AI_Brain · codex/build-online-github-ani-7nmb21

Purpose & architecture

Seeding suite for Hive's 500× expansion. Inserts atomically hashed sentences into Postgres atomic_facts with ON CONFLICT DO NOTHING.

This page records a pinned branch snapshot. Differences are compared with the audited default branch, not assumed to be chronological improvements. Author claims in the source documentation are retained as claims. Source recovery does not establish successful deployment, scientific validity, or equivalence with another branch.

This source generation

This record follows the codex/build-online-github-ani-7nmb21 branch of Janady13/AI_Brain at commit d0f50071c85cddaaebbf9c4dd1313bd7676986da. The snapshot identifies the source inspected in this audit. It is not a statement about which revision is currently running on a device.

Recovered implementation structure

These paths locate the components and entry points represented by this build. The source register contains their inspected revisions.

• .github/workflows/ci.yml
• README.md
• daemon/datasets/agentic_loops.json
• daemon/datasets/agentic_systems.json
• daemon/datasets/amazon_marketplace.json
• daemon/datasets/automation_sprints.json
• daemon/datasets/aws_growth_playbooks.json
• daemon/datasets/channel_investments.json
• daemon/datasets/cj_offers.json
• daemon/datasets/code_automation.json
• daemon/datasets/cognitive_capabilities.json
• daemon/datasets/competitive_landscape.json
• daemon/datasets/ebay_marketplace.json
• daemon/datasets/global_stock_markets.json
• daemon/datasets/influencer_partners.json
• daemon/datasets/knowledge_workflows.json
• daemon/datasets/language_capabilities.json
• daemon/datasets/marketing_benchmarks.json
• daemon/datasets/moe_experts.json
• daemon/datasets/performance_memory.json
• daemon/datasets/statistical_reasoning.json
• daemon/enumerations/agent_500.txt
• daemon/enumerations/language_500.txt
• daemon/enumerations/marketing_500.txt
• daemon/enumerations/math_model_500.txt
• daemon/enumerations/prophecy_500.txt
• daemon/make_enumerations.py
• daemon/seed_agents.py
• daemon/seed_all.sh
• daemon/seed_common.py
• daemon/seed_language.py
• daemon/seed_marketing.py
• daemon/seed_math_models.py
• daemon/seed_prophecy.py
• daemon/seed_statistics.py
• daemon/utils.py
• infra/omnicron_iface.py
• infra/schema.sql
• infra/seed.sql
• requirements-dev.txt
• requirements.txt
• scripts/hive_master_start.sh
• scripts/smoke_seeders.py
• services/knowledge_pipeline.py
• services/seeder_api/README.md
• services/seeder_api/app.py
• services/seeder_api/intelligence.py
• services/seeder_api/requirements.txt
• sub_brains/money_agents/dashboard.py
• sub_brains/money_agents/docker-compose.yml
• sub_brains/money_agents/launch.sh
• sub_brains/money_agents/requirements.txt
• sub_brains/money_agents/sql/init.sql
• sub_brains/money_agents/templates/index.html
• sub_brains/money_agents/templates/panel_jobs.html
• sub_brains/money_agents/templates/panel_logs.html
• sub_brains/money_agents/templates/panel_overview.html
• sub_brains/money_agents/templates/panel_transactions.html
• sub_brains/money_agents/templates/panel_workers.html
• sub_brains/money_agents/worker.py
• tests/conftest.py
• tests/test_generators.py
• tests/test_knowledge_pipeline.py
• tests/test_marketing_intelligence.py
• tests/test_seeder_api.py

Behavior & verification limits

Read-only source inspection. No native engine, generator, database, payment, or production service was executed.

Original project documentation

The following documentation is preserved as a dated source account. Claims in it retain the source’s wording and must be read alongside the implementation notes above.

AI_Brain

Seeding suite for Hive's 500× expansion. Inserts atomically hashed sentences into Postgres atomic_facts with ON CONFLICT DO NOTHING.

