Do we even need to learn coding anymore? The question sounds very timely as tools such as Cursor and Claude Artifacts are making everyone build apps without writing a single line of code. Cursor, which is also basically a glorified fork of the VS Code IDE, is making developers wonder if this transition to building apps in natural language marks the end of traditional IDEs.

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IDE, or integrated development environment, is a code editor that allows developers to write, test, and debug code, combining multiple tools and features into a single environment. IDEs are essential for using programming languages and making useful software. 

The most famous IDE, VS Code, also allows developers to edit code such as IntelliSense code completion. However, with AI editors like Cursor, Zed, Magic, Codeium, or the most recent one Melty, integrating traditional IDEs into a developer’s workflow seems to have become redundant. 

Or Has It?

There was a recent surge of developers actively uninstalling VS Code because of Cursor IDEs. But it is easily possible for VS Code to simply add an update of AI-assisted coding onto its platform, which would eventually mark the end of Cursor. Some people also predict that Microsoft might simply end up acquiring Cursor in the future. 

John Lindquist, creator of egghead.io, said that he had a chat with Harald Kirschner, project manager at VS Code, about Cursor and VS Code recently, and the team is keenly aware of Cursor’s capabilities and there might be several things in the pipeline. “I think we’ll all be pleasantly surprised,” he said.

Other IDEs such as Jetbrains, PyCharm, and IDLE, are also facing a similar crisis as AI-generated coding gains traction. 

Regardless, modern generative AI coding tools are capable of integrating several open-source LLMs instead of a traditional IDE like VS Code, making it handy for several AI developers. “You can select code and ask questions based on that piece of code. So you don’t have to keep switching between the IDE and browser,” explained a developer on X.

But it simply means that most of the IDEs in the future would come with generative AI integration. It would become the default for IDEs as it was for integrating no-code and low-code platforms. 

Moreover, though it is becoming easier for non-developers to build apps, the task of building high-end software is still far away from these autocoding platforms. These tools can enable non-developers or those with minimal coding experience to create apps without needing to interact with an IDE, but those cannot be replicated everywhere. 

When it comes to experienced developers, AI tools can fasten the prototyping process by generating samples of code through prompts to quickly test ideas. But in the long run, when it comes to dealing with complex, customised, and large-scale projects, traditional IDEs still are better at debugging and handling crucial features.

The Future of IDEs is AI-Assisted

Before talking about the end of IDEs, it is essential to understand what they stand for. With IDEs, developers have a standardised environment with a defined codebase for meeting specific requirements. This consistency of code cannot be fully controlled by an automated code generation platform like Cursor, or others. 

On the contrary, future IDEs could take this further by generating larger chunks of code, or even entire modules, based on brief descriptions or prompts. The future of software development is likely to see AI-integrated IDEs becoming the norm. This would be helpful in analysing code in real-time and automatically correcting them. 

Furthermore, with generative AI integrated into IDEs, these code editors would be able to suggest context-aware code, which is not just based on the syntax, and even optimise it. This would also enable a personalised developer style of coding, enabling natural language prompting with coding, much like Cursor and Claude. 

AI-integrated IDEs will likely combine traditional development tools with generative coding, making software development more efficient, intuitive, and accessible while still providing the depth needed for complex projects. This would enable developers who know coding to take generative tools to the next level. 

But at the same time, it would be difficult to manage generative AI code if it is available within the IDEs as people still would be hesitant to trust generative AI for boilerplate code. It could also result in the loss of coding skills for skilled developers. 

The post Will AI Coding Tools Mark the End of IDEs? appeared first on AIM.

“Programming is no longer hard” or “everyone’s a developer” are the most common phrases that one would hear on LinkedIn or X as everyone is basically talking about Cursor, Claude, or GitHub Copilot. But the problem is that most of the people who claim so are not developers themselves. They are merely ‘modern developers.’

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Santiago Valdarrama, founder of Tideily and an ML teacher, who has been actively asking developers if they are using Cursor or not, started another discussion that tools such as Cursor and others are basically tools that can only assist existing developers in writing better code. “Wake me up when companies start hiring these clueless modern ‘developers’,” he added.

He gave an analogy of calling yourself an F1 racer after playing a racing game on an iPad.

In all honesty, it is undeniable that the barrier to entry for becoming a developer has dropped significantly ever since Cursor, even ChatGPT, dropped. People have been able to build software for their personal use and even build apps in mere hours. But, this does not eliminate the fact that it is currently only limited to just creating such apps and low level software.

“You Can’t Correct Code if You Don’t Know How to Code”

Given all this hype around the end of software engineering roles, developers and programmers are getting worried about the future of their jobs. It is indeed true that software engineers have to upskill faster than anyone else, but the fear of getting replaced can be pushed off to at least a few years.

Having tools such as Cursor and Claude are only good enough if a developer actually knows how the code actually works. The real game-changer is how developers who use AI will outpace those who don’t. “The right tools can turn a good developer into a great one. It’s not about replacing talent; it’s about enhancing it,” said Eswar Bhageerath, SWE at Microsoft. 

AI only takes care of the easy part for a developer – writing code. The real skill that a developer has is reasoning and problem solving, apart from fixing the bugs in the code itself, which cannot be replaced by any AI tool, at least anytime soon. Cursor can only speed up the process and write the code but correcting the code is something that only developers can do.

Moreover, bugs generated within code with AI tools are not easily traceable by developers without using any other AI bug detection tool. Andrej Karpathy, who has been actively supporting Cursor AI over GitHub Copilot, also shared a similar thing while working. “it’s slightly too convenient to just have it do things and move on when it seems to work.” This has also led to the introduction of a few bugs when he is coding too fast and tapping through big chunks of code.

These bugs cannot be fixed by modern ‘developers’ who were famously also called ‘prompt engineers’. To put it simply, someone has to code the code for no-code software.

Speaking of prompt engineers, the future will include a lot of AI agents that would be able to write code themselves. The future jobs of software engineers would be managing a team of these AI coding agents, which is not possible for developers who just got into the field by just learning to build apps on Cursor or Claude. It is possible that the size of the teams might decrease soon as there would be no need for low level developers.

Upskilling is the Need of the Hour

That is why existing developers should focus on developing engineering skills, and not just coding skills. Eric Gregori, adjunct professor at Southern New Hampshire University, said that this is why he has been teaching his students to focus more on engineering than just programming. “AI is too powerful of a tool to ignore,” he said, while adding that existing limitations of coding platforms have been removed completely. 

“Hopefully, AI will allow software engineers to spend more time engineering and less time programming.” It is time to bring back the old way of learning how to code as modern developers would be tempted to just copy and paste code from AI tools, and not do the real thinking. 

The F1 driver analogy fits perfectly here. Most people can learn how to drive, but would never be able to become a race driver. The same is the case with the coding tools. But if all people need is prototyping and designing an initial code, AI driven developers would be able to do a decent enough job.

That is why a lot of pioneers of the AI field such as Karpathy, Yann LeCun, Francois Chollet, even Sam Altman, say that there would be 10 million coding jobs in the future, the ones that would require the skills of Python, C++, and others. As everyone in some way would be a ‘modern developer’, and most of the coding would be done by AI agents. 

It is possible that most of the coding in the future would be in English, but most of it would be about debugging and managing the code generated by AI, which is not possible for someone who does not know coding from scratch.

The post Wake Me Up When Companies Start Hiring Clueless Modern ‘Developers’ appeared first on AIM.

The barrier for entry to become a developer is dropping everyday. The most recent phenomenon that everyone is still talking about is Anysphere’s Cursor AI coding tool, which has basically made everyone a developer. Now, there are more tools coming up in the same category such as Codeium, Magic, and Zed AI, all of them trying to come up with the same formulae. 

This definitely brings in the question – what would happen to the software developers of today? Graduating out of colleges with computer science degrees in a world to compete with the people who are becoming software engineers with AI tools, the turmoil of an average software engineer is real.

The solution is easier said than done – upskill yourselves and focus on higher order things such as building foundational AI. Even 8-year-olds are building apps using Cursor AI in 45 minutes. 

