Project Codename

ATLAS

MorningStop.com builds an AI-native publishing, discovery, and rewards engine for creators and readers. This PRD is the build-grade blueprint.

Back to HomeRead Articles
AI-native platform
Creator-first economy
Autonomous agents
Revenue + rewards
Product Summary
Editorial authority meets AI-native automation to reward creators and readers at scale.

ATLAS blends Morningstar-grade research depth, Medium-style publishing, and AI discovery systems to automate curation, generation, ranking, distribution, monetization, and rewards.

The system is designed for a self-reinforcing knowledge economy: humans provide signal; agents handle scale. Every agent decision is logged, auditable, and config-driven.

Primary Goals
Outcomes that shape the platform health and creator economics.
  • Enable creators to publish once and earn continuously.
  • Reward readers for attention, engagement, and curation.
  • Automate creation, moderation, ranking, and monetization.
  • Create a self-reinforcing knowledge economy.
Success Metrics
Operational KPIs tracked daily.
  • DAU / MAU
  • Avg session time
  • Creator retention (30/90 days)
  • Content quality score (AI-rated)
  • Ad CTR & RPM
  • Revenue shared to creators
  • Reader reward redemption rate
User Roles
Distinct incentives per persona.
  • Reader
  • Creator
  • Curator
  • Advertiser
  • System AI Agents
  • Admin
Core Content Types
Surface area for creators and AI agents.
  • Blog posts
  • Newsletters (daily / weekly / custom cadence)
  • AI-generated summaries
  • Tool directories
  • Research reports
  • Opinion / essays
  • Sponsored content (clearly labeled)
Creator System
Onboarding, dashboard tooling, and publishing controls.
  • Email / OAuth onboarding with optional KYC for payouts
  • Wallet creation (custodial or non-custodial)
  • AI skill-level assessment and niche selection
  • AI-assisted, co-writing, or fully delegated drafting
  • RSS/Substack/Medium imports with release scheduling
  • Dashboard for earnings, engagement, and automation toggles
Reader System
Retention, trust, and reward mechanics.
  • Optional login with anonymous read-to-earn via device ID
  • Interest graph + reading history
  • Rewards for read time, upvotes, comments, sharing, and curation
  • Anti-scroll-farming and fraud detection controls
Rewards & Incentives
Points economy and payout logic.
  • Points-based internal ledger with decay to prevent hoarding
  • Convertible to cash, subscriptions, ad-free reading, and tipping
  • Creator rewards driven by engagement quality + longevity
  • Reader rewards reflect trust and spam risk scores
Advertising System
Monetization while protecting UX.
  • Display + native ads + sponsored newsletters + tool placements
  • AI-driven contextual ad placement with CPM optimization
  • User-level ad frequency control and creator opt-in
Discovery & Ranking
AI personalization and trust.
  • AI ranking on originality, depth, trust, freshness, and cross-niche relevance
  • Personalized surfaces: home feed, topic hubs, daily digests
  • Automated What you missed digests and high-signal topic clusters
Multi-Agent Architecture
Modular agents with auditable outputs and config-driven workflows.

Content Scout

  • Crawls web, RSS, social
  • Detects trending topics
  • Flags high-signal opportunities

Content Generator

  • Drafts articles and summaries
  • Creates variants per audience
  • Enforces editorial tone

Editor

  • Fact-checks and bias detection
  • Plagiarism screening
  • Tone and clarity enforcement

Ranking Agent

  • Scores relevance
  • Updates feed positions
  • Decays low-quality content

Monetization Agent

  • Places ads
  • Sets CPM dynamically
  • Suggests sponsorships

Reward Agent

  • Calculates payouts
  • Detects fraud
  • Adjusts incentive curves

Email Distribution

  • Personalizes newsletters
  • Optimizes send times
  • A/B tests subject lines
Daily Autonomous Loop
Zero human input required.
  1. Scout Agent finds topics
  2. Generator creates drafts
  3. Editor validates
  4. Ranking Agent publishes
  5. Monetization Agent inserts ads
  6. Email Agent distributes
  7. Reward Agent logs earnings
Technical Architecture
Scalable, event-driven infrastructure.
  • Frontend: Next.js / React
  • Backend: Node.js + Python microservices
  • Postgres (core data) + Vector DB (embeddings)
  • Queue: Redis / Kafka
  • LLMs for generation + embeddings for ranking
  • Stripe + optional crypto payouts
Deployment Blueprint
One-click production provisioning.
  • Vercel Frontend
  • AWS Lambda + API Gateway
  • Postgres (RDS)
  • Vector Store (Pinecone or Milvus)
  • Redis + S3 media storage
  • SES/SendGrid email + Stripe billing
  • Datadog / Sentry observability
Design Tokens
Visual identity system.
Primary#1F2937
Secondary#4B5563
Accent#EF4444
Background#F9FAFB
API Spec
Core content endpoints.
  • GET /api/posts?topic=...&limit=...
  • POST /api/posts
  • PATCH /api/posts/:id
MVP Scope
Phase one delivery commitments.
  • Publishing
  • Reading
  • Rewards
  • Ads
  • AI generation
  • Email newsletters
Future Roadmap
Next horizon expansions.
  • Creator DAOs
  • Paid communities
  • API access
  • White-label newsletters
  • Enterprise research tiers
  • On-chain reputation
Non-Goals
What ATLAS is not.
  • Not a social media clone
  • Not short-form content
  • Not paywalled knowledge only