AI tools that are worth exploring

📋 Table of Contents

Navigating the generative AI landscape can be overwhelming with hundreds of new tools launching every month. Here is a curated breakdown of the standout AI tools across research, data analysis, document generation, and design.

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1. Fast Reference & Essential Tools

• NotebookLM – Grounded research assistant by Google for uploading PDFs, docs, and links into private notebooks with audio summaries.
• Google Stitch – Google's experimental AI tool for generative interface design and UI prototyping.
• Claude – Anthropic’s flagship conversational assistant with a 200k+ context window and visual Artifacts support.
• Julius AI – Conversational "chat-with-your-data" analyst for executing Python/R code on datasets.

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2. Julius AI: Conversational Data Analysis

Julius AI is an AI-powered data analyst that runs directly in your browser. Instead of requiring you to write complex SQL queries, build Excel pivot tables, or write custom Python code, Julius lets you interact with datasets using plain-English prompts.

Under the hood, Julius translates your questions into Python or R execution environments, allowing it to process large files, clean datasets, perform advanced statistical operations, and generate clean visual charts.

Core Capabilities & Features

A. "Chat-with-Your-Data" & Multi-Format File Intake

• Format Support: Upload Excel spreadsheets (.xlsx), CSVs, PDFs, JSON, Google Sheets, or SPSS files (.sav).
• Large File Memory: Depending on the tier, Julius provides sandbox memory instances (up to 32 GB–64 GB of RAM), allowing it to handle files ranging from 8 GB to 32 GB—significantly larger than standard LLM uploads.
• Live Data Connectors: Higher-tier plans link directly to enterprise data warehouses including Snowflake, PostgreSQL, BigQuery, and SharePoint.

B. Automatic Visualization & Charting

• Generates interactive, presentation-ready charts (bar graphs, scatter plots, heatmaps, time-series distributions, and box plots).
• Allows you to set custom branding and plot styles (e.g., corporate color palettes or print-optimized themes) that automatically apply to all generated graphs.
• Exports charts cleanly as PNG, SVG, or interactive HTML artifacts.

C. Reusable Workflows ("Notebooks")

• Notebook Cells: Similar to Jupyter Notebooks, you can set up sequential workflows consisting of Prompt Cells, File Cells, User Input Cells, and Code Cells.
• Automated & Scheduled Runs: Create an analysis workflow once and re-run it automatically whenever you upload a new dataset or on a set schedule.

D. Statistical Analysis & Data Cleaning

• Data Prep: Automatically detects missing values, removes duplicates, handles null entries, and reformats date/time columns upon request.
• Advanced Math & ML: Performs regression analysis, ANOVA tests, correlation matrices, predictive modeling, and forecasting without requiring manual coding.

Pricing Tiers (2026)

Plan	Monthly Cost	Annual Equivalent	Key Features & Limits
Free	$0	$0	15 messages/month, 2 GB RAM, basic file support.
Plus	$20/mo	~$16/mo	2,000 monthly credits (~250 queries), 32 GB RAM, Google Drive/OneDrive integrations.
Pro	$45/mo	~$37/mo	5,000 credits, 32 GB RAM, live database connectors (Snowflake, BigQuery, Postgres), Notebooks.
Max / Ultra	$200–$500/mo	~$166–$416/mo	Up to 70,000 credits, largest context windows, dedicated Slack support, permanent file storage.
Business	$450/mo	~$375/mo	Team workspaces, shared files/threads, centralized billing, usage dashboards, admin controls.

(Note: Verified students and educators receive a 50% discount across paid plans).

How It Compares to Alternatives

• Julius vs. ChatGPT (Advanced Data Analysis): ChatGPT shows raw Python execution clearly step-by-step, making it great for code transparency. However, Julius is purpose-built for data workflows, supporting much larger file limits (up to 32 GB vs. 512 MB), direct database connections, and custom branding rules.
• Julius vs. Power BI / Tableau: Power BI and Tableau excel at building persistent dashboards for enterprise reporting. Julius is better for exploratory, ad-hoc natural language data analysis and automated report creation.

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3. Best AI Generators for Presentations, Spreadsheets & Documents

Here is a breakdown of the leading AI tools for generating Presentations (.pptx), Spreadsheets (.xlsx), and Documents (.docx / PDF), categorized by what they do best:

A. Presentations (.pptx / Decks)

• Gamma.app
  • Best For: Rapid creation of pitch decks, visual presentations, and web documents from plain text.
  • Key Strengths: Uses customizable "cards" instead of rigid slide templates. Offers built-in layout design, media embeds, instant restyling, and clean export to PPTX and PDF.
• Beautiful.ai
  • Best For: Corporate presentations requiring strict brand compliance and slide formatting.
  • Key Strengths: Smart design engine automatically resizes text, aligns elements, and adapts layouts as you edit content.
• Pitch
  • Best For: Modern team collaboration, startup pitch decks, and investor updates.
  • Key Strengths: Blends AI deck drafting with real-time co-editing and viewer analytics.
• Tome
  • Best For: Interactive storytelling, mobile-first decks, and multimedia presentations.
  • Key Strengths: Generates complete narrative storyboards from prompts with web content and dynamic AI images.

B. Spreadsheets (.xlsx / Data Analysis)

• ChatGPT (Advanced Data Analysis)
  • Best For: Instant Python-based data cleaning, statistical modeling, and downloadable .xlsx files.
  • Key Strengths: Executes Python code in a sandboxed runtime to manipulate datasets, generate multi-tab Excel workbooks, apply formulas, and plot charts.
• Rows.com
  • Best For: AI-powered cloud spreadsheets with live API integrations.
  • Key Strengths: Acts as an AI-native alternative to Google Sheets. Pulls data from public APIs, cleans datasets with plain text prompts, and writes complex formulas automatically.
• Quadratic
  • Best For: Infinite canvas data work blending Excel formulas, Python, SQL, and AI.
  • Key Strengths: Write Python/SQL directly inside spreadsheet cells while leveraging AI to automate data queries and visual charts.
• Julius AI
  • Best For: Conversational dataset exploration, regression analysis, and chart creation.
  • Key Strengths: Connects directly to Excel/CSV files to yield clean spreadsheet outputs and publication-ready graphs.

C. Documents (.docx / Long-Form Reports)

• Claude (Anthropic)
  • Best For: In-depth technical reports, long-form content generation, structured markdown, and dynamic document artifacts.
  • Key Strengths: Handles extensive context windows (200k+ tokens) seamlessly to synthesize large source documents into formatted reports.
• Microsoft 365 Copilot
  • Best For: Direct native document generation inside Word, Excel, and PowerPoint.
  • Key Strengths: Deep Office integration to draft Word documents from email threads, generate slides from Word docs, and analyze Excel sheets.
• Google Workspace (Gemini)
  • Best For: Direct document creation within Google Docs, Sheets, and Slides.
  • Key Strengths: Built directly into Google Drive for seamless drafting and cross-app workflows.

D. Autonomous General Agents (All-in-One)

• Manus AI
  • Best For: Complex, end-to-end multi-format tasks.
  • Key Strengths: An autonomous general agent that researches topics on the web, executes code to process data, writes detailed reports, builds spreadsheets, and creates slides independently.

How to run your LLM locally on your laptop/PC/Mac?? #LMStudio

Interact with the locally deployed LLM using the below Python SDK using LM Studio.

Click here for LM Studio Python SDK documentation -  https://lmstudio.ai/docs/python 

Ollama by Meta is another option for you to run your LLMs locally on your laptop/PC/Mac.

Visit https://ollama.com/ for more details.

Post your questions if any & I shall try to answer.

Follow me on my social media accounts here -  https://linktr.ee/krmadhukar 

Free Google courses for Generative AI

 Google just launched a free learning path for Generative AI.

If you're in tech, business, or just curious, this is worth a look.

