Hello Everyone,

After Github Copilot got successful, a lot of others have come on to the scene in the ChatGPT wave and this includes the likes of Mistral’s recent Codestral LLM. Codestral is Mistral AI's first open-weight generative AI model designed for code generation tasks, automating code completion, generation, and testing across multiple languages.

Mistral of course claims to be empowering developers and democratising coding with Mistral AI.

According to Codesignal, 81% of developers surveyed say they use AI-powered coding assistants.

In recent times some of the coding Assistants I’ve been watching are:

1. GitHub Copilot

2. Amazon Q Developer

3. Tabnine

4. Gemini in Android Studio

5. Watsonx Code Assistant

6. ChatGPT

7. Code Llama

8. Replit AI

Stack Overflow survey maybe aren’t what they used to be but developer surveys are all worth glancing at if you are interested in AI at the intersection of the future of code and software engineers like I am.

Stack Overflow did manage to ask some fairly salient questions though. Like how often CodeGen tools fall short, what challenges developers face with these tools, and what they are doing with all of the free time these tools purport to offer.

• The majority of respondents (76%) let us know they are using or are planning to use AI code assistants.

• Some roles use these tools more than others amongst professional developers: Academic researchers (87%), AI developers (76%), frontend developers (75%), mobile developers (60%), and data scientists (67%) currently use code assistants the most. 

• Other roles indicated they are using code assistants (or planning to) much less than average: data/business analysts (29%), desktop developers (39%), data engineers (39%), and embedded developers (42%).

Who uses these tools? More So

1. Software Engineers

2. Academic Researchers

3. AI Developers

4. Frontend Developers

5. Mobile Devs

6. Data Scientists

Less So

1. Business Analysts

2. Desktop Devs

3. Data Engineers

4. Embedded Devs

For or Against they are Here

• AI coding assistants like GitHub Copilot or ChatGPT are increasingly popular among developers, offering speed and productivity boosts by suggesting code completions based on context.

• AI coding assistants may speed up development but compromise code quality if not used carefully — and code must be reviewed and verified forensically.

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Members of the Cognition AI team in New York. Credit: Levi Mandel for Bloomberg Businessweek

Hey Everyone,

The real AI bros just keep getting younger this year as funding appears limitless for AI startups of this variety recently.

In 2024 we are seeing more AI startups and coding assistants/agents coming to market with demos suggesting they can do a lot of things. I first wrote about Devin here.

Is Devin just another agent-GPT or something more sophisticated? Many are saying it uses GPT-4, which seems likely to me.

Built by Cognition Labs, who were first backed by Peter Thiel is claming a viable coding assistant called Devin.

Generative AI has meant that we are seeing an increasing number of Chinese Americans and American educated Chinese found pretty fascinating startups. A lot of the builders and software engineers most interested in machine learning happen to be Asian. Open a random AI paper in 2024, and you will see what I mean.

Cognition Labs of course is making incredible claims like: “Devin can today in March, 2024 make thousands of decisions, recall relevant context, learn over time, and correct mistakes in code.”

The tool, called Devin, has rattled software engineers across the tech sector. and many are skeptical it’s much more than a GPT-4 skin.

Read the Blog

• See Comments on Hacker News (527 so far).

• Software engineers sound skeptical on the whole.

To start using Devin for engineering work, please reach out here or get in touch at info@cognition-labs.com.

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Hey Everyone,

Apple is full of surprises. For the coders out there, this one is for you and just a short note. In terms of software coding news, this is pretty fascinating.

See the comments on Hacker News.

Check out on GitHub: https://github.com/apple/pkl

Read Apple's Announcement

Apple designed “Pkl” with three overarching goals:

1. To provide safety by catching validation errors before deployment.

2. To scale from simple to complex use-cases.

3. To be a joy to write, with our best-in-class IDE integrations.

It’s an open-source configuration coding language. Sometimes pronounced “pickle”.

🍎 Facilitating Config Pkl by Apple 🍏

We are delighted to announce the open source first release of Pkl (pronounced Pickle), a programming language for producing configuration. When thinking about configuration, it is common to think of static languages like JSON, YAML, or Property Lists.

While these languages have their own merits, they tend to fall short when configuration grows in complexity. For example, their lack of expressivity means that code often gets repeated. Additionally, it can be easy to make configuration errors, because these formats do not provide any validation of their own. To address these shortcomings, sometimes formats get enhanced by ancillary tools that add special logic.

For example, perhaps there's a need to make code more DRY, so a special property is introduced that understands how to resolve references, and merge objects together. Alternatively, there's a need to guard against validation errors, so some new way is created to validate a configuration value against an expected type. Before long, these formats almost become programming languages, but ones that are hard to understand and hard to write.

On the other end of the spectrum, a general-purpose language might be used instead. Languages like Kotlin, Ruby, or JavaScript become the basis for DSLs that generate configuration data.

While these languages are tremendously powerful, they can be awkward to use for describing configuration, because they are not oriented around defining and validating data. Additionally, these DSLs tend to be tied to their own ecosystems. It is a hard sell to use a Kotlin DSL as the configuration layer for an application written in Go.

We created Pkl because we think that configuration is best expressed as a blend between a static language and a general-purpose programming language. We want to take the best of both worlds; to provide a language that is declarative and simple to read and write, but enhanced with capabilities borrowed from general-purpose languages. When writing Pkl, you are able to use the language features you'd expect, like classes, functions, conditionals, and loops. You can build abstraction layers, and share code by creating packages and publishing them. Most importantly, you can use Pkl to meet many different types of configuration needs. It can be used to produce static configuration files in any format, or be embedded as a library into another application runtime.

Learn More

Launched on February 1 2024 into version 0.25, the tech giant’s team have provided a ‘quick tour’ of the language in a text post on the Pkl documentation website.

Installation: https://pkl-lang.org/main/current/pkl-cli/index.html#installation

Moreover, Per that tour, Pkl is designed around a key value structure, in a manner akin to JSON, rather than imperative instructions like many other traditional programming languages, Apple has designed Pkl to specialise in configuration, along with a few neat quality-of-life features to turn heads. Indeed, Pkl supports JSON, XML, and YAML property lists at launch to generate static configuration files.

Why Pickle (Pkl)

• In software development, configuration is a frequent and crucial process that lets engineers alter the look and feel of their environments, systems, and applications.

• Configuration, however, may also be difficult since it frequently entails creating intricate and prone-to-mistake code, handling many formats and languages, and guaranteeing data confidentiality and integrity. 

• Pkl prioritizes data integrity and security through type checking, sandboxing, and least privilege. Its versatility allows for easy configuration of various systems and applications.

• While Pkl has been internally used at Apple for several years, its release as an open-source project opens up opportunities for community contributions and broader adoption.

“In the future, we hope to add support for other popular languages and platforms, realizing our vision of a polyglot config solution based on a single config language.”

Pkl

• Type safety: Pkl supports both complicated kinds (like lists, maps, sets, and enums) and primitive types (like integers, strings, booleans, and null). Additionally, users may do type checking and validation at build time and runtime, as well as construct new types and schemas using Pkl.

• Expressiveness: Pkl allows users to write simple and dynamic configuration code by supporting variables, functions, operators, and control structures. Interpolation is another feature that Pkl offers, enabling users to insert expressions within strings.

• Modularity: Modules are supported by Pkl, enabling users to arrange and share configuration code among various projects and contexts. To allow users to access other resources and libraries, Pkl also allows imports and exports.

• Security: Sandboxing, which restricts the environment in which configuration code may be accessed and executed, is supported by Pkl. The least privilege option, which gives the bare minimum of permissions needed for a configuration job, is likewise supported by Pkl.

Apple is Evolving in Open-Source Software

It seems that the release of Pkl seems to align with Apple’s growing commitment to open-source software. Recent contributions include the Swift programming language and the Foundation libraries, demonstrating their expanding presence in the open-source community.

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Why Use Pkl?

Pkl is intended to be a general-purpose and flexible programming language for configuration that may be applied to a variety of situations, including:

• Infrastructure: Networks, servers, cloud services, containers, and other infrastructure elements may all be configured using Pkl. Pkl is also capable of producing configuration files in XML, JSON, YAML, and property lists, among other forms.

• Applications: Pkl is a tool for configuring user interfaces, features, preferences, and program settings. Additionally, configuration code in several languages, including Java, Kotlin, Swift, and Go, may be generated by Pkl.

• Environments: Staging, production, testing, and development environments may all be configured with Pkl. Claims, environment variables, and credentials can also be generated using Pkl.

Hey Everyone,

I will be posting less often between December 15th and January 8th due to the holidays.

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One of the pet topics of this publication in 2023 has been how AI is impacting the role of software engineers, the future of code and transforming the future of the software engineering and programming professions.

As such, we have to take a closer look at AlphaCode 2.

DeepMind’s LLM prowess in coding is called AlphaCode and presumably they have upgraded it with the second edition about a year later.

Has Alphacode 2 Reached Human Programming Level?

Alpha Code's largest model had 41.4 billion parameters and was trained using 715 gigabytes of code samples from GitHub. After training, AlphaCode solved 34.2 % of all problems in the CodeContests dataset and achieved an average score in the top 54.3 % in ten Codeforce competitions. This means that AlphaCode outperformed almost 46 % of the human participants.

Guet AI for Developers is Open for BUsiness

Here we also have to understand that Google is going up against Microsoft. Duet AI for Developers, the suite of AI-powered assistance tools for code completion and generation the company announced earlier this year, is now generally available and that it will start making use of Google’s more powerful Gemini model in the coming weeks.

How Good is Alpha Code 2?

AlphaCode 2 can understand programming challenges involving “complex” math and theoretical computer science. And, among other reasonably sophisticated techniques, AlphaCode 2 is capable of dynamic programming, explains DeepMind research scientist Rémi Leblond in a prerecorded video.

When AlphaCode 2 was tested on 77 problems across 12 CodeForces contests – where it competed against more than 8,000 programmers in total – it managed to solve 43 percent of them. AlphaCode 2 submitted its answers in C++.

In two contests out of the twelve in which it competed, AlphaCode 2 outperformed 99.5 percent of participants. Although impressive, the competition conditions were different for the machine and for humans.

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Here we go again! Yet another Startup unveiling a tool to ease code development. I’m not going to lie, I find these products fascinating. I’m of the belief that Generative A.I. has a lasting impact on the future of software development, no-code and how we use code and accessibility to coding at large.

So what is Deci? Deci’s deep learning development platform bridges the AI efficiency gap, empowering AI teams to efficiently build next generation deep learning applications. 

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If you want to learn more about Deci there’s a Webinar by someone I know from Linkedin soon.

RSVP the Event

So back in July, Deci, a startup company with 50 employees who are developing a platform to build and optimize AI-powered systems, announced that it closed a $25 million Series B financing round led by Insight Partners with participation from Square Peg, Emerge, Jibe Ventures, Fort Ross Ventures and ICON that brings the company’s total raised to $55.1 million.

Their mission is to empower AI developers with powerful tools for building innovative AI based solutions.

Arrival of DeciCoder

Deci’s DeciCoder is a novel generative AI foundation model designed to streamline code development.

Future of Code? (the pitch by Saboo)

🔥 Introducing DeciCoder - an extraordinary open-source LLM for code generation. It's not only fast and precise, but it's also budget-friendly.

🔍 What is DeciCoder?
It's an auto-regressive language model rooted in the transformer decoder architecture. Tailored for Python, Java, and Javascript enthusiasts, its performance is outstanding with superior memory utilization even on budget GPUs. We've struck the perfect balance between rapid code generation and minimal latency.

🧠 Deci's AutoNAC Revolution
Remember the days when designing the "optimal" neural network structure was a backbreaking task? Say goodbye to manual customizations and exhaustive trials! Deci’s AutoNAC has made strides in uniting accuracy with speedy inference.

DeciCoder was generated using Deci's proprietary Automated Neural Architecture Construction (AutoNAC) engine, the most advanced Neural Architecture Search (NAS)-based technology on the market. AutoNAC identifies the ideal architecture that strikes a perfect balance between accuracy and processing speed, tailored for distinct data features, tasks, performance goals, and inference environment.

From models like Yolo-NAS (for object detection) to DeciBERT (Q&A) and DeciSeg (semantic segmentation), AutoNAC has been the brain behind them. For DeciCoder, it has adeptly maneuvered transformers to craft an architecture fine-tuned for NVIDIA’s A10 cloud GPU. The precision? It's comparable (perhaps superior) to the likes of SantaCoder! 🎅

📊 Key Details:
- 1 Billion parameters
-  Trained on a whopping 6TB of source code across 358 languages, with a primary focus on Python, Java, and Javascript.
- A generous context window of 2048 tokens.

What are you waiting for? 🤔 Try it now for free:
🕹️ Demo: https://lnkd.in/gzxAapuJ
🌎 Access the Model: https://lnkd.in/g-EeMqWs
📜 Detailed Insights: https://lnkd.in/gnMVfh9Q

Run the Demo

In Brief

• With a robust architecture featuring 1 billion parameters and a 2048-token context window, DeciCoder can generate diverse and high-quality code snippets across multiple programming languages.

• DeciCoder’s efficiency outshines SantaCoder, offering faster inference speeds on more affordable hardware without compromising accuracy.

• It outperforms SantaCoder in accuracy across Python, JavaScript, and Java languages.

https://huggingface.co/Deci/DeciCoder-1b

Some of the specs are fairly encouraging for Deci.

Apply for Early Access

I have no affiliations with this company and this is not a sponsored post, I really do find Generative A.I. startups going after coding fairly interesting.

The product was first unveiled on August 15th, 2023.

Apparently, when running on NVIDIA’s A10G, a less expensive hardware, DeciCoder’s inference speed surpasses that of SantaCoder, the most popular model in the 1-billion parameter range, running on the pricier NVIDIA’s A100. Moreover, DeciCoder on the A10 is 3.5 times faster than SantaCoder on the A10 and 1.6 times faster than SantaCoder on the A100. I obviously could not verify these claims of the company.

About Deci

Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company’s deep learning development platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment including cloud, edge, and mobile, allowing them to revolutionize industries with innovative products.

Read the Blog

A NAC/NAS Startup

The platform is powered by Deci’s proprietary automated Neural Architecture Construction technology (AutoNAC), which automatically generates and optimizes deep learning models’ architecture and allows teams to accelerate inference performance, enable new use cases on limited hardware, shorten development cycles and reduce computing costs. Founded in 2019, Deci’s team of deep learning engineers and scientists are dedicated to eliminating production-related bottlenecks across the AI lifecycle.

NAS, (Neural Architecture Search) a family of techniques on which Deci heavily relies, can help automatically discover low-cost, optimal models for a given problem. Deci isn’t unique in this — Google’s Vertex AI service leverages NAS to optimize the performance of models on specific, customer-specified tasks. But Geifman argues that Deci’s platform offers access to NAS capabilities at a lower cost.

The company is based in Israel. Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company’s end-to-end deep learning acceleration platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment, including cloud, edge, or mobile.

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Hey Everyone,

Watching Stack Overflow the in 2023 has been a bit painful to see. It may be a casualty of ChatGPT. About 10 days ago this article went viral on Hacker News outlining it. We knew since at least April that traffic was way down.

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After being critical of AI content and not knowing how to handle it in a moderator sense (e.g.hallucinations), it was peculiar in late July to see Stack Overflow release their own AI.

While traffic to OpenAI’s ChatGPT has been growing exponentially, Stack Overflow has been experiencing a steady decline – losing some of its standings as the go-to source developers turn to for answers to coding challenges.

So what is Overflow AI? For Stack Overflow for Teams, and brand new product areas, like an IDE integration that brings the vast knowledge of 58 million questions and answers from our community right into the area where developers find focus and get work done. They’re putting all this work under the umbrella of OverflowAI. 

How can we have arrived at this point of A.I. scrapping everything without recourse? Some AI models that compete against Stack Overflow were partly trained on the company's own data. 

And I’m not sure OverflowAI can make a difference.

For 15 years, Stack Overflow has been the online community where software engineers go to ask questions and get tips from fellow coders. It doesn’t however have the reputation it once did. On some level I think the traffic reflects that, and just the variety of what’s possible in 2023 with ChatGPT, GitHub Copilot and other tools. It’s easier to get information out of a chatbot than a peer, for quick answers.

"Some of them (competitors) are very explicit about calling out Stack Overflow as a primary source.”

Actually, traffic to Stack Overflow’s community website has been dropping since the beginning of 2022. That may be in part because of a related development, the introduction of the CoPilot coding assistant from Microsoft’s GitHub business. What else could it be?

Microsoft used their monopoly power in their acquisition of GitHub and in developing Copilot it’s been a bit awkward for some of the rest of the internet. CoPilot is built on top of the same OpenAI large language model as ChatGPT, capable of processing both human language and programming language. A plugin to the widely used Microsoft Visual Studio Code allows developers to have CoPilot write entire functions on their behalf, rather than going to Stack Overflow in search of something to copy and paste.

A Less Personal Internet

Overflow AI is a human-centered search, but it may be too little too late. Online communities, like Stack Overflow and Wikipedia, thrived as hubs for experts and curious browsers to come together and share information freely, but Generative A.I. shifts the pendulum back to A.I. and not human sources per se, although all of this is really plagiarism in a blender, if you think about it.

ChatGPT’s rise has been a well documented phenomenon and marks 2023 as different and almost belonging to an exponential age of A.I., as compared with 2022. ChatGPT doesn’t have a year-over-year track record, having only launched at the end of November, but its website (chat.openai.com) has become one of the world’s hottest digital properties in that short time, bigger than Microsoft’s Bing search engine for worldwide traffic. We knew there would be casualties and there will be more.

The Hacker News comments were blunt and obvious. You don’t really rebound from something like this, the arrival of a different sort of beast and a different sort of era of how search and interactions take place.

According to (Business) Insider, this also perhaps reveals a looming problem at the heart of the AI revolution: With less incentive for people to go online and answer questions, the rich, human data that AI needs for training will whither and the quality of these models could degrade. Even the controversy at Reddit ensures human data and human conversations could wane. It’s a scary thought.

Generative A.I. Might Reduce Human Community Online

Overflow AI is a noble attempt at holding on. But how do you respond to such a future when consumer behaviors adapt to new tech? In 2021, Prosus, a major backer of Chinese tech giant Tencent, bought Stack Overflow for $1.8 billion. Visits to the GitHub CoPilot free trial signup page more than tripled from February to March, topping 800,000, just as traffic to Stack Overflow began to plummet.

Microsoft elbowing smaller competition out of existence is nothing new. But if Microsfot could acquire GitHub and super-charge OpenAI with $10 billion and likely another $4 Billion by now, what does it all mean for community online? They acquired LinkedIn too, and have started to train scientists to act like teenage creators. I never knew A.I. researchers could act like threadboise, but it turns out all they needed was a nudge. The AI of corporations can hack people too, it turns out, to change their behaviors and to post in a certain way.

Microsoft Killed Stack Overflow

SO served its purpose for a certain stage of the internet. It feels like in the second half of 2023 we’re building another layer of the internet. I don’t like the platform monopolies that we have going, but there you have it. Stack Overflow’s future is likely not so bright and Microsoft have a bunch of new subscriptions to earn even more revenue, and it is anti-competitive and no they won’t likely get called out for it.

Thanks for reading!

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In recent times I’ve been trying to bring software engineers news on the various A.I. capabilities and LLMs upgrades for coders.

So while GitHub Copilot thrives along with ChatGPT, what Google, Amazon and Meta will build I think will keep improving the field. The coding tool, called CodeCompose, isn’t available publicly — at least not yet. But Meta says its teams use it internally to get code suggestions for Python and other languages as they type in IDEs like VS Code.

Read the Paper

What is It?

CodeCompose is based on the InCoder LLM that merges generative capabilities with bi-directionality. They have scaled up CodeCompose to serve tens of thousands of developers at Meta, across 10+ programming languages and several coding surfaces.

In the paper Meta discusses the unique challenges in terms of user experience and metrics that arise when deploying such tools in large-scale industrial settings. Curiously they also present metrics from their large-scale deployment of CodeCompose that shows its impact on Meta's internal code authoring experience over a 15-day time window, where 4.5 million suggestions were made by CodeCompose.

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Code-Completion LLMs are Getting Better Fast

If you have tried ChatGPT or GitHub Copilot you will understand how these LLMs trained on code work and are getting better. The folks at Meta have also put numbers on it.

Quantitative metrics reveal that (i) CodeCompose has an acceptance rate of 22% across several languages, and (ii) 8% of the code typed by users of CodeCompose is through accepting code suggestions from CodeCompose.

Qualitative feedback indicates an overwhelming 91.5% positive reception for CodeCompose. In addition to assisting with code authoring, CodeCompose is also introducing other positive side effects such as encouraging developers to generate more in-code documentation, helping them with the discovery of new APIs, etc.

• 8% of Code suggested is Accepted

• 92% favorable positive reception within Meta to CodeCompose

On Google’s side, Paid Colab users will also have access to autocomplete suggestions, which can save time and reduce errors when coding. A Codey Bot.

Developers will be able to use the new Codey AI assistant based on Google’s new PaLM 2.

More on CodeCompose

CodeCompose is based on the InCoder LLM and has been trained on 10+ programming languages at Meta. As such, it inherits the property of LLMs to be multi-lingual.

The largest of several CodeCompose models Meta trained has 6.7 billion parameters, a little over half the number of parameters in the model on which Copilot is based.

For all intents and purposes like the Coding tools of other BigTech companies, Meta’s code-generating AI model is a significant step forward in aiding developers and enhancing their productivity.

It’s not clear what will happen with the copyright though. TechCrunch notes that Microsoft, GitHub and OpenAI are being sued (Verge) in a class action lawsuit that accuses them of violating copyright law by allowing Copilot to regurgitate sections of licensed code without providing credit.

While Microsoft Copilot and ChatGPT seem to be the ones prefered by developers so far, I wonder if Google or Meta’s tools will be able to catch up in their efficacy. Amazon has of course CodeWhisperer.

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The competitive arena of LLMs in Code is getting mighty interesting in 2023 it turns out.

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Hey Everyone,

So covering the intersection of coding and LLMs, we shouldn’t neglect StarCoder. AI startup Hugging Face and ServiceNow Research, ServiceNow’s R&D division, have released StarCoder, a free alternative to code-generating AI systems along the lines of GitHub’s Copilot, Google PaLM and others.

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Let’s take a closer look. The complete view of StarCoder is fairly interesting and I have not seen much coverage about it on social media.

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Is this the A.I. programming language of the future?

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If Rust went from a side project to being the world’s most-loved programming language, what can Mojo become? I have a big feeling about this.

AI infrastructure company, Modular AI, recently unveiled Mojo, a new programming language that combines the syntax of Python along with the portability and speed of C, making it ideal for both research and production. 

Stay up to Date

This language is designed specifically for AI applications and is said to bridge the gap between research and production by combining the best of Python syntax with systems programming and metaprogramming.

According to their keynote, Mojo is about 35,000x faster than Python, which is simply incredible. It offers unparalleled programmability of AI hardware and extensibility of AI models, making it a game-changer in the field.

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One of the most exciting features of Mojo is that it allows you to write portable code that’s faster than C and seamlessly inter-op with the Python ecosystem. You can write Python or scale all the way down to the metal with no need for C++ or CUDA. Mojo has a number of other impressive features, such as a unified inference engine, progressive type system, zero-cost abstractions, and an ownership and borrow checker (like Rust).

Although the language is still in development and not publicly available yet, the potential it holds for AI applications is truly exciting. Keen to see how it gets picked up by the community.

Here’s the website to learn more:

Read about Mojo

There are a lot of good YouTubes already about it.

For decades, coders wrote critical systems in C and C++. Now they turn to Rust. But if Python is so popular what will Mojo do to it?

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Sign up to Modular’s Newsletter here. Let’s get a lot more into this!

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Hey Everyone,

On this publication, there will be times I write several times a week, and other times I will go 10 or even 14 days without a Newsletter, that’s just the way things are. Lately in the News I’ve been reading a lot about the layoffs in Technology how many software engineers are being impacted.

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With GitHub Copilot and Google’s PaLM tool and many other auto-complete like A.I. assistants, they say the jobs and roles of coders won’t be disrupted, but can we trust ChatGPT for the future of coding? I’d like to seriously ponder the issue.

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Matt Welsh, a former professor of Computer Science and Google engineer believes that programming jobs, as we know them today, will cease to exist in three years. If ChatGPT and GitHub Copilot improve at the rate they have been improving, won’t GPT Agents realize a lot of coding tasks in the decades ahead?

Thenewstack writes:

Welsh thinks programming is on the cusp of changing from a job that humans do, to one that robots will do, thanks to technologies like ChatGPT and Copilot. In his view, programmers will need to evolve into “teachers” of AI programs — or perhaps product managers or code reviewers, the two human roles he thinks are relatively safe from the robots. But don’t expect to continue your career as an actual programmer, he says, because machines are taking over that role.

Listen to Matt Welsh here.

Just as software development has slowly become more inclusive between the genders, is there actually the possibility it will begin to disrupt these highly skilled knowledge workers?

Welsh, who has held senior engineering positions at Google and Apple, had written an article on this topic for the January 2023 edition of the Communications of the ACM magazine.

I’m fairly interested in how A.I. impacts the knowledge workforce and the future of engineering. If you consider that expensive engineers and their salaries is a huge bottleneck for a lot of startups and innovation! This has incredible implications across industries, if it’s true.

Some are so blinded by ChatGPT hype they say a lot of things, and make some outlandish convictions, predictions and have a weird solidarity with the Venture Capital and BigTech folk who benefit the most.

But we also have to consider the factual possibility?

If A.I. learns to code itself, what can it become?

What could be the impact on coding jobs and demand for actual software engineers, data scientists, data engineerings, MLops, analytics folk, business intelligence and IT Workers broadly speaking and so forth?

While we can ruminate on the top programming languages for A.I. development, will our jobs exist in 5 or 10 years? We may have to pivot in ways that will change over time.

People like Matt Welsh believe that the only thing limiting co-pilot today is more data and compute. Is that true?

Software Engineering Jobs Are not Safe in 2023

Insider wrote a hit piece recently where they said:

• Software engineers have overwhelmingly faced layoffs this year, per data from Revelio Labs.

• Coders made up 14% of employees at tech companies, but represented 20% of layoffs in 2023.

• It's a big shift from data from late last year, which showed recruiters were overwhelmingly cut.

That to me seems like a high number. Even a flashy new job like a prompt-engineer might not exist in three years time as GPT Agents evolve. A lot of the early builders in the Generative A.I. space will be made obsolete by new GPT and foundational models. They are gambling with innovation, at a time when the “speed of A.I.” is fast and peculiar.

Data from the Fall of 2022 pointed to software skilled folk being expendable in the “year of efficiency” in Silicon Valley.

According to data from Revelio labs.

Read their blog

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How could some of our most in-demand knowledge workers actually be disrupted by these tools? Maybe GitHub Copilot just gets better and better much faster than even Microsoft expected? How could this be?

Friend or foe: Can computer coders trust ChatGPT?

• Is this the End of programming? I mean, it makes for a good headline but really? Revelio seems to have gotten their data from layoffs.fyi (a site I mostly check for company wide layoffs).

With margin compressions occuring in Earnings and a slowdown in things like Cloud, PC Sales and Advertising, I guess it’s only inevitable for companies who over-hired in the near zero interest decade of the past.

Think of it this way, while layoffs were previously focused on the future of hiring, by targeting engineers, companies are now focusing layoffs on current business priorities and product priorities, which makes sense. But it’s also a bit scary. Retail and media layoffs I understand, but software engineers? It could be the dawn of a new era.

Were were always told engineering had a bright future full of demand. But at what point is this no longer true, is it some hypothetical 2028? Depending on the speed of A.I.?

The choice of programming language can affect an AI system's performance, efficiency, and accuracy. But what if A.I. itself masters programming relatively easily, and relatively quickly, will it still just be a “co-pilot”? What has Microsoft done?

A lot of coders use a combination of ChatGPT and GitHub Copilot and other tools. It does not mean they will be replaced, right?

Software Engineers are amongst the most common roles vulnerable to the tech layoffs of late 2022 and 2023, and likely some of 2024.

If Recruiters and Customer support are the most impacted, Software engineers aren’t exactly so far behind:

If Tech needs to save cash due to deteriorating margins, it really does make sense:

Maybe it’s not news to some of you, but I found it a bit surprising.

Are Coders the Odd Ones out due to A.I.?

There is one quote that really struck me: (the idea that this is a new normal).

“Before it was just, ‘the more or better, whatever it takes to recruit the best talent,’” said Daniel Keum, an associate professor of management at Columbia University’s business school, said of tech companies. “Now they’re cost-conscious, they want to be optimized and economized.”

He added, “That’s an entirely different mindset. We haven’t seen this before.”

The Tech Bleed Will Continue in 2023

The latest monthly jobs report by tech industry association CompTIA found that even though employment at tech companies (which includes all roles at those companies) declined slightly in March, employment in technical occupations across industry sectors increased by nearly 200,000 positions.