Features - 12-factor env config; dry-run support via DRY_RUN=1. - File-based seeding (SEED_FILE[_CATEGORY]) or programmatic generators (math, agent, language, prophecy, marketing). - Online marketing intelligence endpoint that ranks curated campaigns against prompts. - Statistical marketing intelligence endpoint delivering benchmark-weighted ROI and CTR projections. - Multi-process marketing ANI surface pairing retrieval-augmented guidance (RAG), an Experiment Growth Grid (EGG) planner, optional Google Ads enrichment, CJ affiliate monetization intelligence, crypto market telemetry, Stripe monetization playbooks, PayPal payout orchestration, influencer collaboration intelligence, Amazon marketplace merchandising, eBay partner commerce intelligence, AWS revenue automation, and an autonomous budget allocator. - Budget allocation engine that synthesizes channel investment benchmarks with prompt intent to recommend spend mix, projected revenue, and payback timelines. - Autonomous automation sprint planner that converts ranked campaigns into a three-hour execution loop across the strongest channels. - Closed-loop learning loop that distills historic experiment memory into next actions aligned to each prompt. - Competitive landscape intelligence that surfaces rival plays, threat levels, and counter-moves from curated monitoring feeds. - Executive playbook orchestrator that fuses every enrichment signal into prioritized directives with budgets, actions, and risk mitigation. - Crypto market telemetry layer that synthesizes CoinGecko, CoinCap, Blockstream, and BlockCypher data into actionable asset snapshots for campaigns. - Stripe monetization planner that converts campaign selections into checkout-ready offers (activates live mode when Stripe SDK credentials are configured). - PayPal monetization planner that transforms campaigns into payout-ready flows with configurable payout cadence and merchant linkage. - Influencer collaboration planner that pairs prompts with curated creator reach, projected ROI, and activation notes, with optional live sourcing when API credentials are provided. - Amazon and AWS data pipelines with curated marketplace catalogs and growth playbooks that flip to live SDK-backed enrichment when credentials are supplied. - Marketplace mastery across Amazon (Prime, Buy Box, fulfillment heuristics) and eBay (Top Rated velocity, affiliate margin, shipping speed) plus influencer partnership reach planning so prompts surface revenue-ready partner offers even without live credentials. - Atomic SHA-256 hashing and idempotent inserts. - Simple launcher: daemon/seed_all.sh --env file --dry-run --only math,agent,marketing,....

Quick Start 1. Python deps (for live DB): - python3 -m pip install --user -r requirements.txt 2. Create schema: - psql -h "ATLASSLOT0ENDPGUSER" -d "$PGDATABASE" -f infra/seed.sql 3. Dry-run with examples: - daemon/seed_all.sh --env .env.example --dry-run 4. Live (set your own .env): - daemon/seed_all.sh --env .env

Env Vars - PGHOST, PGDATABASE, PGUSER, PGPORT - DRY_RUN (1 to simulate) - SEED_SOURCE (default Hive500Expansion) - SEED_FILE, SEED_FILE_MATH_MODEL, SEED_FILE_AGENT, SEED_FILE_LANGUAGE, SEED_FILE_PROPHECY, SEED_FILE_MARKETING - GOOGLE_ADS_DEVELOPER_TOKEN, GOOGLE_ADS_CUSTOMER_ID, GOOGLE_ADS_CLIENT_ID, GOOGLE_ADS_CLIENT_SECRET, GOOGLE_ADS_REFRESH_TOKEN, GOOGLE_ADS_LOGIN_CUSTOMER_ID (optional OAuth credentials for live Google Ads metrics) - GOOGLE_ADS_SERVICE_ACCOUNT_INFO, GOOGLE_ADS_SERVICE_ACCOUNT_FILE, or GOOGLE_ADS_SERVICE_ACCOUNT_B64 (service account options for Google Ads SDK authentication; honors GOOGLE_APPLICATION_CREDENTIALS) - CJ_DEVELOPER_KEY (required for live CJ publisher API access) - CJ_WEBSITE_ID, CJ_ACCOUNT_ID (optional metadata forwarded to CJ API requests) - STRIPE_API_KEY (activates live Stripe monetization orchestration) - STRIPE_ACCOUNT_ID (optional Stripe account hint for logging/analytics) - PAYPAL_CLIENT_ID, PAYPAL_CLIENT_SECRET (activate live PayPal checkout orchestration) - PAYPAL_MERCHANT_ID (optional merchant account reference used in generated setup steps) - PAYPAL_ENVIRONMENT (set to live to flip from sandbox heuristics) - AMAZON_PAAPI_ACCESS_KEY, AMAZON_PAAPI_SECRET_KEY, AMAZON_PAAPI_PARTNER_TAG (enables live Amazon Product Advertising API harvesting) - EBAY_APP_ID, EBAY_CERT_ID, EBAY_DEV_ID (required for live eBay Partner Network inventory) - EBAY_REDIRECT_URI, EBAY_CAMPAIGN_ID (optional partner configuration forwarded to the eBay API) - AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN (optional) (activates boto3-backed AWS revenue automation validation) - AWS_REGION, AWS_PROFILE (optional session tuning for AWS integrations) - INFLUENCER_NETWORK_API_KEY (optional API key for sourcing live influencer availability) - INFLUENCER_NETWORK_WORKSPACE (optional workspace identifier forwarded to influencer network requests)

See .env.example for a template.

