Simple Jeff

jeff is a self-hosted, MIT-licensed drop-in implementation of TypeSafe's jev System One API, powered by the GLiFormer encoder model. It accepts classification questions through a compatible SDK or the POST /v1/systemone endpoint and returns choice selections, ordered scores, and noul yes/no probabilities. The server supports CUDA, Apple MPS, CPU, and ONNX Runtime deployments, with batching, bearer-key authentication, rate limits, request limits, health and statistics endpoints, and deployment configurations for Modal. Its repository reports lower hosting costs than jev but weaker results on reasoning-heavy tasks.

Simple Jev is an open-model classifier and scoring server from Featherless AI. It accepts shared context or chat messages plus questions, then reads the model's next-token logits for predefined answer labels instead of generating a prose or JSON completion. The server constructs JSON responses for choice classification, ordered rubric scores, and truth or support judgments, including confidence and label distributions. Its Hugging Face Transformers implementation identifies the exact common token prefix across questions, evaluates that prefix once, reuses its KV cache for question-specific suffixes, and batches the suffixes before scoring the allowed answer tokens. The response-scoring code normalizes the selected logits; shared context is counted once for usage purposes, and no output tokens are generated. The API provides non-streaming /v1/classifier and /v1/systemone endpoints, with local execution supported through Python, PyTorch, and Transformers. A public demo API is available, while production deployments can use Featherless paid plans or run the server themselves. The repository also includes RFDT, which fine-tunes models directly on allowed answer-token logits using the same prompt structure.

MiniMind is an open-source tiny large-language-model project and end-to-end training tutorial developed by Jingyao Gong. Its main dense model is approximately 64M parameters, and the repository provides the model architecture, tokenizer, datasets, inference code, and training pipeline. The pipeline covers pretraining, supervised fine-tuning, hand-written LoRA, DPO, PPO, GRPO, CISPO, model distillation, tool calling, adaptive thinking, and agentic reinforcement learning. Core algorithms are implemented directly with native PyTorch rather than relying on high-level abstractions from third-party training libraries. Its agentic reinforcement-learning path performs multi-turn rollouts, executes generated tool calls, appends tool observations to the context, calculates trajectory-level rewards, and updates the policy; rollout can use local PyTorch generation or an SGLang server. MiniMind supports dense and mixture-of-experts variants, single- and multi-GPU training, YaRN-based RoPE length extrapolation, Transformers-format models, and inference through llama.cpp, vLLM, or Ollama. The repository also includes a Streamlit chat interface and a lightweight OpenAI-compatible API server with tool-call and reasoning fields. It is released under the Apache License 2.0.

Stripe MCP is Stripe's public-preview Model Context Protocol server that lets AI agents interact with the Stripe API and search Stripe's documentation and support knowledge base. It can be configured in clients including Cursor, Claude, Codex, ChatGPT, and VS Code; Stripe's agent plugins install and maintain the server and related agent skills, while manual setup is also available using OAuth or an agent API key. It can be used with Claude to build Stripe payments integrations, including recommendations for Stripe Tax or Checkout and sandbox provisioning with API keys through the Stripe CLI.

OpenMAIC (Open Multi-Agent Interactive Classroom) is an open-source AI learning platform from THU-MAIC that turns topics or uploaded documents into interactive classrooms. It generates slides, quizzes, HTML simulations, and project-based learning activities, with AI teachers and classmates that can speak, draw on a whiteboard, conduct discussions, and respond to learners in real time. Its classic generation pipeline has two stages: an AI-generated lesson outline followed by scene generation for each outline item. The platform also provides a database-backed agent workbench that plans, builds, and revises courses through validated tools, with resumable sessions, follow-up steering, uploaded or web-retrieved materials, reusable skills, and support for importing PPTX files. Multi-agent orchestration uses a LangGraph director graph, while the playback and action engines handle classroom state and actions such as speech, whiteboard drawing, spotlights, and laser effects. OpenMAIC supports browser-only storage by default and can use PostgreSQL or S3-backed storage through its swappable storage packages. It accepts document, image, audio, and video materials through configured extraction providers, supports multiple LLM, media, speech, search, and local-provider configurations, and exports editable PPTX slides, interactive HTML, or classroom ZIP files. The repository is licensed under the MIT License, with separate terms for bundled components including an LGPL-licensed MathML-to-Office-Math package.