Dzintars Skarbovskis’ Post

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

"AI won’t replace developers, but developers who use AI will replace those who don’t." In the rapidly evolving world of coding, AI tools like OpenAI's ChatGPT have redefined how developers approach their work. What was once hours of manual effort has now become a matter of minutes—at least when it comes to writing code. Before OpenAI Coding Time: Developers would traditionally spend around two hours coding, piecing together algorithms and logic manually. Though time-consuming, this process helped developers fully understand the ins and outs of their code. Debugging Time: Post-coding, debugging could easily take up to six hours, as developers meticulously hunted for errors, fixed broken logic, and optimized performance. It was tedious, but a necessary part of delivering functional software. After OpenAI Coding Time: AI tools like ChatGPT have reduced coding to a few minutes. Developers can now generate entire code snippets or solve complex logic problems with a single prompt. Debugging Time: But here’s the catch—debugging has become a much larger task. AI-generated code, while quick, often needs more rigorous testing and refinement. Developers may find themselves debugging for an entire day, troubleshooting unexpected issues in AI-generated logic. AI has changed the game, offering incredible speed but demanding more attention to detail in debugging. It’s not about replacing developers—AI is your sidekick, but your skills still lead the way. At Coding Ninjas, we help you embrace AI while sharpening your expertise. Ready to level up your coding journey? 🚀 #OpenAI #ChatGPT #CodingLife #AIinTech #DeveloperJourney #CodingNinjas

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.

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.

OpsMonsters Typhoons : Why LLMs are not Good for Developers ? Large Language Models (LLMs) like ChatGPT have revolutionized how we interact with technology, effortlessly understanding and generating human-like text. But when it comes to coding, these AI marvels still have a lot to learn. Here’s why: 1. Language Barrier Different Worlds: LLMs are trained on text, which is very different from code. Text is full of variety, while code follows strict rules. Token Trouble: LLMs break text into pieces (tokens) to understand it. This works well for text, but code often has patterns and structures that tokens miss. For example, code indentation is super important, but tokenizers often ignore it. 2. Short-Term Memory Limited Focus: LLMs can only "remember" a small amount of text at a time. Code often has connections between different parts, which LLMs can miss. Big Picture Problem: Long codes with complex structures are hard for LLMs to handle because they can't see the whole thing at once. 3. Learning to Predict One-Way Street: LLMs learn to predict the next word based on what came before. This is great for writing, but code often needs to look both forward and backward. Better Training: New models are learning to look at code from both sides, which helps them understand and generate code better. Final Thoughts While newer iterations of GPT models show improved coding capabilities, they do not necessarily address the core issues directly. Typically, these models use the traditional encoder-decoder transformer architecture and are pre-trained on code bases to develop a strong prior for human-like coding patterns. Task-specific fine-tuning with smaller datasets further enhances their performance. However, despite promising results from fine-tuning and integrating additional components like the ChatGPT code interpreter, some researchers advocate addressing these challenges fundamentally. This approach aims to evolve LLMs beyond relying solely on maximum likelihood estimations to adopting performance-aware code generation strategies. #LLM #ChatGPT #COPILOT #OpsMonsters Don't miss a Geek! Follow us for your daily dose of tech news, insights, and more...

