Every Lambda, every layer, every pipeline, every data store and every AI provider — laid out as one visual blueprint. Built entirely from inline HTML and SVG.
A single user request traverses the edge, the auth layer, a Lambda, the data plane, and (when needed) an AI provider — then the same path back.
Every Lambda from src/lambda_v2/template.yml, grouped by the business capability it owns.
User CRUD, profile, Google/Facebook OAuth binding, ACL checks.
Editing pipelines, FB2 ingest, OpenSearch index, scene composer, media catalog, the global game board.
PayPal order lifecycle, webhook ledger, internal credit accounting, collectible artifacts.
Multi-turn chat, anonymous echo previews, Telegram + admin pipelines, in-product feedback / moderation.
Job orchestration, S3-event-driven post-processing for AI-generated assets and auxiliary uploads.
System bucket loaders, prompt registry, ad-hoc SQL bridge for internal automations.
Lambda Layers carry the code that doesn't belong to any one service. Centralising them keeps function bundles small and behaviour identical across the fleet.
layer_ai/ai_requests.py exposes a single llm_call() function. Model selection, credit accounting and provider fallbacks are decided behind the wall.
Off-AWS, on a dedicated GPU host (kv2_AI_main/app/ scripts), the heavy media work runs as orchestrated pipelines. Each one writes back to the same S3 buckets the Lambdas read.
Each store is chosen for a specific access pattern, not as a default.
Static HTML served from S3 behind CloudFront. Same JS modules, different feature gates per host.
Two Claude-powered scheduled agents, run by cron on the operator's host. Both are advisory: they prepare drafts and digests for human review.
Reddit + X + YouTube scan → scoring → Claude-drafted native-tone replies. Stored in SQLite for human approval.
RSS + arXiv + Medium + X → dedupe → Claude annotation → static HTML brief. Weekly X-influence graph builder runs in parallel.