ide
actix-web
ide | actix-web | |
---|---|---|
8 | 171 | |
422 | 20,290 | |
- | 1.0% | |
9.4 | 9.1 | |
over 2 years ago | about 12 hours ago | |
Rust | Rust | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ide
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Launch HN: Enso (YC S21) – Visual programming and workflow tool for data science
docs (https://enso.org/docs/syntax). We've added many new libraries, so you can do many more things with it now. Oh, and we changed the name to Enso and got accepted to YC! :)
The problem we address is that data analysts still waste up to half of their time on repetitive manual work that can be automated [6]. To give one example, a company we're working with hires business users who use Excel to define data quality rules. These get manually translated to SQL, then manually translated to Python. This is not only error prone, it’s so slow that it takes them 90 days to introduce a single new rule. There’s 60 days’ worth of overhead in this process—it’s insane!
Years ago I (Wojciech) led the in-house development of visual effects (VFX) tools at a motion picture studio. We made tools like cloud renderers and smoke simulation engines. The artists using these tools did not have any programming background, yet they were designing complex algorithms for forces between particles, light subsurface scattering, things like that. Earlier generations of these tools had hundreds of config options, buttons, etc., for masses of different use cases, but this approach got way too complex and people eventually realized that it falls short when you need to do anything that the vendor did not think of. Nowadays they use node-based software (like the Houdini FX) which lets users draw algorithms as a sequence of data processing steps (these steps are often referred to as “nodes”). Later, when I was working in other industries and encountered the same rats’ nests of complex GUIs for solving data processing problems, I realized that the data analytics/science space was in need of the same breakthrough that we had already gone through in the VFX space.
Most visual programming languages / workflow-builders do not scale well because they don't let users express abstractions. Try to build a complex pipeline and you'll end with an unreadable spaghetti of connections—it's like coding a web app in the assembler. Enso is different because we allow you to build abstractions to manage the complexity. As a result, you never have more than 10-20 nodes on the stage in Enso (nodes are hierarchical). You can create custom data types, custom components (functions), catch errors, etc. All this works because under the hood, Enso is a real programming language. However, naive implementations of such systems are super slow. Each component may be built of hundreds, sometimes thousands of lower-level ones. The real trick is making these hierarchical components run fast. For that you need a dedicated compiler and a runtime system, and this is a hard technical space. Our system involves a dedicated JIT compiler based on GraalVM. For details, see https://enso.org/language#compiler. In case this is interesting for you, here is our podcast about how the compiler works under the hood: https://www.youtube.com/watch?v=BibjcUjdkO4.
Enso is interactive, meaning that we recompute the relevant parts of graphs as parameters change, which shortens feedback loops dramatically. Like a lot of people on HN, we were inspired by Bret Victor's classic talk on instant feedback: https://www.youtube.com/watch?v=8QiPFmIMxFc. We’ve also put a lot of effort into extensibility. You can add Java, JavaScript, R, and Python (soon also Ruby, Scala, Kotlin, Rust, and C) directly into Enso nodes without the need to write any wrappers and with a close-to-zero performance overhead.
Enso is open source. Our compiler code is at https://github.com/enso-org/enso and our GUI code at https://github.com/enso-org/ide. Our business model is based on selling domain specific libraries, on-premise installations with enhanced user permission management, and coming soon, a hosted solution called Enso Cloud, which will be our only non-open-source codebase. Since this is Hacker News, I should add that all our alpha releases collect anonymous usage statistics which we use to improve Enso and prepare it for a stable release. Full details about that are always in our release notes (https://github.com/enso-org/ide/releases/latest).
Dear HN Family, we are super excited to show Enso to you. Please, share with us your thoughts, experiences, ideas and feedback. It is insanely important to us, as our dream is to make Enso the most useful data processing platform in your toolbox! Also, in case you’d like to build your projects on top of Enso, we would love to help you do it – describe what you have in mind here, and we will reach out to you: https://airtable.com/shrsnx2mJuRn0MxIS :)
=== Links ===
[1] Luna: Visual and textual functional programming language* - https://news.ycombinator.com/item?id=11144828 - Feb 2016 (100 comments)
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I'm considering Rust, Go, or Julia for my next language and I'd like to hear your thoughts on these
Enso the language is mostly scala, Enso the IDE, which is very much an integral part of the project, is like 90% Rust.
