I've spent lots of hours fixing npm audit to make our #js CI happy. Most of the times - it was garbage.

I've even disabled it in our projects. It was not adding any value, just wasting our time.
But, we still need to check some dependencies to be secure!

Dan Abramov shares his vision about npm audit problems and potential solutions.

Textual - a Text User Interface with Rich as the renderer.

Why is it cool?
1. It has an amazing grid layout
2. It is from the same team as Rich, which proved to be amazing
3. It has nice user-facing API (it even has async parts), way better than curses

But, it is still WIP. We need to wait for a little bit.

https://github.com/willmcgugan/textual

#python

​​Flix: Next-generation reliable, safe, concise, and functional-first programming language.

Flix is inspired by OCaml and Haskell with ideas from Rust and Scala. Flix looks like Scala, but its type system is based on Hindley-Milner. Two unique features of Flix are its polymorphic effect system and its support for first-class Datalog constraints.

Flix aims to offer a unique combination of features that no other programming language offers, including: algebraic data types and pattern matching (like Haskell, OCaml), extensible records (like Elm), type classes (like #haskell, #rust), higher-kinded types (like Haskell), local type inference (like Haskell, OCaml), channel and process-based concurrency (like #go), a polymorphic effect system (unique feature), first-class Datalog constraints (unique feature), and compilation to JVM bytecode (like #scala).

https://flix.dev/

Personal opinion: it has all the features new modern programming language should have. It has nice syntax, powerful ideas, and (hopefully) good JVM integration and code-reuse. That's a recipe for a success. I will keep an eye on this amazing language.

It also has a playground on its website, check it out!
I would love to highlight this example of polymorphic effects:

​​The Open Source Airtable alternative.
Turns any MySQL, PostgreSQL, SQL Server, SQLite & MariaDB into a smart-spreadsheet.

Features:
- Search, sort, filter, hide columns with uber ease
- Create Views: Grid, Gallery, Kanban, Gantt, Form
- Share Views: public & password protected
- Upload images to cells (Works with S3, Minio, GCP, Azure, DigitalOcean, Linode, OVH, BackBlaze)
- Roles: Owner, Creator, Editor, Commenter, Viewer, Commenter, Custom Roles
- Access Control: Fine-grained access control even at database, table & column level
- Programmatic APIs via REST and GraphQL

Personal opinion: I love #nocode instruments like this. I had very pleasant experience with Airtable for several quite different tasks. This is something you really need to consider before writing your own automatizations.

https://github.com/nocodb/nocodb

#js #vue

​​Bit is a modern Git CLI.

bit is an experimental modernized git CLI built on top of git that provides happy defaults and other niceties:
- command and flag suggestions to help you navigate the plethora of options git provides you
- autocompletion for files and branch names when using bit add or bit checkout
- automatic fetch and branch fast-forwarding reducing the likelihood of merge conflicts
- suggestions work with git aliases
- new commands like bit sync that vastly simplify your workflow
- commands from git-extras such as bit release & bit info
- fully compatible with git allowing you to fallback to git if need be
- get insight into how bit works using bit --debug

https://github.com/chriswalz/bit

#git #go

​​Kind: A modern proof language.

A minimal, efficient and practical proof and programming language. Under the hoods, it is basically Haskell, except purer and with dependent types. That means it can handle mathematical theorems just like Coq, Idris, Lean and Agda. On the surface, it aims to be more practical and looks more like TypeScript.

Compared to other proof assistants, Kind has:
- The smallest core. Check FormCore.js or Core.kind. Both are < 1000-LOC complete implementations!
- Novel type-level features. Check out article on super-inductive datatypes.
- An accessible syntax that makes it less scary
- A complete bootstrap: the language is implemented in itself. Check it here.
- Efficient real-world compilers. Check http://uwu.tech/ for a list of apps. (WIP)

Things you can do with it:
- Compile programs and modules to several targets, right now js and scm are supported
- Create live applications. Kind has an interconnected back-end that allows you to create rich, interactive applications without ever touching databases, TCP packets or messing with apis
- Prove theorems: for programmers, they're more like unit tests, except they can involve symbols, allowing you to cover infinitely many test cases. If you like unit tests, you'll love theorems.