The average software engineer base pay in the US is $90,000, according to PayScale, but can be substantially higher at tech firms like Facebook, where such workers also get bonuses and stock options. The get rich move to the Valley card may be coming to an end.

Blame ChatGPT and declining margins. Seriously.

This research builds on data Revelio published with Insider's Aki Ito last September, which found that recruiters were overrepresented in layoffs by nearly 8%, compared to other positions. Then, software engineers were overrepresented in layoffs by nearly 4%.

If ChatGPT and Bard and GitHub Copilot are only getting started with Code! It could be a weird world for many knowledge workers in the Tech umbrella. Sorry Tech bros, your time is up! I’m kidding but I found all this data quite interesting and maybe salient to your world and your job?

I’ll stop because I’m rambling but this topic isn’t over, this is just the beginning.

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Are you looking for an A.I. related tech job, see my collective here. Are you an A.I. company looking to hire, you can join it as well here. Join my Reddit for incredible A.I. links to articles from around the web.

Microsoft now refers to nearly everything Generative A.I. as being a “copilot.” But that used to be a term reserved for GitHub Copilot, which is supposedly boosting productivity of coders, though likely in late 2022 along with ChatGPT.

GitHub Copilot X is their vision for the future of AI-powered software development. Integrated into every part of your workflow.

So now for software engineers, GitHub says it’s using OpenAI’s latest GPT-4 model to go way beyond auto-completing comments and code. What does that even mean?

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As a pioneer in the use of generative AI for code completion, GitHub is not the only one. There is Tabnine and Google’s PaLM based solution that will likely be announced later at their I/O event this Spring or Summer.

Still this is a very reasonable price for enterprise subscriptions where OpenAI’s Codex model, currently writes 46% of the code on the platform and has helped developers code up to 55% faster. By auto-completing comments and code, Copilot serves as an AI pair programmer that keeps developers focused and productive.

GitHub has been one of the more productive acquisitions by Microsoft. Though Nuance the health-tech acquisition is also being upgraded with GPT-4. Microsoft-owned Nuance Communications unveiled its latest voice-enabled medical scribe application integrated with OpenAI's GPT-4. 

Nuance says the new application, Dragon Ambient eXperience (DAX) Express, is the first fully automated clinical documentation application to combine conversational and ambient AI with the advanced reasoning and natural language capabilities of OpenAI’s GPT-4.

So Microsoft’s future in using Generative A.I. in coding, low-code automation and game development seems really bright to me. This is of course only available to paying customers. The rollout of Copilot X from my understanding, includes features and set changes in Copilot for Business and Copilot for Individuals subscription offerings.

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• In essence it seems like GitHub Copilot for Business is getting a bit of a Chat-GPT like upgrade.

• Basically, introducing chat and voice for Copilot, and bringing Copilot to pull requests, the command line, and docs to answer questions on your projects.

• Launching in preview today, Copilot Chat will be available in Visual Studio and VS Code, with support for other IDEs coming later.

Microsoft has an off-chance of making a mobile gaming app store that could impact Android and Apple in a material way. This means upgrading coders and game developers with Generative A.I. is just a massive play for the future of innovation in Microsoft’s gaming and no-code empire.

Microsoft is already the one true rule of software subscriptions, notably with acquisitions like LinkedIn, GitHub and so many others. Meanwhile GitHub’s own R&D team at GitHub Next has been working to move past the editor and evolve GitHub Copilot into a readily accessible AI assistant throughout the entire development lifecycle. Microsoft’s sprawling software partnership ecosystem and Cloud dominance just gives them an unfair advantage at nearly every turn.

Now that they are getting more into Search and Advertising it’s bad news for the rest of BigTech. It makes the $14 Billion investment in OpenAI seem rather worth it when taken from the big picture perspective. It boosts diversification and enables them to do things they wouldn’t have been able to do otherwise a few months before their key rivals.

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As exciting as Adobe Firefly is, Github Copilot is the real “copilot”. Microsoft’s Generative A.I. success may not improve Bing substantially, but for coders ChatGPT and GitHub Copilot X for Business is already a substantial upgrade for roughly $50 a month.

And obviously, Copilot Chat will focus solely on programming questions. As for Microsoft’s unchecked monopolistic assault on gaming, Microsoft could launch a new app store for games as early as next year if regulators approve the company’s $75 billion acquisition of Activision Blizzard. The thing is I expect Generative A.I. to have the most impact on game development over time, of all things.

Microsoft’s copilot for 365 apps and its no-code and automation (RPA-like) efforts is really considerable. There’s so much going on with their GPT-4 upgrades, it’s so much bigger than BingAI. In this sense, Microsoft and Apple have diversification of revenue that Amazon, Google and others just don’t have.

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The AI-Human Coding Duo

For a programmer and software engineer, chatting with A.I. while programming with some level of auto-complete will just be the new normal. GitHub Copilot can sit at the side of your integrated development environment (IDE), ready to accept commands.

In theory for new programming students they will be learning a different trick of the trade, since it is claimed autocomplete already allows developers to greatly improve their productivity, while supposedly this new Copilot will 10x developer productivity. I cannot verify such statements, but it’s an intriguing idea.

If software is augmented with A.I., what’s next?

Hey Everyone,

More programmers are using ChatGPT and GitHub Copilot for Business now than ever before in February, 2023.

GitHub’s OpenAI-powered developer tool with new capabilities goes live with enhanced Codex

So what actually changed this week?

• GitHub today announced that Copilot for Business, the company’s $19/month enterprise version of its AI-powered code completion tool, is now generally available, after a short beta phase that started last December.

• Google is busy working on a competitor to this based on PaLM. I believe they are likely to announce this tool at their I/O Conference in May or June, 2023.

• As you probably already know, first previewed in 2021, Copilot uses OpenAI’s Codex large language model (LLM) to turn textual descriptions into source code.

Among Microsoft’s many weird experiments around A.I. adoption of its software suite productivity, GitHub Copilot is perhaps the one I’m most bullish upon. The reviews of BingAI weren’t so great this week, if we are to believe the journalists and anti-hype PR.

Whatever the case may be, as engineers and software dev enthusiasts if the sum-total of all of these new tools allows us to be more productive, than it’s all worth it, so long as we use the tools as they were intended.

For companies and enterprise, helping Software devs be more productive with a tool that costs just 19$ a month is a very affordable rate. ChatGPT Plus is $20, last time I checked.

So the mid Feb announce comes with additional “enhancements”. Enhancements unveiled February 14 include an update to the underlying OpenAI Codex AI model to support large-scale improvements to code quality suggestions. The time required to deliver suggestions has also been reduced. These enhancements are available for both Copilot for individuals and Copilot for Businesses.

GitHub said that in addition to upgrading the underlying Codex model to improve the quality and responsiveness of its code suggestions, it also developed a new security vulnerability filter to make the tool's code suggestions more secure and help developers identify insecure coding patterns as they work.

Copilot for Business adds features like license management, organization-wide policy management and additional privacy features. Until now, you had to work with GitHub’s sales organization to sign up for the business version, but now there is a self-serve option as well.

Microsoft for Coders

GitHub said in a statement GitHub Copilot is the world’s first at- scale artificial intelligence (AI) developer tool.

I’m not sure that’s technically true but they do seem to have corned this market in the early going.

The benefits they claim are striking if true:

• 55% faster coding?

• 46% code written?

Microsoft of course thinks all of A.I. is helping us with a co-pilot in search and elsewhere. I guess that means GitHub Copilot’s branding is selling well.

Sitting within the editor as a simple extension, GitHub Copilot draws context from a developer’s code to suggest new lines, entire functions, tests, and even complex algorithms.

It’s hard to verify the efficacy of the benefits that Microsoft claims but it does sound like it has momentum.

Specifically, the technical improvements reportedly provide:

• A more powerful client-side AI model with algorithms that improve the quality of code suggestions by 44 percent

• Simple self-serve purchasing, where businesses can sign up for Copilot themselves and immediately assign seats -- even if they don't use the GitHub platform

• AI-based security vulnerability filtering to target common issues

What do you think?

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One thing that GitHub seems to be doing really well is integrating feedback to improve the product.

Better code suggestions

One of the important parts of the LLM life cycle is gathering user feedback and updating models. Since officially launching Copilot, GitHub has used feedback from millions of developers to improve its model, increasing the quality of code suggestions and reducing latency. According to GitHub’s latest report, on average Copilot writes 46% of code for developer users, up from 27% in June 2022.

Microsoft definately shouldn’t be allowed to acquire Activision and even GitHub and LinkedIn weren’t great acquisitions for the community. But anyways:

Their team is constantly refining the models and adding new features as they become available in Azure’s OpenAI Service.

Since its release, GitHub Copilot has transformed developer productivity for more than one million people, helping developers code up to 55 percent faster.

That sounds like a lot, and Twitter influencers are coming out and saying how amazing it is, which sounds like an unsolicited Ad.

Then it turns out he’s joining OpenAI again, sort of ridiculous the manipulation going on, but anyways.

How many users does it even have though? The Visual Studio Code extension has been installed nearly 3.7 million times, while the Visual Studio tool has been installed nearly 154,000 times. According to GitHub, more than 1.2 million developers used Copilot's technical preview in the past 12 months as of September, 2022. I wonder what the numbers are at now?

No ChatGPT like craze, that’s for sure.

As the model gets better, the team is also adding new features, including things like “fill-in-the-middle,” where the model can’t just complete a line but also start adding words in the middle because it knows what sits before and after the current cursor position, for example.

The PR is a bit like a Microsoft commercial and what you might expect:

“With more accurate and responsive code suggestions, we’re seeing a higher acceptance rate [for code suggestions],” Shuyin Zhao, GitHub senior director of product management

Trained on billions of lines of code, GitHub Copilot turns natural language prompts into coding suggestions across dozens of languages.

