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# Introduction
I’ve been immersed in artificial intelligence (AI) tools, not only writing about them but using them every day in my work as a data scientist. They have completely changed how I work, helping me write cleaner code, improve my writing, speed up data analysis and deliver projects much faster.
In this article, I share seven AI tools that have become permanent parts of my workflow. No replacements, no replacements – just the essentials that power everything from machine learning projects to content automation.
# 1. Grammarly AI
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Grammar It’s a tool I’ve been using for almost a decade. It started as a simple grammar and spell check assistant for my assignments and theses, but has evolved into a full AI-powered writing companion.
Now, I can highlight any text and ask it to grammatically improve, rewrite it, adjust tone, or clarify it, and it consistently delivers high-quality results.
After running your content through Grammarly, everything feels faster, more polished, and ready to publish. I use it for my LinkedIn posts, articles, tutorials, project documents, and emails. It’s one of the few tools I really can’t live without.
# 2. You.com
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I am using you.com For two years, and honestly, despite the recent subscription price hike, it’s still worth every penny.
I rely on it for research, planning and learning new topics. Its deep exploration mode is one of the best features. It explores the subjects well and gives detailed reports that I have not seen Chat GPT Or any other AI assistant.
One of the biggest advantages of ap.com is access to top models Anthropicfor , for , for , . Open Eyefor , for , for , . Googleand a range of open source models, all in one place. You can test them, compare them, and even integrate them into your workflow. On top of that, you.com offers a free Model Context Protocol (MCP) server, which makes it incredibly easy to integrate your native AI tools and pull web results in milliseconds.
For research-heavy tasks or exploring new ideas, you.com is easily one of my most trusted tools.
# 3. Cursor
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I have been a fan Cursor Long before it became popular. It is lightweight, intuitive, and one of the first editors to offer native support for agentic AI workflows.
Today, I use it with a cursor Claude Code And several key extensions to test, debug, and ship code very quickly, and I’m loving every bit of it.
I use Cursor for training machine learning models, web development, API building, data analysis, and even assembling projects from scratch. Features like inline AI suggestions, multi-file reasoning, quick refactoring, and context-aware planning make it feel like a true AI duo programmer.
# 4. Deep
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deep It’s my go-to tool for rapid prototyping and testing code. I’ve been using it for five years, and it’s grown into a fully capable data science platform. It’s a cloud-based notebook powered by AI, which means you can simply ask it to analyze your data and it will generate the code step-by-step, run it, fix errors, and generate a clean, structured notebook report for you.
It comes with smart autotemplate, debugging support, and a fast environment, which makes the experience effortless. I use it for my tutorials, demos, and quick experiments, and it has drastically reduced my time to build and test ideas.
I’ve gotten so used to the deep note workflow that I rarely touch native notebooks anymore. Everything stays online, organized and synced. For the kind of work I do, nothing beats it.
# 5. Claude Code
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Honestly, I was skeptical about Cloud Code at first. It felt too expensive, and it didn’t perform well in my initial data science tests. But when I discovered that I could integrate it, everything changed GLM Coding Plan Since then, I’ve been using CloudCode every single day for both personal projects and work.
It feels smooth to use. I have tried open source, Geminifor , for , for , . Codexand even Druid, but I keep coming back to Cloud Code.
Its simplicity, the way it follows instructions, and its ability to handle complex tasks automatically make it incredibly reliable. For fast development, clean reasoning, and handling multi-step coding workflows, nothing else comes close.
# 6. Chat GPT
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Where do I even start with Chat GPT? It has been a part of my daily life since its launch. I use it for everything – coding, research, debugging issues, debugging my system, writing and streamlining my workflow.
Whenever I’m stuck on a complex problem, ChatGPT is the first place I turn for a fast, reliable answer. I ask it personal questions, work-related questions, and anything in between, and it consistently gives useful, context-aware answers thanks to its ability to recall past conversations.
What makes ChatGupt so powerful for me is the combination of interactive memory, flexible inputs, and custom instructions. It adapts to how I work, understands my patterns, and can easily switch between tasks.
Whether I’m developing code, reviewing notebooks, drafting content, or analyzing data, it’s the closest thing to a full-time AI companion sitting next to me for my workflow.
# 7. llama.cpp
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llama.cpp Native AI is the backbone of the ecosystem. It’s completely open source and lets you run large native language models on regular consumer hardware, even without a GPU. It’s lightweight, fast, and incredibly efficient, delivering true bare-metal performance. Recently, the developers even added a clean UI, which makes it feel like a native alternative to ChatGPT.
I use llama.cpp for offline projects and anything that involves sensitive code or private data. It integrates easily with native coding agents, chatbots, and custom tools, and setup is so simple that even Windows users can install it without a problem. Whenever I want to test new open source models, I run them directly on my laptop through Llama CPP and share my experience. I also use it for code generation, writing, and quick question answering.
It’s not at the level of ChatGPT, but if you care about privacy, security, and experimenting with new models for free, LamaCPP is the tool you want in your stack.
# Final thoughts
My primary tools remain the same: Grammarly, Op.com, Cursor, and ChatGupt. The rest change depending on my workflow or when better alternatives appear.
As someone with dyslexia, having AI support at my fingertips has been a real boon. These tools help me understand complex texts, review my writing, and even manage research that would normally take me hours to complete. Exploring Grammarly, ChatGupt, and Cursor felt like a challenge to one of my strengths.
I don’t believe AI is here to replace us. It’s here to help us and create a new generation of workflows where AI becomes a natural part of how we develop, write, learn and create. When used well, it doesn’t take away from your abilities. It amplifies them.
Abid Ali Owan For centuries.@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunication Engineering. His vision is to create an AI product using graph neural networks for students with mental illness.