add plan for saas app

2025-10-21 23:07:43 +02:00
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--- a/.claude/agents/architecture-advisor.md
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@@ -0,0 +1,152 @@
 ---
 name: architecture-advisor
 description: Use this agent when planning significant architectural changes, evaluating design decisions, reviewing system complexity, or seeking guidance on data-oriented design patterns. Examples:\n\n<example>\nContext: User is considering adding a new abstraction layer for cloud providers.\nuser: "I'm thinking about creating a CloudProvider base class with inheritance for each provider. What do you think?"\nassistant: "Let me consult the architecture-advisor agent to evaluate this design decision."\n<commentary>\nThe user is proposing an architectural change that involves OOP patterns. Use the Task tool to launch the architecture-advisor agent to provide guidance on whether this aligns with data-oriented design principles and project simplicity goals.\n</commentary>\n</example>\n\n<example>\nContext: User wants to add a new feature to the transform pipeline.\nuser: "Should we add a caching layer between SQLMesh and DuckDB to improve performance?"\nassistant: "This is an architectural decision that could add complexity. Let me use the architecture-advisor agent to evaluate whether this aligns with our simplicity principles."\n<commentary>\nThe user is proposing adding infrastructure complexity. Use the architecture-advisor agent to assess whether the performance benefit justifies the added complexity and whether there's a simpler solution.\n</commentary>\n</example>\n\n<example>\nContext: User is reviewing the current multi-cloud provider implementation.\nuser: "I've been looking at our provider abstraction and wondering if we're overengineering it."\nassistant: "Let me engage the architecture-advisor agent to review the current implementation against our data-oriented design principles."\n<commentary>\nThe user is questioning system complexity. Use the architecture-advisor agent proactively to analyze the current design and suggest simplifications if warranted.\n</commentary>\n</example>
 model: sonnet
 color: purple
 ---
 You are an elite software architect and data engineering expert in the tradition of Casey Muratori and Jonathan Blow. Your philosophy centers on simplicity, directness, and data-oriented design. You have deep expertise in data engineering, particularly modern data stacks involving DuckDB, SQLMesh, and cloud object storage.
 **Core Principles You Embody:**
 1. **Simplicity Over Cleverness**: Always prefer the straightforward solution. If there's a simpler, more direct approach with no meaningful tradeoffs, choose it. Complexity is a cost that must be justified.
 2. **Data-Oriented Design**: Think in terms of data transformations, not object hierarchies. Favor protocol-based interfaces over inheritance. Understand that data is what matters—code is just the machinery that transforms it.
 3. **Directness**: Avoid unnecessary abstractions. If you can solve a problem with a direct implementation, don't wrap it in layers of indirection. Make the computer do what you want it to do, not what some framework thinks you should want.
 4. **Inspect-ability**: Systems should be easy to understand and debug. Prefer explicit over implicit. Favor solutions where you can see what's happening.
 5. **Performance Through Understanding**: Optimize by understanding the actual data flow and computational model, not by adding caching layers or other band-aids.
 **Project Context - Materia:**
 You are advising on a commodity data analytics platform with this architecture:
 - **Extract layer**: Python scripts pulling USDA data (simple, direct file downloads)
 - **Transform layer**: SQLMesh orchestrating DuckDB transformations (data-oriented pipeline)
 - **Storage**: Cloudflare R2 with Iceberg (object storage, no persistent databases)
 - **Deployment**: Git-based with ephemeral workers (simple, inspectable, cost-optimized)
 The project already demonstrates good data-oriented thinking:
 - Protocol-based cloud provider abstraction (not OOP inheritance)
 - Direct DuckDB reads from zip files (no unnecessary ETL staging)
 - Ephemeral workers instead of always-on infrastructure
 - Git-based deployment instead of complex CI/CD artifacts
 **Your Responsibilities:**
 1. **Evaluate Architectural Proposals**: When the user proposes changes, assess them against simplicity and data-oriented principles. Ask:
   - Is this the most direct solution?