If I was just learning to code, I’d likely try all of these tools including what ChatGPT can do in various languages.

Will Coders Get Automated?

Dohmke expects that soon, Copilot will be able to generate 80% of a developer’s code. Today, that number is about 46% across programming languages — and 61% for Java.

If A.I. buddies in the AI-human hybrid workforce do more of the heavy lifting, it’s going to be a different job if this keeps up. You can almost fathom a time when the no-code revolution means less of a demand for software engineers? I don’t know enough about it but it’s worth speculating about this if you are thinking of a career in software development. On the other hand an expected supply-demand shortage may become more serious.

GitHub FIM

GitHub has also added a few new tricks to improve the Copilot experience. One of them is a new paradigm called “Fill-in-the-Middle” (FIM), which gives Copilot more context to improve code suggestions.

Microsoft is Gaining Adoption via Generative A.I. Hype

“The rise of generative AI models like GitHub Copilot has triggered widespread
recognition that the age of AI has begun. But until now, generative AI has largely
benefited the individual. That’s changing today,” said its Chief Executive Officer Thomas Dohmke.

There’s now 5 million users on the Programming Subreddit, with some interesting debates.

Tim Davis, professor of computer science at Texas A&M, has reported that GitHub Copilot has produced "large chunks of my copyrighted code, with no attribution, no LGPL license" even when the block public code flag is enabled. This is not the only controversy surrounding the tool. The tool is likely to face a lot of legal challenges and lawsuits.

Github owned by Microsoft now claims 400+ organizations are already using GitHub Copilot. That’s not actually that many considering how long this has been out and available. But it does point to how coding is changing in a world of software empowered by A.I.

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Hey Guys,

What could be a secret power of Generative AI? How about coding with voice? It could be a ‘Hey, GitHub!’ moment, or at least Microsoft hopes so.

Copilot officially launched for everyone back in June, costing $10 per month or $100 per year after a free 60-day trial. By 2024 I think the Code generation leaders will have a useful voice indication feature. I see this as having a legit place in the tools software developers might use in the near future.

This is because Generative AI is moving fast now. If GitHub Copilot is doing it, others are too or won’t be far behind which will drive the opportunities here further along faster.

Think about it, how weird would voice prompting be? “Hey, GitHub!” will allow programmers to code with just their voice and no keyboard, just like how you’d speak to Siri, Alexa, or Google Assistant.

Let’s see how this might be useful:

Hey GitHub, you might actually have a future with voice.

Read GitHub Voice Blog

Could Voice be more efficient in some instances?

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Voice search is slowly becoming more popular, so how could GitHub Copilot bring it more into the mainstream?

GitHub is serving access to the new voice feature via a waitlist that’s open for interested developers now, but essentially it will allow developers to activate Copilot’s ears via the “Hey, GitHub” wake word. Obviously, this is just the beginning when it comes to Voice Generative AI at the intersection of coding.

Copilot suggests lines of code to developers inside their code editor, and it’s capable of suggesting the next line of code as developers type in an integrated development environment (IDE) like Visual Studio Code, Neovim, and JetBrains IDEs. While Microsoft is pushing it, it actually has many of really good competitors. These include Tabnine, GPT-Code-Clippy, Amazon CodeWhisperer and so many others.

The idea of autocomplete tools and auto-suggest and voice-coding is not new but they are starting to get to the point where they could be useful and part of the tool kit of new software developers in my opinions especially over the early 2020s as they refine themselves.

It’s also a win for Microsoft in terms of inclusion and accessibility. According to GitHub, Hey, GitHub! can recognize “natural language,” making Copilot a more user-friendly tool among programmers. This also boosts Copilot’s accessibility for other developers who find the conventional process of delivering code inputs difficult.

Other developers might just want to have voice-integration for easy navigation in some cases. So even if a developer doesn’t want any code suggestions, it can serve other practical use-cases such as helping them navigate a codebase by saying something like “Hey GitHub, go to line 34,” or even control the IDE by toggling to zen mode.

I like how Microsoft is pushing the envelope here with great ideas in coding even if some of them don’t stick. I noticed also the reviews for Tabnine were very decent. I think Generative AI startups will also mature in coding that aren’t yet out of stealth or have yet to be born as A.I. labs or startups.

For instance I’d not see Jasper.AI to also offer utility with regards to code as it has expanded into text-to-image. It’s a very well funded startup. When you can raise $125 million you are probably on to something.

What if you are a remote worker and not in an environment where you have your mouse and keyboard, could you actually work? While this is still an early stage experiment developed by an R&D team called GitHub Next (TechCrunch), it could have significant ramifications from an accessibility perspective, as it reduces the amount of interaction that’s required with a mouse and keyboard.

All of this is somewhat speculative and futuristic, and hard to imagine that even a beta Pilot is actually here. But the interest in Generative AI at the intersection of Code is going to be very high. Anything that makes engineers and software developers more productive will have huge implications and be able to attracting funding rather well.

If you are interested in trying out Tabnine, you can start a free trial here.

As for ‘Hey Github’ - What might the average developer actually use this for? I think the answer is speed. Just like some of us have gotten used to bossing around our A.I’s with voice commands the same could hold true at work.

Aside from writing and editing codes, Hey, GitHub! will allow programmers to do code navigation by simply indicating the line where they want to view (e.g., “Hey, GitHub! go to line 34,” “Hey, GitHub! go to method X,” or “Hey, GitHub! go to next block,”). It also accepts other VisualStudio Code commands such as “Toggle zen mode” and “run the program.” Additionally, experiment participants will be able to have quick access to code summarization in Hey, GitHub!, giving them summary explanations of certain code functions.

It’s all pretty interesting!

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Hey Guys,

This is an article about programming trends of the popularity of software development languages. A recent CircleCI's 2022 State of Software Delivery report(opens in new tab) found that TypeScript has now overtaken JavaScript to the number one position, toppling one of the biggest names in the industry.

Software delivery has never been more critical to the success of business in every industry. It’s also never been more complex.

So in 2022, Typescript has essentially overtaken Javascript and Ruby as the most popular DevOps language based on the work of 2 million developers.

TypeScript has Developer Friendly Features

The company says the change is likely down to its developer-friendly features, like allowing them to catch smaller errors locally and to commit working code more frequently compared with JavaScript.

In the annual “State of Software Delivery” report, what this firm does is analyze data from more than 250 million workflows. And one of the main conclusions is that TypeScript has been the most used in the last year to develop DevOps.

According to TechRadar, Top programming languages

From 2019 to 2021, only Python remained consistent in its popularity according to the report, which places the language at fourth place. Other top-ten languages include HTML, Java, and PHP.

Typescript, Rust and Python are overall the most loved programming languages of the last few years from what I have personally read.

Further down the list, HashiCorp (HCL) has entered the top-ten list, coming in at a respectable ninth place, leading the way for both HTML and Swift trailing one and two places behind respectively.

Even as of 2020, TypeScript was second to Rust as the most loved language. This is according to StackOverflow’s survey of over 65,000 developers. So what else can we deduce from the survey?

The landscape of tools, platforms, and architectures is constantly evolving. With this rapid pace of change and the growing challenges of complexity, how can engineering teams not only succeed but beat out the competition?

The recent study: The 2022 State of Software Delivery Report represents the largest collection and evaluation of developer engineering productivity data in the world: over two years of data from over a quarter of a billion workflows, representing almost 50,000 organizations from more than 100 countries, building over a quarter of a million projects on the CircleCI platform.

DevOps

The data shows that the most successful engineering teams routinely meet these 4 benchmarks:

1. They prioritize being in a state of deploy-readiness, rather than the number of workflows run.

2. Their workflow Durations are between five to ten minutes on average.

3. They recover from any failed runs by fixing or reverting in under an hour.

4. Their Success Rates are above 90% for the default branch of their application.

Popularity of Software Languages

These trends highlight changes in the broader industry for development teams at the leading edge of application development. Above are the popularity of languages from 2019 to 2021.

In DevOps — where MTTR is normally referred to as mean time to recovery — MTTR is used to measure how long it takes for the DevOps team to recover from a production failure. Here it's typically calculated as the average production downtime over the last 10 downtime incidents.

According to the report, here are the current leaders:

Success Rate

The high Success Rate of some of the languages at the top reflects low testing. These languages are not known for robust testing — they likely represent steps producing artifacts and output. Go, again, is an outlier here as dynamic languages include build and test steps for each of those languages.

Hack sells itself as the language for fast development and high Throughput, and projects using Hack on our platform show up at the top. Interestingly, Apex shows a growing number of Salesforce projects adopting CI.

Read the original report if the topic interests you.

State of Software Delivery

Typescript overtakes Javascript as most popular DevOps language

TypeScript, say the report’s authors, “is a superset of the very popular JavaScript programming language that adds optional static typing.” Since its introduction by Microsoft in 2012, it has been rapidly adopted by developers as a scalable, human-readable language that facilitates collaboration and accelerates development.

This is all fairly interesting for new software developers.

Since JavaScript code is valid for TypeScript, migrating a JavaScript code base to TypeScript is relatively easy. Teams that have recently completed migrations to large scale from JavaScript to TypeScript include Stripe, Airbnb and Etsy.

According to the study and Gearrice, developers appreciate TypeScript because it offers type safety that allows developers to detect errors directly in their IDE (Integrated Development Environment) instead of at runtime, which reduces the risk of sending production errors. They also allow for quick onboarding and collaboration across projects, so productivity and reliability have been key to enterprise-level adoption of TypeScript.

Thanks for reading!

If you enjoy the content and want to support the channel, I’m very grateful.

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Better Reading on a Browser

Hey Everyone,

You can listen to the VP of GitHub on Lenny’s podcast here. So GitHub Copilot is really trying hard! But Microsoft’s Github Copilot didn’t just want to measure developer productivity, but developer happiness as well.