A Profession Like Never Before

Since there is no barrier to entry, no degree requirements, and no regulations about who can join the market, software engineering has become a profession that has never happened ever in the history. There are plenty of opportunities for developers to upskill.

But “upskill to what?” is what people ask. 

The conversation from LLMs to SLMs to coding assistants to AI agents keeps changing so swiftly, it can be challenging to determine which new skills are worth acquiring. This question reflects a broader uncertainty about how to prioritise learning in a field where the next big thing seems just around the corner.

Saket Agrawal, a developer from IIT Guwahati, said that it is not as much about the technological shift but the advancement of automation tools that reduce the time and efforts for the same skills. “I don’t see any big threat to existing software skills suddenly and software has been the field all the time which needs continuous skills updation based on requirement without leaving your old skills instantly,” he said. 

Another user on X put it in a funny way. “Software engineers need more updates than my grandma’s Windows 95. Ever tried explaining AI to her? It’s like defining gluten-free bread to a caveman!”

It is widely discussed that a lot of software engineering jobs are dying. “Winter is coming for software engineering,” said Debarghya Das from Menlo Ventures, saying that many of the current software engineering jobs would become a distant memory. 

Scott Stouffer adds another layer to this conversation by suggesting that some are experiencing an upgrade in their lives at a pace that surpasses others. This notion of being “upgraded” faster could imply a divide between those who adapt quickly to technological advancements and those who struggle to keep up.

LLMs to Upskill?

While there is a very interesting caveat to all of this conversation around upskilling. Hardcore skilled developers believe that leveraging tools such as Cursor and others can take them to another level where the new developers would never be able to reach. Yann LeCun has already told developers getting into the AI field to not work on LLMs.

Andrej Karpathy recently said that the future of coding is ‘tab tab tab’ referring to auto code completion tools such as Cursor. Further in the thread, he added that with the capabilities of LLM shifting so rapidly, it is important for developers to continually adapt the current capabilities.

Some people are sceptical if they even should get into the computer science field anymore. “…if I was new to programming I would be too tempted to skip actual learning in favour of more LLM usage, resulting in many knowledge gaps,” said a user replying to Karpathy. This truly feels like the true way forward for many developers. 

This is similar to what Francois Chollet, the creator of Keras, said a few months ago. “There will be more software engineers (the kind that write code, e.g. Python, C or JavaScript code) in five years than there are today.” He added that the estimated number of professional software engineers today is 26 million, which would jump to 30-35 million in five years.

This is because developers who are proficient with coding without code generators can never be replaced. People who built programming languages and foundational tools are still very well versed with coding than people who are just using Cursor to build apps. Sure, there can be an abundance of people building apps in the future, but the scope would just be limited to that.

Meanwhile, highly skilled 10x developers would be focusing on leveraging such tools, or possibly finding flaws in them, to create even better software. So to say, creating the next Cursor or ChatGPT.

There is an abundance of things that can be done. For instance, focusing on enhancing hardware or building infrastructure for running future workloads can only be comprehended by experts in the field. For example, companies such as Pipeshift AI, Groq, Jarvis Labs, and many others who are working on different problems than coding. 

The truth is that such AI tools can never replace human intelligence or jobs, only augment them. “Generating working code is only a part of the responsibility,” said VJ’s Insights on a post on X. Though “Yes, if you are someone who *just* writes code, you need to start thinking differently.”

In the near future, there are predictions that the future of software engineering would be about managing a team of AI agent engineers, and telling them how to code. This will make every engineer akin to an engineering manager, delegating basic tasks to coding agents while focusing on higher-level aspects such as understanding requirements, architecting systems, and deciding what to build.

It is high time that software engineers start upskilling themselves, and currently, it looks like using generative AI tools is the best way forward, not without them. Who knows, you might also become a solo-entrepreneur building a billion dollar company alone. 

The post Software Engineers Have to Upskill Faster Than Anyone Else appeared first on AIM.

According to a recent study, only 2.5% of engineers of India possess any AI skills, with only 5.5% qualified with basic programming knowledge. Though Indian IT companies are looking to change that as they actively upskill their employees, this report did not resonate well with the Indian engineers.

In a recent Reddit discussion, a significant number of Indian engineers expressed concerns over the programming skills of college professors, with many claiming that most professors in Indian colleges lack the necessary expertise to teach programming effectively. 

“Most people learn what college teaches them and nothing more. Most college professors themselves don’t know programming,” said a user in the discussion, which was highly resonated amongst college students.

This discussion highlighted a growing discontent among students and professionals who feel that the quality of education in computer science and related fields is being compromised due to the inadequacies of teaching staff.

One of the key points raised in the discussion was the tendency of professors to rely heavily on external resources like YouTube channels and online courses rather than teaching from their own knowledge and experience. “My college professors used to watch GateSmashers videos for teaching Computer Networks to us. They even wrote the same examples from the video, and when asked questions, they told us to watch the videos.” 

The situation is particularly dire in Tier-3 colleges, where the quality of teaching is perceived to be the lowest. A user said that their professor even copy-pasted the same screenshots from the video in his PowerPoint presentation. “I am not sure for what reason professors take salaries if they can’t even make their own original PPTs,” he added. 

Who to Blame?

Earlier, AIM wrote that there is a dire need for Indian researchers and professors to move beyond PhDs as most of them keep publishing papers. “Though Indian universities produce some very good engineers, they are very successful in the West,” Amit Sheth, the chair and founding director of the AIISC, told AIM earlier.

Similarly, Adarsh Shirawalmath, the founder of Tensoic, told AIM that though his college has been helpful, it is still years behind. “We are lagging a bit in terms of where the SOTA is and what we are doing because some of the professors still might be researching on CNN whereas the SOTA is really ahead,” he said. 

The issue is critical. These days, colleges make it mandatory for students to complete online courses on subjects already included in the syllabus. “What’s the point of paying the teachers then?” asked a user, which aptly explains the core issue of it all.

The root of the problem seems to be the significant gap between the salaries offered to college professors and what they could potentially earn in the private sector. “Let’s talk about 95% of teachers working in the rest of the colleges in India. Why would someone work as a teacher getting paid at most 10 to 12 LPA if they were any good?”. This is the valid question that engineers should be asking.

“The market will pay 30 LPA to anyone decent.” This discrepancy in pay makes it difficult for colleges to attract and retain talented professionals, leading to a situation where those who do become professors are often not the best in the field.

This sentiment was echoed by others, who argued that the incentive structure is fundamentally flawed. Nobody half good wants to be a computer science teacher when they can get paid at least double that much in an equivalent experience IT job.

Still Hope

This is a critical issue, as it suggests that the teaching profession in India is not seen as a viable career option for skilled professionals, which in turn affects the quality of education that students receive. On the other hand, becoming a university professor at IITs and NITs is still something engineers strive for. Which is why the faculty at these premier institutes is good enough and training world class talent. 

That is also why a lot of India’s obsession with STEM degrees is creating a generation of jobless graduates, as universities apart from these premier institutes struggle to provide decent skills for the market. Moreover, not all students are capable of self-learning, which further complicates the issue. 

Despite the general dissatisfaction, some users suggested potential solutions to the problem. One such suggestion was to hire more professors with industry experience. Some experienced people should take up the plan to teach in college after retiring from the corporate sector. “We need more professors with industry experience. I would readily learn from some professor with good industry experience of 5 years rather than a PhD professor,” said an engineer. 

The over-reliance on external resources, the disparity in pay between professors and private sector employees, and the lack of practical industry experience among teaching staff are all contributing factors to this issue. 

The ideal way forward is to increase the barrier of entry for becoming a professor for computer science, which would end up making the professors upskill themselves. At the same time, the compensation offered to these professors needs to be substantially increased as well, to match with high paying jobs of the market. 

The post Indian Engineers Say Most College Professor Don’t Know Programming appeared first on AIM.

Cursor, an AI-powered product built by Anysphere, is transforming the developer community, helping them turn ideas into reality across sectors. From empowering an eight-year-old to build a chatbot within an hour to building a financial dashboard using only voice, Cursor AI is currently reeling in its force multiplier era. 

At its core, what pushes Cursor AI ahead of the curve are its seamless features like integration to workflow, customisations, and GPT-4 assistance, that democratise coding. Some of these features are available in its free version, while the paid plan is more suitable for larger teams or those with specific, and more advanced requirements. 

Meanwhile, developers and tech enthusiasts have taken over the internet to share their insights on leveraging Cursor AI. It is interesting to note their custom prompts and workflows that have led to building apps and features in record time. 

Cursor 101

Corbin Brown, a creator and an investor with 49.9K YouTube subscribers, has made a Cursor 101 explainer video for beginners. The video highlights features like codebase integration, AI driven edits, and privacy first approach making the tool extremely secure and easy to use for developers. 

Web Apps using Cursor

Abdul Majed Raja, who specialises in AI, ML and open source, has built a community of 79.7K subscribers on YouTube. He has been covering several aspects of Cursor AI through his lucid tutorials around building a Chrome extension, creating a web app, and a JS game – all from scratch. 

Building Financial Dashboard using Cursor

Mckay Wrigley, the founder of Takeoff, made a tutorial video on building a financial dashboard in 5 minutes. Additionally, he is also offering a paid course with a 20% launch discount for those interested in a comprehensive understanding of the tool and its potential. 

UI/UX Using Cursor

Meng To, a Singapore-based designer, built his dream video editor using prompts within three weeks – something which usually takes way longer. While his post is taking X by storm, he is on a mission to teach design to coders and coding to designers. 

Real-time Problem Solving Using Cursor

Ray Fernando, a former engineer with Apple, built an entire fully functional app in real time on livestream using Cursor AI. His method of teaching is gaining traction online, where he talks about the basics of coding, integrating OpenAI’s GPT-4, and follows up with real-time problem-solving. 

Cursor for Beginners 

Steven, a self-taught developer with eight years of experience, aims to democratise his knowledge about web development and coding through easy-to-learn videos. He delves into the features, user interface, and ways in which it speeds up your workflow. 

Enhance Productivity using Cursor

Zachary Lee, an NYC-based tech generalist, focuses on deconstructing LLMs and AI products in his videos. In this short tutorial, he talks about Cursor AI’s four core features, like inline editing, code generation, and cursor chat that can be leveraged to maximise productivity. 

Build AI Projects with Cursor

Kevin Kernegger, the founder of Macherjek GmbH with over a  decade of experience, teaches a nine-part course on skillpark.ai for $99. In a tailored manner, he helps individuals create their own AI projects. 

With the hype taking over the internet, software engineers claim it is tough to return to a time when this tool didn’t exist. Its adoption in the non-tech industry is also on the rise – especially its potential within the design community.

The post 8 Resources to Help You Learn Coding with Cursor appeared first on AIM.

Microsoft is not shy of giving developers what they want. After Excel, and possibly Windows as well, the company introduced Copilot with GitHub. But somewhere there, VS Code, which has one of the largest footprint as a software for developers in today’s world, got lost for the recognition of its contribution.

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“Not sure if Microsoft can truly comprehend the true footprint of VSCode,” said Jaana Dogan, principal engineer at Google. But at the same time, there is an apparent spree of people not liking what VS Code has become, stale. Though the abundance of repositories on GitHub that are forks of VS Code is undeniable, the new tools in the market are making VS Code struggle.

“Just uninstalled VS Code,” said a developer on X. Why? Probably because of the release of Cursor AI, which is touted as the ChatGPT moment in coding, is like the final nail in the coffin of VS Code. But is it really the end of VS Code?

What are the Problems?

There are several problems with VS Code, which is undeniable. Mohamed Yamani, a front end software engineer, said that VS Code for Python development sucks, explaining that it was not highlighting the problems with his code. Developers in the thread agree that they often end up using other IDE’s such as Jetbrains or even vim for Python. 

Even for C# and other languages, though VS Code has been introducing several updates, developers are not happy. Also, it is very easy to install malicious extensions through VS Code.

Coming to Cursor, which is basically a glorified fork or extension of VS Code, is capable of integrating several open source LLMs instead of an IDE like VS Code, making it handy for several AI developers. “You can select code and ask questions based on that piece of code. So you don’t have to keep switching between the IDE and browser,” explained a developer on X.

VS Code works well, but when it comes to AI development, it falls majorly behind Cursor with the integration of Claude 3.5 Sonnet, and other LLMs. Though VS Code allows integration of other LLMs such as Phi-3.5 or GPT-4, Cursor is just way more flexible with the availability of LLMs including Llama 3.1.

Alex Sidorenko, a React developer, made a video comparing VS Code with Copilot vs Cursor using Claude 3.5 Sonnet for Next.js App Router, where Cursor felt more aware and easier.

Cursor’s coding and AI capabilities should be a wake up call for Microsoft to make VS Code integration with GitHub Copilot a lot easier. “My assumption is that Cursor’s sudden surge in popularity will wake Microsoft up and they’ll make VS Code/GitHub Copilot integration a lot better, landing in a few months,” said Jamon Holmgren, the founder of Infinite Red.

Is it too late for Microsoft’s VS Code or GitHub Copilot to catch up? 

Cursor is Just One VS Code Update Away from Extinction

Exactly a year ago, Cursor was touted as if VS Code and ChatGPT had a baby. Cut to today, it is being called as a replacement for VS Code and GitHub Copilot. Conversations around dropping VS Code started exactly a year ago with the introduction of Cursor. But VS Code is still going strong. 

In a Reddit discussion, as developers were discussing the capabilities of Cursor and VS Code, they were willing to bet big on VS Code in the long run, as Cursor built by a small startup would struggle to compete with Microsoft. “Even if Cursor survives through the next year, I would go with VS Code just to have something standard and mainstream to compare it to,” said a user.

John Lindquist, creator of egghead.io, said that he had a chat with Harald Kirschner, project manager at VS Code, about Cursor and VS Code recently, and the team is keenly aware of Cursor’s capabilities and there might be several things in the pipeline. “I think we’ll all be pleasantly surprised,” he said. 

It seems like a Cursor like update for VS Code is coming soon, which might possibly make Cursor obsolete. Or to survive, Cursor might end by getting acquired by someone like OpenAI or Anthropic. 

But when it comes to VS Code, given the massive footprint it has, plans in the pipeline, it is hard to just uninstall it and move on. The concept of making everyone a developer using natural language, is not unknown to Microsoft. 

The post Why Developers are Uninstalling VS Code appeared first on AIM.

Claude + Cursor = AGI. This is basically what the conversation is around coding right now everywhere on X. The hype around Cursor AI was just not enough, and then Andrej Karpathy added that he is choosing to use it instead of GitHub Copilot from now on. Is the future of coding in natural language already here?

Karpathy is the one who said more than a year back that English is the hottest programming language. Now for Cursor, he says, the future is ‘tab tab tab’ and that “The tool is now a complex living thing.” His support for Cursor even made people question if he is working with, or supporting, the team at Anysphere, the creators of Cursor, in some way.

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Further in the thread, he added that with the capabilities of LLM shifting so rapidly, it is important for developers to continually adapt the current capabilities. This argument has resonated by many developers that it is increasingly becoming difficult to catch up with the new tools in coding.

Is it now time to bring back the old days of learning how to code? A user replied to Karpathy saying that they are very grateful that they had time to learn computer science prior to the advent of AI tools. “…if I was new to programming I would be too tempted to skip actual learning in favour of more LLM usage, resulting in many knowledge gaps,” the user added.

Karpathy agreed that it is a very valid concern and he feels that “it’s slightly too convenient to just have it do things and move on when it seems to work.” This has also led to the introduction of a few bugs when he is coding too fast and tapping through big chunks of code.

But, Cursor AI is Here to Stay

Overall, all of the coding assistant tools have given productivity gains for organisations. Before Cursor, developers in companies were relying on GitHub Copilot for coding faster, which was overall reported to have increased the productivity of the teams. But it definitely brings the question of how people would learn coding from scratch from now on.

