Should I Build It Should I Build It
21 Apr 2025
Artificial Intelligence

An agentic AI based VS code extension that runs Jupyter notebooks only ...

...focused on data scientists, machine learning and AI builders who experiment on notebooks. The extension can write and run multiple cells using agents in the order giving explanations for codes etc.

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Freemium

People love using similar products but resist paying. You’ll need to either find who will pay or create additional value that’s worth paying for.

Should You Build It?

Build but think about differentiation and monetization.

Your are here

You're entering a competitive space with your AI-powered VS Code extension for Jupyter notebooks, as indicated by the 13 similar products we found. The good news is that most products in this category garner medium engagement, which suggests there's interest in AI-driven data science tools. Given this crowded market and the general freemium nature of these tools, the challenge lies in differentiation and monetization. Many users in this space enjoy the utility of these tools, but are often hesitant to pay for them. You will need to figure out what additional value will make your product worth paying for, or which users will see enough value in it to become paying customers.

Recommendations

  1. Start by deeply understanding your target user: data scientists and AI/ML engineers experimenting with notebooks. Conduct user interviews and surveys to pinpoint their biggest pain points in the notebook development workflow. What repetitive tasks can your AI agent automate? What insights can it provide that they're currently missing? This understanding will be crucial for differentiation and feature prioritization.
  2. Given that similar products face criticism related to lacking version control and integration with existing IDEs (like PyCharm and VS Code), prioritize seamless integration and robust version control from the outset. Make it effortless for users to incorporate your extension into their existing workflows and track changes to their notebooks. Consider integrating with popular Git providers or offering a built-in versioning solution.
  3. Focus on transparency and explainability in your AI agent's actions. Users in the similar products category expressed concerns about the lack of domain understanding in auto-code AI tools. Provide clear explanations for the code your agent generates, the reasoning behind its decisions, and the data sources it's using. This will build trust and empower users to understand and customize the agent's behavior.
  4. To counteract the 'buzzword' criticism some similar products receive, focus on delivering tangible value and demonstrable results. Showcase concrete use cases and success stories of how your extension has helped data scientists solve real-world problems. Quantify the time savings, performance improvements, or accuracy gains that users can expect.
  5. Actively engage with the data science community on platforms like GitHub, Stack Overflow, and Reddit. Participate in discussions, answer questions, and solicit feedback on your extension. This will help you build a loyal user base, identify new feature ideas, and address any concerns or criticisms proactively.
  6. Consider the comments about privacy concerns related to sending data to external servers. Offer options for local processing and model execution to address these concerns. This could involve supporting local LLMs or providing a self-hosted version of your extension.

Questions

  1. What specific, measurable benefits (e.g., time savings, error reduction, improved model accuracy) can your AI agent provide to data scientists that justify its cost and differentiate it from existing tools?
  2. How will you address the concerns about transparency and explainability in your AI agent's actions, ensuring that users understand and trust its decisions?
  3. Given the competition in the AI-assisted coding space, what is your long-term strategy for maintaining a competitive edge and attracting new users to your extension?

Your are here

You're entering a competitive space with your AI-powered VS Code extension for Jupyter notebooks, as indicated by the 13 similar products we found. The good news is that most products in this category garner medium engagement, which suggests there's interest in AI-driven data science tools. Given this crowded market and the general freemium nature of these tools, the challenge lies in differentiation and monetization. Many users in this space enjoy the utility of these tools, but are often hesitant to pay for them. You will need to figure out what additional value will make your product worth paying for, or which users will see enough value in it to become paying customers.

Recommendations

  1. Start by deeply understanding your target user: data scientists and AI/ML engineers experimenting with notebooks. Conduct user interviews and surveys to pinpoint their biggest pain points in the notebook development workflow. What repetitive tasks can your AI agent automate? What insights can it provide that they're currently missing? This understanding will be crucial for differentiation and feature prioritization.
  2. Given that similar products face criticism related to lacking version control and integration with existing IDEs (like PyCharm and VS Code), prioritize seamless integration and robust version control from the outset. Make it effortless for users to incorporate your extension into their existing workflows and track changes to their notebooks. Consider integrating with popular Git providers or offering a built-in versioning solution.
  3. Focus on transparency and explainability in your AI agent's actions. Users in the similar products category expressed concerns about the lack of domain understanding in auto-code AI tools. Provide clear explanations for the code your agent generates, the reasoning behind its decisions, and the data sources it's using. This will build trust and empower users to understand and customize the agent's behavior.
  4. To counteract the 'buzzword' criticism some similar products receive, focus on delivering tangible value and demonstrable results. Showcase concrete use cases and success stories of how your extension has helped data scientists solve real-world problems. Quantify the time savings, performance improvements, or accuracy gains that users can expect.
  5. Actively engage with the data science community on platforms like GitHub, Stack Overflow, and Reddit. Participate in discussions, answer questions, and solicit feedback on your extension. This will help you build a loyal user base, identify new feature ideas, and address any concerns or criticisms proactively.
  6. Consider the comments about privacy concerns related to sending data to external servers. Offer options for local processing and model execution to address these concerns. This could involve supporting local LLMs or providing a self-hosted version of your extension.