𝟏. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈:

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↳ Explain how generative AI works

↳ Describe generative AI model types

↳ Describe generative AI applications

𝟐. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐋𝐋𝐌:

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↳ Define large language models (LLMs)

↳ Describe LLM use cases

↳ Explain prompt tuning

↳ Describe Google’s generative AI development tools

𝟑. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐀𝐈:

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↳ Identify the need for a responsible AI practice within an organization

↳ Recognize that decisions made at all stages of a project make an impact in Responsible AI

↳ Recognize that organizations can design an AI infrastructure to fit their own business needs and values

♻️ Repost this to help your network get started 

AI tools for Software Engineers

 

Google I/O 2025 summary

 Google just dropped their biggest Al updates ever during Google I/O 2025. 

Here are 13 new Al updates you can't miss:

1. Gemini Live. You can now turn on your camera, point at anything, and talk to Gemini about it in real time
2. Imagen. Google's best image model yet
3. Veo 3. The first video model with native sound generation
4. Deep Research
5. Project Astra. A JARVIS-like research prototype exploring the capabilities of a universal Al assistant
6. Google Flow. Al filmmaking tool for creators
7. Agent Mode. A new feature in the Gemini app that lets you state a goal, and Gemini will handle the steps to achieve it
8. Google Jules. Jules is an Al-powered coding assistant that can read your code, write tests, fix bugs, and update dependencies
9. Al Mode in Search. Al Mode transforms Google Search into a conversational assistant
10. Real-time speech translation in Google Meet
11. Google Beam. An Al-first video communication platform that turns 2D video streams into realistic 3D experiences
12. Gemma 3n. A new open-source Al model optimized for mobile devices
13. Try-On. Google's Virtual Try-On feature lets you upload a photo of yourself to see how clothes would look on you

What are your thoughts on this?

Free MCP model context protocol course

Worth investing time on learning the Model context protocol using a free course provided by huggingface.

 https://huggingface.co/learn/mcp-course/unit0/introduction

MCP vs RAG (Model Context Protocol vs Retrieval Augmented Generation)

RAG (Retrieval-Augmented Generation) focuses on enhancing AI responses by retrieving external data, while MCP (Model Context Protocol) standardizes how AI interacts with various data sources and tools.

Overview of RAG

Definition: RAG is an AI architecture that improves the accuracy and relevance of responses generated by large language models (LLMs) by pulling in up-to-date information from external sources, such as databases or APIs, before generating a reply.

2
Functionality: When a user submits a query, RAG retrieves relevant content from connected data sources and appends this information to the input prompt, enriching the model's context with real-world relevance. This helps reduce inaccuracies and hallucinations in AI responses by grounding them in verifiable sources.

2
Use Cases: RAG is particularly useful in scenarios where real-time data is crucial, such as customer support, news aggregation, and any application requiring current information.

3 Sources
Overview of MCP

Definition: MCP is a framework developed by Anthropic that standardizes how AI applications interact with various external data sources and tools, aiming to simplify integration and enhance functionality.

2
Functionality: MCP allows LLMs to fetch data from multiple sources, such as databases or APIs, without needing to create custom solutions for each interaction. It operates on a client-server model, where the MCP server manages the logic for data retrieval and the MCP client interfaces with the LLM.

2
Use Cases: MCP is designed for applications that require seamless integration of various tools and data sources, making it suitable for complex workflows and environments where multiple data interactions are necessary.

3 Sources
Key Differences

Scope: RAG is a specific method focused on improving the accuracy of LLM outputs by grounding them in external knowledge, while MCP is a broader protocol that standardizes interactions between AI and various data systems.

1
Data Retrieval: RAG retrieves external data each time a query is made, whereas MCP allows LLMs to access contextual memory and external data more efficiently, reducing the need for repeated data retrieval.

2
Integration: RAG requires specific setups for each data source, while MCP provides a universal framework that simplifies the integration of multiple data sources and tools into AI applications.

3 Sources
Conclusion
Both RAG and MCP play significant roles in enhancing AI capabilities, but they serve different purposes. RAG is ideal for applications needing real-time data retrieval to improve response accuracy, while MCP offers a standardized approach for integrating various tools and data sources, making it easier to build complex AI systems. Understanding these differences is crucial for developers and organizations looking to leverage AI effectively in their applications.