It is almost as if Cursor is bringing a ChatGPT moment for coding. Earlier, Ricky Robinett, VP of developer relations at Cloudflare, posted a video of his eight-year-old daughter building a chatbot on the Cloudflare Developer Platform in just 45 minutes using Cursor AI, documenting the whole process, even the spelling mistakes while giving prompts! Even Jeff Dean, chief scientist at Google DeepMind was fascinated by it.

“It is the future,” said several developers who reposted the video. People who started using Cursor have started implementing their own tricks. The most common one currently is using Claude 3.5 Sonnet with Cursor AI, as it allows people to use other open source LLMs and architectures such as Replit, Tailwind, React, Vercel, Firebase and many more, as well in their workflows.

Developers have built complex projects using Cursor in just hours, without even writing a single line of code. Plus, using LLMs, the code generator could also explain the use cases and meaning of the code, which in the end assists in learning how to code as well. “If you’re a technical founder, Cursor + Claude 3.5 Sonnet is a legit team of senior software engineers,” said Sahil Lavingia, founder of Gumroad.

Wake Up Call for Others?

Francois Chollet, the deep learning guru and creator of Keras, said that he is interested in watching developers stream while programming using Cursor and Claude and another developer who is really good at coding, and compare how generative AI has worked for the former one. 

Chollet had earlier also said that it would be great if someone could fully automate software engineering as he could move on to working on higher things, which is what Cursor AI is slowly ushering in. 

Meanwhile, there is another tool in the market called Zed, which is released in partnership with Anthropic, the creator of Claude, which several developers claim is better than Cursor and VS Code as well. 

The same was the case with GitHub Copilot and even Cognition Labs’ Devin. Cursor’s capabilities should be a wake up call for Microsoft to make VS Code integration with GitHub Copilot a lot easier. Basically, Cursor is also a glorified VS Code extension. 

Devin, on the other hand, is still to be released, which might create a new era of programming as well. Probably, replacing Cursor AI, or an entire software engineering team. 

It is clear that most of the upcoming generation of developers would want their code to be generated completely by AI, which GitHub Copilot in many instances was failing to do. But the issues with generated buggy code still exist with Cursor, which might get fixed with future iterations. 

The post The Future of Coding is ‘Tab Tab Tab’ appeared first on AIM.

Sarvam AI, or what we call the OpenAI of India, is probably one of the most celebrated startups by Indian developers. Its focus on Indian languages and open source philosophy has been the cornerstone for AI development in India. 

Recently, at Sarvam’s recent launch event, the company announced Sarvam 2B, its open source foundational model trained on 4 trillion tokens of data with 50% of it in 10 Indic languages. 

According to co-founder Vivek Raghavan, Sarvam 2B is among other small language models in the market, such as Microsoft’s Phi-3, Meta’s Llama 3, and Google’s Gemma Models, which were all decent enough for Indic AI development. 

But is Sarvam 2B really good enough for Indic AI developers?

First, to clear the air, the model uses a slightly modified Llama architecture, but is trained from scratch. This means there is no continued pre-training or distillation, making it the first foundational model built entirely for Indic languages. 

We can also call it an ‘Indic Llama trained from scratch’. This is several steps ahead of the company’s earlier approach of building OpenHathi, which was a model fine-tuned on top of Llama 2.

When compared to other models, Sarvam 2B outperforms GPT-4o and gives tough competition to Llama 3.1-8B and Gemma-2-9B in speed and accuracy for Indic languages.

The End of Indic Llama?

Models from Google, Meta, or OpenAI, have been increasingly trying to do better at Indic language translation and inference, but have been failing big time. Indian developers have been fine-tuning these models on billions of tokens of Indian language data, but the quality hasn’t necessarily improved. 

Plus, these models have rarely been adopted for any production. “The best thing about Sarvam’s 2B model is that it puts an end to the Indic Llama garbage,” said Raj Dabre, a prominent researcher at NICT in Kyoto and a visiting professor at IIT Bombay. 

Pratik Desai, the founder of KissanAI, agrees with Dabre and said that none of those models were useful for production. He still has those same doubts about Sarvam 2B, even though it is a great stepping stone towards better Indic models.

“I doubt it will be used in production. Actually none of the 2B models are good enough to serve paid customers,” said Desai, adding that even models such as Gemma 2B are not useful – not even for English use cases.

Desai further explained that LLMs with 2 billion parameters struggle with following consistency and instructions even for summarisation, and are only good enough as helper models. But since Sarvam is also planning to release more versions in the future, these issues might get resolved eventually. 

Regardless, the small size of the models make them useful for edge use cases and on-device LLMs. This, along with an audio model called Shuka released by Sarvam, would be helpful for the developer community to experiment with. But taking these models into production is still a hard decision to make as the output can become unreliable for several use cases, as is the case with other English models of the same size.

Along with this, the company also released Samvaad-Hi-v1 dataset, a collection of meticulously curated 100,000 high-quality English, Hindi, and Hinglish conversations, which is an invaluable resource for researchers and developers. 

A Good Start

One of the key aspects of Sarvam 2B’s brilliant performance is it is completely trained on synthetic data, which is similar to Microsoft’s Phi series of models. Even though the debate around using synthetic data is still on, Raghavan said that it is essential to use synthetic data for Indian languages as the data is not available on the open web. 

Furthermore, using synthetic data generated by the Sarvam 2B model would also help in improving the model’s capabilities in the future, or would make it run in circles. 

Raghavan has also said that the cost of running Sarvam 2B would be a tenth of other models in the industry. With APIs and Sarvam Agents in the pipeline, it is clear that Sarvam’s sovereign AI approach is helping them build great models for Indian developers. 

The post Is Sarvam 2B Good Enough for Indian AI Developers? appeared first on AIM.

“A lot can happen in six months,” Ola chief Bhavish Aggarwal, revealed a bunch of announcements about its AI venture Krutrim at Sankalp 2024. The launch of Krutrim six months ago has led to several chains of events, including the Microsoft Azure Cloud and Google Maps exit. Now, Krutrim is finally showing its love for Indian developers.

Apart from a bunch of announcements about electric bikes and the launch of BharatCell, the event was fully dedicated to Krutrim’s announcements and future roadmap. 

According to the company, since its launch six months back, Krutrim Cloud has received around 250 billion API calls and has around 25k developers using the platform, with one trillion tokens generated so far. Aggarwal also added that the company aims to build India’s first AI cloud in its own data centres which will be powered by the company’s own Bodhi chips, slated to be released by 2026, with Bodhi 2 by 2028.

Moreover, Krutrim Cloud will be free for developers till Diwali this year. 

Krutrim has announced a significant expansion of its cloud services, unveiling over 50 new offerings on its Krutrim Cloud platform. These services are designed to cater specifically to the needs of Indian developers and include AI Pods, AI Studio, a language hub with multimodal translation capabilities, and customer experience AI.

Full Stack AI Platform

Back in May, the AI unicorn launched its first LLM, Krutrim-7B-chat, trained on ten Indian languages, on the Databricks Marketplace for free. This was announced by Gautam Bhargava, VP and head of AI engineering at Krutrim.

This time, Bhargava announced that Krutrim has the best Indic tokeniser when compared to GPT-4o and Gemini, but falls behind when compared to Llama 3.1.

Moreover, speaking about the speech models, Krutrim model is on par with AI4Bharat’s IndicTrans2, but beats Gemini and Azure. When it comes to visual world understanding, Krutrim’s model competes closely with Idefics-2.

Krutrim is also launching another AI developer app called Bhashik, a multimodal language hub that can translate videos into different languages. “This event is currently being live-streamed in multiple languages through the Bhashik platform,” said Aggarwal. He also said that edtech startup Unacademy is also leveraging Ola Krutrim’s translation API for its language learning app.

During a scripted demo, Ola Krutrim previewed an upcoming image recognition feature that will be added to the AI model later this year. Aggarwal also showcased how Dashtoon, an early stage startup, uses Krutrim AI Cloud for training and fine-tuning its models. 

