VTeam AI


Top Generative AI tools for Data Scientists and Coders

Welcome to the wild world of huge language models! These AI tools are changing everything - from planning vacations to coding and more. It's a revolution that's made life easier, especially for Data Scientists and AI Engineers. As an AI-obsessed team, we've done the homework for you. We've spent a week testing Gen AI tools and picked out the best ones to help streamline your work. Hang tight as we share our findings from this tech treasure hunt. Let's ride this wave of change together!

It's the world of LLMs, and we are living in it. Ever since the inception of ChatGPT, things are changing rapidly, especially in the tech industry. There is a flurry of tools out there trying to automate almost everything, be it planning your next trip to Vegas, automating Youtube channels with Faceless videos, or managing your finances. Even the most complex of tasks like coding have seen major interventions due to this so-called “Gen AI Revolution”. On top of it, if you’re a Data Scientist or AI Engineer, where already AutoML has made things a lot effortless, this Gen-AI wave has brought in some really useful tools making life a lot easier.

Being an AI firm, we got our hands dirty on your behalf, exploring some of the best Gen AI products out there that can assist fellow AI researchers and Data Scientists to make things easier. So let’s start off with what we have found after a week’s hard work and effort.

We will be not just talking, but doing a demonstration of the following Gen-AI tools

🖥️Tabnine: The coolest code autocompletion tool available out there

📱Codacy:  A great DevOps tool if you’re deploying services in a production environment

🤫Mutable AI: A of its kind,  Mutable AI is making understanding github repos a lot easier

📓Jupyter-AI: What if I can integrate ChatGPT into Jupyter Notebooks? A must to have for Data Scientists

🚴Polymer AI: An AI-driven dashboarding tool that can be a lifesaver if you’re into Data Analysis

🧐And this is not all, we will be having a shallow dive into the core of all these Gen-AI tools i.e. what are LLMs and how they are trained at the end


Tabnine (code auto-completion)


This is something I wished for a very long time. Giving stiff competition to Github CoPilot, Tabnine with its state-of-the-art deep learning algorithms, TabNine takes code completion to a whole new level, significantly boosting productivity and reducing coding time. Here are the standout features that make TabNine a must-have tool for coders:

  1. AI-Powered Autocomplete: TabNine utilizes advanced AI models to predict code completions accurately and contextually, offering intelligent suggestions as you type.
  2. Cross-Language Support: Whether it's Python, JavaScript, Java, C++, or more, TabNine has got you covered.
  3. Smart Learning: Over time, TabNine learns from the developer's coding style and patterns, resulting in increasingly personalized and accurate code suggestions. After actually using the tool, we are actually in awe of this feature.
  4. Code Snippet Expansion: TabNine goes beyond simple code completion by allowing users to create and utilize custom code snippets.
  5. IDE Integrations: TabNine integrates seamlessly with popular integrated development environments (IDEs).

It can be explored: vteam.ai/tools/Tabnine



Codacy is a DevOps Intelligence Platform that offers a code quality automation solution to improve productivity and efficiency within engineering organizations. Some key features this tool provides are:

  1. Code Quality Automation: Codacy provides static code analysis tools that help developers ship high-quality code efficiently in over 40 programming languages. It offers crucial information in every commit and pull request, including static analysis, cyclomatic complexity, duplication, and test coverage.
  2. Seamless Integration: Codacy seamlessly integrates into the existing workflow of development teams. It works with popular version control platforms like GitHub, GitLab, and Bitbucket, allowing developers to receive feedback and notifications directly within their pull requests or on communication platforms like Slack.
  3. Customizable Rulesets: Developers can customize rulesets in Codacy to align with their team's quality standards and eliminate false positives.
  4. Security and Performance Checks: Codacy helps prevent security issues by identifying OWASP's Top 10 vulnerabilities and critical code issues early in the development process.
  5. Continuous Improvement: The platform facilitates continuous improvement by providing detailed tracking of code quality evolution.

Codacy can be explored here: vteam.ai/tools/Codacy

Mutable AI


Mutable AI is one of its kind Code Autocomplete tool which apart from this, serves multiple other purposes as well. 

  1. AI-Autocomplete: Mutable.ai utilizes AI-powered autocomplete to speed up coding based on existing patterns
  2. One-Click Quality Code: With just one click, developers can generate production-ready code.
  3. Prompt-Driven Development: The tool enables developers to give instructions directly to the AI, allowing for modifications to the code based on specific prompts. This is one of the best features I have seen
  4. Automatic test cases generation
  5. Features like understanding any Github repo, auto bug, auto-documentation, and auto standup summaries (based on commit history). Insane !!

