Congrats as well!

We've been loving it for making internal point-and-click tools + external project starters (ex: tutorials, solution engineering, ..).

Today, if you and your users are coding-heavy for data flows (dataset -> pydata wrangling/ml/... -> ui), jupyter notebooks are #1, and if you're in bigco, maybe say databricks notebooks as #2. However, most operational users really want an interactive point-and-click dashboard UI. Tableau and friends don't make as much sense in the pydata world, mostly for simpler SQL-only flows, and the existing python dashboarding tools (voila, panels, plotly, ...) have been too much work, esp. when sharing.

I've been liking Streamlit as it's pretty opinionated + prestyled (less work!), simple interaction model (accessible!), etc. It clearly can be better, but is already so much more accessible than our experiences with other tools here.

As some examples:

* We've been building https://github.com/graphistry/graph-app-kit for people building mini graph apps (one-click self-hosted launch via docker + st dashboard + graphistry viz + optional graph db connectors + optional graph compute tools).

* We're releasing 2-3 more integration + tutorial sets this month, where they're both notebook modes + dashboard modes.

* We just ran a hackathon for the same w/ the TigerGraph team: https://tigergraph-web-app-hack.devpost.com/

* projectdomino.org uses it internal for anti-misinfo dashboard tools. Our devs/data sci/some advanced osint researchers are fine w/ nb's, but everyone else needs dashboard UIs.

I'm excited to see what 2021 + 2022 bring here, and esp. if they can keep increasing accessibility all the way to no-code!

I use it internally to show interactive results and explain stats concepts to PMs/Eng, so for example I was trying to explain exactly how increased sample sizes change the confidence intervals around a binomial proportion as the proportion changes from .5 -> 1, and then relate that to how much it costs for the business, etc. So I made a Streamlit app in a few minutes, and let them interact with it.

I'm using it for internal debug tools. Lookup data from various APIs, databases, show status info for servers, that kind of thing. Often easier to create one and run it locally than write scripts and keep rerunning them with different bits of data.

We are using it for data health dashboards for all our big data projects,hence we are able to observe discrepancies, missing data, etc.