When it comes to Ola Maps, Aggarwal mentioned that it is expanding its API for developers to encompass over 95 per cent of use cases, including routing, places, maps, tiles, and SDKs. Moreover, another startup, Together, is using Ola Maps for enhancing its customer experience, competing with MapmyIndia. “It is the best maps out there,” added Aggarwal.

For developers, Ola unveiled AI Pods for affordable GPU access, an AI Studio for creating complex AI applications, a model catalogue featuring LLMs and vision models, as well as capabilities for no-code/low-code training, fine-tuning, inference, and model evaluation.

Starting today, Krutrim Cloud on AI Studio will be available for developers and provide no-code and low-code computing platforms tailored for Indian use cases, along with offerings like GPU-as-a-service and model-as-a-service, with Krutrim H100 tiny for 1/25th of the price on AI Pods, which is an abstraction service with GPU slicing for resource efficiency.

Moreover, Ola has also launched the Udaan startup programme. Startups will get Ola Maps credit worth up to Rs 50 lakhs free for three years on Krutrim Cloud and access to mentorship and Ola’s partner ecosystem.

The India Chip Revolution Begins 

When it comes to building an AI chip in India, Sambit Sahu came on stage and said that the company is on the roadmap to challenge SOTA AI chip performance by 2028 with Krutrim Silicon. Sahu unveiled three distinct chip families—Bodhi for AI, Sarv for general computing, and Ojas for edge computing.

The Bodhi chip is specifically engineered for complex AI workloads. It focuses on enhancing the speed and efficiency of AI systems. By 2028, Krutrim aims to launch Bodhi 2, an advanced version expected to rank among the top-performing AI chips worldwide. It will be able to run ten trillion parameter models.

Krutrim also plans to produce its first AI silicon chip by 2026.

To achieve its chip development goals, Krutrim has formed strategic partnerships with leading technology firms Arm and Untether AI. “Basically, we would be the best in class in performance per rupee and performance per watt,” added Aggarwal. 

When comparing image classification capabilities, Sahu showcased a demo where Krutrim’s Bodhi 1 was able to classify one million images faster than the leading AI chip, possibly NVIDIA’s offering. Adding to that, Aggarwal said that Krutrim will set up a gigawatt scale data centre by 2028. Today, the company’s capacity is at 20 megawatt. 

“There is no need for developers to look outside Krutrim,” said Aggarwal. 

Ironically, it looks like Ola Krutrim is also trying hard for the sovereign AI approach, similar to Sarvam AI, and naming its products strikingly similar to already existing ones in the market like Sarv-1 with Sarvam and Bhashik with Bhashini.

(FYI: AIM was present at Sankalp 2024 representing the developer community)

The post Ola Krutrim Brings an Early Diwali for Indian AI Developers appeared first on AIM.

The race for building a team of AI software engineers doesn’t stop. After Cognition’s Devin, Cosine, the human reasoning lab, has introduced Genie, hailed as the most capable AI software engineering model globally, achieving 30.08% on SWE-Bench evaluations. 

Genie is designed to emulate the cognitive processes of human engineers, enabling it to solve complex problems with remarkable accuracy and efficiency. “We believe that if you want a model to behave like a software engineer, it has to be shown how a human software engineer works,” said Alistair Pullen, the founder of Cosine.

Moreover, this UK-based AI startup Cosine has secured $2.5 million in funding from SOMA and Uphonest, with additional investment from Lakestar and Focal and is part of the YC-W23 batch. 

As the first AI Software Engineering colleague, Genie is trained on data that mirrors the logic, workflow, and cognitive processes of human engineers. 

This allows it to overcome the limitations of existing AI tools, which are often extensions of foundational models with added features like web browsers or code interpreters. Unlike these, Genie can tackle unseen problems, iteratively test solutions, and proceed logically, akin to a human engineer.

Genie has set a new standard on SWE-Bench, achieving a score of 30.08%, a 57% improvement over the previous best scores held by Amazon’s Q and Code Factory. 

This milestone not only represents the highest score ever recorded but also the largest single increase in the benchmark’s history. Genie’s enhanced reasoning and planning capabilities extend beyond software engineering, positioning it as a versatile tool for various domains.

In its development, Genie was evaluated using SWE-Bench and HumanEval, with a strong focus on its ability to solve software engineering problems and retrieve the correct code for tasks. 

Genie scored 64.27% in retrieving necessary code lines, identifying 91,475 out of 142,338 required lines. This marks significant progress, though Cosine acknowledges room for improvement in this area.

Genie’s development involved overcoming challenges related to training models with limited context windows. Early efforts using smaller models highlighted the need for a larger context model, leading to Genie’s training on billions of tokens. The training mix was carefully selected to ensure proficiency in the programming languages most relevant to users.

Cosine’s innovative approach to Genie’s development included the use of self-improvement techniques, where the model was exposed to imperfect scenarios and learned to correct its mistakes. This iterative process significantly strengthened Genie’s problem-solving abilities.

Looking ahead, Cosine plans to continue refining Genie, expanding its capabilities across more programming languages and frameworks. The company aims to develop smaller models for simpler tasks and larger ones for complex challenges, leveraging their unique dataset. Exciting future developments include fine-tuning Genie on specific codebases, enabling it to understand large, legacy systems even in less common languages.

The post Cosine Unveils Genie, the AI Software Engineer that Beats Cognition’s Devin appeared first on AIM.

Stakeholders are worried about the future of software engineering and everyone is busy making predictions. While some claim that most coding jobs will cease to exist, others say that there will be an abundance of software engineers in the future. What is the truth?

Remember Devin? The AI software engineer made by Cognition Labs propelled people to talk about job displacements. Now, Russell Kaplan, who has joined as the president of Cognition Labs, is making several predictions about the future of software engineering. 

“Models will be extraordinarily good at coding very soon,” he stated, emphasising that research labs are prioritising coding and reasoning improvements above all else, as was evident with OpenAI, Meta, and Google releasing models for maths. 

This investment will soon pay off, transforming the landscape of software development. Kaplan highlights a unique advantage in coding: the potential for superhuman data scaling through “self-play”.

A Team of AI Agents

If models can write code, test it, and check for self-consistency, a form of automatic supervision that is not possible in most domains due to the limits of human expertise will usher in. This capability would allow code to be tested empirically and automatically by AI agents.

As a result of this, software engineering will see radical transformation. “True coding agents, which do tasks end to end, will complement today’s AI copilots,” Kaplan explained. 

This will make every engineer akin to an engineering manager, delegating basic tasks to coding agents while focusing on higher-level aspects such as understanding requirements, architecting systems, and deciding what to build. 

“Any value that humans can bring here is either in leading the direction of deciding what to work on (eg. product), or in working at the cutting edge of what AI cannot do yet (researching new models),” agreed Jeremy Bernier, founding engineer at Canvas.

This is similar to what Franoic Chollet, the creator of Keras, said a few months ago. He predicted that there would be around 10 million more coding jobs in the next five years, but those would be for people who have the expertise in writing code in Python, but most of the coding would be done by AI.

Chollet had earlier said, “If you could fully automate software engineering (my job), I think that would be great, since I could then move on to higher-leverage things. Making software is a means to an end, not the end.” He added that software engineering is not just about copy-pasting code, but about developing mental models of problems and their solutions. 

The Age of Abundance

What’s certain is that the role of software engineers will evolve significantly. “There will be way more software engineers in the future than the present,” Kaplan noted. But “the job will just be very different: more English, less boilerplate coding”. 

According to this prediction, engineers will adapt, much like they did when transitioning from assembly language to Python. Funnily, it’s likely that English teachers would make the best engineers.

Interestingly, this could also usher in the era where advertisements will be focused on AI agents, and not developers. Companies that market to developers will start targeting coding agents as well. “Your agent might decide what cloud you use and which database you choose,” said Kaplan. And there would be an army of those agents.

Just as Reid Hoffman predicted the decline of traditional 9-to-5 jobs due to the rise of the gig economy and a focus on results, the standards for product quality will increase as developers will be able to ship products much faster. This would call for better testing infrastructure, where agents can automatically train, test, and verify models and software. 