Questions

  1. What specific, measurable benefits (e.g., time savings, error reduction, improved model accuracy) can your AI agent provide to data scientists that justify its cost and differentiate it from existing tools?
  2. How will you address the concerns about transparency and explainability in your AI agent's actions, ensuring that users understand and trust its decisions?
  3. Given the competition in the AI-assisted coding space, what is your long-term strategy for maintaining a competitive edge and attracting new users to your extension?

  • Confidence: High
    • Number of similar products: 13
  • Engagement: Medium
    • Average number of comments: 10
  • Net use signal: 9.9%
    • Positive use signal: 15.1%
    • Negative use signal: 5.2%
  • Net buy signal: -1.9%
    • Positive buy signal: 0.0%
    • Negative buy signal: 1.9%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

Similar products

Relevance

Einblick Prompt AI for JupyterLab - A generative AI pair programmer optimized for data science

15 Nov 2023 Artificial Intelligence Analytics Data Science

This Jupyter extension is engineered specifically to solve data problems with Python. Get to answers faster by utilizing generative AI to clean and explore data, create visualizations, train ML models, and much more.

The Product Hunt launch received positive feedback, with users congratulating Emanuel and wishing him good luck. One user expressed interest in a Jupyter app related to A.I. and name pronunciation. Another user indicated they would try the extension and found it helpful.

Avatar
72
3
33.3%
3
72
33.3%
Relevance

ChatGPT code assistant for Jupyter Notebooks

09 Jan 2023 Artificial Intelligence Productivity Chrome Extensions

Hi HN!As much as ChatGPT/Github CoPilot have their flaws; I've come to realize that I miss their productivity benefits when I don't have access to it. Jupyter Notebooks tend to be one of the areas where access to these AI tools is more limited. So as a fun side-project I put together a browser extension that uses ChatGPT + some prompt engineering to provide AI assistance when working in a Jupyter Notebooks or Jupyter Lab.Currently it can improve the formatting of a cell (e.g., add type hints, docstrings, comments), it can explain code, it can explain errors, and it can complete code. It isn't perfect, but I personally enjoy using it, so I hope others will find it helpful too!

A browser extension has been created to provide AI assistance in Jupyter Notebooks. There is a concern about the lack of a publicly available API for ChatGPT.

Users criticized the AI tools for having flaws and limited access in Jupyter Notebooks. Additionally, they pointed out the lack of a publicly available API.

Avatar
2
2
50.0%
2
2
50.0%
Relevance

Einblick Prompt AI - Data notebooks that solve any problem with just one sentence

24 Jul 2023 Artificial Intelligence Data & Analytics Data Science

Tell your data notebook what to do. Meet Einblick, the AI-native notebook that writes and fixes code, plots beautiful charts, builds models, and much more. Provide a prompt and Einblick’s AI agent builds workflows using Python, SQL, and interactive components.

Einblick's Product Hunt launch garnered positive feedback, with users praising its AI features, data notebook capabilities (especially 'Einblick Prompt'), and live collaboration. Users appreciate its potential for small engineering teams. Several comments express excitement, offer congratulations, and highlight the product's solid design and comprehensive landing page. Questions were raised about the dataset size, version control, integration with Jupyter environments, and the specific large language models used. An offer was made to list the app on AI directories.

The primary criticism revolves around the lack of integration with version control systems for the notebooks, which is a crucial feature for collaborative coding and tracking changes.

