The ultimate MRP solution for small manufacturers!
MRPeasy offers an affordable, user-friendly, and cloud-based MRP system tailored specifically for small manufacturing businesses.
Transform your customer orders into manufacturing orders and let the system schedule them automatically. It effortlessly books items from your inventory, and if needed, initiates purchase orders on your behalf. Depending on the real-time requirements, MRPeasy allows for both forward and backward scheduling. Automated checks ensure the availability of workers, workstations, and materials.
Maintain a comprehensive overview of all your operations at all times!
MRPeasy also smoothly integrates with premier accounting software such as QuickBooks and Xero, along with e-commerce platforms like Shopify and WooCommerce. This integration creates an all-encompassing business management solution that meets your every need.
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Epicor Connected Process Control provides a simple-to-use software solution that allows you to configure digital work instructions and enforce process control. It also ensures that operations are error-proof. Connect IoT devices to collect 100% time studies and process data, images and images at the task level. Real-time visibility and quality control on a new level! eFlex can handle any number of product variations or thousands of parts, whether you are a component-based or model-based manufacturer. Work instructions can be linked to Bill of Materials, ensuring that products are built correctly every time, even if changes are made during the process. Work instructions that are part a system that is advanced will automatically react to model and component variations and only display the right work instructions for what's currently being built at station.
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Muse Spark 1.2
Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.
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Inkling
Inkling is Thinking Machines’ open-weights foundation model built for customization, multimodal reasoning, and agentic AI workflows. The model uses a Mixture-of-Experts architecture with 975 billion total parameters and 41 billion active parameters, making it large in capacity while activating only a subset of experts per token. Inkling supports up to a 1 million token context window and was pretrained on 45 trillion tokens spanning text, images, audio, and video. It is designed as a broad generalist model with strengths across coding, reasoning, instruction following, factuality, tool use, vision, audio understanding, forecasting, and safety. Developers can tune its thinking effort to trade off latency, cost, and performance, which is useful for production systems that need efficient reasoning at scale. Inkling can be fine-tuned on Tinker, tested in the Inkling Playground, and deployed through partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, vLLM, SGLang, llama.cpp, and Hugging Face transformers. The model can generate applications, operate tools, create styled artifacts, reason over visual and audio inputs, and support long refinement loops for collaborative work. Thinking Machines also previewed Inkling-Small, a lighter Mixture-of-Experts model with 276 billion total parameters and 12 billion active parameters for lower-cost and lower-latency workloads. By combining open weights, multimodal training, agentic capabilities, efficient reasoning, and fine-tuning support, Inkling gives builders a flexible AI foundation for specialized products and workflows.
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