“There’s never been a better or more productive time to be a builder,” he concluded, adding that softwares like Devin would usher in the age of abundance for software. 

“Code is largely worthless, more of a liability than an asset. Problem-solving is where the value is,” Chollet added in his thread. Because software engineering is not just about writing or copying code or giving prompts to AI models, it is much more than that.

Offices in the future would probably have ‘Human Software Engineers’ managing a team of AI engineers, just as we predicted.

The post Soon, Software Engineering Would be All About Managing AI Coding Agents appeared first on AIM.

Web development is often seen as a chaotic field by many in the software engineering community. This sentiment was aptly captured by a user who famously went on a ranting rampage in his post ‘Web development is f*** stupid’. 

“I have never seen such poorly written languages as JavaScript and TypeScript in my life,” he wrote in his Reddit post.  

He further added that the dependency management with npm and Yarn (JavaScript package managers) is a nightmare and “frameworks like React, Redux, and Next.js are constantly changing for no reason, making the entire process unnecessarily complicated”.

Despite the strong language, his viewpoint resonates with many developers who find web development frustrating. One user commented, “I hate how most of the software engineering is web development.” Another added, “I started off learning video games and desktop apps, but by the time I finished college, I realised most of software engineering is web apps, which I despise.”

Why the Hate?

Many developers feel that web development is plagued by poor management and constant changes. “There’s always some new framework everyone thinks will be the holy grail, but it’s always just as bad as everything else,” one user lamented. 

Another agreed, saying that no other area of software engineering releases as many updates and new frameworks as web apps, making it hard to keep up with.

However, not everyone shares this negative view. Some see the challenges of web development as opportunities for interesting projects. “There are so many interesting projects in web development. People think it’s just building simple websites, but web apps can have so many interesting challenges and depth,” a user noted. 

Another pointed out, “Web applications today are just the desktop applications of yesterday, thanks to advancements in computing power.”

In web development, frameworks often become obsolete within a few years, replaced by newer technologies. Developers must learn these new frameworks, update existing code, and hope for smooth transitions. And now, there are people who say that TypeScript is going to replace JavaScript, which brings back the same conversation about the changing landscape of web development. 

Too Much to Deal With

Web developers in software development agencies face even greater challenges. While some agencies stick to a specific technology stack, most take on any web-related projects they can find.

Others believe that the constant updates and new frameworks are a necessary part of the evolution of technology. “Web development has nothing to do with websites. It has everything to do with web applications. The reason web dev jobs are so common is because 99.9% of software is delivered through a browser or mobile app now,” a user explained.

Despite the criticism, some developers find web development to be intuitive and refreshing. “And no one needs to switch to every latest web framework either. If you have to, it’s not that bad if your foundation is solid.”

Web development may be seen by some as a “necessary evil”, but it remains an integral part of the software engineering landscape. 

“Computers are literally designed based on the principle of abstraction. Even if you’re coding in binary, you’re still abstracting away a ton of stuff,” said a user in the discussion. Web development, with all its layers and complexities, is just another step in the ongoing evolution of technology.

The hate for web development is not new. A simple search on Google, X, Quora, Reddit, or any community platform throws thousands complaining about the constantly changing paradigm of web development for more than a decade.

Regardless, according to the Stack Overflow Developer Survey 2024, JavaScript has remained the most popular programming language in the past decade. This can be attributed to the fact that JS has been used almost everywhere, not because it is loved by everyone.

The post Web Development was Designed by Satan appeared first on AIM.

It is hard to imagine what today’s operating systems will mean to developers 37 years in the future. Going back 37 years to 1986, when MS DOS version 4.0 was released, computers were less complex and more open to modifications at a fundamental level. 

Microsoft, embracing the open source movement, released version 1.1 and 2.0 in 2014. Last week, the company, along with IBM, which was closely associated with building the 4.0 version of DOS, made the source code available on GitHub. 

The open source release also included the beta build and scanned documentation of MT-DOS 4.0 built primarily by Microsoft. MT-DOS introduced multi-tasking, which allowed certain programs to run in the program. This version didn’t see commercial release but was quickly overshadowed by OS/2 which improved upon the feature. 

Microsoft claims to have ‘lost’ its source code.

It was unearthed by Connor ‘Starfrost’ Hyde, who discovered unreleased beta binaries of DOS 4.0 among former Microsoft CTO Ray Ozzie’s collection. 

He worked to publish the code on GitHub along with Scott Hanselman, the VP of developer community, and Jeff Wilcox, head of OS programs office, who reported they will “continue to explore the archives and may update this release if more is discovered”.

In 2001, Bill Gates announced, “Today it really is actually the end of the MS DOS era.” DOS, which ran underneath Windows 3.1, Windows 95, and Windows 98, was being replaced by Windows XP. 

At the time of the announcement, DOS was running on over 400 million PCs worldwide. 

But the popularity of vintage games built on DOS has retained old timers and children. There are plenty of games still being released on DOS barring the limitations of memory management. 

Developers, who reminisced about the time they worked with DOS, were enthusiastic about experimenting with the release and found a few inconsistencies in it. 

Hackers and tinkerers who grew up with DOS are eagerly digging into the source code, reliving their early computing experiences. As one HackerNews user exclaimed, “Holy cow, this is huge! DOS 1.x and 2.x are too old to run a lot of vintage DOS software, but 4.x would run just about everything.”

Nostalgia and the present!

The average person doesn’t often come in contact with systems run on DOS. Apart from George RR Martin, who wrote A Game of Thrones books on DOS, the US Navy that used it till 2011, or old-time nerds who are the market for DOSBox to play retro video games on, the operating system is practically defunct. 

Right after the open source release, developers took to social media to discuss the times they spent working on DOS when alternatives were sparse. While modern operating systems are complex and allow a wide range of functions, they’re more closed and secure. 

A user wrote, “They (kids) can play with code in various sandboxes locally and on the web, but the computer fundamentally belongs to someone else today.” 

Although slow with less memory, the device would allow for programming in languages like C to directly compile on the device itself, and modify the core software. 

This is in contrast to many modern systems, which often restrict such low-level access to enhance security and stability.

Using the source code, one developer built his own version of the DOS. Though he mentioned the difficulties in compiling the code, he successfully demonstrated running various commands and utilities, showing the functionality of the built operating system within the FreeDOS environment.

Bryan Lunduke, a former software engineer of Microsoft, who attempted to compile the source code, found that several files, including GETMSG.ASM and USA.INF, were “mangled” and could not be properly compiled.

A software engineer and historian, Michal Necasek, who runs the OS/2 Museum blog, found that shoving the source code into GitHub caused irreversible damage, such as loss of timestamps and corruption of files that used code page 437 characters. 

Necasek argued that for practical purposes, old source files should be treated as binary and must be preserved without modification. These changes raise concerns about the integrity and historical accuracy of the released source code. 

Despite the issues with the release, many in the community are excited to dig into this piece of computing history. 

As one developer commented on HackerNews, “Even just seeing some of these filenames is a major blast from the past! Love it,” listing off familiar file names like ‘CPY.BAT’, ‘MAKEFILE’, ‘RUNME.BAT’, and ‘SETENV.BAT.’

The Future is Retro 

While the released version of MS-DOS 4.0 may not have much practical use today, the publicly available source code has allowed hackers to explore the inner workings of the operating system that defined their early computing experiences. 

Another user shared, “I never thought I would be able to build DOS from scratch, but here we are!” The real value is in preserving this important part of the history of personal computing. 

Jim Hall, the founder and project coordinator of FreeDOS, explained that the move to use the MIT licence allows developers to freely study, modify and distribute the code. 

Modern developers’ fascination with retro computing extends beyond MS-DOS, as seen in the rise of retrocomputing. One user on X posted, “We completely missed out on the days of programming in C for MS-DOS back when things were so much simpler.” 

Engaging with classic operating systems’ source code and retro media allows developers to gain insights into computing’s evolution, informing future innovations. 

