Author name: Aravind Arumugam

16 Must-Have AI Tools for Every Professional in 2025

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AI is no longer just a buzzword — it’s becoming an everyday tool for working professionals. Whether you’re a student, freelancer, content creator, teacher, developer, or small business owner, the right AI tools can save you time, help you work smarter, and unlock creative ideas. Recently, I found a great video that shows 16 powerful AI tools you should know about in 2025. The video is short and practical, and I’ve also listed all the tools below with direct links so you can explore them easily. Watch the full video here:https://www.youtube.com/watch/356Zl12rgeU 16 Useful AI Tools for Professionals in 2025 Google AI StudioWebsite: https://aistudio.google.com/A real-time learning assistant that helps you with on-screen tutoring while you work or study. Google Gemini Deep ResearchWebsite: https://gemini.google/overview/deep-research/Helps you research smarter by summarising insights from over 100 sources. Notebook LMWebsite: https://notebooklm.google/Reads your documents and creates learning materials, summaries, or notes from them. Napkin AIWebsite: https://www.napkin.ai/Instantly converts your thoughts into diagrams, infographics, and simple animations. Photo AI (Fotor)Website: https://www.fotor.com/A beginner-friendly photo and video editing tool with features like background removal and enhancement. Replicate AIWebsite: https://replicate.com/Run AI models like image or video generators in the cloud without needing a powerful computer. Pinokio AIWebsite: https://pinokio.computer/Lets you install AI tools with one click — no technical skills required. TubeMagicWebsite: https://tubemagic.com/Predicts what kind of YouTube video might go viral based on your past content and trends. Guide AIWebsite: https://guide-ai.com/Automatically creates step-by-step tutorials from your content or processes. Sono AI (Suno)Website: https://suno.com/Generates original songs using AI. Just give it your lyrics or vibe, and it creates the music. Temper AIWebsite: https://www.tempor.ai/Make royalty-free music for your videos, presentations, or content — powered by AI. Gamma AIWebsite: https://gamma.app/Create modern and clean presentations in seconds using just your ideas or a topic. Cursor AIWebsite: https://www.cursor.com/A no-code tool to build apps using plain English descriptions. Wizard AI (Uizard)Website: https://uizard.io/Quickly create UI/UX design mockups from simple text input. Perplexity AIWebsite: https://www.perplexity.ai/A research assistant that gives fact-based, cited answers with a clean and structured format. Otter AIWebsite: https://otter.ai/Transcribes meetings and gives summaries, making remote work and teamwork easier. Why You Should Explore These Tools Save hours of work by automating tasks Improve quality of writing, design, and content Create professional output even without deep technical skills Most of these tools offer free plans to get started Whether you’re building a personal brand, running a business, or just looking to stay ahead in your field, these tools are worth checking out.

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DeepSeek-VL2: The Next Generation of Vision-Language Models

