Streamlit LLM Hackathon

Data Professor · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

The Streamlit LLM Hackathon is a competition where participants can build innovative LLM-based apps using Streamlit and partner technologies such as LangChain, Assembly AI, and Llama Index. The hackathon provides a platform for developers to showcase their skills and creativity in building groundbreaking LLM-based apps.

Full Transcript

today I've got an exciting news to share with you streamlined is hosting the LOM hackathon that is happening from September 5th to September 19th now before we dive into the details of the hackathon let's talk a little bit about trimlet streamlet is a low code web framework that allows you to create data apps in Python using a simple API it's no secret that streamlined has become the go-to platform for building LM based apps in fact they already have over 12 000 apps and counting in their community and developers are unlocking new possibilities using large language models streamlit has partnered with some of the leaders in llm based technology including Lang chain llama index assembly Ai weavate and clarify these Partnerships means you'll have access to top-notch tools and expertise while participating in the appathon so what's this hackathon all about it's your chance to build a groundbreaking llm based streamlit app that incorporates at least one of the following LOM Technologies from these Partners including Lang chain assembly AI weba Lama index or clarify there will be five price categories one for each partner listed above and in each category there will be two lucky app winners you can participate Solo or you could also partner up in a team of two and winners will be announced by October 5th so if you're wondering how your app creation will be judged these are the criterions so they're looking for inventive and error-free apps and that the app should be public on GitHub and also hosted on the community Cloud the app should also use one of the five partner Technologies mentioned here and bonus points if it is addressing common error and pain points like transparency trust accuracy privacy cost reduction or ethics and here's the exciting part there are five most Innovative use prize categories each category corresponding to one of the partner Technologies and in each of the category as already mentioned there will be two lucky app winners aside from the special swag from the two winning apps in each of the category the first 250 apps submitted will receive a pair of streamlined socks Yes you heard that right streamlined socks now before you get started there are plenty of resources to help you on your llm hackathon journey firstly you could join the LM hackathon channel on the streamlined Discord and there are tutorial blocks and starting with these Technologies if you need some inspiration you can check out the example LM apps like building a chat bot building a weeviate magic the Gathering chat building a chat research using Lang chain using custom data sources using llama index or how about analyzing video channels automatically using assembly AI or interact with models via chatbots using clarify when you're done with your app creation don't forget to submit it and be a part of this incredible LM hackathon Journey so mark your calendars you have from September 5th until the 19th to take part in the streamlit LM hackathon it's your chance to shine collaborate and make a real impact using Ln technology I'll also be one of the judge and I can't wait to see what you create until next time happy hackathoning

Original Description

If large language models and generative AI sounds interesting to you, then this video is for you! Briefly, I'll give you a quick overview of the Streamlit LLM Hackathon. In a nutshell, you'll build an LLM app using one or more of the following LLM technology from these partners: - LangChain - AssemblyAI - Weaviate - LlamaIndex - Clarifai 👉 Go to the LLM Hackathon https://streamlit.io/community/llm-hackathon-2023?utm_campaign=2023-Q3%20LLM%20Hackathon&utm_medium=social&utm_source=youtube&utm_content=dataprofessor Here's a quick summary: 📆 When? September 5-19, 2023 🎁 Prizes! Lots of cool swags and prizes! 🗺️ Where? Virtual Hackathon _________________ Support my work: 👪 Join as Channel Member: https://www.youtube.com/channel/UCV8e2g4IWQqK71bbzGDEI4Q/join ✉️ Newsletter http://newsletter.dataprofessor.org 📖 Join Medium to Read my Blogs https://data-professor.medium.com/membership ☕ Buy me a coffee https://www.buymeacoffee.com/dataprofessor Recommended Resources 📚 Books https://kit.co/dataprofessor 😎 Taro (Tech Career Mentorship) https://www.jointaro.com/r/dataprofessor/ 📜 Google Data Analytics Professional Certificate https://click.linksynergy.com/deeplink?id=PNeWWakF7rI&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fgoogle-data-analytics 🤔 Interview Query https://www.interviewquery.com/?ref=dataprofessor 🖥️ Stock photos, graphics and videos used on this channel https://1.envato.market/c/2346717/628379/4662 Subscribe: 🌟 Coding Professor https://www.youtube.com/channel/UCJzlfIoF8nmWqJIv_iWQVRw?sub_confirmation=1 🌟 Data Professor https://www.youtube.com/dataprofessor?sub_confirmation=1 Disclaimer: Recommended books and tools are affiliate links that gives me a portion of sales at no cost to you, which will contribute to the improvement of this channel's contents. #datascience #machinelearning #dataprofessor
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Data Professor · Data Professor · 0 of 60

