Boost is an open source tool with 2.96K GitHub stars and 882 GitHub forks. Here's a link to Boost's open source repository on GitHub.
I am an undergraduate in computer science. (3rd Year)
Then, later, for back-end programming languages, Rust seems like your best bet. Its pros: - it's satisfying to work with (after the learning curve) - it's got potential to grow big in the next year (also with better paying jobs) - it's super versatile (you can do high-perf system stuff, graphics, ffi, as well as your classic api server) It comes with a few cons though: - it's harder to learn (expect to put in years) - the freelancing options are virtually non-existent (and I would expect them to stay limited, as rust is better for long-term software than prototypes)
And if you want to go with python as a secondary tool then i suggest you to learn a python framework (Flask,Django).
Well. Flutter is just a Framework (just like Django btw.) and it uses Dart as a programming language. Django is kind of solving a different problem than Dart. Dart is intened for use in Front End Applications and Django is a Framework for Back-End Web Development.
So if you want to program Flutter Apps (although i wouldn't recommend it for any serious web development yet since Flutter web isn't very mature yet) i would recommend you just lern Dart.
From a management and hiring perspective, I recommend Flutter (Dart). It provides native solutions to both mobile platform ( (Android and IOS) while having the same knowledge. Hiring managers look at this as an advantage since a developer can provide solutions for both platforms whit the same knowledge. The Flutter framework is growing and there is a lot of resources to ground your knowledge and start experimenting. Dart is also a great language that covers most E2E necessities, so again, no further need of learning one language for FE and another for BE and services. It is my belief that Dart will surpass Kotlin soon, and will leverage to Python and Java in the upcoming year.
We are converting AWS Lambdas from Java due to excessive cold start times. Usage: These lambdas handle XML and JSON payloads, they use s3, API Gateway, RDS, DynamoDB, and external API's. Most of our developers are only experienced in java. These three languages (Go, Node.js, and Python) were discussed, but no consensus has been reached yet.
I've worked with all three of these languages and also with Java developers converting to these languages and far and away Go is the easier one to convert to. With the improved cold-start times and the ease of conversion for a Java developer, it is a no-brainer for me.
The hardest part of the conversion though is going to be the lack of traditional Classes so you have to be mindful of that, but Go Structs and interfaces tend to make up for what is lost there.
Full Disclosure: I'm a 95% Go convert (from Python) at this point in time.
Go would provide the easiest transition for Java programmers -- its IDE/tooling is second to none (just install Goland) and the deploy/distribution story is extremely clean and lends itself to work well in lambda: single, static binaries with quick startup. No need to set up a full environment or package dependencies on your lambda AMIs, just copy a file.
So I'd agree, on the strength of AWS Lambda support and the solid performance of Go, it seems like your best choice here for Lambdas (and I'm going to need to consider that myself going forward... pardon the pun).
Telegram Messenger has frameworks for most known languages, which makes easier for anyone to integrate with them. I started with Golang and soon found that those frameworks are not up to date, not to mention my experience testing on Golang is also mixed due to how their testing tool works. The natural runner-up was JS, which I'm ditching in favor of TS to make a strongly typed code, proper tests and documentation for broader usage. TypeScript allows fast prototyping and can prevent problems during code phase, given that your IDE of choice has support for a language server, and build phase. Pairing it with lint tools also allows honing code before it even hits the repositories.
In 2015 as Xelex Digital was paving a new technology path, moving from ASP.NET web services and web applications, we knew that we wanted to move to a more modular decoupled base of applications centered around REST APIs.
To that end we spent several months studying API design patterns and decided to use our own adaptation of CRUD, specifically a SCRUD pattern that elevates query params to a more central role via the Search action.
Once we nailed down the API design pattern it was time to decide what language(s) our new APIs would be built upon. Our team has always been driven by the right tool for the job rather than what we know best. That said, in balancing practicality we chose to focus on 3 options that our team had deep experience with and knew the pros and cons of.
That left us with two options. We went a very unconventional route for deciding between the two. We built MVP APIs on both. The interfaces were identical and interchangeable. What we found was easily quantifiable differences.
We were able to iterate on our Node based APIs much more rapidly than we were our C# APIs. For us this was owed to the community coupled with the extremely dynamic nature of JS. There were tradeoffs we considered, latency was (acceptably) higher on requests to our Node APIs. No strong types to protect us from ourselves, but we've rarely found that to be an issue.
As such we decided to commit resources to our Node APIs and push it out as the core brain of our new system. We haven't looked back since. It has consistently met our needs, scaling with us, getting better with time as continually pour into and expand our capabilities.
Sign up to add or upvote prosMake informed product decisions
Sign up to add or upvote consMake informed product decisions
What is Boost?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions