| Last commit: Oct 2026 Total commits: 3,337
Python (40.8%) HCL (34.7%) JavaScript (18.6%) |
| smarter-sh/smarter | Smarter is used as an instructional tool at University of British Columbia
for teaching AI.
The Smarter Project is an open source, cloud-native
platform and
developer framework
for building sophisticated AI applications without writing a single line of
Python. An application that searches the web, queries your databases, calls
your APIs, uses the tools of any MCP server, and defends itself against prompt
injection and data leaks is described in one short YAML file, and deployed with
one command:
smarter apply -f my-ai-application.yaml
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| Last commit: Oct 2026 Total commits: 7,447
Python (43.8%) JavaScript (35.0%) CSS (11.2%) |
| FullStackWithLawrence/azureml-example | Demonstrate basic usage of Azure Machine Learning's Automated ML service. Implements the following:
AzureAIMLWorkspace: Azure AI ML Studio workspace helper class. generates an authenticated instance of a workspace.AzureAIMLAssetsDataset: Azure AI ML Studio data set. Provides helpers for managing ML Studio data sets, and for porting to/from kaggle data sets and local csv and Excel files.AzureAIMLStudioComputeCluster: Azure AI ML Studio Compute - compute cluster object with helpers for instantitation.AzureAIMLStudioAssetsBatchEndpoint: Azure AI ML Studio Assets - batch end point. Providers helpers for managing end points.AzureAIMLStudioAuthoringAutomatedML: Azure AI ML Studio Authoring - Automated ML. Helper class for managing life cycle of 'automated ml' jobs.
There is also an example production deployment of a model created using AutomatedML. See important deployment details, here.
Note the following:
- Model training is a computationally intensive task and it is not free. You'll need a paid Azure Subscription. Expect to spend in the neighborhood of $0.10/per hour ($USD) when training models.
- This repo is currently based on Python3.9 even though this is several versions behind the latest stable version of Python. DO NOT ARBITRARILY UPGRADE TO LATER VERSIONS OF PYTHON. It will break. This repo is actively maintained. We monitor this. You have been warned.
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| Last commit: Apr 2026 Total commits: 112
Python (56.6%) Jupyter Notebook (36.0%) Makefile (3.6%) |
| FullStackWithLawrence/openai-embeddings | 🤖 Retrieval Augmented Generation and Hybrid Search 🤖
A Hybrid Search and Augmented Generation prompting solution using Python OpenAI API Embeddings persisted to a Pinecone vector database index and managed by LangChain. Demonstrates the following:
- System Prompting. How do use the system prompt to modify LLM text completion behavior.
- Templates. How to create templates in order keep your prompts DRY.
- LangChain. How to setup a project using LangChain as an alternative to vendor specific LLM PyPi packages.
- PDF Loader. a command-line pdf loader program that extracts text, vectorizes, and
loads into a Pinecone dot product vector database that is dimensioned to match OpenAI embeddings.
- Pinecone. How to create, load, and query a Pinecone vector database.
- Retrieval Augmented Generation (RAG). A chatGPT prompt based on a hybrid search retriever that locates relevant documents from the vector database and includes these in OpenAI prompts.
Secondarily, I also use this repo for demonstrating how to setup Pydantic to manage your project settings and how to safely work with sensitive credentials data inside your project.
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| Last commit: Aug 2026 Total commits: 1,443
Python (94.3%) JavaScript (2.9%) Makefile (2.6%) |
| Last commit: Oct 2026 Total commits: 2,187
Python (62.2%) HCL (30.0%) Makefile (2.9%) |
| FullStackWithLawrence/006-scikit-learn-logistic-regression | A simple example of how to setup a Logistic Regression predictive model
using Python with Pandas, MatPlotLib, Seaborn and Scikit Learn.
This is the source code for FullStackWithLawrence Youtube Video -- "Scikit-learn Logistic Regression model in 10 minutes".
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| Last commit: Aug 2023 Total commits: 30
HTML (63.6%) Jupyter Notebook (36.2%) Python (0.2%) |
| FullStackWithLawrence/007-scikit-learn-svm | A simple example of how to setup a Support Vector Machine predictive model
using Python with Pandas, MatPlotLib, Seaborn and Scikit Learn.
This is the source code for FullStackWithLawrence Youtube Video -- "????".
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| Last commit: Jun 2023 Total commits: 9
Jupyter Notebook (51.2%) HTML (48.6%) Python (0.2%) |
| Last commit: Jun 2023 Total commits: 9
HTML (51.5%) Jupyter Notebook (48.3%) Python (0.2%) |
| Last commit: Jun 2023 Total commits: 8
HTML (51.8%) Jupyter Notebook (48.0%) Python (0.1%) |