It is just me, or is there an underlying desperation in their marketing?

Their event is also happening in November:

🎉 #GitHubUniverse 2022 👇
🐦 20% off early bird tickets https://github.co/3edd84R
🗓 Nov. 9-10 (with a virtual component as well).

Microsoft as you know bills GitHub Copilot as your A.I. pair programmer. In theory I really like the idea!

GitHub Copilot uses the OpenAI Codex to suggest code and entire functions in real-time, right from your editor. It is generally available to all developers for $10 USD/month or $100 USD/year. It will also be free to use for verified students and maintainers of popular open source projects.

But does it really make you more productive and happy? Those are big claims.

Some of the claims are sort of creepy, somewhere between sketch and cringe, but that’s sort of normal for Microsoft’s branding. Still if there claims are true, it’s sort of a big deal. This is not a sponsored post, I really wanted to understand their claims and so-called study.

👨🏻‍💻 What Exactly is GitHub Copilot?

GitHub Copilot is an AI-based system that will take notice of what you are coding and make suggestions for what it is you are about to type. You can think of it as IntelliSense on steroids, but users are claiming that it is a big step up. The idea is all based on the sort of AI that lead to the GP-3 language system. Take a very big network and train it on a huge corpus of natural language data and it will extract the regularities.

The GitHub Next team conducted research using a combination of surveys and experiments designed to try to operationalize the benefits in an academic sense.

When GitHub first launched a technical preview of GitHub Copilot in 2021, their hypothesis was that it would improve developer productivity and, in fact, according to the company, early users shared reports that it did. In the months following its release, they wanted to better understand and measure its effects with quantitative and qualitative research. To do that, they first had to grapple with the question: what does it mean to be productive?

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👓 Microsoft has Big Dreams when they Acquired GitHub

Microsoft acquired GitHub for $7.5 Billion in 2018, just four years ago.

Because AI-assisted development is a relatively new field, as researchers they have little prior research to draw upon. Obviously if a company measures its own product, they will want to paint it in a favorable light, but let’s try to pretend that they are objective academics.

They wanted to measure GitHub Copilot’s effects, but what are they? After early observations and interviews with users, they surveyed more than 2,000 developers to learn at scale about their experience using GitHub Copilot. Then, they designed their research approach with three points in mind:

1. Holistic

2. First-hand perspectives

3. Everyday development scenarios.

• Look at productivity holistically. At GitHub we like to think broadly and sustainably about developer productivity and the many factors that influence it. We used the SPACE productivity framework to pick which aspects to investigate.

• Include developers’ first-hand perspective. We conducted multiple rounds of research including qualitative (perceptual) and quantitative (observed) data to assemble the full picture. We wanted to verify: (a) Do users’ actual experiences confirm what we infer from telemetry? (b) Does our qualitative feedback generalize to our large user base?

• Assess GitHub Copilot’s effects in everyday development scenarios. When setting up our studies, we took extra care to recruit professional developers, and to design tests around typical tasks a developer might work through in a given day.

Do you want to start using GitHub Copilot today? Get started with a 60-day free trial, and check out our pricing plans. It’s free to use for verified students and maintainers of popular open source software.

🖥️ Benefits of Using GitHub Copilot

I’m not a developer so I’ll just state what the company says here:

GitHub Copilot enables developers to

• Get AI-based coding suggestions: Get code suggestions that match a project’s context and style conventions, and cycle through different options to decide what to accept, reject, or edit.

• Use your preferred environment: Integrate GitHub Copilot with popular editors, including Neovim, JetBrains IDEs, Visual Studio, and Visual Studio Code as an unobtrusive extension.

• Code confidently in unfamiliar territory: Code in new languages or try something new, and let GitHub Copilot suggest syntax and code in dozens of languages—so you can spend more time learning by doing.

💾 Results of Surveys

• Being more productive with repetitive tasks seems to be a featured benefit.

Curiously Microsoft wanted to portray how it could make “coding more fun”.

(With Copilot) I have to think less, and when I have to think it’s the fun stuff. It sets off a little spark that makes coding more fun and more efficient.

- Senior Software Engineer

I found this particularly amusing. Being augmented by A.I. in our job it turns out makes developers and software engineers happier.

GitHub even conducted a test to show how much faster and speedier GitHub Copilot made software engineers. Apparently, developers who used GitHub Copilot completed the task significantly faster–55% faster than the developers who didn’t use GitHub Copilot.

So then the PR is to say that Copilot is like your coding buddy, or new BFF. The new study by GitHub shows that Copilot, its AI code programming assistant, results in a significant increase in developer productivity and happiness. Copilot uses Codex, a specialized version of GPT-3 trained on gigabytes of software code, to autocomplete instructions, generate entire functions, and automate other parts of writing source code.

Lenny Podcast on GitHub

Listen guys whatever makes developers and Microsoft happy, I’m all for A.I. tools augmenting us in our repetitive tasks. I totally understand Microsoft wants ROI and revenue from its acquisitions.

GitHub Copilot wouldn’t be possible without GitHub’s vibrant community of students and creators. While AI-assisted coding is still a new field and needs more research, GitHub’s study provides a good look at what to expect from tools such as Copilot. At at time when rage and awe are concerning artists and designers regarding text-to-image generation, I wonder what other sorts of tasks upgrades will do to us in other professions.

If you’re a student and want to participate in the program, apply for the GitHub Student Pack to get started. And if you’re an open source maintainer, check out our FAQ to see if you qualify to start using GitHub Copilot at no cost.

Try GitHub Copilot Yourself

As for happiness and job satisfaction that’s going pretty far to claim. According to the GitHub’s findings, 60–75% of developers feel “more fulfilled with their job, feel less frustrated when coding, and can focus on more satisfying work” when using its Copilot tool. My internal devil’s advocate alarm bells are raging, but maybe you are more chill. Since I’m not a developer I can’t test it myself to see if I’m pacified into a new coding zone with Microsoft’s AI brains beside me.

Most academics who have looked at the study closely say that it's probably premature to say that Copilot has had an effect on students, because the software has only been publicly available for a few months.

Still I found it pretty interesting that Microsoft and GitHub are trying to quantify the benefits like this. I wonder if this means their uptake of GitHub Pilot is going well or going poorly. According to GitHub, more than 1.2 million developers used Copilot's technical preview in the past 12 months. Given how many actually use GitHub, that doesn’t sound to me like a huge number.

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I’ve started a new Newsletter where I will try to post content for software engineers and technology workers called Data Analytics Future, which might evolve into something interesting.

If I don’t see you again, have a good weekend.

Hey Guys,

It’s been a while since I’ve seen this topic covered. As a fan of both ESG and Green Technology and programming languages I was curious. So I looked it up.

Tl;dr here are the results:

I somewhat suspected C and Rust would be near the top here. This is based on a 2021, the original study was in 2017.

Top Green Programming Languages

1. C

2. Rust

3. Java

4. Swift

5. Haskell

6. C#

7. Go

8. Dart

9. JavaScript

10. TypeScript

“This paper presents a study of the runtime, memory usage, and energy consumption of twenty-seven well-known software languages. We monitor the performance of such languages using ten different programming problems, expressed in each of the languages. Our results show interesting findings, such as slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. We show how to use our results to provide software engineers support to decide which language to use when energy efficiency is a concern.”

This study implemented 10 benchmark problems in 27 different programming languages and measure execution time, energy consumption, and peak memory use.

𝗖 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲, 𝘄𝗵𝗶𝗹𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗮𝗻𝗱 𝗣𝗲𝗿𝗹 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗹𝗲𝗮𝘀𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀.

Full text: https://medium.com/codex/what-are-the-greenest-programming-languages-e738774b1957.

The original report with updated info: https://sites.google.com/view/energy-efficiency-languages/.

Post credit: I saw it on Dr. Milan Milanović’s account.

Read the PDF Paper

🌐 A very common misconception when analyzing energy consumption in software is that it will behave in the same way execution time does. In other words, reducing the execution time of a program would bring about the same amount of energy reduction. However power doesn’t follow this rule.

The Computer Language Benchmarks Game

The CLBG initiative includes a framework for running, testing and comparing implemented coherent solutions for a set of well-known, diverse programming problems.

You can see the complete results here.

I think in some ways the paper is very theoretical and novel and may not fully be totally representative of the challenge of the question, but is pretty amusing at least. I wonder a few years later if this would have changed using the same methodology. As new languages are rolled out like Carbon I wonder if they take this into consideration:

In Case you Missed It

Datascience Learning Center

Google Carbon Wants to be the Successor of C++

August 6th, 2022 8:00 PM MONTREAL, CANADA. I have a summer discount going to reward my early backers and most passionate readers. I’m really grateful for the support. Who actually reads on the weekend anyways? Hey Guys, At the end of July, 222 as you know, Google announced its new software programming language Carbon. The headlines were about how…

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4 years ago · 3 likes · Michael Spencer

The study is now about five years old:

The paper was popularized by the Rust people from AWS, and many software engineers don’t take it entirely seriously. However it’s highly shareable and stirs up some useful debate.

You can read a 2021 update to this question here.

There are many variables the study did not take into account according to commentators:

• Skill of the engineers themselves.

• Debugging energy

• Lifecycle energy cost

• Impact of JIT compilers of many languages

Still the article is a rare example of asking an important and tough question and trying to find the most relevant benchmarks.

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Hey Guys,

Thanks for reading Data Science Learning Center, I’m still getting the hang of this, this is still a relatively new publication. Recently I made columns at the top I will attempt to tackle (in so far as one person can do so!). They are:

• Data Science

• Machine Learning

• Programming

• Visualization

• Hacker News

• Business Science Center

Eventually I’m going to have to find other guest contributors who can fill out some of this content with me who are more qualified. If you or someone you know fits the bill get them to DM me on Twitter or LinkedIn. #AMA anything too, I’m always up for chat.