The series ‘Fallout’, for instance, draws inspiration from vintage technology, MS-DOS 4.0 release exemplifies this retro revolution, inspiring a new generation to embrace the hacker legacy.

Bojan Tunguz aptly said, “The future is here and it’s totally retro.”

The post Open-Source MS-DOS 4.0 Inspires Aspiring Developers to Embrace Retro Revolution appeared first on AIM.

2023 has undeniably been the year of GPT. Developers worldwide have been captivated by the limitless possibilities that the next generation of large language models (LLMs) offers. Taking the advancements forward MachineHack, in collaboration with Intel^®, is presenting the ‘oneAPI Hackathon: The LLM Challenge’ scheduled from September 14 to October 8, 2023.  

All About the Hackathon

While text-based tasks are present everywhere, one of the most compelling objectives is the development of a question-answering system tailored to textual data. Imagine a system capable of sifting through vast datasets, identifying ‘span_start’ and ‘span_end’ positions within the ‘Story’ text, extracting the relevant ‘span_text,’ and generating responses that align perfectly with the provided ‘Answer’ for each question.

This is precisely the challenge that awaits participants in the oneAPI Hackathon: The LLM Challenge. The goal is to use LLMs, leveraging their text understanding and generation capabilities to put up accurate, context-aware answers. Moreover, it needs to be done with a level of accuracy that pushes the boundaries of what language models can do. Beyond just creating a robust LLM, participants will be challenged to build an application that utilises their language model effectively and pen down their insights in a blog post.

Start Date: Sep 14, 2023 6:00 PM(IST)

End Date: Oct 8, 2023 6:00 PM(IST)

Register Now

The contest kicks off on September 14 at 6:00 PM IST, setting the stage for a month-long journey of problem-solving. But wait, there’s more! The competition is divided into two intriguing phases:

Phase #1: Metric-based Evaluation (Sept 14 to Sept 29)

In this phase, participants will put their models to the test, to get the highest level of accuracy in Q&A tasks.

Phase #2: Application Building + GitHub/Blog Writing (Sept 25 to Oct 8)

This phase is where developers will step outside their comfort zones of dealing with algorithms, document their journey in a GitHub repository and write a detailed blog about their insights.

Amid the neck-to-neck competition, participants won’t be left to fend for themselves. Further, on September 21, a workshop on “Intel® AI Software Optimization Using Intel® AI Analytics Toolkit with Demo on Intel® Developer Cloud” will provide guidance, tips, and tricks from experts to refine strategies for the final stretch of the hackathon. (To save your spot, register here.)

The excitement doesn’t end with the submission of entries. Winners and their language models will be lauded in October during India’s biggest AI conference — Cypher 2023. Furthermore, the top performers will be rewarded with the following rewards that recognize their achievements.

• 1st prize: Apple iPhone 14 512GB
• 2nd prize: Apple iPad Air 2022 WiFi 256GB
• 3rd prize: Samsung Galaxy Watch
• Top 10 runners-up: Amazon Vouchers worth INR 5K

Judging Criteria

The final score will be judged based on:

1. 40% on #Phase 1

• Score on MachineHack Leaderboard

2. 60% on #Phase 2

• Usage of Intel oneAPI optimizations in the final application
• Re-usability and ease of deploying the application on Intel® Developer Cloud
• Structure of Github Repo and ReadMe description
• A short blog highlighting the features of the application

** NOTE: Participants need to sign up to the Intel DevMesh Discord channel: https://discord.com/invite/ycwqTP6

The oneAPI Hackathon: The LLM Challenge is more than a competition; it’s an opportunity to be at the forefront of the LLM revolution. It’s a chance to push the boundaries of what language models can do. The clock is ticking, and the world is watching. The future of language models is in your hands, and the oneAPI Hackathon is the right place to be at.

For more information and registration details, visit MachineHack. 

Note: Upon registration, participants can proceed to download the dataset directly from the MachineHack page and the notebook will be on Intel New Developer Cloud.

Calling all aspiring developers! If you’re ready to take on the exhilarating challenge of cracking the LLM code, register now.

Don’t wait a moment longer – seize the opportunity by registering yourself right here, right now. Unleash your coding prowess and embark on a thrilling journey towards conquering the code! 

Start Date: Sep 14, 2023, 6:00 PM(IST)

End Date: Oct 8, 2023, 6:00 PM(IST)

Register Now

The post Call For Developers to Build the Next-Gen LLM In the ‘oneAPI Hackathon: The LLM Challenge’ appeared first on AIM.

In a world of fast-paced technology, coding has quickly become a sought-after skill. Being skilled in coding opens up a world of opportunities for techies in terms of better career prospects. 

Several online platforms offer lucrative insight into the world of coding for all levels of developers—from amateurs to advanced and help hone their skills. These platforms provide level-specific challenges, assessments, quizzes and interview guidance to help professionals obtain prestigious tech jobs and succeed in their roles.

MachineHack

MachineHack is one of the leading online platforms for Machine Learning competitions, assessment and hiring. MachineHack attempts to bridge the gap between industries to obtain the right fit for the requirements and talents. Along with hackathons, MachineHack allows users to practice in diverse aspects of coding such as Array, String, Tuple, Matrix, NumPy, SciPy and more.

You can check the practice section here.

LeetCode

LeetCode is a well-known platform for interview guidance in coding-specific profiles at top tech firms. 

It provides support for over 18 programming languages with a dynamic community for solving doubts. 

Courses on Systems Design, SQL Language, Dynamic programming, Arrays, Machine Learning and others are also available at LeetCode. It also provides detailed interview guidance for top tech companies such as Meta, Microsoft, Amazon, Adobe and Uber. 

The platform has problems that cater to various levels of difficulty like Array, String, Hash Table, Dynamic Programming, sorting, depth-first search, binary search, binary tree, bit manipulation, stack, string matching, probability and statistics and more.

LeetCode also offers Premium access priced at $35/month–$159/year to select problems and other exclusive material.

HackerRank

HackerRank is a prominent, Bengaluru-based platform focused on competitive coding for consumers and businesses alike. 

The programming challenges at HackerRank can be solved in different programming languages like Java, C++, Python, SQL and JavaScript. 

Challenges on data structures like Arrays, 2D Array, Dynamic Arrays, Sparse Array, Array Manipulation and on C (Functions in C, pointers in C, conditional statements in C, Bitwise operators and more) among others are also provided on the platform along with interview preparation kits for Arrays, dictionaries and hashmaps, sorting, string manipulation, dynamic programming, stacks and queues, graphs, recursion and backtracking.

CodeChef

In 2009, CodeChef began as an educational initiative for programmers by Directi, an Indian software company. Recently acquired by Unacademy, it offers practice in basic programming, arrays, strings, binary structures, data structures, dynamic programming, graphs and segment trees. 

CodeChef hosts several challenges every month to encourage competitive programming.

Coderbyte

Coderbyte is a platform best known for the sizable range of coding challenges it offers. These challenges range in difficulty and can be attempted in the platform’s online editor. 

From an employment perspective, CoderByte offers a technical screening product to help companies recruit swiftly and better personalised to their requirements. 

The platform provides challenges in various areas like algorithms, front-end, back-end and database challenges with automated grading. Additionally, it prepares students for real-world interviews with the help of expert content and solutions provided by leading tech companies.

FreeCodeCamp

FreeCodeCamp provides credible certifications to young professionals to improve their skills and explore opportunities in tech. Five projects must be built to earn each certification along with advancing through level-specific tests. 

Most sought-after certifications at FreeCodeCamp include JavaScript algorithm and data structures, data visualisation, back end development and APIs certification, data analysis with Python certification, machine learning with Python certification and more.

Codewars

The Codewars platform allows participation in small coding exercises named ‘kata’. Each kata developed by the Codewars community aims to help participants improve their coding skills. 

The platform also supports over 55 programming languages to master. The ‘kata’ can be solved in the browser and the test cases (TDD) can be used to check participants’ progress. It also allows participants to compare solutions with peers after each kata to check their progress and improve their understanding of problems.

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The post Best Coding Practice Sites in 2024 appeared first on AIM.