Avatar
184
16
18.8%
16
184
18.8%
Relevance

Adding Mistral Codestral and GPT-4o to Jupyter Notebooks

02 Jul 2024 Artificial Intelligence Developer Tools

Hey HN! We’ve forked Jupyter Lab and added AI code generation features that feel native and have all the context about your notebook. You can see a demo video (2 min) here: https://www.tella.tv/video/clxt7ei4v00rr09i5gt1laop6/viewTry a hosted version here: https://pretzelai.appJupyter is by far the most used Data Science tool. Despite its popularity, it still lacks good code-generation extensions. The flagship AI extension jupyter-ai lags far behind in features and UX compared to modern AI code generation and understanding tools (like https://www.continue.dev and https://www.cursor.com). Also, GitHub Copilot still isn’t supported in Jupyter, more than 2 years after its launch. We’re solving this with Pretzel.Pretzel is a free and open-source fork of Jupyter. You can install it locally with “pip install pretzelai” and launch it with “pretzel lab”. We recommend creating a new python environment if you already have jupyter lab installed. Our GitHub README has more information: https://github.com/pretzelai/pretzelaiFor our first iteration, we’ve shipped 3 features:1. Inline Tab autocomplete: This works similar to GitHub Copilot. You can choose between Mistral Codestral or GPT-4o in the settings2. Cell level code generation: Click Ask AI or press Cmd+K / Ctrl+K to instruct AI to generate code in the active Jupyter Cell. We provide relevant context from the current notebook to the LLM with RAG. You can refer to existing variables in the notebook using the @variable syntax (for dataframes, it will pass the column names to the LLM)3. Sidebar chat: Clicking the blue Pretzel Icon on the right sidebar opens this chat (Ctrl+Cmd+B / Ctrl+Alt+B). This chat always has context of your current cell or any selected text. Here too, we use RAG to send any relevant context from the current notebook to the LLMAll of these features work out-of-the-box via our “AI Server” but you have the option of using your own OpenAI API Key. This can be configured in the settings (Menu Bar > Settings > Settings Editor > Search for Pretzel). If you use your own OpenAI API Key but don’t have a Mistral API key, be sure to select OpenAI as the inline code completion model in the settings.These features are just a start. We're building a modern version of Jupyter. Our roadmap includes frictionless, realtime collaboration (think pair-programming, comments, version history), full-fledged SQL support (both in code cells and as a standalone SQL IDE), a visual analysis builder, a VSCode-like coding experience powered by Monaco, and 1-click dashboard creation and sharing straight from your notebooks.We’d love for you to try Pretzel and send us any feedback, no matter how minor (see my bio for contact info, or file a GitHub issue here: https://github.com/pretzelai/pretzelai/issues)

Users have expressed concerns about the product's maturity, interface differences from Jupyter, and preference for Jupyter among data scientists. Criticisms include the relevance of hex.tech, AI code license violations, expectations for IDE features, and a controversial license change. There's uncertainty about the product's similarity to existing tools, local model support, and suitability for Jupyter users. Limitations on forking, lack of Copilot support, and a focus on programming over other features were also noted. Issues with service functionality, account credits, and plan resets were mentioned, alongside questions about performance and technical specifics like file formats and default ports.

Avatar
269
49
14.3%
-2.0%
49
269
20.4%
Relevance

Thread – AI-powered Jupyter Notebook built using React

10 Jun 2024 Artificial Intelligence Developer Tools

Hey HN, we're building Thread (https://thread.dev/) an open-source Jupyter Notebook that has a bunch of AI features built in. The easiest way to think of Thread is if the chat interface of OpenAI code interpreter was fused into a Jupyter Notebook development environment where you could still edit code or re-run cells. To check it out, you can see a video demo here: https://www.youtube.com/watch?v=Jq1_eoO6w-cWe initially got the idea when building Vizly (https://vizly.fyi/) a tool that lets non-technical users ask questions from their data. While Vizly is powerful at performing data transformations, as engineers, we often felt that natural language didn't give us enough freedom to edit the code that was generated or to explore the data further for ourselves. That is what gave us the inspiration to start Thread.We made Thread a pip package (`pip install thread-dev`) because we wanted to make Thread as easily accessible as possible. While there are a lot of notebooks that improve on the notebook development experience, they are often cloud hosted tools that are hard to access as an individual contributor unless your company has signed an enterprise agreement.With Thread, we are hoping to bring the power of LLMs to the local notebook development environment while blending the editing experience that you can get in a cloud hosted notebook. We have many ideas on the roadmap but instead of building in a vacuum (which we have made the mistake of before) our hope was to get some initial feedback to see if others are as interested in a tool like this as we are.Would love to hear your feedback and see what you think!