← Previous Next →
1 How a Biologist became a Data Scientist
How a Biologist became a Data Scientist
Data Professor
2 WEKA Tutorial #1.1 - How to Build a Data Mining Model from Scratch
WEKA Tutorial #1.1 - How to Build a Data Mining Model from Scratch
Data Professor
3 WEKA Tutorial #1.2 - How to Build a Data Mining Model from Scratch
WEKA Tutorial #1.2 - How to Build a Data Mining Model from Scratch
Data Professor
4 WEKA Tutorial #1.3 - How to Build a Data Mining Model from Scratch
WEKA Tutorial #1.3 - How to Build a Data Mining Model from Scratch
Data Professor
5 Computational Drug Discovery: Machine Learning for Making Sense of Big Data in Drug Discovery
Computational Drug Discovery: Machine Learning for Making Sense of Big Data in Drug Discovery
Data Professor
6 Quotes #1 on Big Data and Data Science
Quotes #1 on Big Data and Data Science
Data Professor
7 Quotes #2 on Big Data and Data Science
Quotes #2 on Big Data and Data Science
Data Professor
8 Quotes #3 on Big Data and Data Science
Quotes #3 on Big Data and Data Science
Data Professor
9 Quotes #4 on Big Data and Data Science
Quotes #4 on Big Data and Data Science
Data Professor
10 Quotes #5 on Big Data and Data Science
Quotes #5 on Big Data and Data Science
Data Professor
11 Data Science 101: Starting a Data Science / Data Mining Project
Data Science 101: Starting a Data Science / Data Mining Project
Data Professor
12 Data Science 101: CRISP-DM - Data Mining / Data Science in 6 Steps
Data Science 101: CRISP-DM - Data Mining / Data Science in 6 Steps
Data Professor
13 R Programming 101: How to Define Variables
R Programming 101: How to Define Variables
Data Professor
14 R Programming 101: Read and Write CSV files
R Programming 101: Read and Write CSV files
Data Professor
15 Data Science 101: Basic Command-Line for Data Science
Data Science 101: Basic Command-Line for Data Science
Data Professor
16 Strategies for Learning Data Science in 2020 (Data Science 101)
Strategies for Learning Data Science in 2020 (Data Science 101)
Data Professor
17 Building your Data Science Portfolio with GitHub (Data Science 101)
Building your Data Science Portfolio with GitHub (Data Science 101)
Data Professor
18 R Programming 101: Setting up R programming environment (R, RStudio and RStudio.cloud)
R Programming 101: Setting up R programming environment (R, RStudio and RStudio.cloud)
Data Professor
19 Exploratory Data Analysis in R: Towards Data Understanding
Exploratory Data Analysis in R: Towards Data Understanding
Data Professor
20 Exploratory Data Analysis in R: Quick Dive into Data Visualization
Exploratory Data Analysis in R: Quick Dive into Data Visualization
Data Professor
21 Machine Learning in R: Building a Classification Model
Machine Learning in R: Building a Classification Model
Data Professor
22 Machine Learning in R: Repurpose Machine Learning Code for New Data
Machine Learning in R: Repurpose Machine Learning Code for New Data
Data Professor
23 Data Science 101: Deploying your Machine Learning Model
Data Science 101: Deploying your Machine Learning Model
Data Professor
24 Machine Learning in R: Deploy Machine Learning Model using RDS
Machine Learning in R: Deploy Machine Learning Model using RDS
Data Professor
25 Data Pre-processing in R: Handling Missing Data
Data Pre-processing in R: Handling Missing Data
Data Professor
26 Machine Learning in R: Speed up Model Building with Parallel Computing
Machine Learning in R: Speed up Model Building with Parallel Computing
Data Professor
27 Data Science 101: Overview of Machine Learning Model Building Process
Data Science 101: Overview of Machine Learning Model Building Process
Data Professor
28 Web Apps in R: Building your First Web Application in R | Shiny Tutorial Ep 1
Web Apps in R: Building your First Web Application in R | Shiny Tutorial Ep 1
Data Professor
29 Web Apps in R: Build Interactive Histogram Web Application in R | Shiny Tutorial Ep 2
Web Apps in R: Build Interactive Histogram Web Application in R | Shiny Tutorial Ep 2
Data Professor
30 Web Apps in R: Building Data-Driven Web Application in R | Shiny Tutorial Ep 3
Web Apps in R: Building Data-Driven Web Application in R | Shiny Tutorial Ep 3
Data Professor
31 Web Apps in R: Building the Machine Learning Web Application in R | Shiny Tutorial Ep 4
Web Apps in R: Building the Machine Learning Web Application in R | Shiny Tutorial Ep 4
Data Professor
32 Web Apps in R: Build BMI Calculator web application in R for health monitoring | Shiny Tutorial Ep 5
Web Apps in R: Build BMI Calculator web application in R for health monitoring | Shiny Tutorial Ep 5
Data Professor
33 Machine Learning in R: Building a Linear Regression Model
Machine Learning in R: Building a Linear Regression Model
Data Professor
34 What programming language to learn for Data Science? R versus Python
What programming language to learn for Data Science? R versus Python
Data Professor
35 How to Become a Data Scientist (Learning Path and Skill Sets Needed)
How to Become a Data Scientist (Learning Path and Skill Sets Needed)
Data Professor
36 Using Python in R
Using Python in R
Data Professor
37 Interpretable Machine Learning Models
Interpretable Machine Learning Models
Data Professor
38 Making Scatter Plots in R [Data Visualisation in R series]
Making Scatter Plots in R [Data Visualisation in R series]
Data Professor
39 Machine Learning in Python: Building a Classification Model
Machine Learning in Python: Building a Classification Model
Data Professor
40 Compare Machine Learning Classifiers in Python
Compare Machine Learning Classifiers in Python
Data Professor
41 Hyperparameter Tuning of Machine Learning Model in Python
Hyperparameter Tuning of Machine Learning Model in Python
Data Professor
42 Practical Introduction to Google Colab for Data Science
Practical Introduction to Google Colab for Data Science
Data Professor
43 File Handling in Google Colab for Data Science
File Handling in Google Colab for Data Science
Data Professor
44 Pandas for Data Science: Create and Combine DataFrames / Rename Columns
Pandas for Data Science: Create and Combine DataFrames / Rename Columns
Data Professor
45 Machine Learning in Python: Building a Linear Regression Model
Machine Learning in Python: Building a Linear Regression Model
Data Professor
46 Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data
Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data
Data Professor
47 How to Plot an ROC Curve in Python | Machine Learning in Python
How to Plot an ROC Curve in Python | Machine Learning in Python
Data Professor
48 Installing conda on Google Colab for Data Science
Installing conda on Google Colab for Data Science
Data Professor
49 Use native R on Google Colab for Data Science
Use native R on Google Colab for Data Science
Data Professor
50 How to Save and Download files from Google Colab
How to Save and Download files from Google Colab
Data Professor
51 Easy Web Scraping in Python using Pandas for Data Science
Easy Web Scraping in Python using Pandas for Data Science
Data Professor
52 Data Science for Computational Drug Discovery using Python (Part 1)
Data Science for Computational Drug Discovery using Python (Part 1)
Data Professor
53 Pandas Profiling for Data Science (Quick and Easy Exploratory Data Analysis)
Pandas Profiling for Data Science (Quick and Easy Exploratory Data Analysis)
Data Professor
54 Exploratory Data Analysis in Python using pandas
Exploratory Data Analysis in Python using pandas
Data Professor
55 Quick tour of PyCaret (a low-code machine learning library in Python)
Quick tour of PyCaret (a low-code machine learning library in Python)
Data Professor
56 How to Upload Files to Google Colab
How to Upload Files to Google Colab
Data Professor
57 How to Install and Use Pandas Profiling on Google Colab
How to Install and Use Pandas Profiling on Google Colab
Data Professor
58 How to Adjust the Style of Pandas DataFrame
How to Adjust the Style of Pandas DataFrame
Data Professor
59 How to use Bamboolib for Data Wrangling in Data Science
How to use Bamboolib for Data Wrangling in Data Science
Data Professor
60 How to use Pandas Profiling on Kaggle
How to use Pandas Profiling on Kaggle
Data Professor

