AskOracle Blog

TL;DR I run a small dev-shop. Every product I ship needs an LLM call somewhere — content generation, security analysis, classification, summaries. The economics only work if these calls average near-zero . The trick: never pay for what a free tier can do. Stack 5+ providers in a deterministic fallback chain so that when one rate-limits, account-bans, or hikes prices, the next one takes over within the same request — invisible to the user. This post is the actual production code from audit_routes.py powering askoracle.site/audit — a 12-question crypto security scan that costs me $0 per scan in practice, while still producing real AI reports anchored to 2024-25 incident cases. --- The fallback chain User request │ ▼ ┌──────────────────────────────────────────────────────────┐ │ Groq llama-3.3-70b (5 keys, sequential, free tier) │ Tier 1 └──────────────┬───────────────────────────────────────────┘ │ all 5 rate-limited / Cloudflare-blocked? ...

I had ~2 hours on a Saturday. A committee of two AI models (Gemini Pro + Claude) had just argued me into building a "gamified security audit" tied to our existing in-app currency (💎). 500💎 → 12-question scan → AI report. $49 upsell to a human pentest. Here's the technical breakdown. The product in one sentence User answers 12 questions about their crypto hygiene (seed storage, 2FA, DeFi approvals, etc.). They get a 0–100 score, a per-category breakdown, and a top-3 vulnerabilities report with named tools and 2024–25 incident cases. They burned 500💎 they earned through activity. Live: askoracle.site/audit — try it. Question schema Every question carries a weight (sum across all 12 = exactly 100) and each option has a risk value language-independent of the labels. Example: { "id": "seed_storage", "category": "wallet", "weight": 14, # contributes max 14 points to score loss "...