Loved Mutable.ai? Explore it here: vteam.ai/tools/Mutable

Jupyter-AI (chrome extension)


If you have ever used Jupyter Notebook/Lab, you might have noticed some extra buttons in the panel in the above image for jupyter notebook. Let’s find out what they are for.

The "ChatGPT-powered AI assistant for Jupyter Notebooks" is a browser extension designed to offer a range of AI helper functions within Jupyter Notebooks and Jupyter Lab. The primary functions of this assistant include:

  1. Format: Automatically adds comments, docstrings, and formatting to code cells for enhanced readability and structure.
  2. Explain: Provides explanations of the content within code cells using an "Explain Like I'm 5" (ELI5) style, making complex concepts easier to understand.
  3. Debug: Assists in debugging by helping identify and resolve errors in code cells.
  4. Complete: Helps complete code snippets within code cells, saving time and reducing typing efforts.
  5. Review: Conducts code reviews on code cells, offering suggestions and improvements to enhance code quality.
  6. Ask a Question: Enables users to pose custom questions to ChatGPT for insightful responses.
  7. Voice Command: Allows users to interact with ChatGPT through voice commands using a microphone.

Add this extension to your Chrome browser through this link: vteam.ai/tools/Jupyter-AI



PolymerSearch is a Business Intelligence (BI) platform that aims to empower users to explore, visualize, and present their data confidently, without the need for advanced technical skills or extensive training.

The tool focuses on three core pillars:

   a. No-code accessibility, making it easy for non-technical users to work with data directly.

   b. Fast data processing, allowing users to work with data efficiently and save time.

   c. A visually appealing interface for data, ensuring a pleasant user experience during data analysis.

PolymerSearch utilizes AI algorithms to analyze data, suggest insights, and automatically generate beautiful dashboards, thereby simplifying the process of gaining valuable insights from data. The tool provides data connectors or allows users to upload datasets to start exploring and visualizing data without technical complexities.

PolymerSearch offers a 14-day free trial, making it convenient for users to experience its features before making a purchase decision. 

Polymer Search is available here: vteam.ai/tools/PolymerSearch



Mintlify is a platform that provides developer-friendly documentation solutions, generating documentation and docstrings on the fly. Here are the key points about Mintlify:

  1. Developer-Centric Approach: Mintlify offers a developer-first approach to documentation, allowing developers to seamlessly integrate content with code using MDX.
  2. Performance-Oriented Design: The platform is meticulously designed and optimized for an excellent user experience. Performance considerations are a priority, ensuring that the documentation loads quickly and provides a smooth navigation experience.
  3. Documentation Analytics: Mintlify provides built-in analytics, enabling users to gain insights into how their audience engages with the documentation.
  4. AI-Enabled Capabilities: One of Mintlify's strengths is its integration of AI capabilities within the documentation. Users can harness the limitless power of AI directly within their documentation, enhancing its functionality and utility.
  5. Mintlify Doc Writer: Mintlify Doc Writer is a specialized feature that offers seamless integration with popular code editors like Visual Studio Code (VS Code). It allows developers to generate documentation effortlessly and save time by automatically creating documentation as they build their code.

Mintlify is a must to have which is accessible here: vteam.ai/tools/Mintlify/


That’s all from the curated list of AI products we shortlisted for you. But wait, have you ever thought about what powers these tools?

The answer, as you must have assumed is LLMs or Large Language Models, which powers ChatGPT as well. But what are these LLMs?

Large Language Models (LLMs) are a type of advanced machine learning model specifically designed to work with human languages. They are built using deep learning algorithms and rely on a special architecture called transformer-based attention mechanism (From Attention is All You Need), which allows them to understand and generate human-like text. The "large" in LLM refers to their huge size due to billions of parameters to train. 
The core idea on which any LLM is based is Self-Attention which helps to focus on specific words in a sentence.  

The process of training LLMs involves:

  • Pre-Training: Training the model on some general, huge corpus (mostly from the internet) helping to understand the language.
  • Fine-Tuning: This training is more task-specific and uses specific, related data according to the task. Also, it aims at just refining the weights we got after pre-trainin.
  • Reinforcement Learning with Human Feedback: A new concept, RLHF aims at training the model to generate a human-like response using a combination of reward model which rewards the responses generated by the model based on “how humanly the response is” alongside the SOTA Reinforcement Learning technique called as PPO (Proximal Policy Optimization).

So, this is all we got for you in this post. But don’t worry, we will come back very soon with another curated list of Gen-AI tools that you can use for improved productivity.

Disclaimer: The views and opinions expressed in this blog post are solely those of the authors and do not reflect the official policy or position of any of the mentioned tools. This blog post is not a form of advertising and no remuneration was received for the creation and publication of this post. The intention is to share our findings and experiences using these tools and is intended purely for informational purposes.