AI_Brain · codex/build-online-github-ani

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 branch of Janady13/AI_Brain at commit 49db80a641b10ab62d16cc7643bd0b367f93951c. 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/marketing_benchmarks.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/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/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_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, and optional Google Ads enrichment. - 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_CLIENT_ID, GOOGLE_ADS_CLIENT_SECRET, GOOGLE_ADS_REFRESH_TOKEN, GOOGLE_ADS_CUSTOMER_ID, GOOGLE_ADS_LOGIN_CUSTOMER_ID (optional; enables live Google Ads metrics in the marketing ANI)

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: 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, and a google_ads collection containing either live Google Ads performance (when credentials are configured) or benchmark-driven simulations.

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.