Playing with Streamlit and LLMs.
This post describes how to use Streamlit to build a simple interface for interacting with large language models (LLMs). It also includes code examples that show how to use Streamlit to display text, images, and tables, and to interact with LLMs through prompts and queries.
https://lethain.com/streamlit-llms/
This post describes how to use Streamlit to build a simple interface for interacting with large language models (LLMs). It also includes code examples that show how to use Streamlit to display text, images, and tables, and to interact with LLMs through prompts and queries.
https://lethain.com/streamlit-llms/
Lethain
Playing with Streamlit and LLMs.
Recently I’ve been chatting with a number of companies who are building out internal LLM labs/tools for their teams to make it easy to test LLMs against their internal usecases. I wanted to take a couple hours to see how far I could get using Streamlit to…
localrf
An algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video.
https://github.com/facebookresearch/localrf
An algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video.
https://github.com/facebookresearch/localrf
GitHub
GitHub - facebookresearch/localrf: An algorithm for reconstructing the radiance field of a large-scale scene from a single casually…
An algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. - facebookresearch/localrf
Remote Interactive Debugging of Python Applications Running in Kubernetes
In this tutorial we will create a setup for remote debugging of Python applications running in Kubernetes, which will allow you to set breakpoints, step through code, and interactively debug your applications without any change to your code or deployment.
https://martinheinz.dev/blog/99
In this tutorial we will create a setup for remote debugging of Python applications running in Kubernetes, which will allow you to set breakpoints, step through code, and interactively debug your applications without any change to your code or deployment.
https://martinheinz.dev/blog/99
martinheinz.dev
Remote Interactive Debugging of Python Applications Running in Kubernetes
<p>
Let's imagine a situation - you have multiple Python applications running on Kubernetes that interact with each other. There's bug that you can't repro...
Let's imagine a situation - you have multiple Python applications running on Kubernetes that interact with each other. There's bug that you can't repro...
arguably
arguably turns functions into command line interfaces (CLIs). arguably has a tiny API and is extremely easy to integrate.
https://github.com/treykeown/arguably
arguably turns functions into command line interfaces (CLIs). arguably has a tiny API and is extremely easy to integrate.
https://github.com/treykeown/arguably
GitHub
GitHub - treykeown/arguably: The best Python CLI library, arguably.
The best Python CLI library, arguably. Contribute to treykeown/arguably development by creating an account on GitHub.
The Annotated S4
This post provides an overview of the Structured State Space for Sequence Modeling (S4) architecture which is a new approach to very long-range sequence modeling tasks for vision, language, and audio, showing a capacity to capture dependencies over tens of thousands of steps. It also includes code implementations that allow readers to experiment with the S4 architecture.
https://srush.github.io/annotated-s4
This post provides an overview of the Structured State Space for Sequence Modeling (S4) architecture which is a new approach to very long-range sequence modeling tasks for vision, language, and audio, showing a capacity to capture dependencies over tens of thousands of steps. It also includes code implementations that allow readers to experiment with the S4 architecture.
https://srush.github.io/annotated-s4
Designing Pythonic library APIs
The article discusses some principles for designing good Python library APIs, including structure, naming, error handling, type annotations, and more. The author argues that Python's flexibility can be a double-edged sword, and that it's important to design APIs that are easy to use and understand.
https://benhoyt.com/writings/python-api-design/
The article discusses some principles for designing good Python library APIs, including structure, naming, error handling, type annotations, and more. The author argues that Python's flexibility can be a double-edged sword, and that it's important to design APIs that are easy to use and understand.
https://benhoyt.com/writings/python-api-design/
Benhoyt
Designing Pythonic library APIs
Principles I've found useful for designing good Python library APIs, including structure, naming, error handling, and type annotations.
ChristianLempa / videos
This is my video documentation. Here you'll find code-snippets, technical documentation, templates, command reference, and whatever is needed for all my YouTube Videos.
https://github.com/ChristianLempa/videos
This is my video documentation. Here you'll find code-snippets, technical documentation, templates, command reference, and whatever is needed for all my YouTube Videos.
https://github.com/ChristianLempa/videos
GitHub
GitHub - ChristianLempa/videos: This is my video documentation. Here you'll find code-snippets, technical documentation, templates…
This is my video documentation. Here you'll find code-snippets, technical documentation, templates, command reference, and whatever is needed for all my YouTube Videos. - ChristianLempa/videos
Geospatial Data in your Graph
In this stream we explore some techniques for working with geospatial data in Neo4j. We will cover some basic spatial Cypher functions, spatial search, routing algorithms, and different methods of importing geospatial data into Neo4j.
https://www.youtube.com/watch?v=djMsdSxvd2E
In this stream we explore some techniques for working with geospatial data in Neo4j. We will cover some basic spatial Cypher functions, spatial search, routing algorithms, and different methods of importing geospatial data into Neo4j.
https://www.youtube.com/watch?v=djMsdSxvd2E
YouTube
Neo4j Live: Geospatial Data in your Graph
In this stream we explore some techniques for working with geospatial data in Neo4j. We will cover some basic spatial Cypher functions, spatial search, routing algorithms, and different methods of importing geospatial data into Neo4j.