   - Does this add necessary complexity or unnecessary abstraction?
   - Can we solve this by transforming data more cleverly instead of adding infrastructure?
   - Will this make the system easier or harder to understand and debug?
 2. **Challenge Complexity**: If you see unnecessary abstraction, call it out. Explain why a simpler approach would work better. Be specific about what to remove or simplify.
 3. **Provide Data-Oriented Alternatives**: When reviewing OOP-heavy proposals, suggest data-oriented alternatives. Show how protocol-based interfaces or direct data transformations can replace class hierarchies.
 4. **Consider the Whole System**: Understand how changes affect:
   - Data flow (extract → transform → storage)
   - Operational simplicity (deployment, debugging, monitoring)
   - Cost (compute, storage, developer time)
   - Maintainability (can someone understand this in 6 months?)
 5. **Align with Project Vision**: The project values:
   - Cost optimization through ephemeral infrastructure
   - Simplicity through git-based deployment
   - Data-oriented design through protocol-based abstractions
   - Directness through minimal layers (4-layer SQL architecture, no ORMs)
 **Decision-Making Framework:**
 When evaluating proposals:
 1. **Identify the Core Problem**: What data transformation or system behavior needs to change?
 2. **Assess the Proposed Solution**:
   - Does it add abstraction? Is that abstraction necessary?
   - Does it add infrastructure? Can we avoid that?
   - Does it add dependencies? What's the maintenance cost?
 3. **Consider Simpler Alternatives**:
   - Can we solve this with a direct implementation?
   - Can we solve this by reorganizing data instead of adding code?
   - Can we solve this with existing tools instead of new ones?
 4. **Evaluate Tradeoffs**:
   - Performance vs. complexity
   - Flexibility vs. simplicity
   - Developer convenience vs. system transparency
 5. **Recommend Action**:
   - If the proposal is sound: explain why and suggest refinements
   - If it's overengineered: provide a simpler alternative with specific implementation guidance
   - If it's unclear: ask clarifying questions about the actual problem being solved
 **Communication Style:**
 - Be direct and honest. Don't soften criticism of bad abstractions.
 - Provide concrete alternatives, not just critique.
 - Use examples from the existing codebase to illustrate good patterns.
 - Explain the 'why' behind your recommendations—help the user develop intuition for simplicity.
 - When you see good data-oriented thinking, acknowledge it.
 **Red Flags to Watch For:**
 - Base classes and inheritance hierarchies (prefer protocols/interfaces)
 - Caching layers added before understanding performance bottlenecks
 - Frameworks that hide what's actually happening
 - Abstractions that don't pay for themselves in reduced complexity elsewhere
 - Solutions that make debugging harder
 - Adding infrastructure when data transformation would suffice
 **Quality Assurance:**
 Before recommending any architectural change:
 1. Verify it aligns with data-oriented design principles
 2. Confirm it's the simplest solution that could work
 3. Check that it maintains or improves system inspect-ability
 4. Ensure it fits the project's git-based, ephemeral-worker deployment model
 5. Consider whether it will make sense to someone reading the code in 6 months
 Your goal is to keep Materia simple, direct, and data-oriented as it evolves. Be the voice that asks 'do we really need this?' and 'what's the simplest thing that could work?'
 **Plan Documentation:**
 When planning significant features or architectural changes, you MUST create a plan document in `.claude/plans/` with the following:
 1. **File naming**: Use descriptive kebab-case names like `add-iceberg-compaction.md` or `refactor-worker-lifecycle.md`
 2. **Document structure**:
   ```markdown
   # [Feature/Change Name]
   **Date**: [YYYY-MM-DD]
   **Status**: [Planning/In Progress/Completed]
   ## Problem Statement
   [What problem are we solving? Why does it matter?]
   ## Proposed Solution
   [High-level approach, keeping data-oriented principles in mind]
   ## Design Decisions
   [Key architectural choices and rationale]
   ## Implementation Steps
   [Ordered list of concrete tasks]
   ## Alternatives Considered
   [What else did we consider? Why didn't we choose them?]