How to deply Ollama & open web-ui on your laptop

How to deploy Ollama

 Installation:

• Download Ollama: Get the Ollama package from the GitHub repository. 
• Install Dependencies: Ensure you have any required dependencies, including libraries for your specific model. 
Verify Installation: Use ollama --version to confirm Ollama is installed correctly. 

2. Model Deployment and Usage:

Pull the Model: Use the ollama pull <model_name> command to download the desired model. 
Run the Model: Use ollama run <model_name> to initiate the model's execution. 
Interacting with the Model: Ollama provides an API at http://localhost:11434/api/generate for interacting with the model. 
Optional: Web UI: Explore Open WebUI for a user-friendly interface to manage and interact with models. 
Optional: Custom Applications: Build custom applications using libraries like FastAPI and Gradio to integrate Ollama models into your workflows. 

How to deploy open-webui

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG, making it a powerful AI deployment solution.

How to Install 🚀

Installation via Python pip 🐍

Open WebUI can be installed using pip, the Python package installer. Before proceeding, ensure you're using Python 3.11 to avoid compatibility issues.

1. Install Open WebUI: Open your terminal and run the following command to install Open WebUI:

   pip install open-webui

2. Running Open WebUI: After installation, you can start Open WebUI by executing:

   open-webui serve

This will start the Open WebUI server, which you can access at http://localhost:8080

To upgrade the Open-webui components

pip install open-webui --upgrade

DeepSeek R1: A Technical Deep Dive into the Next-Gen AI Search and Conversational Tool

 Artificial intelligence has become a cornerstone of modern technology, with tools like DeepSeek R1 and ChatGPT leading the charge in transforming how we interact with machines. While both are powered by advanced AI, they cater to different use cases and employ distinct technical architectures. In this article, we’ll explore the technical underpinnings of DeepSeek R1, compare it with ChatGPT, and highlight their unique capabilities.

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What is DeepSeek R1?

DeepSeek R1 is an AI-driven search and conversational platform designed to deliver real-time, context-aware, and highly personalized results. Unlike traditional search engines, which rely on keyword matching and static datasets, DeepSeek R1 leverages cutting-edge natural language processing (NLP), machine learning (ML), and real-time data integration to provide dynamic and accurate responses.

The "R1" in its name stands for Real-time, Relevance, and Reliability, reflecting its core strengths. It is built to handle complex queries, process multimodal inputs (text, images, audio, and video), and integrate seamlessly with external systems, making it a versatile tool for both individual and enterprise use.

Technical Architecture of DeepSeek R1

1. Natural Language Processing (NLP) Engine

   - Transformer-Based Models: DeepSeek R1 utilizes transformer-based architectures, similar to those used in models like GPT and BERT, to understand and generate human-like text. These models are trained on massive datasets to capture the nuances of language.

   - Contextual Embeddings: Unlike traditional word embeddings (e.g., Word2Vec), DeepSeek R1 employs contextual embeddings (e.g., BERT-style embeddings) to understand the meaning of words in context. This allows it to handle ambiguous queries and provide more accurate results.

   - Intent Recognition: DeepSeek R1 uses advanced intent recognition algorithms to classify user queries into specific categories (e.g., informational, navigational, transactional). This helps tailor responses to the user’s needs.

2. Real-Time Data Processing

   - Streaming Data Pipelines: DeepSeek R1 is equipped with streaming data pipelines that allow it to process and analyze real-time data from various sources, such as APIs, databases, and IoT devices.

   - Dynamic Knowledge Graphs: It constructs and updates knowledge graphs in real-time, enabling it to connect disparate pieces of information and provide comprehensive answers.

   - Caching Mechanisms: To ensure low latency, DeepSeek R1 employs intelligent caching mechanisms that store frequently accessed data while still prioritizing real-time updates.

