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3,642 results · Page 19 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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simple_GRPO

AI developer platforms

simple_GRPO is a lightweight open-source GRPO implementation for reproducing the reasoning capabilities of R1-like large language models. The code is only about 200 lines, spread across two files, and depends on standard libraries such as deepspeed and torch, with no need for ray. It draws on the core loss calculation formula from Hugging Face trl, and supports saving GPU memory, quickly understanding the RL process, trying multi-answer generation, regrouping, KL penalty, and parameter tuning, among other things. An "Aha moment" can be observed in the early stages of training. The project provides a decoupled reference model design, allowing it to run on different GPUs or even different machines, and supports vLLM-accelerated inference and the reinforce++ algorithm. Experiments show that training is completed within 1 hour on an A800, and the Qwen2.5-7B and 3B models exhibit an Aha moment within 30 steps, with accuracy stably above 60% and format compliance reaching 100%.

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simpleRL-reason

AI developer platforms

simpleRL-reason is an open-source reinforcement learning training codebase for enhancing reasoning ability, released on GitHub by the NLP team at the Hong Kong University of Science and Technology. It provides a simple reinforcement learning training scheme that can improve the reasoning ability of base models using only rule-based rewards and the GSM8K/Math datasets. The project has successfully conducted zero RL training on 10 different base models, including Llama3 8B, Mistral 7B/24B, DeepSeekMath 7B, Qwen2.5 0.5B/1.5B/7B/14B/32B, and Qwen2.5-Math-7B. Using limited data of 8K examples, the accuracy improvement reaches 10 to more than 20 absolute percentage points. The project has open-sourced the code, model checkpoints, and analysis tools, and provides related papers and blogs. The code is implemented based on Verl, supports custom environment settings and FSDP training, uses the GRPO algorithm, and accelerates training with Ray and vLLM.

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Skillry

AI developer platforms

Skillry is a platform that provides reusable Agent Skills for AI agents such as Claude Code, Codex, and Cursor. Its skills are independently designed, written, and tested by the team, packaged in the form of SKILL.md, and cover tasks such as slides, web pages, images, and videos. Users can install skills via the web or CLI, enabling agents to generate corresponding finished products according to preset workflows. The platform has accumulated more than 400 skills, including free and paid content; paid offerings include monthly and annual plans, as well as a one-time lifetime purchase option. Some image or video skills require users to provide their own API keys for external AI models (such as FAL), with the corresponding provider charging based on usage. Free skills only require registering for a free account, and each skill page explains the actual output and required conditions.

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Skywork-OR1

AI developer platforms

Skywork-OR1 (Open Reasoner 1) is a series of math and code reasoning models and accompanying training solutions open-sourced by SkyworkAI. This series uses large-scale rule-based reinforcement learning, combined with carefully designed datasets and training recipes, and includes two general reasoning models, Skywork-OR1-7B and Skywork-OR1-32B. According to official materials, Skywork-OR1-32B outperforms Deepseek-R1 and Qwen3-32B on math tasks (AIME24 and AIME25) and performs comparably on code tasks (LiveCodeBench); Skywork-OR1-7B is competitive in math and coding scenarios among models of the same scale. The project has open-sourced model weights, training data, and code, and provides training and evaluation scripts, supports reproduction via Docker and Conda environments, and supports multi-node training using Ray. The official team also released a technical report (arXiv) and Notion Blog, sharing detailed training recipes, experimental results, and analysis. The project also provides the Skywork-OR1-RL-Data training dataset, as well as evaluation scripts for AIME24/AIME25 and LiveCodeBench. Evaluation uses Avg@K as the primary metric rather than Pass@1, to reduce the impact of randomness.

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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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