Users find the product's landing page and naming need improvement for better searchability and to avoid confusion. There's interest in IDE integration, particularly with PyCharm and Jupyter, and a desire for local use with open LLMs. The product's remote server requirement and API key are points of contention. Users are curious about the target audience, use cases, and architecture, and compare it to GitHub Copilot and Hex. Privacy concerns are raised about AGPL and sending data to external servers. Some users appreciate the tool and its progress, while others suggest it's becoming a buzzword and lacks features like integrated auto-completion.

Users criticized the product for its overused and confusing name 'Thread', the annoyance of needing an API key, and the requirement for PyCharm Professional. Concerns were raised about the lack of domain understanding in auto-code AI tools, the necessity for OpenAI server details, and policy restrictions on accessing OpenAI. The product competes with free tools like VS Code and Jupyter, which already offer good features, and there's skepticism about its benefits over existing tools. Data processing in the cloud raises privacy issues, and the AGPL license is seen as a vulnerability. The product's presentation is considered unclear, not easily searchable, and filled with buzzwords.

Avatar
162
35
8.6%
-2.9%
35
162
17.1%
Relevance

Labpilot – comment driven development with Jupyter lab

03 Aug 2023 Artificial Intelligence Developer Tools

Hello,I've been working on this project of mine these last couple of months, and with the release of Jupyter AI, I just had to go ahead and release my own project into the wild.I call the paradigm, "comment driven development". You use special comments, "#@" in the notebook cells, to steer the LLM to write the code you need. Got a bug in your code? - The debug feature takes the stacktrace and fixes the code for you. It also has a explain feature if some of the code is hard to understand.I'm also working on a more experimental "autogpt" feature that can be accessed from the Labpilot terminal, which is built into Jupyter lab.It's been really fun to build and play with. I hope somebody will find it useful. I appreciate any feedback I can get. Thank you.

Avatar
1
1
Relevance

Agentic AI coding tool that's easy to use, transparent and customizable

24 Sep 2024 Artificial Intelligence Developer Tools

Hey HN,I’d like to share a new agentic AI coding assistant I’ve been working on called OppyDev. Rather than trying to build something that will write the code for you this is aiming for something more collaborative. You can follow along as the AI makes updates and add your own changes on top. We’ve made the inner workings of the agent accessible to the user by outputting its workflow into the UI and allowing most actions to be triggered manually. You can also use the AI to create your own custom plugins to enhance the existing functionality. Here are some videos available demonstrating its usage:- https://www.youtube.com/watch?v=g9FrwVOHTdE&t=187s- https://www.youtube.com/watch?v=6JlQwnYn7RY&t=119s- https://oppydev.ai/documentation/#getting-startedThe current version has been released in beta for PC and Mac as an electron app, with a VSCode plugin in the works. You can see a demo video and the full list of our feature set here, https://oppydev.ai. There is more about building plugins at https://oppydev.ai/documentation/#plugins.You can get started for free with your own Pinecone and OpenAI API keys or you can message me for a promo code to get started with free credit. We're looking for feedback so we can improve the tool. We’re also offering free inference costs and support for several small teams that are interested in using the tool and providing feedback on an ongoing basis.Contact me at:feedback@oppydev.aihttps://x.com/Alex_OppyDevhttps://www.linkedin.com/in/alex-oppydev/

Criticizes LinkedIn and Xitter, suggests moving away.

LinkedIn lacks technical value, Xitter is racist, conspiratorial.

Avatar
2
1
-100.0%
1
2
Relevance

Codel – Autonomous Open Source AI Developer Agent

23 Mar 2024 Artificial Intelligence Open Source Developer Tools

Users have compared the product to the Devin project, encountered issues with Docker API compatibility on macOS, and found discrepancies with the readme instructions. There's curiosity about the choice of Postgres over SQLite, the necessity of an OpenAI account, and whether it has been evaluated against SWEbench or can be used with local models like ollama. Some appreciate the project while others note that OpenAI simplifies tasks but doesn't handle everything. A user also mentioned the absence of content.

Users criticized the Show HN product for a Docker API version mismatch, questioned the choice of Postgres over SQLite, and noted a requirement for an OpenAI account. There were also claims that the assertion about OpenAI doing all the work is incorrect, and some users reported that the product does not work according to the provided readme instructions.

Avatar
48
10
-10.0%
-10.0%
10
48
Top