The Streamlit LLM Hackathon is an opportunity for developers to build innovative LLM-based apps using Streamlit and partner technologies. Participants can learn how to integrate LLM technologies into Streamlit apps and create groundbreaking solutions. The hackathon provides a platform for developers to showcase their skills and creativity in building LLM-based apps.

Key Takeaways
  1. Join the LLM Hackathon channel on Streamlit Discord
  2. Explore tutorial blocks and starting points for LLM technologies
  3. Choose a partner technology to work with
  4. Design and build an LLM-based app using Streamlit
  5. Submit the app for judging
💡 The hackathon provides a unique opportunity for developers to learn about LLM technologies and build innovative solutions using Streamlit and partner technologies.

Related AI Lessons

Claude AI vs ChatGPT: Which One Is Actually Better in 2026?
Compare Claude AI and ChatGPT based on real-world usage and benchmarking to determine which one is better in 2026
Medium · AI
Claude AI vs ChatGPT: Which One Is Actually Better in 2026?
Compare Claude AI and ChatGPT to determine which AI model is better for your needs in 2026
Medium · Programming
IntelliBooks: Classic RAG vs Graph RAG vs Agentic RAG – Choosing the Right AI Retrieval Architecture for Enterprise AI
Learn to choose the right AI retrieval architecture for enterprise AI between Classic RAG, Graph RAG, and Agentic RAG
Dev.to AI
Fluid, natural voice translation with Gemini 3.5 Live Translate
Learn about Gemini 3.5 Live Translate, a new voice translation technology that enables fluid and natural conversations across languages
Dev.to AI
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →