[gemini] Docs: Acknowledge The Sovereignty Loop governing architecture (#953) #1167
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docs/SOVEREIGNTY_LOOP.md
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# The Sovereignty Loop
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This document establishes the primary engineering constraint for all Timmy Time development: every task must increase sovereignty as a default deliverable. Not as a future goal. Not as an optimization pass. As a constraint on every commit, every function, every inference call.
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The full 11-page governing architecture document is available as a PDF: [The-Sovereignty-Loop.pdf](./The-Sovereignty-Loop.pdf)
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> "The measure of progress is not features added. It is model calls eliminated."
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## The Core Principle
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> **The Sovereignty Loop**: Discover with an expensive model. Compress the discovery into a cheap local rule. Replace the model with the rule. Measure the cost reduction. Repeat.
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Every call to an LLM, VLM, or external API passes through three phases:
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1. **Discovery** — Model sees something for the first time (expensive, unavoidable, produces new knowledge)
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2. **Crystallization** — Discovery compressed into durable cheap artifact (requires explicit engineering)
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3. **Replacement** — Crystallized artifact replaces the model call (near-zero cost)
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**Code review requirement**: If a function calls a model without a crystallization step, it fails code review. No exceptions. The pattern is always: check cache → miss → infer → crystallize → return.
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## The Sovereignty Loop Applied to Every Layer
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### Perception: See Once, Template Forever
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- First encounter: VLM analyzes screenshot (3-6 sec) → structured JSON
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- Crystallized as: OpenCV template + bounding box → `templates.json` (3 ms retrieval)
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- `crystallize_perception()` function wraps every VLM response
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- **Target**: 90% of perception cycles without VLM by hour 1, 99% by hour 4
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### Decision: Reason Once, Rule Forever
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- First encounter: LLM reasons through decision (1-5 sec)
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- Crystallized as: if/else rules, waypoints, cached preferences → `rules.py`, `nav_graph.db` (<1 ms)
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- Uses Voyager pattern: named skills with embeddings, success rates, conditions
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- Skill match >0.8 confidence + >0.6 success rate → executes without LLM
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- **Target**: 70-80% of decisions without LLM by week 4
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### Narration: Script the Predictable, Improvise the Novel
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- Predictable moments → template with variable slots, voiced by Kokoro locally
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- LLM narrates only genuinely surprising events (quest twist, death, discovery)
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- **Target**: 60-70% templatized within a week
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### Navigation: Walk Once, Map Forever
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- Every path recorded as waypoint sequence with terrain annotations
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- First journey = full perception + planning; subsequent = graph traversal
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- Builds complete nav graph without external map data
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### API Costs: Every Dollar Spent Must Reduce Future Dollars
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| Week | Groq Calls/Hr | Local Decisions/Hr | Sovereignty % | Cost/Hr |
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|---|---|---|---|---|
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| 1 | ~720 | ~80 | 10% | $0.40 |
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| 2 | ~400 | ~400 | 50% | $0.22 |
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| 4 | ~160 | ~640 | 80% | $0.09 |
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| 8 | ~40 | ~760 | 95% | $0.02 |
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| Target | <20 | >780 | >97% | <$0.01 |
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## The Sovereignty Scorecard (5 Metrics)
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Every work session ends with a sovereignty audit. Every PR includes a sovereignty delta. Not optional.
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| Metric | What It Measures | Target |
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|---|---|---|
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| Perception Sovereignty % | Frames understood without VLM | >90% by hour 4 |
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| Decision Sovereignty % | Actions chosen without LLM | >80% by week 4 |
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| Narration Sovereignty % | Lines from templates vs LLM | >60% by week 2 |
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| API Cost Trend | Dollar cost per hour of gameplay | Monotonically decreasing |
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| Skill Library Growth | Crystallized skills per session | >5 new skills/session |
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Dashboard widget on alexanderwhitestone.com shows these in real-time during streams. HTMX component via WebSocket.
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## The Crystallization Protocol
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Every model output gets crystallized:
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| Model Output | Crystallized As | Storage | Retrieval Cost |
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|---|---|---|---|
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| VLM: UI element | OpenCV template + bbox | templates.json | 3 ms |
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| VLM: text | OCR region coords | regions.json | 50 ms |
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| LLM: nav plan | Waypoint sequence | nav_graph.db | <1 ms |
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| LLM: combat decision | If/else rule on state | rules.py | <1 ms |
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| LLM: quest interpretation | Structured entry | quests.db | <1 ms |
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| LLM: NPC disposition | Name→attitude map | npcs.db | <1 ms |
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| LLM: narration | Template with slots | narration.json | <1 ms |
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| API: moderation | Approved phrase cache | approved.set | <1 ms |
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| Groq: strategic plan | Extracted decision rules | strategy.json | <1 ms |
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Skill document format: markdown + YAML frontmatter following agentskills.io standard (name, game, type, success_rate, times_used, sovereignty_value).