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DeepSeek-VL2 is a cutting-edge Vision-Language Model series designed to redefine how AI interacts with multimodal data. Built with a Mixture-of-Experts (MoE) architecture, it offers unparalleled performance and computational efficiency. The model is highly capable across various advanced tasks such as visual question answering, OCR, document analysis, and data interpretation from charts and tables. This blog delves into the technical details of DeepSeek-VL2 and its powerful capabilities, based on its official research and design. I’ve also conducted a detailed professional test of the model’s capabilities, which you can watch on my YouTube channel. The links to test scenarios for specific features are included throughout this post. Key Features of DeepSeek-VL2 Dynamic Tiling Strategy One of the core innovations in DeepSeek-VL2 is its dynamic tiling strategy, which ensures efficient processing of high-resolution images with varying aspect ratios. This feature divides images into smaller, manageable tiles, allowing for detailed processing without losing essential visual information. 📺 Watch the test case on Dynamic Tiling: [YouTube Link for Dense Scene QA Test] Multi-Head Latent Attention and MoE Architecture DeepSeek-VL2 leverages Multi-Head Latent Attention to compress image and text representations into compact vectors. This design enhances processing speed and accuracy. Its Mixture-of-Experts architecture uses sparse computation to distribute tasks among expert modules, which improves scalability and computational efficiency. 📺 Watch the test case on Object Localization: [YouTube Link for Object Localization Test] Vision-Language Pretraining Data The training process of DeepSeek-VL2 uses a rich combination of datasets such as WIT, WikiHow, and OBELICS, along with in-house datasets designed specifically for OCR and QA tasks. This diversity ensures the model performs well in real-world applications, including multilingual data handling and complex visual-text alignment. 📺 Watch the test case on OCR Capabilities: [YouTube Link for OCR Test] Applications and Use Cases DeepSeek-VL2 excels in several practical applications: General Visual Question Answering: It provides detailed answers based on image inputs, making it ideal for complex scene understanding. OCR and Document Analysis: The model’s ability to extract text and numerical information from documents makes it a valuable tool for automated data entry and analysis. Table and Chart Interpretation: Its advanced reasoning enables the extraction of meaningful insights from visualised data like bar charts and tables. 📺 Watch the test case on Chart Interpretation: [YouTube Link for Chart Data Interpretation Test] 📺 Watch the test case on Visual Question Answering: [YouTube Link for General QA Test] Training Methodology The model’s training involves three critical stages: Vision-Language Alignment: This phase aligns the visual and language encoders, ensuring seamless interaction between both modalities. Pretraining: A diverse set of datasets is used to teach the model multimodal reasoning and text recognition. Supervised Fine-Tuning: Focused on improving the model’s instruction-following abilities and conversational accuracy. 📺 Watch the test case on Multi-Image Reasoning: [YouTube Link for Multi-Image Reasoning Test] Benchmarks and Comparisons DeepSeek-VL2 has been benchmarked against state-of-the-art models like LLaVA-OV and InternVL2 on datasets such as DocVQA, ChartQA, and TextVQA. It delivers superior or comparable performance with fewer activated parameters, making it a highly efficient and scalable model for vision-language tasks. 📺 Watch the test case on Visual Storytelling: [YouTube Link for Visual Storytelling Test] Conclusion DeepSeek-VL2 represents a leap forward in the development of Vision-Language Models. With its dynamic tiling strategy, efficient architecture, and robust training process, it is well-suited for a range of multimodal applications, from OCR and QA to chart interpretation and beyond. While the model excels in many areas, there is still potential for improvement in creative reasoning and storytelling. Overall, DeepSeek-VL2 stands out as a reliable, efficient, and versatile tool for researchers and developers alike. Resources To explore DeepSeek-VL2 in more detail, download the resources below: Presentation Slides as PDF prepared for youtube by Aravind Arumugam: @mr_viind_DeepSeek-VL2-Mixture-of-Experts-Vision-Language-Models-for-Advanced-Multimodal-Understanding-3Deepseek-VL2-official-document Official DeepSeek-VL2 Research Document: Deepseek-VL2-official-document Call to Action If you enjoyed this detailed overview of DeepSeek-VL2, make sure to check out the test scenarios and results on my YouTube channel. Don’t forget to subscribe, like on youtube, and share your thoughts in the comments in youtube. Let me know if there’s a specific AI model or technology you’d like me to explore next! Stay tuned for more deep dives into cutting-edge AI technologies!  

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How Google Chrome’s New IP Protection Feature Will Protect You from Online Tracking

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Your IP address is a unique identifier that can be used to track your online activity. Advertisers and other third-party companies often use IP tracking to build profiles of users and target them with personalized ads. Google Chrome’s new IP Protection feature aims to protect users from this type of tracking by masking their IP addresses. When the feature is enabled, Chrome will route users’ traffic through a proxy server, which will hide their real IP address from the websites they visit. This feature is still under development, but it has the potential to significantly improve user privacy online. Here are some of the ways that the IP Protection feature can protect you from online tracking: Prevent cross-site tracking: Cross-site tracking is a technique that allows advertisers to track users across multiple websites. This is done by placing cookies on users’ browsers that can be used to identify them on different websites. The IP Protection feature can prevent cross-site tracking by masking the user’s IP address from websites. Protect your location: Your IP address can be used to determine your approximate location. Advertisers and other third-party companies often use this information to target users with location-based ads. The IP Protection feature can protect your location by masking your IP address. Make it more difficult to track your online activity: The IP Protection feature can make it more difficult for advertisers and other third-party companies to track your online activity. By masking your IP address, you can make it more difficult for them to build a profile of your online interests and target you with personalized ads. Overall, the IP Protection feature is a promising new privacy feature in Google Chrome. It has the potential to significantly reduce the amount of data that advertisers and other third-party companies can collect about you online. Additional details: The IP Protection feature will be opt-in at first, but Google plans to make it the default setting in the future. The feature will initially only work for specific domains, but Google plans to expand it to include more domains in the future. The IP Protection feature is not a replacement for a VPN. A VPN encrypts all of your traffic and routes it through a VPN server, while the IP Protection feature only masks your IP address for specific domains. How to enable the IP Protection feature: Open Google Chrome. Click the three dots in the top right corner of the window. Click “Settings.” Click “Privacy and security.” Under “Advanced,” click “Site Settings.” Click “Additional permissions.” Click “IP address.” Select “Block all requests.” Conclusion: Google Chrome’s new IP Protection feature is a promising new privacy feature that can help to protect users from online tracking. It is still under development, but it has the potential to significantly improve user privacy online.

