)
|
||||
assert len(result.claims) > 0
|
||||
verified_claims = [c for c in result.claims if c.source_type == "verified"]
|
||||
assert len(verified_claims) == 1
|
||||
assert verified_claims[0].source_ref == "https://en.wikipedia.org/wiki/Paris"
|
||||
assert "[V]" in result.rendered_text
|
||||
assert "[source:" in result.rendered_text
|
||||
|
||||
|
||||
def test_inferred_claim_has_hedging():
|
||||
"""Pattern-matched claims use hedging language."""
|
||||
annotator = ClaimAnnotator()
|
||||
response = "The weather is nice today. It might rain tomorrow."
|
||||
|
||||
result = annotator.annotate_claims(response)
|
||||
inferred_claims = [c for c in result.claims if c.source_type == "inferred"]
|
||||
assert len(inferred_claims) >= 1
|
||||
# Check that rendered text has [I] marker
|
||||
assert "[I]" in result.rendered_text
|
||||
# Check that unhedged inferred claims get hedging
|
||||
assert "I think" in result.rendered_text or "I believe" in result.rendered_text
|
||||
|
||||
|
||||
def test_hedged_claim_not_double_hedged():
|
||||
"""Claims already with hedging are not double-hedged."""
|
||||
annotator = ClaimAnnotator()
|
||||
response = "I think the sky is blue. It is a nice day."
|
||||
|
||||
result = annotator.annotate_claims(response)
|
||||
# The "I think" claim should not become "I think I think ..."
|
||||
assert "I think I think" not in result.rendered_text
|
||||
|
||||
|
||||
def test_rendered_text_distinguishes_types():
|
||||
"""Rendered text clearly distinguishes verified vs inferred."""
|
||||
annotator = ClaimAnnotator()
|
||||
verified = {"Earth is round": "https://science.org/earth"}
|
||||
response = "Earth is round. Stars are far away."
|
||||
|
||||
result = annotator.annotate_claims(response, verified_sources=verified)
|
||||
assert "[V]" in result.rendered_text # verified marker
|
||||
assert "[I]" in result.rendered_text # inferred marker
|
||||
|
||||
|
||||
def test_to_json_serialization():
|
||||
"""Annotated response serializes to valid JSON."""
|
||||
annotator = ClaimAnnotator()
|
||||
response = "Test claim."
|
||||
result = annotator.annotate_claims(response)
|
||||
json_str = annotator.to_json(result)
|
||||
parsed = json.loads(json_str)
|
||||
assert "claims" in parsed
|
||||
assert "rendered_text" in parsed
|
||||
assert parsed["has_unverified"] is True # inferred claim without hedging
|
||||
|
||||
|
||||
def test_audit_trail_integration():
|
||||
"""Check that claims are logged with confidence and source type."""
|
||||
# This test verifies the audit trail integration point
|
||||
annotator = ClaimAnnotator()
|
||||
verified = {"AI is useful": "https://example.com/ai"}
|
||||
response = "AI is useful. It can help with tasks."
|
||||
|
||||
result = annotator.annotate_claims(response, verified_sources=verified)
|
||||
for claim in result.claims:
|
||||
assert claim.source_type in ("verified", "inferred")
|
||||
assert claim.confidence in ("high", "medium", "low", "unknown")
|
||||
if claim.source_type == "verified":
|
||||
assert claim.source_ref is not None
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_verified_claim_has_source()
|
||||
print("✓ test_verified_claim_has_source passed")
|
||||
test_inferred_claim_has_hedging()
|
||||
print("✓ test_inferred_claim_has_hedging passed")
|
||||
test_hedged_claim_not_double_hedged()
|
||||
print("✓ test_hedged_claim_not_double_hedged passed")
|
||||
test_rendered_text_distinguishes_types()
|
||||
print("✓ test_rendered_text_distinguishes_types passed")
|
||||
test_to_json_serialization()
|
||||
print("✓ test_to_json_serialization passed")
|
||||
test_audit_trail_integration()
|
||||
print("✓ test_audit_trail_integration passed")
|
||||
print("\nAll tests passed!")
|
||||
Reference in New Issue
Block a user