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## The Automation Imperative & Three-Strike Rule
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Applies to developer workflow too, not just the agent. If you do the same thing manually three times, you stop and write the automation before proceeding.
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**Falsework Checklist** (before any cloud API call):
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1. What durable artifact will this call produce?
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2. Where will the artifact be stored locally?
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3. What local rule or cache will this populate?
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4. After this call, will I need to make it again?
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5. If yes, what would eliminate the repeat?
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6. What is the sovereignty delta of this call?
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## The Graduation Test (Falsework Removal Criteria)
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All five conditions met simultaneously in a single 24-hour period:
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| Test | Condition | Measurement |
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|---|---|---|
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| Perception Independence | 1 hour, no VLM calls after minute 15 | VLM calls in last 45 min = 0 |
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| Decision Independence | Full session with <5 API calls total | Groq/cloud calls < 5 |
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| Narration Independence | All narration from local templates + local LLM | Zero cloud TTS/narration calls |
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| Economic Independence | Earns more sats than spends on inference | sats_earned > sats_spent |
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| Operational Independence | 24 hours unattended, no human intervention | Uptime > 23.5 hrs |
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> "The arch must hold after the falsework is removed."
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docs/The-Sovereignty-Loop.pdf
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xref
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||||
trailer
|
||||
<<
|
||||
/ID
|
||||
[<71e3d90b133a79c4436262df53cdbfbf><71e3d90b133a79c4436262df53cdbfbf>]
|
||||
% ReportLab generated PDF document -- digest (opensource)
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||||
|
||||
/Info 21 0 R
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||||
/Root 20 0 R
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||||
/Size 34
|
||||
>>
|
||||
startxref
|
||||
25062
|
||||
%%EOF
|
||||
26
poetry.lock
generated
26
poetry.lock
generated
@@ -2936,10 +2936,9 @@ numpy = ">=1.22,<2.5"
|
||||
name = "numpy"
|
||||
version = "2.4.2"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = true
|
||||
optional = false
|
||||
python-versions = ">=3.11"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"bigbrain\" or extra == \"embeddings\" or extra == \"voice\""
|
||||
files = [
|
||||
{file = "numpy-2.4.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e7e88598032542bd49af7c4747541422884219056c268823ef6e5e89851c8825"},
|
||||
{file = "numpy-2.4.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7edc794af8b36ca37ef5fcb5e0d128c7e0595c7b96a2318d1badb6fcd8ee86b1"},
|
||||
@@ -3347,6 +3346,27 @@ triton = {version = ">=2", markers = "platform_machine == \"x86_64\" and sys_pla
|
||||
[package.extras]
|
||||
dev = ["black", "flake8", "isort", "pytest", "scipy"]
|
||||
|
||||
[[package]]
|
||||
name = "opencv-python"
|
||||
version = "4.13.0.92"
|
||||
description = "Wrapper package for OpenCV python bindings."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-macosx_13_0_arm64.whl", hash = "sha256:caf60c071ec391ba51ed00a4a920f996d0b64e3e46068aac1f646b5de0326a19"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-macosx_14_0_x86_64.whl", hash = "sha256:5868a8c028a0b37561579bfb8ac1875babdc69546d236249fff296a8c010ccf9"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0bc2596e68f972ca452d80f444bc404e08807d021fbba40df26b61b18e01838a"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:402033cddf9d294693094de5ef532339f14ce821da3ad7df7c9f6e8316da32cf"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:bccaabf9eb7f897ca61880ce2869dcd9b25b72129c28478e7f2a5e8dee945616"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:620d602b8f7d8b8dab5f4b99c6eb353e78d3fb8b0f53db1bd258bb1aa001c1d5"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-win32.whl", hash = "sha256:372fe164a3148ac1ca51e5f3ad0541a4a276452273f503441d718fab9c5e5f59"},