Make Targets - make enumerations – generate 5×500 lists under daemon/enumerations/ - make seed-dry – dry-run using .env.example - make seed-live – live run using .env (ensure DB is reachable)

Testing - python -m pip install -r requirements-dev.txt - pytest

Intelligence API

The marketing ANI runs within the FastAPI service and now includes a statistical surface.

• Run locally: uvicorn services.seeder_api.app:app --reload
• Baseline ranking endpoint: curl -X POST http://localhost:8000/intelligence/marketing -H 'Content-Type: application/json' -d '{"prompt": "accelerate demos for B2B SaaS founders"}'
• Statistical endpoint with ROI/CTR projections: curl -X POST http://localhost:8000/intelligence/marketing/statistical -H 'Content-Type: application/json' -d '{"prompt": "Optimize ROI for AI founders"}'
• Statistical responses include campaign-level ROI, CTR, confidence estimates, and a portfolio rollup summarizing the blended strategy.
• Diagnostics endpoint for prompt intelligence: curl -X POST http://localhost:8000/intelligence/marketing/diagnostics -H 'Content-Type: application/json' -d '{"prompt": "Pipeline is stalled for B2B founders"}'
• Diagnostic responses label funnel stage, urgency, ICP focus, recommended KPIs, opportunity plays, and risk signals to accelerate decisioning.
• Process orchestration endpoint for RAG + EGG + Google Ads + CJ affiliate + crypto market + Stripe + PayPal + Amazon marketplace + AWS revenue + budget intelligence + closed-loop learning + competitive counter-moves: curl -X POST http://localhost:8000/intelligence/marketing/processes -H 'Content-Type: application/json' -d '{"prompt": "Need urgent conversion plays for enterprise SaaS"}'
• Process responses include a rag block with prioritized campaigns, an egg array of experiment playbooks, a google_ads collection containing either live Google Ads performance (when credentials are configured) or benchmark-driven simulations, a cj_affiliate catalog with monetizable programs from CJ (live when keys configured, simulated otherwise), crypto_markets snapshots summarizing CoinGecko/CoinCap/Blockstream/BlockCypher signals, stripe checkout plans that switch to live Stripe SDK execution when credentials are available, paypal payout flows that flip to live PayPal SDK orchestration when credentials are provided, influencer_collaborations that surface reach, ROI, and activation notes for curated partner creators (upgrade with API credentials), amazon_marketplace merchandising plays from Amazon Product Advertising/AWS Marketplace sources, ebay_marketplace Top Rated offers tuned for affiliate margin and shipping velocity, aws_cloud revenue automation programs sourced from AWS growth playbooks or live boto3 validation, competitive_landscape counter-moves summarizing curated rival signals, an executive_playbook blueprint that prioritizes directives with dependencies, timelines, and risk mitigation, and a scenario_planning payload that quantifies optimistic/base/conservative revenue envelopes with accelerators and stabilizers.
• Process responses now also provide a budget_optimizer plan that allocates spend across the ranked campaigns with projected leads, revenue, ROI, and payback horizon based on channel investment benchmarks (override daemon/datasets/channel_investments.json to customize).
• The automation payload delivers a scripted three-hour sprint with sequential tasks, tooling, and KPIs that can drive autonomous execution; update daemon/datasets/automation_sprints.json to tailor time blocks or focus overlays.
• The learning_loop payload taps into daemon/datasets/performance_memory.json and blends funnel diagnostics, campaign rankings, and historic wins/losses into next-action guidance ready for operators or automation cadences.
• The competitive_landscape payload leverages daemon/datasets/competitive_landscape.json to spotlight rival strengths, weaknesses, and recommended counter-moves; tweak the dataset to match your market intelligence feeds.
• The excellence summary distills ROI, CTR, confidence, and process coverage into a composite maturity index so you can quickly understand how "world-class" a given prompt’s orchestration is and where to reinforce integrations.
• The scenario_planning payload feeds the new scenario engine which fuses ranked campaigns, budget allocations, and enrichment telemetry (Google Ads ROAS, CJ revenue, Amazon sentiment, eBay margin velocity, AWS ROI, Stripe pricing, PayPal payout readiness, influencer reach momentum, crypto volatility) into optimistic, base, and conservative projections. Each projection surfaces accelerators, stabilizers, and watchouts so operators can decide how aggressively to push spend or hedge risk in autonomous loops.

Continuous Integration This repository is wired up with a GitHub Actions workflow (CI) that runs on every push and pull request. The workflow installs the project dependencies, executes the dry-run seeding smoke test (make seed-dry), runs the automated pytest suite, and compiles the Python sources to validate imports. This provides quick feedback that seeding pipelines and services stay healthy before changes are merged.