Or let me know what you want me to write about more.

This topic is for active supporters, I really appreciate your investment in me as I try to create educational and inspirational content for young aspiring software development engineers and the Tech crowd in general.

As such I have a time-limited Summer Deal. I really want to reward early supporters.

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So let’s get into it:

Programming languages: Meta’s Top Picks

Meta's primary supported server-side languages are Hack, C++, Rust, and Python. For performance-sensitive back-end services, we encourage C++ and Rust.

C++? Buy why not Carbon?

Facebook's Meta will give cash to support Python for Instagram.

About two weeks ago, Meta signed off on four main programming languages for developers at Meta. I found this story really interesting.

Can someone make a Substack on what software engineering questions FAANG companies ask in their interviews?

Software engineers and developers still want to work at Meta. With among the highest average mean salaries in Silicon Valley, who would blame them? (Video: Facebook Salary progression as a Software engineer).

Meta importantly is really powering Python and Rust currently.

Meta, which owns Facebook, has given $300,00 to the Python Software Foundation (PSF), the group that maintains Core Python (CPython) – the open-source programming language that powers most machine learning (ML) and artificial intelligence apps.

The company’s own Hack programming language is among the four supported languages moving forward; it’s a typesafe variant of the open-source PHP language that works on the HipHop Virtual Machine, and has been around since 2014 where it was first implemented by Facebook.

• Python

• Rust

• Hack

• C++

Thanks to the Reddit archives of Subreddit: Programming, I found the release notes on Hack, you know, 8 years ago.

In March, 2022 Meta invested more into Python’s Ecosystems. As the backbone to many Meta Services ranging from Instagram to its AI and data infrastructure, Python is very important to Meta.

CPython is the basis for other implementations of the language such as Anaconda (ZDnet) and Cinder, Facebook's implementation of it, which aims to boost Python performance for sharing photos on Instagram. 

"It’s important that every language we adopt is the best fit for a particular use case, so we do a high level of diligence whenever we evaluate a language," Meta told The Register.

Understanding’s Meta, Google and Microsoft’s relationship to the programming language ecosystem and evolution is relatively interesting.

"Language decisions tend to stick once they’re made, so we want to be deliberate from the onset to give our engineers the best tools to work with."

Meta’s Big 4 Programming Languages

TL;dr:

1. Hack

2. Python

3. C++

4. Rust

According to the Register, as might be expected, Meta has designated homegrown, open sourced Hack as a supported language. Hack is a typesafe variant of PHP that relies on the HipHop Virtual Machine (HHVM) and is the basis for much of Facebook. Meta recommends Hack for business logic and relatively stateless applications.

Python, one of the most popular programming languages around, also got the nod. Meta recommends Python for data science, ML applications, and Instagram-related code.

C++, the widely used general purpose programming language, received Meta's blessing for performance-sensitive backend services.

So too did Rust, also recommended for backend services focused on performance and for CLI tools. "There's a rapidly increasing Rust footprint in our products and services, and we're committing to Rust long term and welcome early adopters," the company said.

Meta’s Community Supported Languages

Other programming languages are said to be "community supported," which means those using them are left to their own devices to ensure things work properly. Java, Erlang, Haskell, and Go, are endorsed for specific use cases, but aren't supported outside of those situations.

• Java

• Erlang

• Haskell

• Go

Highest Earning Software Languages in 2022

According to Octoparse:

1. Go

2. Scala

3. Objective-C

4. CoffeeScript

5. R

6. TrypeScript

7. SQL

8. Java

9. Python

10. JavaScript

11. C++

12. C#

13. Perl

14. PHP

15. IOS/Swift

Go

Go programmers get paid at an average of $110,000 per year, and it has been on the top in recent years.

It has been used in:

1. Uber

2. SoundCloud

3. Netflix

4. Dropbox

5. Twitch

6. SendGrid

7. DailyMotion

8. Trivago

9. Alibaba

10. Capital One

11. PayPal

12. Slack and others.

Scala

Programmers equipped with Scala earn up to $110,000 a year.

Designed to be concise, many of Scala's design decisions aimed to address criticisms of Java.

1. LinkedIn.

2. AirBnb

3. Twitter.

4. Foursquare.

5. Netflix.

6. Tumblr.

7. The Guardian.

8. precog.

9. Sony, and others.

To see their full list go here.

That’s all for today. DSLC post frequency will vary from week to week and in posting time.

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I have yet another Newsletter that’s a “surprise box” of futurism topics here.

August 6th, 2022 8:00 PM MONTREAL, CANADA.

I have a summer discount going to reward my early backers and most passionate readers. I’m really grateful for the support. Who actually reads on the weekend anyways?

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Hey Guys,

At the end of July, 222 as you know, Google announced its new software programming language Carbon. The headlines were about how Google's Carbon is trying to be be a better C++. While Rust is super popular I wonder if over-time Carbon could compete with it?

Frustrated by the slow evolution of the C++, Google engineers have launched a new “experimental” open source programming language, called Carbon, as a possible successor to the venerable but aging C++.

Think about it, the way BigTech molds programming languages really is for their own benefit: Just as Microsoft built (NewStack) Typescript to update JavaScript, and Kotlin was created to shore up weaknesses in Java, Carbon could serve as a successor language (9to5) to C++, one that offers an easy jumping off point for developers to a newer language that addresses modern development concepts such as memory safety and generics.

First Look (Video)

According to Google developer Chandler Carruth, Carbon could serve as a successor language to C++. The language was unveiled on July 19th, 2022.

Dubbed the experimental successor to C++, I’m very curious to see what this becomes.

What is C++ Used for?

C++ is a powerful general-purpose programming language. It can be used to develop operating systems, browsers, games, and so on. C++ supports different ways of programming like procedural, object-oriented, functional, and so on. This makes C++ powerful as well as flexible.

It powers search engines, VR applications, air travel, movie production, oh okay!

• Operating Systems

• Browsers

• Search Engines

• VR applications

Bro, that sounds a lot like Carbon could be useful for the Metaverse!

Long the language of choice for building performance-critical applications, C++ is plagued with a number of issues that hamper modern developers, Carruth explained on a GitHub page.

Carbon on GitHub

Dart was one of the programming languages launched by Google which was object-oriented and a web-based programming language. Dart programming language didn’t gain a huge response from the developers and hence it never got the position of mainstream programming language.

The Rust Foundation has a budget of $625,000 (generously donated by AWS, Huawei, and Google) to run a grants program for the Rust community in 2022. Microsoft also joined Rust’s Foundation. How BigTech are involved in how programming languages evolve is pretty significant.

But anything is better than bureaucracy. Apparently the evolution of C++ is presently and historically stymied by a bureaucratic committee process, oriented around standardization rather than design. Which can make it difficult to add new features.  C++ has largely a sequestered development process, in which a select committee makes the important decisions, in a waterfall process that can take years.

Chandler is pretty low key. In that fateful early July, 2022 at the Cpp North convention in Toronto, as shared by Conor Hoekstra who was in attendance and documented the slides, Googler Chandler Carruth shared the vision for a new programming language called Carbon.

I could only find half a dozen articles on Carbon and you know copypastas. Do you guys have any insights on this?

Leave a comment

You can Get Started.

This might be a good time to learn Carbon, as it could likely be in demand in the years to come. While Carbon began as a Google internal project, the development team ultimately wants to reduce contributions from Google, or any other single company, to less than 50% by the end of the year.

The Rust - Carbon Connection in the Future of Code

As usual Google says it ultimately want to  hand the project off to an independent software foundation, where its development will be led by volunteers. Who knows who really pulls the strings in these Foundations? But I have my more deviant assumptions.

So who is Rust perhaps involved? Even as it becomes a favorite at Meta?

Many have dubbed Rust a C++ successor, however speaking at a recent event, Google Principal Software Engineer Chandler Carruth explained how the programming language that was initially a Mozilla product doesn’t have the same “bi-directional interoperability” as other tools, which introduces a type of ‘language barrier’ when ‘translating’ between different programming language.

Think about that.

Facebook parent Meta now encourages its engineers to use C++ and Rust programming languages for coding high-performance backend services. But for how much longer? Could there be another changing of the guard in a few years time?

As such, the newly announced Carbon should be interoperable with the popular C++ code, however for users looking to make the full switch, the migration should be fairly easy. This could potentially disrupt some adoption of Rust.

C++ has been struggling to improve and meet developers' needs for quite some time.

Carbon: In the Tooling section, we will go over the following to set up our environment:

1. Homebrew

2. Bazel

3. LLVM

4. Clone Carbon language

5. Run the Explorer

Succession Wars

I really quite like the “succession” metaphor in utility and natural selection of code.

Android developers well know that Kotlin serves as a successor to Java, just as iOS developers know Swift is the successor to Objective-C. TypeScript, from Microsoft, has thoroughly enhanced JavaScript, while remaining comfortable to use and able to be “transpiled” back to JavaScript.

Carbon < Rust > C++

Rust felt like a darling (AIM) of 2021 and especially 2020, but could its stock be fading as we head over into 2023?

Rust has been ranked as the most liked language by its users for two years in surveys but some programmers say otherwise. What could be Google’s motive for disrupting Rust’s momentum and all the Meta and Microsoft adoption it is seeing? Oh I wonder….

Down with those ideological “Rusticans!” They are getting too popular! We at Google who don’t know how to launch products, protests, we protest!

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I’m joking. This could go any number of ways, but be aware software is always eating the world and that means software is always evolving.

I have a summer discount going to reward my early backers and most passionate readers. I’m really grateful for the support. Who actually writes on the weekend anyways? I do.

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Interested in debating the future of Rust vs. Carbon? There’s a Reddit thread for that.

I’m doing a summer discount deal promotion here to kickstart my own motivation into another gear. If you want to “buy me a cup of coffee”, here is your chance.