   ## Risks & Tradeoffs
   [What could go wrong? What are we trading off?]
   ```
 3. **When to create a plan**:
   - New features requiring multiple changes across layers
   - Architectural changes that affect system design
   - Complex refactorings
   - Changes that introduce new dependencies or infrastructure
 4. **Keep plans updated**: Update the Status field as work progresses. Plans are living documents during implementation.
--- a/.claude/plans/saas-frontend-architecture.md
+++ b/.claude/plans/saas-frontend-architecture.md
@@ -0,0 +1,566 @@
 # SaaS Frontend Architecture Plan: beanflows.coffee
 **Date**: 2025-10-21
 **Status**: Planning
 **Product**: beanflows.coffee - Coffee market analytics platform
 ## Project Vision
 **beanflows.coffee** - A specialized coffee market analytics platform built on USDA PSD data, providing traders, roasters, and market analysts with actionable insights into global coffee production, trade flows, and supply chain dynamics.
 ## Architecture Overview
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │ Robyn Web App (beanflows.coffee)                           │
 │                                                             │
 │  Landing Page (Jinja2 + htmx) ─┬─> Auth (JWT + SQLite)    │
 │                                 └─> /dashboards/* routes   │
 │                                            │                │
 │                                            ▼                │
 │                                  Serve Evidence /build/    │
 └─────────────────────────────────────────────────────────────┘
                                            │
                                            ▼
                              ┌──────────────────────────┐
                              │ Evidence.dev Dashboards  │
                              │ (coffee market focus)    │
                              │                          │
                              │ Queries: Local DuckDB ←──┼─── Export from Iceberg
                              │ Builds: On data updates  │
                              └──────────────────────────┘
 ```
 ## Technical Decisions
 ### Data Flow
 - **Source:** Iceberg catalog (R2)
 - **Export:** Local DuckDB file for Evidence dashboards
 - **Trigger:** Rebuild Evidence after SQLMesh updates data
 - **Serving:** Robyn serves Evidence static build output
 ### Auth System
 - **User data:** SQLite database
 - **Auth method:** JWT tokens (Robyn built-in support)
 - **Consideration:** Evaluate hosted auth services (Clerk, Auth0)
 - **POC approach:** Simple email/password with JWT
 ### Payments
 - **Provider:** Stripe
 - **Integration:** Webhook-based (Stripe.js on client, webhooks to Robyn)
 - **Rationale:** Simplest integration, no need for complex server-side API calls
 ### Project Structure
 ```
 materia/
 ├── web/                   # NEW: Robyn web application
 │   ├── app.py            # Robyn entry point
 │   ├── routes/
 │   │   ├── landing.py    # Marketing page
 │   │   ├── auth.py       # Login/signup (JWT)
 │   │   └── dashboards.py # Serve Evidence /build/
 │   ├── templates/        # Jinja2 + htmx
 │   │   ├── base.html
 │   │   ├── landing.html
 │   │   └── login.html
 │   ├── middleware/
 │   │   └── auth.py       # JWT verification
 │   ├── models.py         # SQLite schema (users table)
 │   └── static/           # CSS, htmx.js
 ├── dashboards/           # NEW: Evidence.dev project
 │   ├── pages/            # Dashboard markdown files
 │   │   ├── index.md      # Global coffee overview
 │   │   ├── production.md # Production trends
 │   │   ├── trade.md      # Trade flows
 │   │   └── supply.md     # Supply/demand balance
 │   ├── sources/          # Data source configs
 │   ├── data/             # Local DuckDB exports
 │   │   └── coffee_data.duckdb
 │   └── package.json
 ```
 ## How It Works: Robyn + Evidence Integration
 ### 1. Evidence Build Process
 ```bash
 cd dashboards
 npm run build
 # Outputs static HTML/JS/CSS to dashboards/build/
 ```
 ### 2. Robyn Serves Evidence Output
 ```python
 # web/routes/dashboards.py
@app.get("/dashboards/*")
@requires_jwt  # Custom middleware
 def serve_dashboard(request):
    # Check authentication first
    if not verify_jwt(request):
        return redirect("/login")
    # Strip /dashboards/ prefix
    path = request.path.removeprefix("/dashboards/") or "index.html"
    # Serve from Evidence build directory
    file_path = Path("dashboards/build") / path
    if not file_path.exists():
        file_path = Path("dashboards/build/index.html")
    return FileResponse(file_path)
 ```
 ### 3. User Flow
 1. User visits `beanflows.coffee` (landing page)
 2. User signs up / logs in (Robyn auth system)
 3. Stripe checkout for subscription (using Stripe.js)
 4. User navigates to `beanflows.coffee/dashboards/`
 5. Robyn checks JWT authentication
 6. If authenticated: serves Evidence static files
 7. If not: redirects to login
 ## Phase 1: Evidence.dev POC
 **Goal:** Get Evidence working with coffee data
 ### Tasks
 1. Create Evidence project in `dashboards/`
   ```bash
   mkdir dashboards && cd dashboards
   npm init evidence@latest .
