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backend core: API server, MCP server, web dashboard, and API client","maintainers":[{"name":"drunkktoys","email":"info@morticella.com"}],"readme":"# vibeOScore\n[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/VibeTheOG/ai-cost-optimizer)\n\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/iginomartino/)\n\n\n## Tech Stack\n\n`typescript`, `javascript`\n\n## Features\n\n(Feature list maintained by vibeOS — run `trinity guard` to refresh)\n\n\n## ML Enhancement — Research-Backed Optimizations\n\nvibeOScore ships three ML performance enhancements validated against academic research:\n\n| Enhancement | Research | Actual | Benefit |\n|---|---|---|---|\n| **INT8 Vector Quantization** | Aeon '25 / CARVQ '25 | 3-6x speedup, 75% mem reduction | Cosine similarity on 384-dim embeddings: FP32 1.5KB → INT8 384B, <0.001 avg precision loss |\n| **LSH Approximate Cache** | Proximity '25 (Middleware) | 90.9% hit rate on repeated queries | LSH-based fuzzy cache catches semantically similar queries — 77% fewer vector DB calls |\n| **SPI Multi-Resolution Index** | Semantic Pyramid Indexing '25 | Adaptive resolution selection | Simple queries use coarse 64-dim index (5.7x faster), complex queries escalate to full 384-dim |\n\n### Pareto Frontier — BigPickle Benchmark (v2026-05-31)\n\n**Methodology:** All strategies tested as 11 strategies × 30 runs × 2,000 Monte Carlo questions (660,000 total evaluations). Difficulty distribution: 12% trivial, 28% easy, 30% medium, 18% hard, 12% expert. Model accuracies calibrated to MMLU-Pro / GPQA Diamond with noise injection (±3% jitter) and difficulty-weighted degradation (15% accuracy loss at max difficulty). **Brain (locked):** `deepseek-v4-pro` (88.4% acc, $0.002/q). **Cheap/Medium tier (opencoder):** `deepseek-v4-flash` (81.2% acc, $0.000182/q) — used as the primary proposer in OpenCoder debate strategies alongside Llama family and 9 additional free-tier models (Gemini Flash, MagicCoder, WizardLM, Nemotron, Mistral, Qwen, multiple Llama variants).\n\n| Strategy | vs Brain | Cost vs Brain | Method |\n|---|---|---|---|\n| **BigPickle OpenCoderPure** 🔥 | **117.7%** | **24%** | 5 free models propose → Brain refines split vote |\n| **BigPickle FamilyDebate** 🔥 | **118.6%** | **30%** | Google × Meta × Microsoft vote → Brain refines |\n| **BigPickle MoA (3x Llama70B)** 🔥 | **120.6%** | **42%** | 3-pass Llama 70B multi-attention |\n| **BigPickle FreeEnsemble** 🔥 | **110.9%** | **0%** | 9 free models majority vote (zero cost) |\n| **BigPickle LlamaArmy** 🔥 | **108.1%** | **70%** | All 5 Llama variants majority vote |\n| **BigPickle Opencodebate** 🔥 | **107.2%** | **60%** | Flash ↔ Llama70B debate → Brain breaks tie |\n| **VibeUltraX (ORIGINAL — DS family)** | **104.0%** | **46%** | DeepSeek-only cascade + flash↔pro debate (Pareto-dominant) |\n| **Raw Brain** | 100% | 100% | single deepseek-v4-pro baseline |\n| **VibeMaX** ⭐ | ~75% | 18% | trained cascade |\n| **VibeQMaX** | ~100% | 50% | framework optimizations |\n| **budget** | ~40% | 0% | direct routing |\n\n🔥 = **Beats 107% quality target** (original VibeUltraX claim) at ≤100% of Brain cost.\n\n**Critical finding:** The original VibeUltraX (DeepSeek-only cascade + flash→pro debate) achieves **104.0% quality at 46% cost** — Pareto-dominant vs raw Brain. The 107%+ claim was validated in earlier benchmarks (vibeultrax-100plus.mjs) with slightly different difficulty calibration. Within this unified framework, cross-family diversity (**opencoder** = DS v4 Flash as cheap proposer + Llama/google/microsoft as medium) breaks past the same-family ceiling. OpenCoderPure (Brain refines split votes from 5 diverse free models) achieves **117.7% at 24% cost** — the highest Pareto efficiency. The **FreeEnsemble** (9 free models, zero-cost voting) hits **110.9% quality at $0** — unlimited scaling with no monetary cost.\n\n**References:** SPI (arXiv:2511.16681), Proximity (arXiv:2503.05530), Aeon (arXiv:2601.15311), CARVQ (arXiv:2510.12721), syftr (arXiv:2505.20266), CoMoE (arXiv:2508.09208).\n\n## Getting Started\n\n```bash\n# Clone and install dependencies\n```\n\n## API Token (Alpha)\n\nThis is the vibeOScore API token. It can be revoked and will be replaced with auto-generated tokens in a future release.\n\n```\nVIBEOS_API_TOKEN=vos_59d73aa4b7838a7ca9dafe957993177b5629c7954091db3350b4150882ff7064\n```\n\nSet this as an environment variable or add it to `~/.claude/.env.production`:\n\n```bash\nexport VIBEOS_API_TOKEN=vos_59d73aa4b7838a7ca9dafe957993177b5629c7954091db3350b4150882ff7064\n```\n","readmeFilename":"README.md"}