OpsMonsters’ Post

Exciting news in the world of AI🚀 OpenAI introduce Canvas a look like of Anthropic Claude artifact, a revolutionary new feature for ChatGPT that's set to transform how we approach writing and coding projects. Canvas opens in a separate window, creating a shared workspace where you and ChatGPT can collaborate side-by-side, moving beyond simple chat to truly co-create and refine ideas together. Built with our advanced GPT-4o model, Canvas brings a suite of powerful tools to your fingertips. For writers, it offers shortcuts to suggest edits, adjust length, change reading levels, and even add emojis for emphasis. Coders will love the ability to review code, add logs and comments, fix bugs, and easily port between languages like JavaScript, Python, and Java. What sets Canvas apart is its intuitive interface and context-aware capabilities. You can highlight specific sections for focused revisions, and ChatGPT will provide inline feedback and suggestions, acting like a skilled copy editor or code reviewer. With features like version history and a back button, you always maintain control over your project's direction. We're rolling out Canvas to ChatGPT Plus and Team users starting today, with Enterprise and Edu users gaining access next week. And great news for our free users – we plan to make Canvas available to you too once it's out of beta! Canvas represents our commitment to making AI more useful and accessible, and it's our first major update to ChatGPT's visual interface since launch. We're excited to see how you'll use Canvas to enhance your productivity and creativity. Try it out and let us know what you think! #ChatGPT #AIInnovation #FutureOfWork #innovation #ai #openai #canvas #chatgpt #gpt #gpt4

Can AI Code Like Us? New Study Shows ChatGPT's Good Bits and Weak Spots We programmers have been making AI models for ages. Now, get this - AI is making code itself! But the question is, can it do it as well as us? A new study gives us some ideas. So, a bunch of smart people just published a study in this big journal called IEEE Transactions on Software Engineering. They checked out how good OpenAI's ChatGPT is at making code. Key points: 📌Turns out, ChatGPT isn't always amazing at making code work right. Its success rate jumps around a lot (between 0.66% and 89%) depending on how hard the problem is, what coding language it's using, and other stuff. 📌But here's the cool part - it's really good at solving those coding puzzles on LeetCode from before 2021 (easy: 89%, medium: 71%, hard: 40%).  📌However, if it sees a problem that's new (from after 2021), it does a lot worse (easy: 52%, hard: 0.66%). This might be because it wasn't trained on those new problems. 📌While it can fix simple mistakes in the code, it struggles with the real brain teasers where you need to understand the whole thing. In these cases, it needs a helping hand from a human programmer. Why This is a Big Deal: 🚀This study basically tells us that AI has the potential to become really useful for programmers, taking care of the boring, repetitive tasks. This can free us up to do the more interesting stuff. But, and this is important, it also shows that AI still needs us humans to keep an eye on things and make sure the code is up to scratch, especially when it comes to the tricky bits. #ai #coding #programming #chatgpt #softwaredevelopment #artificialintelligence #machinelearning #innovation #openai #llm #genai #technology #future

🚀 Just Wrapped Up an Exciting Project on Corporate Diversification and AI Strategy! 🤖📊 I’m thrilled to share the completion of a unique project I worked on with Professor Christopher Law for a class at Mays Business School - Texas A&M University. The task? Create a case study focusing on how some of the world's leading tech companies are tackling corporate diversification amid the rise of AI in the 2020s. I leveraged AI to automate the entire process of creating a comprehensive case study. From gathering requirements and conducting research to generating structured content and citing references, I used a combination of ChatGPT APIs and Perplexity to bring the project to life. 💡 One key feature I utilized was chat completions API, which allowed me to: Efficiently generate structured responses based on specific prompts. Automate research and content creation while ensuring accurate citations. Transform the raw data into a cohesive, well-organized case study. "Learn more about ChatGPT API: https://lnkd.in/dp_5C4PT" "Explore the Perplexity API: https://lnkd.in/dbeiY_Au" This project gave me a deeper understanding of how AI can streamline content generation and allowed me to refine my Prompt Engineering skills in a dynamic, real-world context. A huge thank you to Professor Christopher Law for trusting me with this opportunity, the constant support, and for allowing me to explore how AI can reshape traditional content creation processes. Feel free to check out the GitHub Repository, where I have generalized the process to automate case study creation using AI tools like ChatGPT and Perplexity. The repository contains the code I used to automate requirements gathering, research, content generation, and gathering appropriate references. It's an adaptable solution that others can extend for their own projects or research purposes. 🔗 GitHub Repository: https://lnkd.in/dvyu3W44 Feel free to check it out! 🚀 #AI #GenerativeAI #ChatGPT #Perplexity #ChatCompletions #PromptEngineering #Automation #CaseStudy #AIContentGeneration #ArtificialIntelligence #TechInnovation #AIResearch #DigitalTransformation