3. Multimodal Capabilities

   - Cross-Modal Learning: DeepSeek R1 is trained on multimodal datasets, allowing it to understand and generate responses based on text, images, audio, and video inputs. For example, it can analyze an image and provide a textual description or answer questions about a video.

   - Unified Embedding Space: It uses a unified embedding space to represent different modalities (e.g., text and images) in a shared vector space, enabling seamless cross-modal interactions.

4. Personalization and User Modeling

   - Reinforcement Learning (RL): DeepSeek R1 employs RL techniques to learn from user interactions and improve its responses over time. This allows it to adapt to individual preferences and behaviors.

   - User Profiling: It builds detailed user profiles by analyzing historical interactions, search patterns, and preferences. These profiles are used to deliver personalized recommendations and responses.

 5. Integration with External Systems

   - API-First Design: DeepSeek R1 is built with an API-first approach, making it easy to integrate with third-party platforms, enterprise systems, and cloud services.

   - Data Connectors: It includes pre-built connectors for popular data sources, such as CRM systems, social media platforms, and IoT devices, enabling it to pull data from multiple sources.

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 DeepSeek R1 vs. ChatGPT: A Technical Comparison

While both DeepSeek R1 and ChatGPT are built on transformer-based architectures, they differ significantly in their design, training, and application. Here’s a detailed technical comparison:

 1. Model Architecture

   - DeepSeek R1: Uses a hybrid architecture that combines transformer-based NLP models with real-time data processing pipelines and knowledge graphs. This allows it to handle both static and dynamic data effectively.

   - ChatGPT: Primarily relies on a transformer-based generative model (GPT-3.5 or GPT-4) trained on a large corpus of text data. It excels at generating coherent and contextually relevant text but lacks real-time data integration.

 2. Training Data

   - DeepSeek R1: Trained on a combination of static datasets and real-time data streams. This enables it to provide up-to-date information and adapt to changing contexts.

   - ChatGPT: Trained on a fixed dataset up to its last update (e.g., October 2023 for GPT-4). While it has a broad knowledge base, it cannot access or process real-time data.

 3. Use Cases

   - DeepSeek R1: Optimized for search, data analysis, and personalized recommendations. Its real-time capabilities make it ideal for applications like financial analysis, healthcare diagnostics, and e-commerce.

   - ChatGPT: Designed for conversational AI, content generation, and customer support. It is widely used for tasks like drafting emails, writing code, and answering general knowledge questions.

 4. Interaction Style

   - DeepSeek R1: Focuses on precision and relevance. Its responses are concise, data-driven, and tailored to the user’s intent.

   - ChatGPT: Emphasizes engagement and creativity. It can generate longer, more detailed responses and is capable of storytelling, brainstorming, and humor.

 5. Integration Capabilities

   - DeepSeek R1: Built for seamless integration with external systems, making it a powerful tool for enterprise applications. It supports APIs, data connectors, and cloud integrations.

   - ChatGPT: While it can be integrated into various platforms, its primary strength lies in standalone conversational applications.

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 Applications of DeepSeek R1

DeepSeek R1’s technical capabilities make it suitable for a wide range of applications, including:

1. Enterprise Search: Enhancing internal search engines by providing real-time, context-aware results.

2. E-Commerce: Delivering personalized product recommendations based on user behavior and preferences.

3. Healthcare: Assisting in diagnostics by analyzing patient data and medical literature in real-time.

4. Finance: Providing up-to-date market analysis, risk assessments, and investment recommendations.

5. Customer Support: Offering instant, accurate responses to customer queries by integrating with CRM systems.

 The Future of AI: DeepSeek R1 and Beyond

DeepSeek R1 represents a significant leap forward in AI-powered search and conversational tools. Its ability to process real-time data, understand context, and deliver personalized results sets it apart from traditional AI models like ChatGPT. As AI continues to evolve, tools like DeepSeek R1 will play a crucial role in bridging the gap between humans and machines, enabling smarter decision-making and more intuitive interactions.

