pplonski86

What is more, many people expect that software that they are trying will be open source and free. It makes really hard to create a new desktop app that is paid as a solo founder. Congratulations!

I'm creating a desktop app, a Python editor designed for beginner data scientists. The biggest shift for me was changing how I talk about it. Instead of focusing on the app's features, I started talking about the benefits it gives users. For example, the app automatically installs Python and all the needed packages. I now describe this as: "Painless Python setup—install everything you need automatically." Thinking more about users and their problems has made a big difference in both marketing and sales.

I think that there should be a guard to check the code before running it. It can be human or another LLM checking code based on its safety. I'm working on an AI assistant for data science tasks. It works in a Jupyter-like environment, and humans execute the final code by running a cell.

Yes, I tried Jupyter Desktop. It is fantastic, I like that you can double click on notebook file to open app. However, it might be a little to complicated for beginners, you need to setup Python and select kernels. That's too much.

Congratulations on the launch! It's great to see alternatives to Jupyter. JupyterLab is an excellent, however creating editor for broad audience is challenging. I've found Jupyter difficult to use, especially for beginners. Managing kernels, Python environments, and installing new packages can be quite cumbersome. Are you planning to address these challenges in Zasper?

Mixing code, markdown and execution results in one output file gives Jupyter notebooks superpower. You can really have anything in the ipynb file, stored in JSON. I wish there was two types of ipynb files, one for file with just code and markdown (for example ipynbc), and one for keeping code+markdown+results.

BTW, sometime ago I wrote an article about surprising things that you can build with Jupyter notebook https://mljar.com/blog/how-to-use-jupyter-notebook/ You will find in the list: blog, book, website, web app, dashboards, REST API, even full packages :)

Congrats on launch! What is target seniority level? Is it for seniors only? What is business model for your service? There are typos on webiste, for example 'sing in' in top menu.

Let's encrypt saved me :) I love to use it with certbot in docker-compose :) deploying really can be simple

How does it compare to Polars?

EDIT: I've found some benchmarks https://fireducks-dev.github.io/docs/benchmarks/

Would be nice to know what are internals of FireDucks

MLJAR Studio - IDE for Machine Learning. It is a Python notebook based editor with set of interactive code recipes and AI assistant. We would like to create an editor that would be suitable for Data Scientists on any skill level. It is a mix of no-code mixed with Python :)

Do you plan to support LLM in BF Machine Learning platform? LLM with BF output?

I have few ML pipelines that are simply using nbconvert to execute notebooks. Regarding python script vs notebook debate I think it all depends on your use case. I like that I can display plots in notebooks without any additional work.

the first thought was, there are no other hackers in my city ... let's wait and we will see :)

Please create issue at https://github.com/mljar/supertree it might be a bug. We are working on docs

Thank you! Tree based algorithms work very good on tabular data. There is a lack for good tools to visualise trees and make them readable. That's why we created supertree.

You are right, it wasn't setup. In case you are interested in purchase, please contact us by email.

We would like to keep package sustainable. Earlier, we've created package for AutoML which is MIT license (https://github.com/mljar/mljar-supervised), and it is very hard to monetise it, and you need to have funds to keep package maintained and work on it.

Regarding purchasing, we just don't have time create landing page with buy button :) we will add it soon. The package cost will be 499 USD/yearly. We already have few finance companies interested.

Thank you. You are right, the software is free for evaluation and personal use. For commercial use, you need to buy license.

We would like to make this package sustainable :) There are a lot of open source packages that developers can't further develop and maintain because lack of funding.

Im working on MLJAR Studio, new Python editor perfect for beginners. It has set of interactive code recipes with Graphical Interface. You can create scripts with it. Additionally, it automatically install and import missing packages. It is a kind a new way for visual programming. Most of recupes are ML and AI focused. You can read more on https://mljar.com/docs

I'm working on visual programming for Python. I created an Python editor, that is notebook based (similar to Jupyter) but each cell code in the notebook has graphical user interface. In this GUI you can select your code recipe, a simple code step, for example here is a recipe to list files in the directory https://mljar.com/docs/python-list-files-in-directory/ - you fill the UI and the code is generated. You can execute code cells in the top to bottom manner. In this approach you can click Python code. If you can't find UI with recipe, then you can ask AI assistant (running Llama3 with Ollama) or write custom python code. The app is called MLJAR Studio and it is a desktop based application, so all computations are running on your machine. You can read more on my website https://mljar.com

Thank you for response. Recently I found blog of Jason Cohen from WP Engine, he has a lot of articles describing how he was doing customers interviews to find product market fit. I

Are you YC backed? https://www.ycombinator.com/companies/vizly Why you didnt do post with Launch HN?

Congrats on launching! What is your ideal target user? What is the hardest use case that is solved with thread.dev that cant be solved with existing tools? What is the architecture of thread? Frontend is in react and you start jupyter server in background?

I'm running a SaaS for serving Python notebooks as web apps [0]. We offer widgets for notebooks and server, both as open source [1]. In open source you are managing the server instance with default Django Admin Panel. In the SaaS version, we have a dashboard for managing site (adding users, setting visibility, usage analytics), the dashboard is closed source. The open source version by default is single site, but can be switched to multi-tenant (multiple domains and subdomains) just by adding instances in the database. In case of update, sometimes it is required to update both code bases. Employees have access to both code bases.

We started with open-source first, and added SaaS offering after ~2 years. The code base split was a natural choice. At first, I didn't want to add SaaS, because managing servers is a lot of work. But, we have a lot of requests for such service, and it makes really easy to deploy notebook online (with few clicks you have unique domain and notebook running). I'm happy with this code base split.

[0]: https://runmercury.com

[1]: https://github.com/mljar/mercury

I build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.

My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/

disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury

Hi HN,

I would like to share a project I've been developing: a REST API server that integrates email management capabilities directly into ChatGPT. This integration is designed to enhance the functionality of custom GPTs by using the GPT builder, allowing them to read and send emails.

This integration is specifically designed for custom GPTs created in the GPT builder, adding a new layer of functionality to ChatGPT. The system is configured to handle only a single email account.

ChatGPT Email Demo https://chat.openai.com/share/8542aae6-74d7-47d3-bf2c-8f0c02...

The complete code for this project is available on GitHub for those interested in the technical details https://github.com/pplonski/mail4gpt.

I welcome any feedback, thoughts, or suggestions on further development or potential applications of this integration.

Ideas are talking to me so ofte , that it is hard to decide with which one to go. Which one people need?

I was testing GPT builder. I found that example REST API endpoint api.example-weather-app.com is not working ... Why? Simply, there was no such endpoint.

I've checked the domain, and it was free. So, I set quickly FastAPI endpoint with hard-coded JSON response. It is available at api.example-weather-app.com/location so we can now test our GPTs :)

Im doing demo with my projects and record them as videos. They are on youtube, so if I want to show it to someone I just send link to youtube.

Im forced to have fb account to conntact people from various groups on messanger. Is there any way to have messanger without fb?