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The obvious Getting started
I have been immersed in artificial intelligence (AI) tools, not just writing about them but using them every day in my work as a data scientist. They have completely changed how I get things done, 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 replacement, no replacement – just powerful content all from machine learning projects to content entry.
The obvious 1. Grammarly ai
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Grammarly it's a tool I've been using for almost ten years. It started as a simple helper and spell checker for my assignments and thesis, but has evolved into a perfect AI-powered writing companion.
Now, I can highlight any text and query the grammar to improve it, rewrite it, adjust the tone, or even upset the top results.
After running my content through grammar, everything feels sharp, very political, and ready to publish. I use it for my social media posts, articles, tutorials, project documents, and emails. It's one of the few tools I can't live without.
The obvious 2. You.com
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I have been using You.com Two years, and honestly, even with the recent price hike, it's still worth every penny.
I rely on it to research, organize, and learn new topics. Its research mode is one of the best; It examines the subjects thoroughly and provides detailed reports that I have never seen before The chatgt or other AI assistant.
One of the biggest benefits of i.com is access to top models from -Obubizin, Open it, Googleand a list of open source models, all in one place. You can test them, compare them, and integrate them into your workflow. In addition, you.com Provide a Protocol Model Contactor Protocol
For research – heavy work or exploring new ideas, Joy.com is easily one of my trusted tools.
The obvious 3. Indicator
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I became a lover of A cheater Long before love. It's lightweight, intuitive, and one of the first editors to offer native support for agentic AI workflows.
Today, I use the pointer with Claude code And several important extensions to test, debug, and ship code very quickly, and I like every bit of it.
I use pointers for machine model training, Web development, API creation, data analysis, and collecting full projects from scratch. Features such as inline ai support, Multi-file consultation, fast printing, and context pre-editing make it feel like a true AI pair-programmer.
The obvious 4. Deepnote
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Emphasis Does my tool go to Prestotyping and test code. I've been using it for five years, and it's grown into an absolutely capable scientific platform. It's a cloud-based notebook powered by AI, which means you can just ask it to analyze your data and it will generate step-by-step code, run it, and fix errors for a clean notebook.
It comes with Smart AutoComplete, debugging support, and fast loading environment, which makes experimenting effortless. I use it for my tutorials, demos, and quick tests, and it reduces my time to build and test ideas.
I've gotten so used to the long-winded workflow that I can't even touch the local notebooks anymore. Everything is always online, organized, and synchronized. For the type of work I do, nothing beats it.
The obvious 5. Claude code
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Honestly, I was skeptical about Code Claude at first. It felt too expensive, and it didn't do well in some of my early science tests. But that all changed when I discovered that I could integrate GLM coding scheme With it. Since then, I have been using Claude's code every single day for both projects and work.
Using it feels seamless. I tried the open code, Gemini, Codexand even a droid, but I keep coming back to Claude's code.
Its simplicity, the way it follows commands, and its ability to handle complex tasks automatically make it very reliable. With fast development, clean thinking, and handling of multiple code workflows, nothing else comes close.
The obvious 6. Chatgpt
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Where do I start with chatgpt? It has been a part of my daily life since the day it was launched. I use it for everything – coding, research, debugging issues, troubleshooting, writing, and revising my workflow.
Whenever I'm stuck with a complex problem, chatgpt is the first place I turn for a quick, reliable answer. I ask it personal questions, work-related questions, and anything in between, and it always gives helpful, knowledgeable answers thanks to its ability to recall past conversations.
What makes chatgpt so powerful for me is the combination of chat memory, dynamic input, and custom commands. It adapts to the way I work, understands my methods, and can change between jobs.
Whether I'm generating code, reviewing textbooks, writing content, or writing down, or analyzing data, it's the closest thing to having a full-time AI partner sitting next to me as I go.
The obvious 7. Llama.cpp
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Llama.cpp it is the natural backbone of the local area. It is completely open source and allows you to run large language models on a standard Serker Hardware environment, or without a GPU. It is lightweight, fast, and very efficient, delivering the performance of real steel. Recently, the developers even added a clean UI, which makes it seem like a restoration of chatgpt.
I use llama.cpp for offline projects and any work that involves sensitive code or private data. It easily integrates with local Conting agents, Chatbots, and custom tools, and setup is so simple that Windows users can install it without any hassle. Whenever I want to test new open models, I run them directly on my laptop with llama.cpp and share my experience. I also use it to tackle code generation, writing, and answering quick questions.
It's not chatgpt standard, but if you care about privacy, security, and experimenting with new models for free, llama.cpp is a tool you want in your stack.
The obvious Final thoughts
My basic tools stay the same: grammar, wena.com, pointer, and chatgpt. Rest changes to the flow of my work or when better alternatives arise.
As someone with dyslexia, having AI support at my fingertips has been a real benefit. These tools have helped me understand complex text, revise my writing, and even manage research that would often take me hours to complete. Finding the grammar, chatgpt, and pointer turned into what felt like a challenge to some of my strengths.
I don't believe AI is here to take over. It is here to support and shape a new generation of workflows where AI becomes a natural part of how we build, write, learn and create. When used well, it doesn't take away your skills; it increases itself.
Abid Awan Awan (@ 1Abidaliawan) is a certified trainer for a scientist with a passion for machine learning models. Currently, he specializes in content creation and technical blogging on machine learning and data science technologies. Avid holds a master's degree in technology management and a bachelor's degree in telecommunication engineering. His idea is to build an AI product using a graph neural network for students struggling with mental illness.