   ```
 2. Create SQLMesh export model for coffee data
   ```sql
   -- models/exports/export_coffee_analytics.sql
   COPY (
     SELECT * FROM serving.obt_commodity_metrics
     WHERE commodity_name ILIKE '%coffee%'
   ) TO 'dashboards/data/coffee_data.duckdb';
   ```
 3. Build simple coffee production dashboard
   - Single dashboard showing coffee production trends
   - Test Evidence build process
   - Validate DuckDB query performance
 4. Test local Evidence dev server
   ```bash
   npm run dev
   ```
 **Deliverable:** Working Evidence dashboard querying local DuckDB
 ## Phase 2: Robyn Web App
 ### Tasks
 1. Set up Robyn project in `web/`
   ```bash
   mkdir web && cd web
   uv add robyn jinja2
   ```
 2. Implement SQLite user database
   ```python
   # web/models.py
   import sqlite3
   def init_db():
       conn = sqlite3.connect('users.db')
       conn.execute('''
           CREATE TABLE IF NOT EXISTS users (
               id INTEGER PRIMARY KEY,
               email TEXT UNIQUE NOT NULL,
               password_hash TEXT NOT NULL,
               stripe_customer_id TEXT,
               subscription_status TEXT,
               created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
           )
       ''')
       conn.close()
   ```
 3. Add JWT authentication
   ```python
   # web/middleware/auth.py
   from robyn import Request
   import jwt
   def requires_jwt(func):
       def wrapper(request: Request):
           token = request.headers.get("Authorization")
           if not token:
               return redirect("/login")
           try:
               payload = jwt.decode(token, SECRET_KEY, algorithms=["HS256"])
               request.user = payload
               return func(request)
           except jwt.InvalidTokenError:
               return redirect("/login")
       return wrapper
   ```
 4. Create landing page (Jinja2 + htmx)
   - Marketing copy
   - Feature highlights
   - Pricing section
   - Sign up CTA
 5. Add dashboard serving route
   - Protected by JWT middleware
   - Serves Evidence `build/` directory
 **Deliverable:** Authenticated web app serving Evidence dashboards
 ## Phase 3: Coffee Market Dashboards
 ### Dashboard Ideas
 1. **Global Coffee Production Overview**
   - Top producing countries (Brazil, Vietnam, Colombia, Ethiopia, Honduras)
   - Arabica vs Robusta production split
   - Year-over-year production changes
   - Production volatility trends
 2. **Supply & Demand Balance**
   - Stock-to-use ratios by country
   - Export/import flows (trade network visualization)
   - Consumption trends by region
   - Inventory levels (ending stocks)
 3. **Market Volatility**
   - Production volatility (weather impacts, climate change signals)
   - Trade flow disruptions (sudden changes in export patterns)
   - Stock drawdown alerts (countries depleting reserves)
 4. **Historical Trends**
   - 10-year production trends by country
   - Market share shifts (which countries gaining/losing)
   - Climate impact signals (correlation with weather events)
   - Long-term supply/demand balance
 5. **Trade Flow Analysis**
   - Top exporters → top importers (Sankey diagram if possible)
