Serializing package requirements in marimo notebooks
Marimo now allows notebooks to serialize their package requirements as top-level comments, enabling users to run notebooks in isolated virtual environments with a single command. This feature, powered by the uv package manager, enhances reproducibility and sharing of notebooks by eliminating the need for separate requirements files and preventing environment pollution.
https://marimo.io/blog/sandboxed-notebooks
Marimo now allows notebooks to serialize their package requirements as top-level comments, enabling users to run notebooks in isolated virtual environments with a single command. This feature, powered by the uv package manager, enhances reproducibility and sharing of notebooks by eliminating the need for separate requirements files and preventing environment pollution.
https://marimo.io/blog/sandboxed-notebooks
marimo.io
Serializing package requirements in marimo notebooks
How marimo enables sandboxed notebooks, reproducible down to the package environment
Let’s build and optimize a Rust extension for Python
Python code too slow? You can quickly create a Rust extension to speed it up.
https://pythonspeed.com/articles/intro-rust-python-extensions/
Python code too slow? You can quickly create a Rust extension to speed it up.
https://pythonspeed.com/articles/intro-rust-python-extensions/
Python⇒Speed
Let’s build and optimize a Rust extension for Python
Python code too slow? You can quickly create a Rust extension to speed it up.
LLaMA-Omni
LLaMA-Omni is a low-latency and high-quality end-to-end speech interaction model built upon Llama-3.1-8B-Instruct, aiming to achieve speech capabilities at the GPT-4o level.
https://github.com/ictnlp/LLaMA-Omni
LLaMA-Omni is a low-latency and high-quality end-to-end speech interaction model built upon Llama-3.1-8B-Instruct, aiming to achieve speech capabilities at the GPT-4o level.
https://github.com/ictnlp/LLaMA-Omni
GitHub
GitHub - ictnlp/LLaMA-Omni: LLaMA-Omni is a low-latency and high-quality end-to-end speech interaction model built upon Llama-3.1…
LLaMA-Omni is a low-latency and high-quality end-to-end speech interaction model built upon Llama-3.1-8B-Instruct, aiming to achieve speech capabilities at the GPT-4o level. - ictnlp/LLaMA-Omni
Things I've learned serving on the board of the Python Software Foundation
https://simonwillison.net/2024/Sep/18/board-of-the-python-software-foundation/
https://simonwillison.net/2024/Sep/18/board-of-the-python-software-foundation/
Simon Willison’s Weblog
Things I’ve learned serving on the board of the Python Software Foundation
Two years ago I was elected to the board of directors for the Python Software Foundation—the PSF. I recently returned from the annual PSF board retreat (this one was in …
Ask HN: Kotlin SpringBoot vs. Python Django for Min Viable Product
https://news.ycombinator.com/item?id=41584157
https://news.ycombinator.com/item?id=41584157
FastAgency
The fastest way to bring multi-agent workflows to production.
https://github.com/airtai/fastagency
The fastest way to bring multi-agent workflows to production.
https://github.com/airtai/fastagency
GitHub
GitHub - airtai/fastagency: The fastest way to bring multi-agent workflows to production.
The fastest way to bring multi-agent workflows to production. - airtai/fastagency
Deploying a Django app with Kamal, AWS ECR, and Github Actions
The article provides a comprehensive guide on deploying a Django app using Kamal, AWS ECR, and GitHub Actions, offering a streamlined approach to containerized deployment. It covers setting up a VPS, creating a Dockerfile, configuring AWS ECR, setting up Kamal, and automating the deployment process with GitHub Actions, aiming to simplify the deployment workflow for developers.
https://dylancastillo.co/posts/deploy-a-django-app-with-kamal-aws-ecr-and-github-actions.html
The article provides a comprehensive guide on deploying a Django app using Kamal, AWS ECR, and GitHub Actions, offering a streamlined approach to containerized deployment. It covers setting up a VPS, creating a Dockerfile, configuring AWS ECR, setting up Kamal, and automating the deployment process with GitHub Actions, aiming to simplify the deployment workflow for developers.
https://dylancastillo.co/posts/deploy-a-django-app-with-kamal-aws-ecr-and-github-actions.html
Dylan Castillo
Deploying a Django app with Kamal, AWS ECR, and Github Actions – Dylan Castillo
A guide to deploy a Django app with Kamal, AWS ECR, and Github Actions
Building an Advanced RAG System With Self-Querying Retrieval
https://www.mongodb.com/developer/products/atlas/advanced-rag-self-querying-retrieval
https://www.mongodb.com/developer/products/atlas/advanced-rag-self-querying-retrieval
Mongodb
Building an Advanced RAG System With Self-Querying Retrieval | MongoDB
In this tutorial, we will see how to build an advanced RAG system with self-query retrieval.
