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3,641 results · Page 16 of 102

ShipAI

AI developer platforms

ShipAI is a full-stack SaaS starter kit positioned as "built for AI agents." It provides production-grade baselines out of the box for authentication, billing, database, background jobs, observability, SEO, and AI workflows, and comes with complete source code and a commercial license. It packages 21 pre-integrated tools and includes three product interfaces: a marketing site, the core application, and documentation. Its tech stack covers Bun, Next.js 16, TypeScript 5.5, PostgreSQL 17, Drizzle ORM, Redis 7.4, Vercel AI SDK v6, Groq and OpenAI, Qdrant, Neo4j 5.26, Docker, MinIO/S3, BullMQ, Tailwind v4, and Shadcn UI. It can run locally with Docker Compose or be deployed to any container environment, with no mandatory managed services. The official website also states that it includes built-in Telegram bot integration, 11 modular AI processors, a deep research workflow, an admin back office, 60+ UI components, and a programmatic SEO foundation, along with AGENTS.md and SKILL.md conventions for Claude Code, Codex, OpenCode, and GitHub Copilot to help autonomous coding agents understand and extend the project.

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slime

AI developer platforms

slime is an LLM post-training framework developed by THUDM, designed for scaled reinforcement learning (RL) training. By connecting Megatron with SGLang, it provides two core capabilities: high-performance training and flexible data generation, and unifies training, rollout, data buffering, reward computation, verifier feedback, and environment interaction into a single path. The framework has been used for the release training of models such as GLM-5.3-Flash, GLM-5.3, GLM-5.2, GLM-5.1, GLM-5, GLM-4.7, GLM-4.6, and GLM-4.5, while also supporting model families such as Qwen, DeepSeek, and Llama 3. The design goal of slime is to let training and data generation reinforce each other, without having to cobble together a heavy tech stack composed of fragmented trainers, rollout services, and agent frameworks. As a result, it is small in size, easy to understand and extend, and yet validated through a complete training loop.

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Slow_Thinking_with_LLMs

AI developer platforms

STILL (Slow Thinking with LLMs) is a series of technical research projects on slow-thinking reasoning systems for large language models conducted by the RUCAIBox team at Renmin University of China, with long-term exploration of o1-like reasoning systems. The project has open-sourced a large amount of code, models, training data, and evaluation benchmarks, covering directions such as reinforcement learning exploration mechanisms, tool-augmented reasoning, code-integrated reasoning, autonomous web search, and hallucination mitigation, and has released STILL-1, STILL-2, STILL-3, and multiple derivative models and benchmarks.

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