Cheat Sheet: htt…
Cheat Sheet: htt…
Building Real-time Machine Learning Foundations at Lyft
The article highlights Lyft's efforts in developing real-time machine learning foundations to enhance their platform's performance and user experience. It explores the challenges faced and the strategies employed to build scalable and reliable machine learning systems within the context of a ride-sharing company.
https://eng.lyft.com/building-real-time-machine-learning-foundations-at-lyft-6dd99b385a4e
The article highlights Lyft's efforts in developing real-time machine learning foundations to enhance their platform's performance and user experience. It explores the challenges faced and the strategies employed to build scalable and reliable machine learning systems within the context of a ride-sharing company.
https://eng.lyft.com/building-real-time-machine-learning-foundations-at-lyft-6dd99b385a4e
Medium
Building Real-time Machine Learning Foundations at Lyft
In early 2022, Lyft already had a comprehensive Machine Learning Platform called LyftLearn composed of model serving, training, CI/CD…
embedchain
Framework to easily create LLM powered bots over any dataset.
https://github.com/embedchain/embedchain
Framework to easily create LLM powered bots over any dataset.
https://github.com/embedchain/embedchain
GitHub
GitHub - mem0ai/mem0: Memory for AI Agents; Announcing OpenMemory MCP - local and secure memory management.
Memory for AI Agents; Announcing OpenMemory MCP - local and secure memory management. - mem0ai/mem0
When NumPy is too slow
What do you do when your NumPy code isn’t fast enough? We’ll discuss the options, from Numba to JAX to manual optimizations.
https://pythonspeed.com/articles/numpy-is-slow/
What do you do when your NumPy code isn’t fast enough? We’ll discuss the options, from Numba to JAX to manual optimizations.
https://pythonspeed.com/articles/numpy-is-slow/
Python⇒Speed
When NumPy is too slow
What do you do when your NumPy code isn’t fast enough? We’ll discuss the options, from Numba to JAX to manual optimizations.
PromtEngineer / localGPT
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
https://github.com/PromtEngineer/localGPT
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
https://github.com/PromtEngineer/localGPT
GitHub
GitHub - PromtEngineer/localGPT: Chat with your documents on your local device using GPT models. No data leaves your device and…
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private. - GitHub - PromtEngineer/localGPT: Chat with your documents on your local device using ...
A Tale of Debugging: The Competitive Programmer Approach
Have the computer find the bugs for you.
https://albexl.substack.com/p/a-tale-of-debugging-the-competitive
Have the computer find the bugs for you.
https://albexl.substack.com/p/a-tale-of-debugging-the-competitive
Algorithmically Speaking
A Tale of Debugging: The Competitive Programmer Approach
Have the computer find the bugs for you...
Caching in Django with Redis
A step-by-step guide on implementing caching with Redis in Django.
https://fly.io/django-beats/caching-in-django-with-redis/
A step-by-step guide on implementing caching with Redis in Django.
https://fly.io/django-beats/caching-in-django-with-redis/
Fly
Caching in Django with Redis
A step-by-step guide on implementing caching with Redis in Django
Automating Python code quality
The article emphasizes the importance of code quality in Python software development, discussing various aspects such as style consistency, code readability, testing, and documentation. It provides practical tips and best practices to improve code quality and maintainability, ultimately enhancing the overall software development process.
https://blog.fidelramos.net/software/python-code-quality
The article emphasizes the importance of code quality in Python software development, discussing various aspects such as style consistency, code readability, testing, and documentation. It provides practical tips and best practices to improve code quality and maintainability, ultimately enhancing the overall software development process.
https://blog.fidelramos.net/software/python-code-quality
blog.fidelramos.net
Automating Python code quality
In this article I explain what I mean by code quality and how it benefits developers. In the first half I discuss general concepts and workflows that apply to most software projects. Even if you are not writing Python code you might learn something from it.…