# streamlit Published articles for streamlit. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From Project Description to Funding Opportunity DevFeed: [From Project Description to Funding Opportunity]() Original publisher: [Read original article]() Author: Gabriel Preda Published: 2026-08-31T05:03:07Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [Artificial Intelligence](), [Google AI](), [Streamlit](), [Cloud](), [PostgreSQL](), [postgresql clusters]() Tags: [adk](), [ai](), [building](), [cloud](), [developer](), [gemini](), [google](), [google-adk](), [google-cloud-sql](), [pgvector](), [postgresql](), [search](), [streamlit]() ### AI overview This article describes GrantMatch AI, a Streamlit application that matches project descriptions with potentially relevant grant and funding opportunities. It explains how hybrid keyword and embedding search, Gemini, ADK, Cloud SQL, PostgreSQL, pgvector, and Streamlit are used to connect related concepts despite different wording. ### Source excerpt Building GrantMatch AI with hybrid keyword and embedding search using Gemini, ADK, Cloud SQL, PostgreSQL, pgvector, and Streamlit Continue reading on Google Developer Experts " ## Measuring Commercial Impact at Scale at Canva DevFeed: [Measuring Commercial Impact at Scale at Canva]() Original publisher: [Read original article]() Author: Jun Ye Published: 2025-06-20T00:00:01Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [experiments](), [Streamlit](), [data analytics](), [App](), [Software as a service]() Tags: [data-analytics](), [development](), [experiment](), [experimentation](), [metrics](), [product](), [streamlit]() ### AI overview Canva describes its IMPACT app, an internal data product built with Snowflake, Streamlit, Snowpark, and Cortex. The app provides self-service estimates of commercial impact for experiment analysis using standardized logic aligned with Canva's finance model. ### Source excerpt How We Built Canva's IMPACT App with Streamlit in Snowflake ## Introducing Heroku-Streamlit: Seamless Data Visualization DevFeed: [Introducing Heroku-Streamlit: Seamless Data Visualization]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2025-05-01T08:00:09Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [Streamlit](), [Template](), [Python](), [web applications](), [Cloud](), [Data visualization](), [Data Science](), [Open Source]() Tags: [ai](), [data-analytics](), [data-visualization](), [deploy](), [deployment](), [developer-tools](), [dockerignore-usage](), [getting-started](), [heroku](), [news](), [open-source](), [prototyping](), [python](), [streamlit](), [web-applications]() ### AI overview Heroku announces Heroku-Streamlit, a ready-to-deploy template for deploying interactive Streamlit data visualization applications on Heroku. The template combines Heroku's cloud platform with Streamlit's Python framework and includes one-click deployment, sample Uber pickup data, customization options, and manual deployment instructions. ### Source excerpt We're excited to announce the release of Heroku-Streamlit, a template that makes deploying interactive data visualization applications on Heroku simpler than ever before. Streamlit is an open-source app framework built for machine learning and data science projects. This Streamlit App brings together Heroku's scalable cloud platform and Streamlit's intuitive Python-based data application framework. Whether you're [...] The post Introducing Heroku-Streamlit: Seamless Data Visualization appeared first on Heroku. ## A Better Mastodon Client DevFeed: [A Better Mastodon Client]() Original publisher: [Read original article]() Published: 2023-12-19T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Mastodon](), [client](), [Streamlit](), [Embeddings](), [SQLite](), [Algorithm](), [Vibe coding](), [Artificial Intelligence](), [scikit-learn](), [tailscale](), [OpenAI]() Tags: [client](), [clustering](), [embeddings](), [github](), [github-copilot](), [mastodon](), [sqlite](), [streamlit](), [tailscale]() ### AI overview A developer describes building a Mastodon timeline dashboard that caches posts in SQLite, creates embeddings, clusters posts by topic, summarizes the clusters with an LLM, and makes the dashboard accessible on a phone through Tailscale. ### Source excerpt Last night I had an idea and went ahead and built it. I'd like to tell you about it. Find the source code here. ## Supabase Beta July 2023 DevFeed: [Supabase Beta July 2023]() Original publisher: [Read original article]() Author: Ant Wilson Published: 2023-08-02T07:00:00Z Content type: news Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [Development](), [Android](), [Kotlin](), [Next.js](), [OAuth](), [PostgreSQL](), [Deno](), [Python](), [React](), [Streamlit]() Tags: [android](), [announcement](), [article](), [kotlin](), [launch](), [next-js](), [oauth](), [postgresql](), [python](), [react-native](), [streamlit](), [web-development]() ### AI overview Supabase's July 2023 update previews Launch Week 8 and highlights a new Android Kotlin client library, pgvector performance work, a Figma OAuth provider, Resend custom SMTP support, and updates to Postgres tooling. It also features community projects, tutorials, and courses involving Supabase with technologies such as Next.js, Python, Streamlit, Deno, and React Native. ### Source excerpt Launch Week 8 is coming - but we still shipped some goodies during July ## Making a Streamlit Machine Learning App into a SystemD Service for Deployment via Ansible DevFeed: [Making a Streamlit Machine Learning App into a SystemD Service for Deployment via Ansible]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-11-13T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Streamlit](), [systemd](), [Ansible](), [Deployment](), [Python](), [Shell](), [Ubuntu]() Tags: [ansible](), [deployment](), [git](), [linux](), [python](), [shell](), [streamlit](), [systemd](), [ubuntu]() ### AI overview This tutorial describes deploying a Streamlit machine learning application written in Python on an Ubuntu server. It presents Ansible automation and recommends replacing nohup and manual process management with a systemd service that can be started, stopped, and monitored with systemctl. ### Source excerpt As I documented recently, I've become quite the fan of Streamlit for taking Machine Learning applications written in Python and making them easily available on the web. Streamlit is a very cool toolkit for taking a Python Machine Learning app and adding a simple UI to it and then hosting it via a built in web server. I haven't seen anything really like in the Ruby world and I'm quite impressed by its elegance and simplicity. Note: Before you start down the path of implementing this the way I did, see the last section titled "Note: " after the Jenkins section. So we have a custom internal app using Streamlit and I am, at present, the only person who can deploy it. That's, well, stupid. Deployment always needs to be something that anyone on the technical team should be able to do. This became increasingly obvious to me when I had to pull over, on Monday afternoon, and deploy fixes to it - in the first snow of the season. And, yes, I've been a consultant for a lot of my career so I'm ok with this but it still is sub optimal. Here's what a deploy using Streamlit onto an Ubuntu server looks like: SSH into the box. Change into the right directory. Do a ps auwwx grep streamlit and grab the pid (process id). Do a kill pid. Do a git pull. Source the python virtual environment with: source ./venv/bin/active Restart it with the syntax: nohup streamlit run dashboard.py All of this should be able to be automated with Ansible. Well, let's make that most of it. The flaw in the ointment is the last thing. Apparently the only thing that Ansible can't automate is something with nohup. Now I'm sure if I dug in enough I could either understand it find a way around it but a seemingly solid Stack Overflow post argues that the right approach is to use SystemD and services rather than nohup and that seems like a good idea actually. Ansible can easily start and stop SystemD services so we can throw out the entire pid / kill stuff. A Shell Script and a System D Unit File I don't claim to be