AI_Brain · codex/build-online-github-ani-7v4kug

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-7v4kug branch of Janady13/AI_Brain at commit 5cd0bbf744b0aee9445913337e852889d9a8098e. 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/investing_platforms.json
• daemon/datasets/knowledge_workflows.json
• daemon/datasets/language_capabilities.json
• daemon/datasets/marketing_benchmarks.json
• daemon/datasets/marketing_knowledge.json
• daemon/datasets/meta_learning_capabilities.json
• daemon/datasets/moe_experts.json
• daemon/datasets/payment_platforms.json
• daemon/datasets/performance_memory.json
• daemon/datasets/physics_economics.json
• daemon/datasets/quantum_simulations.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
• joseph_custom_agents.py
• mega_money_brain.py
• requirements-dev.txt
• requirements.txt
• scripts/bulk_update.py
• scripts/hive_master_start.sh
• scripts/quantum_hyperloop.py
• scripts/smoke_seeders.py
• services/knowledge_pipeline.py
• services/quantum_harness.py
• services/seeder_api/README.md
• services/seeder_api/app.py
• services/seeder_api/intelligence.py
• services/seeder_api/requirements.txt
• sub_brains/hive-brain-deployment/index.js
• 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_bulk_update.py
• tests/test_generators.py
• tests/test_knowledge_pipeline.py
• tests/test_marketing_intelligence.py
• tests/test_quantum_harness.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, payment integration runbooks for Stripe/PayPal/Cash App/Venmo, investing platform monetization intelligence, 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. - Meta-learning planner that layers reinforcement loops, distillation, and governance so campaign intelligence keeps improving between cycles. - 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. - Global stock market and bitcoin forecasting layer that converts curated macro datasets into statistical 30/90-day return outlooks and revenue multipliers. - Statistical reasoning engine that blends curated priors with live campaign performance, projects success probabilities, and optionally persists insights to Postgres for longitudinal learning. - 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. - Payment integration planner that ingests Stripe, PayPal, Cash App Pay, and Venmo documentation to provide troubleshooting and codefix-ready playbooks for campaign launches. - 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 - STATISTICAL_REASONING_PERSIST (set to 1 to persist statistical reasoning summaries into Postgres statistical_reasoning_insights) - 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) - SQUARE_ACCESS_TOKEN, SQUARE_APPLICATION_ID, SQUARE_LOCATION_ID (enable Cash App Pay enrichment through Square APIs) - VENMO_CLIENT_ID, VENMO_CLIENT_SECRET, VENMO_ACCESS_TOKEN (optional Venmo checkout readiness hints for live deployments) - 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) - GITHUB_TOKEN, GITLAB_TOKEN, BITBUCKET_USERNAME, BITBUCKET_APP_PASSWORD (optional source-control credentials for activating live code automation loops) - SONAR_TOKEN, SONAR_HOST_URL, SONARCLOUD_TOKEN (optional SonarQube/SonarCloud credentials powering AI CodeFix remediation) - AZURE_DEVOPS_ORG_SERVICE_URL, AZURE_DEVOPS_EXT_PAT (optional Azure DevOps pipeline access for enterprise automation routing)

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

Bulk Update Runner

Need to rehearse or execute "million update" style refresh cycles without manually triggering the FastAPI endpoint? Use the bulk update helper which simulates or runs high-volume orchestrations while reporting timing projections:

python scripts/bulk_update.py --iterations 1000000 --batch-size 5000 --limit 3

The command defaults to a dry-run that samples five iterations and estimates how long the requested run would take. Pass --execute to perform the full cycle and receive stderr progress logs every batch.