Earlier, the field of AI was more academic and was mostly researched by PhD holders. However, the area has now become approachable with software developers and other tech professionals taking advantage of the new AI paradigms to build a whole new scenario of use cases for the world.

More developers are becoming interested in exploring tools and techniques such as TensorFlow and other open-source tools to explore the field. With frameworks such as TensorFlow, companies such as Google are also making it easy for a software developer who doesn’t necessarily have academic expertise or a PhD in AI/ML, allowing them to ML models.

New Paradigm

Developers are using machine learning in their applications as a way to stand out in the crowd. For instance, they are adding machine learning in apps as a way to make their product unique. Having that new AI programming paradigm has cracked open all of these new scenarios that weren’t previously possible. 

Given the same devices are loaded with sensors, they are capturing so much data which has made new application scenarios. A good example is a cloud kitchen which can connect thousands of restaurants and help deliver food instantly by mapping and connecting users with the available food. Behind such a complex task, AI is used extensively. Social media is another application scenario for app development with machine learning, as witnessed on platforms like TikTok, Instagram and Snapchat.

The availability of affordable compute power is another factor in helping developers enter the field of AI/ML. The dropping prices of chips, particularly GPUs, allows developers the extra power within a laptop to carry many ML workloads. This way, even if a developer does not have complete theoretical knowledge, he/she can showcase their skills as a developer and hopefully build apps.

Open Source Frameworks Bring AI For All

Open source tools like Tensorflow and Keras have unlocked new possibilities for exploring AI such as integrating AI in websites for web developers. For instance, if a coder wants to build a model that runs inference in the cloud and the developer wants the server to be able to upload a picture and have a server return the contents of that picture, that’s also possible as a web developer today using such tools. You can also have the inference happen on a device, within a browser or server.

An open-source ML framework such as TensorFlow can make it easier for developers to enter the field, even if they don’t have a degree in computer science. It can be used to create machine learning models easily, be it low-level machine learning models that use graph-based execution or even higher level models using a high-level API. Keras is a high-level API which can be used to train models and run inference on it. The great thing about TensorFlow is that it is not just a tool, but there is an entire open-source ecosystem attached to it. More and more developers are getting involved, and it’s becoming more and more welcoming for people who don’t have PhDs, or they don’t have advanced mathematics knowledge. 

With TensorFlow, developers only need 10-20 lines of code to have a neural network set up. Google, at the launch of TensorFlow 2.0, introduced high-level APIs to reach to the broader developer audience, instead of just AI/ML researchers, data scientists and scholars. In the past, the API’s were almost researcher focused, but with TensorFlow 2.0, it has enhanced the ability to have high-level APIs focusing on areas such as model architecture, prototyping and more. Thanks to open-source, researchers now don’t necessarily need to know all of the underlying mechanics of how complex systems such as neural networks work. 

What Do You Need To Know To Get Started

For developers who have never done any machine learning before, they can start with knowing the basic Python skills and they’re good to go. And the fact is that Python is one of the simplest languages in the world to learn with a robust ecosystem and learning resources make it easy to learn. Then start by building models, even the basic ones as it can help with getting started to understand the field. 

According to experts, in the coming years, AI/ML will become part of that developer toolbox, just like the web skills. The field is vastly expanding with a shortage of people who can write AI/ML code and turn that into useful applications which businesses can use. All you need to do to get started is basic programming skills.

With the ever-evolving tech community around AI/ML, ease of learning these techniques with many free resources available, along with certifications, only means that exploring the field of AI has become easier than ever before. There are many instances where professionals from various fields are making a transition into the field of data science and AI. Theoretical knowledge, therefore, is not a mandate for developers to explore this field of AI. 

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Data Profiling is one of the most crucial and the first step in data quality assessment. It involves the evaluation of data values within a given set of data for uniqueness, consistency and logic, which are the three key quality metrics. As described in our previous article, it differs from data mining, which is a process of identifying patterns in a pre-built database. 

Here we list 10 data profiling tools that you can use to bring about data quality and accurate assessment of your structured, unstructured or real-time data. 

The list is in chronological order and includes both paid and free tools. 

1. Aggregate Profiler

An open-source data quality and data profiling tool, Aggregate Profiler carries out data profiling and analysis in file formats such as RDBMS, flat files, XML and XLS. Aggregate Profiler tools can be used for data quality check, corrections and profiling as it can perform cardinality checks between different tables within one data source. It can also be used for random generation of data, populating database values, looking into database metadata, and more. Some of the other tasks it can carry out are metadata discovery, anomaly detection, basket analytics, similarity checks and more.

2. Atlan

An autonomous data profiling tool, Atlan provides a modem data profiling solution. With an auto-generated profile, crowdsourced metadata, data dictionary and README editor, the tool offers a solution for every need at one place. It can do data profiling from a variety of different sources and connect with the software you choose. It is one of the most accessible data profiling tools to get the data quality corrected in the required context and format while facilitating BI software integrations, native Excel plugins and more. 

3. IBM InfoSphere Information Analyser

This data profiling tool by IBM helps in identifying the data quality, content and structure. It can carry out various profiling functions such as column analysis, primary key analysis, natural key analysis, cross-domain analysis, natural key analysis and more. It works perfectly for big data, business intelligence, data warehousing, data management and more while analysing different kinds of formats. It also has machine learning capabilities that can auto-tag data and identify potential issues. It offers more than 200 built-in data quality rules that control the ingestion of bad data.

4. Informatica Data Explorer

This tool provides data profiling and data quality solutions allowing developers to carry out a faster and thorough analysis of data in the repository. Available in standard and advanced modes, it can scan every single data record from any source to detect anomalies and hidden relationships. It can work on highly complex datasets with much ease and find connections between data sources. This tool also comes with pre-built rules that can be applied to the data for profiling. It supports all types of structured and unstructured data. 

5. Melissa Data Profiler

It can carry out functions such as data profiling, data enrichment, data matching and data verification. Relatively easy-to-use, this tool can get all kinds of data analysed efficiently while carrying out general formatting, content analysis and more. Its profiling capabilities help to check data before it arrives into the data warehouse and helps ensure consistency and quality of data. It can carry out tasks such as identification and extraction of data, monitoring data quality process and more. It can enhance data governance, create a metadata repository, maintain data standardisation, and convenient management of data. 

6. Microsoft DOCS

The Data Profiling task by Microsoft DOCS provides functionality such as data extractions, transformation and loading data. It allows for an efficient analysis of source data while understanding it better and preventing data quality problems before they are introduced into the data warehouse. With built-in features such as reading broad data types, it does an efficient job of ensuring data quality. 

7. SAP BODS

Business Objects Data Services (BODS) is one of the best and popular data profiling tools to carry out analysis of inconsistencies in data and other data problems. It provides features such as data quality monitoring, metadata management, and data profiling in one package. It can undergo redundancy, sparseness, pattern distribution, analyse cross-system data dependencies, among others. The standard profiling using BODS allows an understanding of the unique values present in the columns, whereas relationship profiling carries out detailed profiling.

8. SAS DataFlux

DataFlux combines data quality, data integration and master data management. It provides high-performance environments to create and explore a data profile, design data standardisation schemes, and more. It can extract data, profile it, standardise it, monitor it and verify the data in a significantly faster and secured way. It ensures high-quality data in every business process.

9. Talend Open Studio

A free downloadable tool, Talend Open Studio offers deep visibility into organisations’ data. It is a flexible tool which can carry data quality analysis of different types of fields, databases and file types. This is one of the best free data profiling tools that offers a sophisticated framework that includes pre-built connectors and monitoring tools. The tool can address data deduplication, validation and standardisation. 

10. TIBCO Clarity

Essentially a data cleansing tool, it provides a data profiling function to check and collect statistics and information about data by generating row or column analysis reports. It can efficiently work on large volumes of data to analyse them accurately for data quality. The tool is also efficient in validating, standardising, transforming, addressing deduplication, cleansing and visualising all major data sources and file types. The application supports strong editing functions that let users manage columns, cells and tables, apart from allowing users to analyse and regroup data according to numerous criteria.

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