nb_conda_kernels VS datasette

If you use conda there are extensions that can help with this by automatically registering any available conda environments that include ipykernel in your Jupyter Lab environment.

nb_conda_kernels is pretty reliable but not actively maintained. Gator from the mamba folks is new and still a bit rough around the edges but looks like it will be pretty slick eventually.

https://github.com/Anaconda-Platform/nb_conda_kernels

https://github.com/mamba-org/gator

it sounds like what you're trying to do is what https://github.com/Anaconda-Platform/nb_conda_kernels does, except with pipenv. I unfortunately do not have a solution for you for pipenv, but nb_conda_kernels does seamlessly allow you to launch kernels in environments other than the environment Jupyter is running in without having to start multiple instances of jupyter.

Simon Willison's github would be a great place to get started imo -

https://github.com/simonw/datasette

I'm a fan on simonw's datasette/dogsheep ecosystem https://datasette.io/

I use Anki the exact same way. After a lifetime of learning I have accepted that I will never read over anything I write for myself voluntarily - so my two options are:

1. Write an article so good I can publish it and look it over myself later on. I did this last year with https://andrew-quinn.me/fzf/, for example.

2. Create Anki cards out of the material. Use the builtin Card Browser or even https://datasette.io/ on the underlying SQLite database in a pinch to search for my notes any time I have to.

Were you aware of, or tempted by https://datasette.io/ for creating your solution?

Browsing HN, GitHub and the like we get to see a huge variety of software products and code bases.

I often see products and think - if this product X, got together with Y, it would be pretty cool - kind of like if they had a kid together.

Not too literally, but more on the conceptual level - my level of programming is low.

E.g. Just some....

- pocketable.io & datasette (+with some more charting) [https://pocketbase.io, https://datasette.io]

You might like the Datasette project: https://datasette.io/

I don't think they are desperate for contributions but it's a welcoming environment and a fun project to hack on. You'll learn a lot just from reading the source and the incredibly informative PRs. The creator is a really talented developer with a great blog which shows up on the HN front page often.

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

> uploads of boring raw excel data and receive a nice UI

https://datasette.io/