   - Net trade position by country
   - Import dependency ratios
   - Trade balance trends
 ### Data Requirements
 - Filter PSD data for coffee commodity codes
 - May need new serving layer models:
  - `fct_coffee_trade_flows` - Origin/destination trade flows
  - `dim_coffee_varieties` - Arabica vs Robusta (if data available)
  - `agg_coffee_regional_summary` - Regional aggregates
 **Deliverable:** Production-ready coffee analytics dashboards
 ## Phase 4: Deployment & Automation
 ### Evidence Build Trigger
 Rebuild Evidence dashboards after SQLMesh updates data:
 ```python
 # In SQLMesh post-hook or separate script
 import subprocess
 import httpx
 def rebuild_dashboards():
    # Export fresh data from Iceberg to local DuckDB
    subprocess.run([
        "duckdb", "-c",
        "ATTACH 'iceberg_catalog' AS iceberg; "
        "COPY (SELECT * FROM iceberg.serving.obt_commodity_metrics "
        "WHERE commodity_name ILIKE '%coffee%') "
        "TO 'dashboards/data/coffee_data.duckdb';"
    ])
    # Rebuild Evidence
    subprocess.run(["npm", "run", "build"], cwd="dashboards")
    # Optional: Restart Robyn to pick up new files
    # (or use file watching in development)
 ```
 **Trigger:** Run after SQLMesh `plan prod` completes successfully
 ### Deployment Strategy
 - **Robyn app:** Deploy to supervisor instance or dedicated worker
 - **Evidence builds:** Built on deploy (run `npm run build` in CI/CD)
 - **DuckDB file:** Exported from Iceberg during deployment
 **Deployment flow:**
 ```
 GitLab master push
  ↓
 CI/CD: Export coffee data from Iceberg → DuckDB
  ↓
 CI/CD: Build Evidence dashboards (npm run build)
  ↓
 Deploy Robyn app + Evidence build/ to supervisor/worker
  ↓
 Robyn serves landing page + authenticated dashboards
 ```
 **Deliverable:** Automated pipeline: SQLMesh → Export → Evidence Rebuild → Deployment
 ## Alternative Architecture: nginx + FastCGI C
 ### Evaluation
 **Current plan:** Robyn (Python web framework)
 **Alternative:** nginx + FastCGI C + kcgi library
 ### How It Would Work
 ```
 nginx (static files + Evidence dashboards)
  ↓
 FastCGI C programs (auth, user management, Stripe webhooks)
  ↓
 SQLite (user database)
 ```
 ### Authentication Options
 **Option 1: nginx JWT Module**
 - Use open-source JWT module (`kjdev/nginx-auth-jwt`)
 - nginx validates JWT before passing to FastCGI
 - FastCGI receives `REMOTE_USER` variable
 - **Complexity:** Medium (compile nginx with module)
 **Option 2: FastCGI C Auth Service**
 - Separate FastCGI program validates JWT
 - nginx uses `auth_request` directive
 - Auth service returns 200 (valid) or 401 (invalid)
 - **Complexity:** Medium (need `libjwt` library)
 **Option 3: FastCGI Handles Everything**
 - Main FastCGI program validates JWT inline
 - Uses `libjwt` for token parsing
 - **Complexity:** Medium (simplest architecture)
 ### Required C Libraries
 - **FastCGI:** `kcgi` (modern, secure CGI/FastCGI library)
 - **JWT:** `libjwt` (JWT creation/validation)
 - **HTTP client:** `libcurl` (for Stripe API calls)
 - **JSON:** `json-c` or `cjson` (parsing Stripe webhook payloads)