Personal opinion: I am a big fan of ML-family languages, but not a big fan of their syntaxes. I love that new products solve their biggest issue for me. I really hope that some of these new functional languages will get eventually popular.

https://github.com/uwu-tech/kind

#js #haskell

The open-source #js Calendly alternative.

Let's face it: Calendly and other scheduling tools are awesome. It made our lives massively easier. We're using it for business meetings, seminars, yoga classes and even calls with our families. However, most tools are very limited in terms of control and customisations. That's where Calendso comes in. Self-hosted or hosted by us. White-label by design. API-driven and ready to be deployed on your own domain. Full control of your events and data. Calendso is to Calendly what GitLab is to GitHub.

Personal opinion: why paying for Calendly, when you can deploy everything to K8S pay for your own infrastructure and manage it yourself?!

https://github.com/calendso/calendso

​​Vieb is the Vim Inspired Electron Browser

Features:
- Free, open source, fast and secure
- Local first adblocking, auto-complete, AMP protection, custom redirects and more, all without web requests
- Privacy with strict permission system, navigator overrides, firefox mode, custom WebRTC policy and more
- Accessible with custom themes, full interface & fontsize scaling, page zooming, spellcheck and mouse support
- Security settings with permissions, cache usage, cookie management, (auto-)download settings and more
- Window splitting with buffer, split, Vexplore and Ctrl-w bindings, for multi-window browsing
- Map commands for completely custom keyboard sequences, keystrokes, commands and actions
- Viebrc config file for all custom/Vim/Vieb commands to configure settings permanently
- Set command for runtime setting configuration exactly like Vim
- Vim-compatible options: showcmd, timeout, colorscheme, maxmapdepth, spelllang, splitright, smartcase etc.
- Container tabs with colored grouping, auto-clearing, individual cookies and tab restore from containers
- Ad-blocker with cosmetic filtering, optional updater, custom lists and uses easylist/easyprivacy by default
- Tabs including audio indicator, a toggle for multi-line tabs, pinned tabs, muted tabs and suspended tabs

https://vieb.dev/

#js

​​A static analysis tool for securing #go code

GoKart is a static analysis tool for Go that finds vulnerabilities using the SSA (single static assignment) form of Go source code. It is capable of tracing the source of variables and function arguments to determine whether input sources are safe, which reduces the number of false positives compared to other Go security scanners. For instance, a SQL query that is concatenated with a variable might traditionally be flagged as SQL injection; however, GoKart can figure out if the variable is actually a constant or constant equivalent, in which case there is no vulnerability.

The motivation for GoKart was to address this: could we create a scanner with significantly lower false positive rates than existing tools? Based on our experimentation the answer is yes. By leveraging source-to-sink tracing and SSA, GoKart is capable of tracking variable taint between variable assignments, significantly improving the accuracy of findings. Our focus is on usability: pragmatically, that means we have optimized our approaches to reduce false alarms.

https://github.com/praetorian-inc/gokart

Rust Memory Safety & Undefined Behavior Detection.

Rudra is a static analyzer to detect common undefined behaviors in #rust programs. It is capable of analyzing single Rust packages as well as all the packages on crates.io.

Features:
- Panic Safety (Unsafe code that can create memory-safety issues when panicked)
- Higher Order Invariant (Assumed properties about traits)
- Send Sync Variance (Unrestricted Send or Sync on generic types)

https://github.com/sslab-gatech/Rudra

​​pdb++, a drop-in replacement for pdb (the #python debugger)

This module is an extension of the pdb module of the standard library. It is meant to be fully compatible with its predecessor, yet it introduces a number of new features to make your debugging experience as nice as possible.

pdb++ features include:
- colorful TAB completion of Python expressions (through fancycompleter)
- optional syntax highlighting of code listings (through Pygments)
- sticky mode
- several new commands to be used from the interactive (Pdb++) prompt
- smart command parsing (hint: have you ever typed r or c at the prompt to print the value of some variable?)
- additional convenience functions in the pdb module, to be used from your program

Personal opinion: I don't like pdb and debuggers in general. Whenever I open a debugger - it means that I have a really big problem. But, this one seems like the best solution (except PyCharm, of course).

https://github.com/pdbpp/pdbpp

​​⚡️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

​​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

​​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/

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

​​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

​​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

​​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

​​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

#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