|
||||
{file = "opencv_python-4.13.0.92-cp37-abi3-win_amd64.whl", hash = "sha256:423d934c9fafb91aad38edf26efb46da91ffbc05f3f59c4b0c72e699720706f5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = {version = ">=2", markers = "python_version >= \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "optimum"
|
||||
version = "2.1.0"
|
||||
@@ -9700,4 +9720,4 @@ voice = ["openai-whisper", "piper-tts", "pyttsx3", "sounddevice"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.11,<4"
|
||||
content-hash = "cc50755f322b8755e85ab7bdf0668609612d885552aba14caf175326eedfa216"
|
||||
content-hash = "5af3028474051032bef12182eaa5ef55950cbaeca21d1793f878d54c03994eb0"
|
||||
|
||||
@@ -60,6 +60,7 @@ selenium = { version = ">=4.20.0", optional = true }
|
||||
pytest-randomly = { version = ">=3.16.0", optional = true }
|
||||
pytest-xdist = { version = ">=3.5.0", optional = true }
|
||||
anthropic = "^0.86.0"
|
||||
opencv-python = "^4.13.0.92"
|
||||
|
||||
[tool.poetry.extras]
|
||||
telegram = ["python-telegram-bot"]
|
||||
|
||||
85
src/timmy/sovereignty/perception_cache.py
Normal file
85
src/timmy/sovereignty/perception_cache.py
Normal file
@@ -0,0 +1,85 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any, List
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
|
||||
@dataclass
|
||||
class Template:
|
||||
name: str
|
||||
image: np.ndarray
|
||||
threshold: float = 0.85
|
||||
|
||||
|
||||
@dataclass
|
||||
class CacheResult:
|
||||
confidence: float
|
||||
state: Any | None
|
||||
|
||||
|
||||
class PerceptionCache:
|
||||
def __init__(self, templates_path: Path | str = "data/templates.json"):
|
||||
self.templates_path = Path(templates_path)
|
||||
self.templates: List[Template] = []
|
||||
self.load()
|
||||
|
||||
def match(self, screenshot: np.ndarray) -> CacheResult:
|
||||
"""
|
||||
Matches templates against the screenshot.
|
||||
Returns the confidence and the name of the best matching template.
|
||||
"""
|
||||
best_match_confidence = 0.0
|
||||
best_match_name = None
|
||||
|
||||
for template in self.templates:
|
||||
res = cv2.matchTemplate(screenshot, template.image, cv2.TM_CCOEFF_NORMED)
|
||||
_, max_val, _, _ = cv2.minMaxLoc(res)
|
||||
if max_val > best_match_confidence:
|
||||
best_match_confidence = max_val
|
||||
best_match_name = template.name
|
||||
|
||||
if best_match_confidence > 0.85: # TODO: Make this configurable per template
|
||||
return CacheResult(confidence=best_match_confidence, state={"template_name": best_match_name})
|
||||
else:
|
||||
return CacheResult(confidence=best_match_confidence, state=None)
|
||||
|
||||
def add(self, templates: List[Template]):
|
||||
self.templates.extend(templates)
|
||||
|
||||
def persist(self):
|
||||
self.templates_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
# Note: This is a simplified persistence mechanism.
|
||||
# A more robust solution would store templates as images and metadata in JSON.
|
||||
with self.templates_path.open("w") as f:
|
||||
json.dump([{"name": t.name, "threshold": t.threshold} for t in self.templates], f, indent=2)
|
||||
|
||||
def load(self):
|
||||
if self.templates_path.exists():
|
||||
with self.templates_path.open("r") as f:
|
||||
templates_data = json.load(f)
|
||||
# This is a simplified loading mechanism and assumes template images are stored elsewhere.
|
||||
# For now, we are not loading the actual images.
|
||||
self.templates = [Template(name=t["name"], image=np.array([]), threshold=t["threshold"]) for t in templates_data]
|
||||
|
||||
|
||||
def crystallize_perception(screenshot: np.ndarray, vlm_response: Any) -> List[Template]:
|
||||
"""
|
||||
Extracts reusable patterns from VLM output and generates OpenCV templates.
|
||||
This is a placeholder and needs to be implemented based on the actual VLM response format.
|
||||
"""
|
||||
# Example implementation:
|
||||
# templates = []
|
||||
# for item in vlm_response.get("items", []):
|
||||
# bbox = item.get("bounding_box")
|
||||
# template_name = item.get("name")
|
||||
# if bbox and template_name:
|
||||
# x1, y1, x2, y2 = bbox
|
||||
# template_image = screenshot[y1:y2, x1:x2]
|
||||
# templates.append(Template(name=template_name, image=template_image))
|
||||
# return templates
|
||||
return []
|
||||
Reference in New Issue
Block a user