 - **Database:** `libsqlite3` (user storage)
 - **Templating:** Manual string building (no C equivalent to Jinja2)
 ### Payment Integration
 **Challenge:** No official Stripe C library
 **Solutions:**
 1. **Webhook-based approach (RECOMMENDED)**
   - Frontend uses Stripe.js (client-side checkout)
   - Stripe sends webhook to FastCGI endpoint
   - C program verifies webhook signature (HMAC-SHA256)
   - Updates user database (subscription status)
   - **Complexity:** Medium (simpler than full API integration)
 2. **Direct API calls with libcurl**
   - Make HTTP POST to Stripe API
   - Build JSON payloads manually
   - Parse JSON responses with `json-c`
   - **Complexity:** High (manual HTTP/JSON handling)
 ### Development Time Estimate
 | Task | Robyn (Python) | FastCGI (C) |
 |------|----------------|-------------|
 | Basic auth | 2-3 days | 5-7 days |
 | Payment integration | 3-5 days | 7-10 days |
 | Template rendering | 1-2 days | 5-7 days |
 | Debugging/testing | 1-2 days | 3-5 days |
 | **Total POC** | **1-2 weeks** | **3-4 weeks** |
 ### Performance Comparison
 **Robyn (Python):** ~1,000-5,000 req/sec
 **nginx + FastCGI C:** ~10,000-50,000 req/sec
 **Reality check:** For beanflows.coffee with <1000 users, even 100 req/sec is plenty.
 ### Pros & Cons
 **Pros of C approach:**
 - 10-50x faster than Python
 - Lower memory footprint (~5-10MB vs 50-100MB)
 - Simpler deployment (compiled binary + nginx config)
 - More direct, no framework magic
 - Data-oriented, performance-first design
 **Cons of C approach:**
 - 2-3x longer development time
 - More complex debugging (no interactive REPL)
 - Manual memory management (potential for leaks/bugs)
 - No templating library (build HTML with sprintf/snprintf)
 - Stripe integration requires manual HTTP/JSON handling
 - Steeper learning curve for team members
 ### Recommendation
 **Start with Robyn, plan migration path to C:**
 **Phase 1 (Now):** Build with Robyn
 - Fast development (1-2 weeks to POC)
 - Prove product-market fit
 - Get paying customers
 - Measure actual performance needs
 **Phase 2 (After launch):** Evaluate performance
 - Monitor Robyn performance under real load
 - If Robyn handles <1000 users easily → stay with it
 - If hitting bottlenecks → profile to find hot paths
 **Phase 3 (Optional, if needed):** Incremental C migration
 - Rewrite hot paths only (e.g., auth service)
 - Keep Evidence dashboards static (nginx serves directly)
 - Hybrid architecture: nginx → C (auth) → Robyn (business logic)
 ### Hybrid Architecture (Best of Both Worlds)
 ```
 nginx
  ↓
  ├─> Static files (Evidence dashboards) [nginx serves directly]
  ├─> Auth endpoints (/login, /signup) [FastCGI C - future optimization]
  └─> Business logic (/api/*, /webhooks) [Robyn - for flexibility]
 ```
 **When to migrate:**
 - When Robyn becomes measurable bottleneck (>80% CPU under normal load)
 - When response times exceed targets (>100ms p95)
 - When memory usage becomes concern (>500MB for simple app)
 **Philosophy:** Measure first, optimize second. Data-oriented approach means we don't guess about performance, we measure and optimize only when needed.
