Opensource Findings

2021-09-13 12:30:00 ​​Automatically replaces jQuery for you in legacy codebases.

This CLI automatically finds jQuery methods from existing projects and generate vanilla #js alternatives.

> I've been working on removing jQuery dependency from multiple projects including lightGallery lately. Most of the projects use only 15% to 20% or less than 30% of the jquery methods And in most of the cases, I didn't want to support all the edge cases or legacy browsers. The hardest part was finding the jQuery methods in the existing project and writing the alternative vanilla js methods without making much changes in the codebase. So I wrote this library which automatically finds jquery methods in any particular JavaScript file and generates readable, chainable vanilla js alternatives. This can also be useful if you want to generate your own utility methods similar to jQuery.

https://github.com/sachinchoolur/replace-jquery Open / Comment

2021-09-10 10:10:00 #rust 1.55 is here.

New features:
- Cargo deduplicates compiler errors
- Faster, more correct float parsing
- std::io::ErrorKind variants updated
- Open range patterns added

https://blog.rust-lang.org/2021/09/09/Rust-1.55.0.html Open / Comment

2021-09-09 09:09:00 ​​Deno + React: No build, no bundle, all streaming

Ultra is a web framework that leans hard into your browser's native features. Embrace the future of ES Modules, Import Maps, and Web Streams. All while supporting some of the non-standards that many normal people love for some reason (JSX and TypeScript).

It's driven by the following hot-takes:
- ESM is non-negotiable in {currentYear}
- SSR is non-negotiable in {currentYear}
- Bundling is an anti-pattern in {currentYear}
- Data can be requested anywhere, and is accessible on the server, always
- Lazy routing with dynamic imports trumps FS routing
- Less magic in tooling and frameworks is a good thing
- Simplify your workflow and tech stack at all costs - life is too short
- Streams are neat

https://github.com/exhibitionist-digital/ultra

Personal opinion: this looks exciting! I am in deno's hypetrain for almost a year now and I love to see new cool use-cases.

#deno #react #js #ts Open / Comment

2021-09-07 16:30:00 ​​Ain is a terminal HTTP API client. It's an alternative to postman, paw or insomnia.

Features:
- Flexible organization of API:s using files and folders.
- Use shell-scripts and executables for common tasks.
- Put things that change in environment variables or .env-files.
- Share the resulting curl, wget or httpie command-line.
- Pipe the API output for further processing.
- Tries hard to be helpful when there are errors.

Ain was built to enable scripting of input and further processing of output via pipes. It targets users who work with many API:s using a simple file format. It uses curl, wget or httpie to make the actual calls.

https://github.com/jonaslu/ain

Personal opinion: I don't like GUIs, they are harder to work with. So, any tools that can be replaced should be replaced.

#go Open / Comment

2021-09-06 13:10:00 ​​Dataframes for #elixir

Explorer is a dataframe library for Elixir. First and foremost, Explorer is an API for data manipulation. Its high-level features are:
- Simply typed series: :float, :integer, :boolean, :string, :date, and :datetime.
- A powerful but constrained and opinionated API, so you spend less time looking for the right function and more time doing data manipulation.
- Pluggable backends, providing a uniform API whether you're working in-memory or (forthcoming) on remote databases or even Spark dataframes.
- The first (and default) backend is based on NIF bindings to the blazing-fast polars library.

The API is influenced heavily by Tidy Data and borrows much of its design from dplyr.

The aim here isn't to have the fastest dataframe library around (though it certainly helps that we're building on one of the fastest). Instead, we're aiming to bridge the best of many worlds:
- the elegance of dplyr
- the speed of polars
- the joy of Elixir

That means you can expect the guiding principles to be 'Elixir-ish'. For example, you won't see the underlying data mutated, even if that's the most efficient implementation. Explorer functions will always return a new dataframe or series.

https://github.com/elixir-nx/explorer Open / Comment

2021-09-04 17:51:53 ​​Livebook is a web application for writing interactive and collaborative code notebooks for #elixir, built with Phoenix LiveView. Inspired by Jupyter and Deepnote.

