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skyagi

AI developer platforms

SkyAGI is a Python package for demonstrating the emergent abilities of large language models (LLMs) in simulating believable human behavior. It implements the idea of Generative Agents and provides a role-playing game that creates a very interesting user experience. Unlike previous AI-based NPC systems, SkyAGI's NPCs can generate very believable human reactions. The interesting observations in this demo show that there is great potential for rethinking many aspects of game development, such as NPC scripting. To demonstrate this, SkyAGI provides example characters from The Big Bang Theory and The Avengers as a starting point. Users can also define custom characters by creating a configuration file similar to customized_character.json.

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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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Slaide

AI office

Slaide is an AI presentation and document generation tool. Users can describe their needs in one sentence, or upload an existing PowerPoint or PDF, and Slaide will plan the structure, write the content, and design it into real PowerPoint, Word, Excel, or PDF files. It supports preserving the original design, brand settings, web research with source citations, and each page is automatically rendered before presentation to check for overflow and layout issues. Documents are stored in the EU, and materials are not used to train AI models. A free trial is offered along with paid plans such as Plus, Pro, and Max, and it can also be used within existing workflows via ChatGPT, Claude, Grok, or a Chrome extension.

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Slide-Easy

AI marketing

Slide-Easy is a Mac app for indie app developers that turns App Store links and real user reviews into Instagram Reels, carousels, and Stories. It renders labeled AI characters on-device, generates a week of posts, and lets users review and publish with one click. The first 7 posts are free, then a one-time payment of $49, with no subscription and no credits. It requires an Apple Silicon Mac (M1 or newer), 16GB of memory, macOS 15+, and downloads about 14GB of models during installation.

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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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