Hy 1.0.0, the Lisp dialect for Python, has been released
https://github.com/hylang/hy/discussions/2608
https://github.com/hylang/hy/discussions/2608
GitHub
Hy 1.0.0, the Lisp dialect for Python, has been released · hylang hy · Discussion #2608
I'm pleased to announce the release of Hy 1.0.0, after nearly 12 years of on-and-off development and lots of real-world use. Hy is a Lisp dialect embedded in Python. See Hylang.org for an intro...
13 Python Quirks That Will Surprise You
This video presents 13 peculiar aspects of Python programming, with the final example being particularly confusing for newcomers to the language. Each quirk is demonstrated through code examples, accompanied by explanations for their existence and behavior.
https://www.youtube.com/watch?v=eufjIfVOm8s
This video presents 13 peculiar aspects of Python programming, with the final example being particularly confusing for newcomers to the language. Each quirk is demonstrated through code examples, accompanied by explanations for their existence and behavior.
https://www.youtube.com/watch?v=eufjIfVOm8s
YouTube
13 Python Quirks That Will Surprise You
💡 Learn how to design great software in 7 steps: https://arjan.codes/designguide.
In this video, I’ll show you 13 things in Python that are just weird. The last one is really confusing, especially if you’re a new Python developer. I’ll go through each of…
In this video, I’ll show you 13 things in Python that are just weird. The last one is really confusing, especially if you’re a new Python developer. I’ll go through each of…
rerankers: A Lightweight Python Library to Unify Ranking Methods
Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, all with different implementation methods. To mitigate this, we propose rerankers, a Python library which provides a simple, easy-to-use interface to all commonly used re-ranking approaches.
https://www.answer.ai/posts/2024-09-16-rerankers.html
Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, all with different implementation methods. To mitigate this, we propose rerankers, a Python library which provides a simple, easy-to-use interface to all commonly used re-ranking approaches.
https://www.answer.ai/posts/2024-09-16-rerankers.html
Answer.AI
rerankers: A Lightweight Python Library to Unify Ranking Methods – Answer.AI
Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, all with different implementation methods. To mitigate this, we propose rerankers, a Python library which provides a simple, easy-to-use interface…
Spiderweb
A small web framework, just big enough for a spider. Also check an
https://github.com/itsthejoker/spiderweb
A small web framework, just big enough for a spider. Also check an
https://github.com/itsthejoker/spiderweb
GitHub
GitHub - itsthejoker/spiderweb: A small web framework, just big enough for a spider.
A small web framework, just big enough for a spider. - itsthejoker/spiderweb
meta-llama / llama-stack
Model components of the Llama Stack APIs
https://github.com/meta-llama/llama-stack
Model components of the Llama Stack APIs
https://github.com/meta-llama/llama-stack
GitHub
GitHub - meta-llama/llama-stack: Composable building blocks to build Llama Apps
Composable building blocks to build Llama Apps. Contribute to meta-llama/llama-stack development by creating an account on GitHub.
skrub
A Python library that facilitates prepping your tables for machine learning.
https://github.com/skrub-data/skrub/
A Python library that facilitates prepping your tables for machine learning.
https://github.com/skrub-data/skrub/
GitHub
GitHub - skrub-data/skrub: Prepping tables for machine learning
Prepping tables for machine learning. Contribute to skrub-data/skrub development by creating an account on GitHub.
An In-Depth Guide to Contrastive Learning: Techniques, Models, and Applications
Discover the fundamentals of contrastive learning, including key techniques like SimCLR, MoCo, and CLIP. Learn how contrastive learning improves unsupervised learning and its practical applications.
https://myscale.com/blog/what-is-contrastive-learning
Discover the fundamentals of contrastive learning, including key techniques like SimCLR, MoCo, and CLIP. Learn how contrastive learning improves unsupervised learning and its practical applications.
https://myscale.com/blog/what-is-contrastive-learning
Myscale
An In-Depth Guide to Contrastive Learning: Techniques, Models, and Applications
Discover the fundamentals of contrastive learning, including key techniques like SimCLR, MoCo, and CLIP. Learn how contrastive learning improves unsupervised learning and its practical applications.