In conclusion, while ChatGPT excels in creative and conversational tasks, DeepSeek R1 is designed for precision, real-time data processing, and enterprise integration. Together, these tools showcase the diverse potential of AI, paving the way for a future where technology is more intelligent, adaptive, and human-centric.

Power of AI - Podcast about my tech blog techbytes-madhukar.com

The podcast is auto-generated by https://notebooklm.google.com 

Techbytes-madhukar.com is a blog created by Madhukar Rupakumar where he shares his insights and findings on various technology-related topics. [1] The blog features articles categorized by labels such as ".NET", "AI", "Apple products", "Blockchain", "Cloud technology", and many more. [2] Rupakumar, a Principal Systems Engineer at Hewlett Packard Enterprise with expertise in storage products, uses his platform to discuss a wide array of subjects related to technology and software. [1]

The blog contains posts covering topics like:Linux commands for beginners. [3]
Interview preparation guides for software engineers. [4]
Free AI/ML LLM Fundamentals Courses. [5]
Cloud computing and data storage terminology. [6]
Free courses on various topics such as Generative AI, React, Angular, SEO, and data science. [7]
Learning resources for data structures and algorithms. [8]

The blog also includes a section where Rupakumar shares details about his professional background and interests.

Free online courses from Nvidia

NVIDIA just released FREE online courses in AI.

Here are 5 courses you can't afford to miss:

__________

1. Generative AI Explained
What you'll learn:

• Generative AI and how it works.

• Various Generative AI applications.

• Challenges and opportunities in Generative AI
Link: https://lnkd.in/gTAJ-sKa
__________

2. Building A Brain in 10 Minutes
What you'll learn:

• Exploring how neural networks use data to learn

• Understanding the math behind a neuron

Link: https://lnkd.in/gvVrqwZF




__________




3. Augment your LLM with Retrieval Augmented Generation:




What you'll learn:




• Basics of Retrieval Augmented Generation

• RAG retrieval process

• NVIDIA AI Foundations and RAG model components

Link: https://lnkd.in/g8hYube9

__________

4. AI in the Data Center:

What you'll learn:

• AI use cases, Machine Learning, Deep Learning, and their workflows.

• GPU architecture and its impact on AI.

• Deep learning frameworks, and deployment considerations.

Link: https://lnkd.in/gvNzawxe
__________

5. Accelerate Data Science Workflows with Zero Code Changes:

What you'll learn:

• Learn benefits of unified CPU and GPU workflows

• GPU-accelerate data processing and ML without code changes

• Experience faster processing times

Link: https://lnkd.in/gRmxxVn8



𝐅𝐑𝐄𝐄 𝐨𝐧𝐥𝐢𝐧𝐞 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝐀𝐈 𝐲𝐨𝐮 𝐜𝐚𝐧'𝐭 𝐦𝐢𝐬𝐬 🔥

All courses can be found here: https://lnkd.in/dahEz8tx

1️⃣ Easily Develop Advanced 3D Layout Tools on NVIDIA Omniverse https://lnkd.in/dsXcjeV4


2️⃣ How to Build Custom 3D Scene Manipulator Tools on NVIDIA Omniverse https://lnkd.in/dhr6vKZY


3️⃣ AI in the Data Center

https://lnkd.in/d3DdNxq9


4️⃣ Building a Brain in 10 Minutes

https://lnkd.in/gtWMPJZK


5️⃣ Networking Introduction

https://lnkd.in/dTsJ9iDa


6️⃣ Mastering Recommender Systems https://lnkd.in/gXYVgvKg


7️⃣ Accelerate Data Science Workflows with Zero Code Changes https://lnkd.in/ghNhRjPg


8️⃣ Building RAG Agents with LLMs

https://lnkd.in/gZVuM679


9️⃣ Generative AI Explained

https://lnkd.in/gFxY6kZh


🔟 Augment your LLM Using Retrieval Augmented Generation https://lnkd.in/g87DCW_V

______

🤳 Contact us if you made a great AI tool to be featured: https://lnkd.in/d5VZ-W8H

_________




💡Share this knowledge with your network to help others.