KamuSEO
It's a complete visitor and SEO analytics, a great tool to analyze your site's visitors and analyze any site's information. It has the ability to analyze your own website's information. It has the ability to analyze any other website's information. It has a native API by which developers can integrate its facilities with another app. KamuSEO is an app to analyze your site visitors and analyze any site's information such as Alexa data, similar web data, whois data, social media data, Moz check, search engine index, Google page rank, IP analysis, malware check, etc. Input a domain name and you will get a js code. Copy the embedded js code and paste it into your web page. You will get a daily report about your website. You will get some bonus utility tools such as email encoder/decoder, metatag generator, tag generator, plagiarism check, valid email check, duplicate email filter, URL encoder/decoder, etc.
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Amazon CodeWhisperer
Build apps faster with ML-powered coding companion. Accelerate application development with automatic code recommendations based on the code and comments in your IDE. Empower developers to use artificial intelligence (AI) responsibly to create syntactically correct and secure applications. Generate entire functions and logical code blocks without having to search and customize code snippets from the web. Stay focused and never leave the IDE, with real-time customized code recommendations for all your Java, Python, and JavaScript projects. Amazon CodeWhisperer is a machine learning (ML)–powered service that helps improve developer productivity by generating code recommendations based on their comments in natural language and code in the integrated development environment (IDE). Accelerate frontend and backend development by empowering developers with automatic code recommendations. Save time and effort by using CodeWhisperer to generate code to build and train your ML models.
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Mu
Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. Hardware-aware tuning, including a 2/3–1/3 encoder–decoder parameter split, weight sharing between input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, enables fast inference at over 200 tokens per second on devices like Surface Laptop 7 and sub-500 ms response times for settings queries.
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GLM-OCR
GLM-OCR is a multimodal optical character recognition model and open source repository that provides accurate, efficient, and comprehensive document understanding by combining text and visual modalities into a unified encoder–decoder architecture derived from the GLM-V family. Built with a visual encoder pre-trained on large-scale image–text data and a lightweight cross-modal connector feeding into a GLM-0.5B language decoder, the model supports layout detection, parallel region recognition, and structured output for text, tables, formulas, and complicated real-world document formats. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization, achieving state-of-the-art benchmarks on major document understanding tasks.
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