AI won’t replace developers, but developers who use AI will replace those who don’t. AI tools like ChatGPT have sped up coding, but debugging has become even more important. While AI helps with quick solutions, it’s our skills that ensure everything works perfectly. Embrace AI, but never stop honing your expertise! #AI #SoftwareDevelopment #Coding #AIandDevelopers

Debugging AI generated code is very tasking and time consuming. Personally, I try to avoid generating code from scratch with chatgpt because most times the code always break and trying to fix it takes a lot of time and effort. I prefer to generate my code and give it chatgpt to debug not the other way round.

Similarly, in translation/localization, carefully consider where and how to involve AI in your work, just as you would when selecting a new employee.

Bard vs ChatGPT: Which is better for coding 👨💻 ? Bard vs ChatGPT: What’s the difference 🖊? The Large Language Models (LLMs) that underpin ChatGPT and Bard represent the primary distinction between the two. Bard employs the Language Model for Dialogue Applications (LaMBDA), whereas ChatGPT uses the Generative Pre-trained Transformer 4 (GPT-4). Moreover, OpenAI produced ChatGPT, whereas Google developed Bard. Both are capable of doing fairly comparable tasks. ChatGPT can be used by programmers for: Suggestions: For functions and other code constructions, both models are capable of recommending the appropriate syntax and arguments. Code that you have begun writing can be finished by it. -Debugging: It can assist you in locating mistakes and issues in your code. -Explanation: It can provide an explanation for the code you enter or that it creates. A sizable dataset, comprising Common Crawl, Wikipedia, books, articles, documents, and information retrieved from the internet, was used to train both models. Bard, on the other hand, differs slightly from ChatGPT in that it was trained mostly on generic information that had been scraped, whereas Bard was trained on online discussions and dialogues. 👉 In conclusion, ChatGPT governs, although both are helpful! #ChatGPT #Gemini #bard #LLMs #LaMBDA #Google #OpenAi More information: "https://lnkd.in/dQCSvMA4"

🚀 **The Evolution of Coding with AI** 🤖 Before tools like OpenAI, coding used to take hours. As developers, we would spend *2 hours coding* followed by *6 hours debugging*—a never-ending loop of trials and errors. 🧑💻🔧 But now, with AI like ChatGPT, it's a whole new game! The *code generation* time has dropped to just *5 minutes*, but the *debugging*? Well... it's a different kind of challenge! 🔄😅 While AI accelerates the coding process, it’s a reminder that **problem-solving** and **debugging** remain an art form we are still perfecting. AI is powerful, but it's not magic. It takes collaboration between human ingenuity and machine learning to create robust solutions. 💡💻 #AIinDevelopment #CodingWithAI #ChatGPT #SoftwareEngineering #DeveloperJourney

Recently I came across this post. It shows how AI is playing a major role in our life. It replaced everyone. Soon it might be replace developers also. In my point of view, we have to reduce the use of AI and related technologies. It is little bit scary about AI

The graphics is a very good depiction on what I have observed with few experimentation I have done with ChatGPT and couple of other GenAI tools. In my experimentation the code generated and explanation were good, but not good enough to have it successfully execute without doing some extensive debugging. The code generation will get better, but it is not replacing software developers, especially when it comes to building real complex software applications.