It features:
- Code notebooks with Markdown support and Elixir cells where code is evaluated on demand.
- Shareable: notebooks are stored in the .livemd format, which is a subset of Markdown with annotations and KaTex for mathematical formulas. This means your notebooks can be saved, easily shared, and play well with version control.
- Interactive widgets via Kino: manipulate Vega-Lite charts, tables, and more.
- Rich code editor through Monaco: with support for autocompletion, inline documentation, code formatting, etc.
- Reproducible: Livebook ensures your code runs in a predictable order, all the way down to package management. It also tracks your notebook state, annotating which parts are stale.
- Custom runtimes: when executing Elixir code, you can either start a fresh Elixir instance, connect to an existing node, or run it inside an existing Elixir project, with access to all of its modules and dependencies. This means Livebook can be a great tool to provide live documentation for existing projects.
- Persistence: persist your notebooks to disk or any S3-compatible cloud storage.
- Collaboration: multiple users can work on the same notebook at once. It works out-of-the-box either in single-node or multi-node deployments - without a need for additional tooling.

https://github.com/livebook-dev/livebook Open / Comment

2021-09-01 13:10:00 Paranoid Scientist is a #python module for verifying scientific software.

It provides:
- Runtime verification of entry and exit conditions written in pure Python, including hyperproperties.
- Conditions specified using pure Python refinement types, i.e. types are defined by predicates.
- Automated unit testing of individual functions.

It is inspired by contract-oriented programming, type classes, static type checking, and fuzz testing.

Paranoid Scientist is a tool to make sure scientific code is correct. Verification is extremely important for scientific software because, unlike most software, we don’t know what the output will be until we run the program. In fact, the program is written in order to examine the output. However, we have no robust way of knowing whether the output is due to a software bug. For example, code performing a complex statistical test could normalize the wrong column, an error which would likely go undetected.

Paranoid Scientist attempts to remedy this situation by providing some key tools from the software verification community to the scientific community. Traditional program verification asks the question, “If I run my code, will it run correctly?” In practice, this is time consuming and requires highly specialized training. For scientific programming, it is acceptable to instead ask, “If I already ran my code, did it run correctly?” In other words, it is not as important to know before executing the program whether it will run correctly. Paranoid Scientist is already in use in scientific software.

https://github.com/mwshinn/paranoidscientist Open / Comment

2021-08-31 11:40:00 ​​Type4Py: Deep Similarity Learning-Based Type Inference for #python

Over the past decade, machine learning (ML) has been applied successfully to a variety of tasks such as computer vision and natural language processing. Motivated by this, in recent years, researchers have employed ML techniques to solve code-related problems, including but not limited to, code completion, code generation, program repair, and type inference.

Dynamic programming languages like Python and TypeScript allows developers to optionally define type annotations and benefit from the advantages of static typing such as better code completion, early bug detection, and etc. However, retrofitting types is a cumbersome and error-prone process. To address this, we propose Type4Py, an ML-based type auto-completion for Python. It assists developers to gradually add type annotations to their codebases.

https://github.com/saltudelft/type4py
Announcing post: https://mirblog.net/index.php/2021/07/31/development-and-release-of-type4py-machine-learning-based-type-auto-completion-for-python/ Open / Comment

2021-08-30 15:25:00 ​​Bubbleprof is a new, completely unique, approach to profiling your Node.js code

Bubbleprof is a completely new way to visualize the operation of your Node.js processes. It observes the async operations of your application, groups them, measures their delays, and draws a map of the delays in your application's async flow.

The size of each bubble represents time within a group of operations. These are grouped where the flow stayed within either your own code, a module, or node core. Tiny adjacent groups are also grouped to reduce clutter. The length of arrows connecting bubbles shows the delays while the flow moves from one group to another. Inner coloured lines indicate the mix of types of async operation responsible for this delay. Click to explore. Line lengths between and around the bubbles and numeric labels reflect the aggregated delay in miliseconds (ms).

https://clinicjs.org/bubbleprof/

#js Open / Comment

2021-08-26 11:33:05 ​​ Breeaking news!

Big project, first public release! From the creator of FastAPI and Typer: SQLModel.

SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust.

SQLModel is based on Python type annotations, and powered by Pydantic and SQLAlchemy.
SQLModel is, in fact, a thin layer on top of Pydantic and SQLAlchemy, carefully designed to be compatible with both.

The key features are:
- Intuitive to write: Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs.
- Easy to use: It has sensible defaults and does a lot of work underneath to simplify the code you write.
- Compatible: It is designed to be compatible with FastAPI, Pydantic, and SQLAlchemy.
- Extensible: You have all the power of SQLAlchemy and Pydantic underneath.
- Short: Minimize code duplication. A single type annotation does a lot of work. No need to duplicate models in SQLAlchemy and Pydantic.

https://github.com/tiangolo/sqlmodel Open / Comment