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Oracle3: open-source trading engine and MCP server for prediction markets. Finds fee-adjusted no-arbitrage violations across related event contracts and trades them live on Kalshi, Polymarket and Solana, or on paper, under pre-trade risk limits. 13 MCP tools, JSON CLI, agent skills, 600+ tests.

A PyPI mirror client according to PEP 381 + PEP 503 + PEP 691

  • mirror
  • mirroring
  • pypi
  • pypi-mirror-client

TranslatedQoder international and China multi-account reverse proxy gateway, a multi-account OpenAI-compatible gateway hub for Qoder's international and China dual regions — COSY-signed inference, stable device fingerprints to avoid risk control, OAuth device authorization for client-free login, daily check-in and Pro benefits, background scheduled task scheduler, web monitoring dashboard, supporting Codex / Claude Code and standard OpenAI clients

Translated斑海豹 (Spotted Seal) — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions.

  • bilingual
  • calibration
  • cpu-inference
  • decision-making

RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.

Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Multi-user anti-filtering panel, with an effortless installation and supporting more than 20 protocols to circumvent filtering plus the telegram proxy.

Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

  • semantic-segmentation
  • lidar
  • semantickitti
  • 3d-lidar-data

A serialization/deserialization/validation library for strings, mappings and lists.

  • validation
  • forms
  • serialization
  • deserialization

Build AI text detectors whose decisions are mechanistically grounded and human-auditable (EMNLP 2026)

  • ai-text-detection
  • explainable-ai
  • interpretability
  • mechanistic-interpretability