TinyPNG (by Tinify) is a free image optimization service built for developers and designers. It utilizes smart lossy compression to reduce the file sizes of JPEG, PNG, WebP, and AVIF files by up to 80% with no visible quality loss. That means faster load times, better SEO, and lower bandwidth.
You can compress, convert, and resize images via a clean web interface or integrate it into your workflow with the API. The platform also provides an image CDN for fast global delivery of optimized assets. SDKs are available for Python, Node.js, PHP, Java, Ruby, and .NET. WordPress plugin included, plus plenty of community-driven integrations.
No tuning, no noise, Tinify just works. Whether you're optimizing a handful of images or processing millions, it scales effortlessly. All plans include a generous free tier, and support is quick when you need it.
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At MyQ, the core belief is that print solutions should be automated, personalized, and easy to use, allowing people to focus on what matters most in their daily work. This principle is reflected in MyQ’s approach to our product design, combining intuitive user experiences with strong data security and efficient document workflows.
MyQ’s print management solutions strengthen document security while helping organizations reduce costs, save time, and lower their environmental impact.
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GLM-OCR
GLM-OCR is an advanced multimodal optical character recognition system and an open-source framework that excels in delivering precise, efficient, and thorough document comprehension by integrating textual and visual elements within a cohesive encoder-decoder design inspired by the GLM-V series. This model features a visual encoder that has been pre-trained on extensive image-text datasets alongside a streamlined cross-modal connector that channels information into a GLM-0.5B language decoder. It offers capabilities for layout detection, simultaneous recognition of various regions, and structured outputs for diverse content types, including text, tables, formulas, and intricate real-world document formats. Furthermore, it employs Multi-Token Prediction (MTP) loss and robust full-task reinforcement learning techniques to enhance training efficiency, boost recognition accuracy, and improve generalization across various tasks, leading to remarkable performance on significant document understanding challenges. This innovative approach not only sets new benchmarks but also opens up possibilities for further advancements in the field of document analysis.
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DeepSeek-VL
DeepSeek-VL is an innovative open-source model that integrates vision and language capabilities, catering to practical applications in real-world contexts. Our strategy revolves around three fundamental aspects: we prioritize gathering diverse and scalable data that thoroughly encompasses various real-life situations, such as web screenshots, PDFs, OCR outputs, charts, and knowledge-based information, to ensure a holistic understanding of practical environments. Additionally, we develop a taxonomy based on actual user scenarios and curate a corresponding instruction tuning dataset that enhances the model's performance. This fine-tuning process significantly elevates user satisfaction and effectiveness in real-world applications. To address efficiency while meeting the requirements of typical scenarios, DeepSeek-VL features a hybrid vision encoder that adeptly handles high-resolution images (1024 x 1024) without incurring excessive computational costs. Moreover, this design choice not only optimizes performance but also ensures accessibility for a broader range of users and applications.
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