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 + global stock forecasts + 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, stock_markets forecasts that merge global equities and bitcoin signals into statistical 30/90-day return projections, 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, payment_integrations maturity-scored playbooks covering Stripe/PayPal/Cash App/Venmo troubleshooting and codefix guidance, investing portfolio readiness plans translating Yieldstreet/SoFi/Webull/Schwab/Stash monetization into activation steps, a cognitive_architecture array translating machine learning, reasoning, problem-solving, perception, language, prompt engineering, cache-augmented generation, knowledge-augmented generation, Haystack, LlamaIndex, agent automations, vector databases, and prompt management into actionable orchestration steps, a marketing_knowledge payload that maps Notion, Airtable, Confluence, Google Drive, and Slack repositories into automated ingestion and connector readiness plans, a language_intelligence suite that scores NLP, NLU, NLG, and conversational AI workflows for revenue readiness, a code_automation planner that orchestrates AI CodeFix, intelligent automation, and agentic automation loops, a meta_learning planner that keeps reinforcement, distillation, and governance loops adaptive, an agentic_systems payload that fuses agent-based planning, database connectivity, feature stores, vector memory, prompt management hubs, and trading automation into maturity-scored activation plans, a physics_economics array blending quantum mechanics, hardware compression, and global economics so compute-constrained agents stay revenue-resilient, 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, a scenario_planning payload that quantifies optimistic/base/conservative revenue envelopes with accelerators and stabilizers, and a statistical_reasoning payload summarizing posterior ROI, success probability, and driver evidence.
• 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, stock_markets forecasts that merge global equities and bitcoin signals into statistical 30/90-day return projections, 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, a marketing_knowledge payload that maps Notion, Airtable, Confluence, Google Drive, and Slack repositories into automated ingestion and connector readiness plans, a cognitive_architecture array translating machine learning, reasoning, problem-solving, perception, language, prompt engineering, cache-augmented generation, knowledge-augmented generation, Haystack, LlamaIndex, agent automations, vector databases, and prompt management into actionable orchestration steps, a language_intelligence suite that scores NLP, NLU, NLG, and conversational AI workflows for revenue readiness, a code_automation planner that orchestrates AI CodeFix, intelligent automation, and agentic automation loops, a meta_learning planner that keeps reinforcement, distillation, and governance loops adaptive, an agentic_systems payload that fuses agent-based planning, database connectivity, feature stores, vector memory, prompt management hubs, and trading automation into maturity-scored activation plans, a physics_economics array blending quantum mechanics, hardware compression, and global economics so compute-constrained agents stay revenue-resilient, 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, a scenario_planning payload that quantifies optimistic/base/conservative revenue envelopes with accelerators and stabilizers, and a statistical_reasoning payload summarizing posterior ROI, success probability, and driver evidence.
• New rag_workflows and knowledge_graphs payloads expose the indexing, retrieval, and governance steps powering retrieval- and knowledge-augmented generation, while mixture_of_experts highlights active Google/Stripe/CJ/Amazon expert routing and agentic_loops documents the perception→reasoning→action→reflection automation cycle.
• Run python -m services.knowledge_pipeline "<your query>" to execute a lightweight, dependency-free RAG+KAG demo that indexes the knowledge workflow dataset, traverses the accompanying knowledge graph, and prints a combined answer payload mirroring the production API surface.
• 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 stock_markets payload reads from daemon/datasets/global_stock_markets.json, blending global equity and bitcoin benchmarks into forecasted returns—edit this dataset or wire live feeds for bespoke macro coverage.
• The cognitive_architecture payload is powered by daemon/datasets/cognitive_capabilities.json, mapping machine learning, reasoning, perception, language understanding, prompt engineering, cache-augmented generation, knowledge-augmented generation, Haystack, LlamaIndex, agent applications, vector databases, and prompt management patterns into maturity-scored playbooks.
• The language_intelligence payload reads from daemon/datasets/language_capabilities.json, aligning NLP, NLU, NLG, and conversational AI orchestration with marketing prompts, campaign telemetry, and configured providers to keep copy, chats, and assistants revenue-ready.
• The marketing_knowledge payload taps daemon/datasets/marketing_knowledge.json, mapping Notion vaults, Airtable campaign bases, Confluence revenue wikis, Google Drive creative libraries, and Slack signal digests into automated ingestion plans; set NOTION_API_KEY, AIRTABLE_API_KEY, CONFLUENCE_API_TOKEN, SLACK_BOT_TOKEN, GOOGLE_APPLICATION_CREDENTIALS, or DROPBOX_TOKEN to upgrade the automation maturity with live connector telemetry.
• The code_automation payload taps into daemon/datasets/code_automation.json, mapping AI CodeFix, intelligent automation, and agentic automation plays into maturity-scored plans with tooling, guardrails, and orchestration steps that keep shipping velocity revenue-safe.
• The meta_learning payload reads from daemon/datasets/meta_learning_capabilities.json, blending reinforcement learning, meta-learning, distillation, and governance heuristics into adaptive learning playbooks with tooling, datasets, and deployment guidance.
• The agentic_systems payload reads from daemon/datasets/agentic_systems.json, translating agent-based orchestration, database fusion, feature store governance, predictive ML labs, market intelligence reactors, CX command centers, and prompt intelligence fabrics into readiness-scored playbooks with toolchains, metrics, and risk controls.
• The quantum_simulation payload leverages daemon/datasets/quantum_simulations.json, running deterministic quantum gate sequences to stress test Google Ads, AWS, marketing funnels, crypto, equity markets, Stripe, Cash App Pay, Venmo, PayPal, and agentic workflows so scenario planning, excellence scoring, and executive directives stay resilient. Each plan now exposes a coverage ratio and domain stability map so you can instantly see which revenue surfaces were stress-tested and how close each measurement landed to the target probability. Plans additionally surface per-domain agent capacity, node requirements for sustaining one million agents, compression ratios, and the shot count used to calculate stability so you can reason about deployment scale directly from the API response.
• Need to stress test at scale without wiring real quantum hardware? Run python scripts/quantum_hyperloop.py "<prompt>" --nodes 16 --agents 1000000 --iterations 5 --pretty to orchestrate repeated simulations via the new QuantumDeploymentHarness. The harness (implemented in services/quantum_harness.py) replays every stress scenario, reports whether continuous operation can be sustained, and works with deterministic amplitudes by default while supporting Monte Carlo sampling for variance analysis.
• The physics_economics payload pulls from daemon/datasets/physics_economics.json, fusing marketing algorithms with quantum mechanics, physics-driven data compression, and economics heuristics so compute-constrained agents keep campaigns, payments, and trading loops stable.
• The investing payload is sourced from daemon/datasets/investing_platforms.json, translating Yieldstreet, SoFi, Webull, Charles Schwab, and Stash setup, monetization, and compliance guidance into maturity-scored investing activation plans.
• Knowledge orchestration now layers dedicated RAG pipelines and KAG knowledge graphs that surface indexing and retrieval plans, embedding/tooling selections, and governance guardrails to keep responses grounded.
• A mixture-of-experts router blends Google Ads, Stripe, CJ Affiliate, and Amazon marketplace experts so monetization guidance reflects each provider’s strengths, with agentic loops coordinating perception→action execution cycles.
• 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, stock market trends, cognitive readiness, and agentic tool automation signals) 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.