2026-02-21 16:53:16 +00:00
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from typing import Literal
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2026-02-19 19:31:48 +00:00
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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# Ollama host — override with OLLAMA_URL env var or .env file
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ollama_url: str = "http://localhost:11434"
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# LLM model passed to Agno/Ollama — override with OLLAMA_MODEL
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ollama_model: str = "llama3.2"
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# Set DEBUG=true to enable /docs and /redoc (disabled by default)
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debug: bool = False
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2026-02-22 17:16:12 +00:00
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# Telegram bot token — set via TELEGRAM_TOKEN env var or the /telegram/setup endpoint
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telegram_token: str = ""
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2026-02-25 01:11:14 +00:00
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# Discord bot token — set via DISCORD_TOKEN env var or the /discord/setup endpoint
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discord_token: str = ""
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2026-02-21 16:53:16 +00:00
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# ── AirLLM / backend selection ───────────────────────────────────────────
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# "ollama" — always use Ollama (default, safe everywhere)
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# "airllm" — always use AirLLM (requires pip install ".[bigbrain]")
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# "auto" — use AirLLM on Apple Silicon if airllm is installed,
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# fall back to Ollama otherwise
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feat: add Grok (xAI) as opt-in premium backend with monetization
- Add GrokBackend class in src/timmy/backends.py with full sync/async
support, health checks, usage stats, and cost estimation in sats
- Add consult_grok tool to Timmy's toolkit for proactive Grok queries
- Extend cascade router with Grok provider type for failover chain
- Add Grok Mode toggle card to Mission Control dashboard (HTMX live)
- Add "Ask Grok" button on chat input for direct Grok queries
- Add /grok/* routes: status, toggle, chat, stats endpoints
- Integrate Lightning invoice generation for Grok usage monetization
- Add GROK_ENABLED, XAI_API_KEY, GROK_DEFAULT_MODEL, GROK_MAX_SATS_PER_QUERY,
GROK_FREE config settings via pydantic-settings
- Update .env.example and docker-compose.yml with Grok env vars
- Add 21 tests covering backend, tools, and route endpoints (all green)
Local-first ethos preserved: Grok is premium augmentation only,
disabled by default, and Lightning-payable when enabled.
https://claude.ai/code/session_01FygwN8wS8J6WGZ8FPb7XGV
2026-02-27 01:12:51 +00:00
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timmy_model_backend: Literal["ollama", "airllm", "grok", "auto"] = "ollama"
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2026-02-21 16:53:16 +00:00
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# AirLLM model size when backend is airllm or auto.
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# Larger = smarter, but needs more RAM / disk.
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# 8b ~16 GB | 70b ~140 GB | 405b ~810 GB
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airllm_model_size: Literal["8b", "70b", "405b"] = "70b"
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feat: add Grok (xAI) as opt-in premium backend with monetization
- Add GrokBackend class in src/timmy/backends.py with full sync/async
support, health checks, usage stats, and cost estimation in sats
- Add consult_grok tool to Timmy's toolkit for proactive Grok queries
- Extend cascade router with Grok provider type for failover chain
- Add Grok Mode toggle card to Mission Control dashboard (HTMX live)
- Add "Ask Grok" button on chat input for direct Grok queries
- Add /grok/* routes: status, toggle, chat, stats endpoints
- Integrate Lightning invoice generation for Grok usage monetization
- Add GROK_ENABLED, XAI_API_KEY, GROK_DEFAULT_MODEL, GROK_MAX_SATS_PER_QUERY,
GROK_FREE config settings via pydantic-settings
- Update .env.example and docker-compose.yml with Grok env vars
- Add 21 tests covering backend, tools, and route endpoints (all green)
Local-first ethos preserved: Grok is premium augmentation only,
disabled by default, and Lightning-payable when enabled.
https://claude.ai/code/session_01FygwN8wS8J6WGZ8FPb7XGV
2026-02-27 01:12:51 +00:00
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# ── Grok (xAI) — opt-in premium cloud backend ────────────────────────
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# Grok is a premium augmentation layer — local-first ethos preserved.
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# Only used when explicitly enabled and query complexity warrants it.
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grok_enabled: bool = False
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xai_api_key: str = ""
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grok_default_model: str = "grok-3-fast"
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grok_max_sats_per_query: int = 200
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grok_free: bool = False # Skip Lightning invoice when user has own API key
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feat: integrate Spark Intelligence into Timmy swarm system
Adds a self-evolving cognitive layer inspired by vibeship-spark-intelligence,
adapted for Timmy's agent architecture. Spark captures swarm events, runs
EIDOS prediction-evaluation loops, consolidates memories, and generates
advisory recommendations — all backed by SQLite consistent with existing
patterns.