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Hey Guys,

On LinkedIn I have an absurdly high audience of software programmers and software engineers. While I don’t personally know how to code, I’ve always been pretty curious about which languages are used for what and in what domain.

I’m definately code curious.

Software development folk can’t ever know too much about all these various tools and languages and I’m going to try to eventually cover these topics better. In a Newsletter, sometimes curation is more informative to your audience than totally original content or analysis.

The world needs software engineers and software developers to build the future. What languages you learn and know will often determine to some extent some of the existing opportunities. For as long as “software is eating the world” there will be a demand for software engineers.

Software Develops in the Great Resignation

Even as we’ve had two consecutive quarters of GPD declines, the labor force is so strong. Enterprise software developers are joining the Great Resignation, as they struggle to keep up with increased business demands and are bogged down by repetitive tasks in the wake of the COVID-19 pandemic, according to a new survey conducted by Salesforce's Mulesoft.

Stress on Software Developers

The top three factors leading to developer stress, according to the survey, were increased workloads and demands from other teams (39%), growing pressure to transform digitally (37%), and the need to constantly learn new skills to adapt to new technologies and approaches (35%).

• Increased workloads

• Digital transformation

• Need to constantly learn and adapt

Leave a comment

I’m curious how software programmers are navigating this bizarre period of the Great Resignation, stocks going down, startups faltering with less funding and less access to capital and the pressures of remote work and life-work balance.

That being said, let’s get into it:

Now let’s just read some generic fun stuff. The ordering of these may also seem somewhat arbitrary, which is okay.

Top Software Programming Languages for Quantum Computing

Quantum programming languages are the foundations to interpret ideas into instructions to be carried out by a quantum computer .

Physicists do tend to code in a lot of programming languages - like Matlab, Fortran, C++ - and getting access to a real quantum computer has historically been difficult. However, cloud quantum computing systems have opened up opportunities for anyone to start working with quantum computers. Python likely remains the best to learn for this.

Imperative and functional QC languages are also interesting to note. Imperative programming languages consist of step-by-step instructions to be performed in order to accomplish the desired result. In classical computers imperative languages include C, JavaScript, Pascal, Python, etc.

1. QMASM

Quantum macro assembler was published in 2016. It is a kind of low-level language that is specially used for quantum annealing. The significance of QMASM relieves the programmer from having to know system-specific hardware details while still allowing programs to be expressed at a low level of abstraction.

2. QML

QML was published in 2007, it is a Haskell-like quantum programming language that is based on strict linear logic. It has the capability to integrate reversible and irreversible quantum computations. It is a user interface specification and programming languages that allows developers and designers alike to create highly performant, fluidly animated, and visually appealing applications.

3. Silq

Silq was originally published in 2020. It is a high-level programming language when compared to the QCL and AMASM. It is written in D language, see it on GitHub.

4. QCL

Quantum computing language is one of the first implemented quantum programming languages that resembles C language in regards to syntax and data types. It is usually used for writing programs for quantum computers. As every quantum machine has to be controlled by classical devices, the pre-existing quantum programming languages incorporate classical control structures like loops and conditional execution and allow them to operate on classical and quantum data.

5. QFC and QPL

Semantically QFC and QPL are equivalent. However, in QFC, quantum programs are represented using flowchart syntax, but in the QPL syntactic structure of quantum, programs are represented using textual representations.

6. Quantum Lambda Calculus

Quantum Lambda Calculus: it is based on classical lambda calculus introduced in 1930 and was first defined for quantum calculations in 1996. It uses high-order functions (λx.x^3) Therefore, it is stronger than the standard quantum computational models, such as quantum Turing machine or the quantum circuit model.

Top Software Programming Languages for Web 3

1. Rust

Unlike Solidity, Rust is not crypto specific, making it accessible to the existing pool of developers. It is used by popular protocols such as NEAR, Compound and Solana. Like Solidity, Rust can be used to write smart contracts but is also popular in the gaming community.

2. Solidity

Solidity, one of the most established blockchain programming languages, was created for writing smart contracts on the Ethereum blockchain. As the first smart contract language running on top of the Ethereum virtual machine (EVM), it has been widely adopted. It is also heavily based around the well-known programming language JavaScript, making it approachable for developers hoping to learn new code.

“Solidity has a fantastic user community that supports a number of development tools for application development. Solidity is quickly establishing itself as a leading development language in the Blockchain community.” says Jim Sullivan, author of Programming in Solidity – Your Guide to Building Smart Contracts.

Although it is mostly associated with Ethereum, Solidity is growing in popularity, and it is positioned to dominate blockchain development. It gives users the ability to create quality decentralized applications. Several other blockchains such as Hyperledger Fabric support Solidity.

3. Python

Python has been ranked as the most in-demand coding language for 2022, according to  CodingNomads, which analyzed thousands of job postings in the U.S. and Europe. Renowned for its simplicity, it has a large community of followers worldwide.

Many blockchain developers opt for a hybrid approach when creating bespoke applications that have different requirements. This action may lead to Python being used as part of the overall development and another language used for the core architecture of a project.

Python is an open-source language that supports object-oriented programming. The code is said to be beginner friendly –– it also offers a huge library of assets and can be typed dynamically so that the user does not have to remember the complete architecture of the system they are building. This language has been used to create the LUNA blockchain, to create contracts for NEO and to develop smart contracts for Hyperledger.

4. Substrate

Substrate is a project born from the Polkadot blockchain. It is a completely free- and open-source project built using Rust programming language. The development of substrate happens through public channels like Github and Riot with contributors across a wide spectrum of projects. It is designed for creating fast and inherently safe software with a firm focus on popularizing a new decentralized internet infrastructure.

Substrate connects applications on public blockchains as well as creates bridges to other networks. When Polkadot is launched, you can feel secure that the technology that backs your blockchain is the same technology that powers multiple other production-level blockchains.

Greg Zaytsec and Alexander Mitrovich, founders of Unique Network believe that “flexibility is the strongest side of Substrate –– it allows unmatched customization that is needed to handle non-fungible asset classes on-chain. We used it to create the next generation NFT chain, capable of supporting innovative use cases and liberating NFTs from the collectables.”

5. C++

Many operating systems and games use C++ as their base language, making it popular in developer circles globally. With its long-standing reputation, it has multiple low- and high-level tools, plus extended versions that can be used. C++ is deemed to be a highly efficient and optimizable language suitable for blockchain transactions that require speed. As C++ is one of the oldest languages, it may be easier for developers to adopt. Significant blockchains like Bitcoin, Litecoin, Stellar and Monero use C++.

The principles of this language complement those of many blockchain projects in that the data is securely hidden and extra layers of security prevent data alteration.

Top Undervalued Programming Languages that Pay Well

I like the weird ones:

1. Haskell

Haskell is a general-purpose, statically-typed, purely functional programming language with type inference and lazy evaluation. Designed for teaching, research and industrial applications, Haskell has pioneered a number of programming language features such as type classes, which enable type-safe operator overloading.

2. Rust

Sponsored by Mozilla Research, Rust focuses on “type safety, memory safety, concurrency, and performance.” You can use Rust for distributed client/server applications and reliable system-level programming. Perhaps its newness is why fewer people queuing up to learn it. Going by this post, it doesn’t look like Rust will be on this list for long. Rust seems to have a much brighter future. It is one of the most undervalued programming languages that actually pays well.

3. Erlang

Erlang is a programming language used to build massively scalable soft real-time systems with requirements on high availability. Some of its uses are in telecoms, banking, e-commerce, computer telephony, and instant messaging. Although Erlang is certainly useful, it’s less accessible for beginners. The steeper learning curve can be discouraging for developers looking for a side project or for beginners who might prefer an easier-to-learn language. It is one of the most undervalued programming languages that actually pays well.

4. Dart

At one time, Google’s dart was all set to dethrone JavaScript as the language of choice for web development. Unfortunately, Dart got left behind by JS and the tech giant remodeled it along the lines of CoffeeScript (Dart-to-JavaScript compiler). Customer-facing web applications of AdSense and AdWords use Dart. Dart has users outside Google, such as Blossoms and Workiva. Despite its stronghold within Google, Dart will have to be sold to outside developers.

5. Ada

Ada is an internationally standardized, general-purpose programming language used in a wide variety of applications — from missile control to payroll processing to air traffic control. Ada is a state-of-the art programming language that development teams worldwide are using for critical software: from microkernels and small-footprint, real-time embedded systems to large-scale enterprise applications, and everything in between. It is one of the most undervalued programming languages that actually pays well.

Top Software Languages used by GitHub Contributors

Here I removed the Big3: Python, Java and JavaScript. So what we left with?

1. TypeScript

TypeScript is one of the fastest-growing programming languages. Its functionality is quite similar to JavaScript and possesses new features such as enumeration type and floating numbers.

2. C++

C++ is the enhanced version of C and is one of the most popular languages among Github contributors. It can create operating systems, games, and even more, which is why it is one of the most-used languages that has seen an increase in its performance ranking since last year.

3. Kotlin

Kotlin is a general-purpose programming language that is extremely interoperable with Java and supports functional programming languages. The language is extensively used for Android applications, web, desktop, and server-side application development. Kotlin was built to be a more prominent option than Java.

4. Ruby

Ruby was initially designed to have a more human-friendly syntax while still being flexible from the object-oriented architecture perspective, supporting procedural and functional programming notation. The facilities provided by this programming language have enabled several of its users to tout it as beginner-friendly.

5. PHP

PHP provides a strong ecosystem of resources for developers with a simple syntax on GitHub. PHP is a short form for Hypertext Pre-processor with an open-source scripting language to be executed on the server. This programming language on GitHub is deep enough to run on large social networks and the files consist of text, HTML, JavaScript, and PHP code. It helps to generate dynamic page content, collect form data, and encrypt data.

Anyways guys I hope you found this issue mildly informative or amusing.

Thanks for reading!

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