
Epicor Connected Process Control offers an intuitive software solution designed to create and manage digital work instructions while maintaining strict process control, effectively minimizing the chances of errors in operations. By integrating IoT devices, it captures comprehensive time studies and detailed process data, including images, at the task level, providing unprecedented real-time visibility and quality oversight. The eFlex system is versatile enough to accommodate countless product variations and thousands of components, catering to both component-based and model-based manufacturers alike. Furthermore, work instructions seamlessly connect to the Bill of Materials, guaranteeing that products are assembled correctly every time, even when modifications occur during production. This advanced system intelligently adapts to variations in models and components, ensuring that only the relevant work instructions for the current build at the station are presented, enhancing efficiency and accuracy throughout the manufacturing process. In this way, Epicor empowers manufacturers to maintain high standards of quality control while adapting to the dynamic nature of production demands.
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Discover the perfect MRP solution designed specifically for small manufacturers!
MRPeasy provides a cost-effective, intuitive, and cloud-based MRP system customized for the unique needs of small manufacturing enterprises.
Easily convert customer orders into manufacturing orders, with the system handling the scheduling automatically. It efficiently manages inventory by booking items as needed, and can generate purchase orders if required. With real-time requirements in mind, MRPeasy offers both forward and backward scheduling options, while automated checks make certain that workers, workstations, and materials are readily available.
Keep a detailed view of all your operations at any moment!
Additionally, MRPeasy seamlessly integrates with leading accounting software like QuickBooks and Xero, as well as e-commerce platforms such as Shopify and WooCommerce, creating a comprehensive business management solution that fulfills all your operational needs. This integration not only enhances efficiency but also streamlines processes across various aspects of your business.
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Muse Spark 1.2
Muse Spark 1.2 is a coding-focused AI model from Meta designed to support advanced software engineering tasks through Muse Code and the Meta Model API. The model builds on Muse Spark 1.1 with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan repository changes, write code, validate outputs, and work across large codebases. Muse Code uses persistent async background agents that stay active throughout a session to reduce redundant information gathering and support difficult multi-step work. The runtime uses a local event log where model calls, tool runs, approvals, and edits are appended, making sessions replay-exact and restart-safe. Muse Spark 1.2 was co-trained with Muse Code so the model can take advantage of its toolset, harness workflows, goals, compaction, and subagent architecture. Meta significantly scaled training compute on coding tasks and expanded training environment diversity to improve the model’s engineering capabilities. The model was also trained on long-horizon coding tasks, including whole-repository generation, large end-to-end projects, auto-research, and extended iterative work. Its training approach uses planning, goal conditioning, context compaction, rejection-sampled harness trajectories, and self-improvement data generated with Muse Spark 1.1. Meta also tested Muse Spark 1.2 on long-running GPU kernel optimization workflows where the model wrote, compiled, profiled, and improved Triton kernels over many tool calls. By combining coding-focused training, agentic runtime integration, persistent subagents, long-horizon reasoning, replay-safe execution, and API availability, Muse Spark 1.2 helps developers and AI agents complete complex software engineering work with less intervention.
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Inkling
Inkling is an open-weights multimodal AI model from Thinking Machines built to support customization, agentic workflows, coding, reasoning, vision, audio, and enterprise AI use cases. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, 256 routed experts per MoE layer, and six routed experts active per token. It supports context windows up to 1 million tokens and was pretrained on 45 trillion tokens across text, images, audio, and video. Inkling is designed as a broad foundation model rather than a narrowly optimized benchmark model, giving it balanced capabilities across reasoning, coding, factuality, instruction following, vision, audio, tool use, and safety. Its controllable thinking effort lets developers adjust how much computation and generated reasoning the model uses, helping teams balance quality, latency, and cost for different production needs. The model can run agentic coding tasks, use tools, create web apps, generate polished multi-page artifacts, reason over long contexts, and work through iterative refinement loops. For multimodal tasks, Inkling can process images, answer questions about visual content, transcribe and reason over audio, follow spoken instructions, and combine visual reasoning with code-based tools such as Python. Thinking Machines trained Inkling for calibration, instruction following, factual reliability, refusal behavior, and safety across multiple modalities, including evaluations for dangerous capabilities and human-AI threat vectors. Inkling is available on Tinker for fine-tuning, with 64K and 256K context options, an Inkling Playground for testing, cookbook recipes, and support for multimodal post-training workflows. Its full weights are available on Hugging Face, and deployment support is available through APIs and infrastructure partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, SGLang, vLLM, llama.cpp, and transformers.
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