New modules:
- spark/memory.py — event capture with importance scoring + memory consolidation
- spark/eidos.py — EIDOS cognitive loop (predict → observe → evaluate → learn)
- spark/advisor.py — ranked advisory generation from accumulated intelligence
- spark/engine.py — top-level API wiring all subsystems together
Dashboard:
- /spark/ui — full Spark Intelligence dashboard (3-column: status/advisories,
predictions/memories, event timeline) with HTMX auto-refresh
- /spark — JSON API for programmatic access
- SPARK link added to navigation header
Integration:
- Coordinator hooks emit Spark events on task post, bid, assign, complete, fail
- EIDOS predictions generated when tasks are posted, evaluated on completion
- Memory consolidation triggers when agents accumulate enough outcomes
- SPARK_ENABLED config toggle (default: true)
Tests: 47 new tests covering all Spark subsystems + dashboard routes.
Full suite: 538 tests passing.
https://claude.ai/code/session_01KJm6jQkNi3aA3yoQJn636c
2026-02-24 15:51:15 +00:00
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# ── Spark Intelligence ────────────────────────────────────────────────
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# Enable/disable the Spark cognitive layer.
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# When enabled, Spark captures swarm events, runs EIDOS predictions,
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# consolidates memories, and generates advisory recommendations.
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spark_enabled: bool = True
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feat: add full creative studio + DevOps tools (Pixel, Lyra, Reel personas)
Adds 3 new personas (Pixel, Lyra, Reel) and 5 new tool modules:
- Git/DevOps tools (GitPython): clone, status, diff, log, blame, branch,
add, commit, push, pull, stash — wired to Forge and Helm personas
- Image generation (FLUX via diffusers): text-to-image, storyboards,
variations — Pixel persona
- Music generation (ACE-Step 1.5): full songs with vocals+instrumentals,
instrumental tracks, vocal-only tracks — Lyra persona
- Video generation (Wan 2.1 via diffusers): text-to-video, image-to-video
clips — Reel persona
- Creative Director pipeline: multi-step orchestration that chains
storyboard → music → video → assembly into 3+ minute final videos
- Video assembler (MoviePy + FFmpeg): stitch clips, overlay audio,
title cards, subtitles, final export
Also includes:
- Spark Intelligence tool-level + creative pipeline event capture
- Creative Studio dashboard page (/creative/ui) with 4 tabs
- Config settings for all new models and output directories
- pyproject.toml creative optional extra for GPU dependencies
- 107 new tests covering all modules (624 total, all passing)
https://claude.ai/code/session_01KJm6jQkNi3aA3yoQJn636c
2026-02-24 16:31:47 +00:00
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# ── Git / DevOps ──────────────────────────────────────────────────────
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git_default_repo_dir: str = "~/repos"
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# ── Creative — Image Generation (Pixel) ───────────────────────────────
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flux_model_id: str = "black-forest-labs/FLUX.1-schnell"
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image_output_dir: str = "data/images"
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image_default_steps: int = 4
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# ── Creative — Music Generation (Lyra) ────────────────────────────────
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music_output_dir: str = "data/music"
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ace_step_model: str = "ace-step/ACE-Step-v1.5"
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# ── Creative — Video Generation (Reel) ────────────────────────────────
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video_output_dir: str = "data/video"
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wan_model_id: str = "Wan-AI/Wan2.1-T2V-1.3B"
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video_default_resolution: str = "480p"
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# ── Creative — Pipeline / Assembly ────────────────────────────────────
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creative_output_dir: str = "data/creative"
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video_transition_duration: float = 1.0
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default_video_codec: str = "libx264"
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2026-02-25 18:19:22 +00:00
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# ── L402 Lightning ───────────────────────────────────────────────────
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# HMAC secrets for macaroon signing and invoice verification.
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# Generate with: python3 -c "import secrets; print(secrets.token_hex(32))"
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2026-02-25 15:32:19 -05:00
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# In production (TIMMY_ENV=production), these MUST be set or the app will refuse to start.
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l402_hmac_secret: str = ""
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l402_macaroon_secret: str = ""
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2026-02-25 18:19:22 +00:00
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lightning_backend: Literal["mock", "lnd"] = "mock"
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2026-02-25 15:32:19 -05:00
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# ── Privacy / Sovereignty ────────────────────────────────────────────
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# Disable Agno telemetry for air-gapped/sovereign deployments.
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# Default is False (telemetry disabled) to align with sovereign AI vision.
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telemetry_enabled: bool = False
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feat: replace GitHub page with embedded Timmy chat interface
Replaces the marketing landing page with a minimal, full-screen chat
interface that connects to a running Timmy instance. Mobile-first design
with single vertical scroll direction, looping scroll, no zoom, no
buttons — just type and press Enter to talk to Timmy.
- docs/index.html: full rewrite as a clean chat UI with dark terminal
theme, looping infinite scroll, markdown rendering, connection status,
and /connect, /clear, /help slash commands
- src/dashboard/app.py: add CORS middleware so the GitHub Pages site can
reach a local Timmy server cross-origin
- src/config.py: add cors_origins setting (defaults to ["*"])
https://claude.ai/code/session_01AWLxg6KDWsfCATiuvsRMGr
2026-02-27 00:35:33 +00:00
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# CORS allowed origins for the web chat interface (GitHub Pages, etc.)
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# Set CORS_ORIGINS as a comma-separated list, e.g. "http://localhost:3000,https://example.com"
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cors_origins: list[str] = ["*"]
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2026-02-25 15:32:19 -05:00
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# Environment mode: development | production
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# In production, security settings are strictly enforced.