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[News] [Project] Enso 2.0 is out! Visual programming language for Data Science. It lets you code in a visual way in Python, Java, R, and JavaScript. Written in Rust and running in WebGL.
The whole IDE (cloud + desktop one) is written in Rust. It lives in this repo: https://github.com/enso-org/ide :)
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Enso 2.0 is out! Visual programming in Python, Java, R, and JavaScript. Written in Rust and running in WebGL.
These issues with random zoom in/out or with selection problems are not known. We haven't seen them before. Would you be so nice to create a screencast and post it as an issue / issues on our issue tracker? This would allow us to track it and fix it for the next release: https://github.com/enso-org/ide/issues .
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Enso 2.0 alpha (formerly Luna) twitch about using Java in a visual way is live
Hi, Wojciech, one of Enso founders here! We are just preparing for Enso 2.0 release. If you want to play with it, you can download the current build from our GitHub releases page (https://github.com/enso-org/ide/releases) and see intro tutorials here: https://www.youtube.com/channel/UC4oMK7cL1ElfNR_OhS-YQAw
:)
actix-web
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Empowering Web Privacy with Rust: Building a Decentralized Identity Management System
Actix Web Documentation: Detailed documentation on using Actix-web, including examples and best practices for building web applications with Rust.
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Ntex: Powerful, pragmatic, fast framework for composable networking services
I can't speak to the "is it any good" part, but (after a bit of research) I can share what I've found. I'll try to represent things as best as I understand, but I may have some finer details mixed up.
ntex is written by the same person that started actix-web, Nikolay Kim (fafhrd91 on GitHub). There was a bunch of drama a while back due to actix-web using (what many reasoned to be) avoidable unsafe code, which was later found to be buggy. Nikolay was pilloried online, resulting in him transferring leadership of actix-web to someone else. ntex is, as I understand it, essentially Nikolay picking back up on his ideals for what could have been actix-web, if people hadn't pushed him out of his own project.
How ntex compares to the pre-/post-leadership change of actix-web, I don't know.
Here are some jumping points if you want more of the backstory.
https://www.theregister.com/2020/01/21/rust_actix_web_framew...
https://steveklabnik.com/writing/a-sad-day-for-rust
https://github.com/actix/actix-web/issues/1289
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Building a REST API for Math Operations (+, *, /) with Rust, Actix, and Rhai🦀
Are you ready to embark on another journey in Rust? Today, we'll explore how to create a REST API that performs basic mathematical operations: addition, multiplication, and division. We'll use Actix, a powerful web framework for Rust, together with Rhai, a lightweight scripting language, to achieve our goal.
- Actix-Web: v4.5.0
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Getting Started with Actix Web - The Battle-tested Rust Framework
Within actix-web, middleware is used as a medium for being able to add general functionality to a (set of) route(s) by taking the request before the handler function runs, carrying out some operations, running the actual handler function itself and then the middleware does additional processing (if required). By default, actix-web has several default middlewares that we can use, including logging, path normalisation, access external services and modifying application state (through the ServiceRequest type).
- Show HN: Play Euchre with AI Bots
- Actix-Web: v4.4.0
- Choosing the Right Rust Web Framework: An Overview
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Building a Rust app with Perseus
Rust is a popular system programming language, known for its robust memory safety features and exceptional performance. While Rust was originally a system programming language, its application has evolved. Now you can see Rust in different app platforms, mobile apps, and of course, in web apps — both in the frontend and backend, with frameworks like Rocket, Axum, and Actix making it even easier to build web applications with Rust.
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Introducing SQLPage : write websites entirely in SQL
actix to handle HTTP requests
What are some alternatives?
enso - Hybrid visual and textual functional programming.
axum - Ergonomic and modular web framework built with Tokio, Tower, and Hyper
graalpython - A Python 3 implementation built on GraalVM
Rocket - A web framework for Rust.
parametric_surfaces - Parametric surfaces drawn using the Rust + WASM toolchain with WebGL, React, and TypeScript.
Tide - Fast and friendly HTTP server framework for async Rust
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
tonic - A native gRPC client & server implementation with async/await support.
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
hyper - An HTTP library for Rust
fastr - A high-performance implementation of the R programming language, built on GraalVM.
salvo - A powerful web framework built with a simplified design.