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Building a Docker container for my react.js app

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To build a Docker container for React.js app, you can follow these steps: Create a Dockerfile. The Dockerfile is a text file that contains instructions for building a Docker image. Create a new file called Dockerfile in the root directory of your project. Add the following instructions to the Dockerfile: FROM node:16 WORKDIR /app COPY package.json . RUN npm install COPY . . EXPOSE 3000 CMD [“npm”, “start”] The FROM instruction specifies the base image that will be used to build the new image. In this case, we are using the node:16 image, which contains the Node.js 16 runtime environment. The WORKDIR instruction sets the working directory for the container. The COPY instruction copies the package.json file and the entire contents of the current directory into the container. The EXPOSE instruction exposes the specified ports on the container. In this case, we are exposing ports 3000. The CMD instruction specifies the command that will be run when the container is started. In this case, we are running the npm command with the start script. Build the Docker image. Once you have created the Dockerfile, you can build the Docker image using the following command: docker build -t my-app . The docker build command builds a Docker image from a Dockerfile. The -t flag specifies the name of the image. In this case, we are naming the image my-app. Run the Docker container. Once you have built the Docker image, you can run it using the following command: docker run -p 3000:3000 my-app The docker run command runs a Docker image. The -p flag maps the specified ports on the host machine to the exposed ports on the container. In this case, we are mapping port 3000 on the host machine to the corresponding ports on the container. Once the Docker container is running, you can access the React.js UI app at http://localhost:3000. To share the Docker container image with others, you can push it to a Docker registry, such as Docker Hub. Once the image is pushed to the registry, others can pull it down and run it.

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Meta Code Llama: The AI Tool That Can Code for You 

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Meta Code Llama is a state-of-the-art Open source large language model (LLM) that can be used to generate code, translate languages, write different kinds of creative content, and answer your questions in an informative way. It is still under development, but it has learned to perform many kinds of tasks, including Generating code in a variety of programming languages, including Python, C++, Java, PHP, Typescript (Javascript), C#, and Bash. Translating code from one programming language to another. Answering questions about code, such as how to use a particular library or API, or how to debug a piece of code. Writing code documentation. Generating test cases for code. Code Llama is a code-specialized version of Llama 2 that was created by further training Llama 2 on its code-specific datasets, sampling more data from that same dataset for longer. Essentially, Code Llama features enhanced coding capabilities, built on top of Llama 2. It can generate code, and natural language about code, from both code and natural language prompts (e.g., “Write me a function that outputs the fibonacci sequence.”) It can also be used for code completion and debugging. It supports many of the most popular languages being used today, including Python, C++, Java, PHP, Typescript (Javascript), C#, and Bash. Code Llama is available in three model sizes: 7B, 13B, and 34B parameters. The larger models are more capable, but they also require more computing power. Why should you use Code Llama? There are many reasons why you should use Code Llama. Here are just a few: It can save you time: Code Llama can generate code for you, which can free up your time to focus on other tasks. It can improve the quality of your code: Code Llama can help you to identify errors and problems in your code. It can help you to learn new things: Code Llama can generate code examples and explain complex concepts. It can make you laugh: Code Llama can generate funny code, which can be a great way to lighten the mood in a software development team. Here is an example of a funny code snippet that Code Llama generated: Python def print_hello_world_in_pig_latin(): print(“elloHay worldLay!”) print_hello_world_in_pig_latin() This code snippet will print the message “elloHay worldLay!” to the console. The word “hello” is reversed and the suffix “-ay” is added to the end of the word, which is a simple way to translate words into Pig Latin. . Overall, Code Llama is a powerful and versatile tool that can be used by developers of all levels to improve their productivity and to write better code.

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