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timmy_env: Literal["development", "production"] = "development"
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2026-02-25 17:18:58 -05:00
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# ── Self-Modification ──────────────────────────────────────────────
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# Enable self-modification capabilities. When enabled, Timmy can
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# edit its own source code, run tests, and commit changes.
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self_modify_enabled: bool = False
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self_modify_max_retries: int = 2
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self_modify_allowed_dirs: str = "src,tests"
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self_modify_backend: str = "auto" # "ollama", "anthropic", or "auto"
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2026-02-26 10:27:08 -05:00
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# ── Work Orders ──────────────────────────────────────────────────
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# External users and agents can submit work orders for improvements.
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work_orders_enabled: bool = True
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work_orders_auto_execute: bool = False # Master switch for auto-execution
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work_orders_auto_threshold: str = "low" # Max priority that auto-executes: "low" | "medium" | "high" | "none"
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feat: add in-browser local model support for iPhone via WebLLM
Enable Timmy to run directly on iPhone by loading a small LLM into
the browser via WebGPU (Safari 26+ / iOS 26+). No server connection
required — fully sovereign, fully offline.
New files:
- static/local_llm.js: WebLLM wrapper with model catalogue, WebGPU
detection, streaming chat, and progress callbacks
- templates/mobile_local.html: Mobile-optimized UI with model
selector, download progress, LOCAL/SERVER badge, and chat
- tests/dashboard/test_local_models.py: 31 tests covering routes,
config, template UX, JS asset, and XSS prevention
Changes:
- config.py: browser_model_enabled, browser_model_id,
browser_model_fallback settings
- routes/mobile.py: /mobile/local page, /mobile/local-models API
- base.html: LOCAL AI nav link
Supported models: SmolLM2-360M (~200MB), Qwen2.5-0.5B (~350MB),
SmolLM2-1.7B (~1GB), Llama-3.2-1B (~700MB). Falls back to
server-side Ollama when local model is unavailable.
https://claude.ai/code/session_01Cqkvr4sZbED7T3iDu1rwSD
2026-02-27 00:03:05 +00:00
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# ── Browser Local Models (iPhone / WebGPU) ───────────────────────
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# Enable in-browser LLM inference via WebLLM for offline iPhone use.
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# When enabled, the mobile dashboard loads a small model directly
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# in the browser — no server or Ollama required.
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browser_model_enabled: bool = True
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# WebLLM model ID — must be a pre-compiled MLC model.
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# Recommended for iPhone: SmolLM2-360M (fast) or Qwen3-0.6B (smart).
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browser_model_id: str = "SmolLM2-360M-Instruct-q4f16_1-MLC"
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# Fallback to server when browser model is unavailable or too slow.
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browser_model_fallback: bool = True
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2026-02-26 17:06:00 +00:00
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# ── Scripture / Biblical Integration ──────────────────────────────
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# Enable the sovereign biblical text module. When enabled, Timmy
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# loads the local ESV text corpus and runs meditation workflows.
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scripture_enabled: bool = True
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# Primary translation for retrieval and citation.
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scripture_translation: str = "ESV"
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# Meditation mode: sequential | thematic | lectionary
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scripture_meditation_mode: str = "sequential"
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# Background meditation interval in seconds (0 = disabled).
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scripture_meditation_interval: int = 0
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2026-02-19 19:31:48 +00:00
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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extra="ignore",
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)
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settings = Settings()
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2026-02-25 18:19:22 +00:00
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# ── Startup validation ───────────────────────────────────────────────────────
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2026-02-25 15:32:19 -05:00
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# Enforce security requirements — fail fast in production.
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2026-02-25 18:19:22 +00:00
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import logging as _logging
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2026-02-25 15:32:19 -05:00
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import sys
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2026-02-25 18:19:22 +00:00
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_startup_logger = _logging.getLogger("config")
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2026-02-25 15:32:19 -05:00
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# Production mode: require secrets to be set
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if settings.timmy_env == "production":
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_missing = []
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if not settings.l402_hmac_secret:
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_missing.append("L402_HMAC_SECRET")
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if not settings.l402_macaroon_secret:
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_missing.append("L402_MACAROON_SECRET")
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if _missing:
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_startup_logger.error(
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"PRODUCTION SECURITY ERROR: The following secrets must be set: %s\n"
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"Generate with: python3 -c \"import secrets; print(secrets.token_hex(32))\"\n"
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"Set in .env file or environment variables.",
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", ".join(_missing),
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)
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sys.exit(1)
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_startup_logger.info("Production mode: security secrets validated ✓")
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else:
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# Development mode: warn but continue
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if not settings.l402_hmac_secret:
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_startup_logger.warning(
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"SEC: L402_HMAC_SECRET is not set — "
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"set a unique secret in .env before deploying to production."
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)
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if not settings.l402_macaroon_secret:
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_startup_logger.warning(
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"SEC: L402_MACAROON_SECRET is not set — "
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"set a unique secret in .env before deploying to production."
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)
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