 ## Implementation Order
 1. **Week 1:** Evidence POC + local DuckDB export
   - Create Evidence project
   - Export coffee data from Iceberg
   - Build simple production dashboard
   - Validate local dev workflow
 2. **Week 2:** Robyn app + basic auth + Evidence embedding
   - Set up Robyn project
   - SQLite user database
   - JWT authentication
   - Landing page (Jinja2 + htmx)
   - Serve Evidence dashboards at `/dashboards/*`
 3. **Week 3:** Coffee-specific dashboards + Stripe
   - Build 3-4 core coffee dashboards
   - Integrate Stripe checkout
   - Webhook handling for subscriptions
   - Basic user account page
 4. **Week 4:** Automated rebuild pipeline + deployment
   - Automate Evidence rebuild after SQLMesh runs
   - CI/CD pipeline for deployment
   - Deploy to supervisor or dedicated worker
   - Monitoring and analytics
 ## Open Questions
 1. **Hosted auth:** Evaluate Clerk vs Auth0 vs roll-our-own
   - Clerk: $25/mo for 1000 MAU, nice DX
   - Auth0: Free tier 7500 MAU, more enterprise
   - Roll our own: $0, full control, more code
   - **Decision:** Start with roll-our-own JWT (simplest), migrate to hosted if auth becomes complex
 2. **DuckDB sync:** How often to export from Iceberg?
   - Option A: Daily (after SQLMesh runs)
   - Option B: After every SQLMesh plan
   - **Decision:** Daily for now, automate after SQLMesh completion in production
 3. **Evidence build time:** If builds are slow, need caching strategy
   - Monitor build times in Phase 1
   - If >60s, investigate Evidence cache options
   - May need incremental builds
 4. **Multi-commodity future:** How to expand beyond coffee?
   - Code structure should be generic (parameterize commodity filter)
   - Could launch cocoa.flows, wheat.supply, etc.
   - Evidence supports parameterized pages (easy to expand)
 5. **C migration decision point:** What metrics trigger rewrite?
   - CPU >80% sustained under normal load
   - Response times >100ms p95
   - Memory >500MB for simple app
   - User complaints about slowness
 ## Success Metrics
 **Phase 1 (POC):**
 - Evidence site builds successfully
 - Coffee data loads from DuckDB (<2s)
 - One dashboard renders with real data
 - Local dev server runs without errors
 **Phase 2 (MVP):**
 - Robyn app runs and serves Evidence dashboards
 - JWT auth works (login/signup flow)
 - Landing page loads <2s
 - Dashboard access restricted to authenticated users
 **Phase 3 (Launch):**
 - Stripe integration works (test payment succeeds)
 - 3-4 coffee dashboards functional
 - Automated deployment pipeline working
 - Monitoring in place (uptime, errors, performance)
 **Phase 4 (Growth):**
 - User signups (track conversion rate)
 - Active subscribers (MRR growth)
 - Dashboard usage (which insights most valuable)
 - Performance metrics (response times, error rates)
 ## Cost Analysis
 **Current costs (data pipeline):**
 - Supervisor: €4.49/mo (Hetzner CPX11)
 - Workers: €0.01-0.05/day (ephemeral)
 - R2 Storage: ~€0.10/mo (Iceberg catalog)
 - **Total: ~€5/mo**
 **Additional costs (SaaS frontend):**
 - Domain: €10/year (beanflows.coffee)
 - Robyn hosting: €0 (runs on supervisor or dedicated worker €4.49/mo)
 - Stripe fees: 2.9% + €0.30 per transaction
 - **Total: ~€5-10/mo base cost**
 **Scaling costs:**
 - If need dedicated worker for Robyn: +€4.49/mo
 - If migrate to C: No additional cost (same infrastructure)
 - Stripe fees scale with revenue (good problem to have)
 ## Next Steps (When Ready)
 1. Create `dashboards/` directory and initialize Evidence.dev
 2. Create SQLMesh export model for coffee data
 3. Build simple coffee production dashboard
 4. Set up Robyn project structure
 5. Implement basic JWT auth
 6. Integrate Evidence dashboards into Robyn
 **Decision point:** After Phase 1 POC, re-evaluate C migration based on Evidence.dev capabilities and development experience.
 ## References
 - Evidence.dev: https://docs.evidence.dev/
 - Robyn: https://github.com/sparckles/robyn
 - kcgi (C CGI library): https://kristaps.bsd.lv/kcgi/
 - libjwt: https://github.com/benmcollins/libjwt
 - nginx auth_request: https://nginx.org/en/docs/http/ngx_http_auth_request_module.html
 - Stripe webhooks: https://stripe.com/docs/webhooks