𝗧𝗵𝗲 𝗗𝗮𝘄𝗻 𝗼𝗳 𝗔𝗜 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀? 𝗠𝗲𝗲𝘁 𝗗𝗲𝘃𝗶𝗻, 𝘁𝗵𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗠𝗮𝗿𝘃𝗲𝗹 An exciting breakthrough has emerged in artificial intelligence that could shake up the world of software engineering. Cognition Labs, an AI startup, has unveiled "Devin" - an autonomous AI system that showcases remarkable coding abilities, potentially giving human software engineers some serious competition. What makes Devin truly innovative is its focus on analogical reasoning instead of simply regurgitating pre-existing answers from its training data. This novel approach allows Devin to tackle complex coding challenges, impressively clearing coding marathons and even passing sophisticated coding interviews at leading AI companies. Even more mind-blowing? Devin has successfully taken on and completed real coding jobs through platforms like Upwork. The numbers speak for themselves - in benchmark tests, Devin resolved an impressive 13.86% of coding problems entirely on its own, nearly 10 times better than OpenAI's ChatGPT. Devin's skills extend to self-generating API keys through its own code and fine-tuning different large language models, showcasing its versatility in the coding domain. While the notion of AI potentially replacing human software engineers is a hotly debated topic, tools like Devin could prove to be valuable aids, automating more tedious coding tasks and freeing up developers to channel their creativity into more innovative projects. As the age of advanced AI continues to unfold, pioneering systems like Devin represent the remarkable progress happening in this field and hint at the transformative potential AI holds for various industries, including software development. 𝘞𝘩𝘢𝘵 𝘢𝘳𝘦 𝘺𝘰𝘶𝘳 𝘵𝘩𝘰𝘶𝘨𝘩𝘵𝘴 𝘰𝘯 𝘋𝘦𝘷𝘪𝘯 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘦𝘮𝘦𝘳𝘨𝘦𝘯𝘤𝘦 𝘰𝘧 𝘈𝘐 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴? 𝘚𝘩𝘢𝘳𝘦 𝘺𝘰𝘶𝘳 𝘱𝘦𝘳𝘴𝘱𝘦𝘤𝘵𝘪𝘷𝘦𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘣𝘦𝘭𝘰𝘸. #ai #softwareengineering #innovation #DevinAI #CognitionLabs #techprogress

CodiumAI - Coding Assistant I've experimented with several AI coding assistants, but CodiumAI is the first one that genuinely enhances my productivity compared to using ChatGPT or Gemini alone. The integration with VS Code is incredibly smooth and unobtrusive. It doesn't force unwanted code completions or pop-ups, simply waiting until I need it. The ability to define the context of is a game-changer. I can easily specify which files to include, whether to check recent changes in Git, and more. This feature alone has been a missing piece for me in other tools. I always understood the context AI needs, but I was hesitant to copy-paste numerous files before asking a question. Now, I effortlessly add folders to the context. And the best part? It's free! If you're seeking an AI coding tool that's user-friendly, seamlessly integrates with VS Code, I highly recommend giving CodiumAI a try. You might find that it clicks for you too – pun intended! https://www.codium.ai/ #CodiumAI #Coding #VSCode #Codiumate #AI #SoftwareDevelopment #Free

🚀 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

🍓 Creative Names, Serious Impact: What OpenAI’s Strawberry and DIMC Services Have in Common 🍓 -------- OpenAI just launched Strawberry—and while the name sounds sweet, this release is packed with powerful features. It’s designed for advanced reasoning, excelling in complex coding and math tasks. Strawberry isn't just about efficiency—it makes sophisticated AI feel more approachable. At DIMC Services, we share this passion for creativity. All our products are wrapped in bursting creativity and savory elements to make complex IT solutions and architecture more digestible. That’s why we created Sugar Script—a fun name for our ChatGPT-Einführungsschulungsservice: https://lnkd.in/dbCAtp29 In this 2-month program, we guide your team through mastering ChatGPT’s business applications. And, just like with any dessert, you’ll need a little Sugar 🍭 before diving into the full power of Strawberry! 🍓 With Sugar Script, your employees will gain the foundational knowledge and hands-on experience they need to use ChatGPT efficiently, transforming how they interact with AI in the workplace. Our program includes practical exercises, interactive learning materials, and ongoing support—ensuring your team gets the most out of this cutting-edge tool. So, whether it’s exploring OpenAI’s latest innovations or preparing your team to harness AI’s full potential, we’ve got you covered. Ready to sweeten your AI strategy? Let’s talk about how we can help your business grow with Sugar Script. #AI #Innovation #DigitalTransformation #DIMCServices #ChatGPTTraining #StrawberryAI #SugarScript #KMU #Unternehmen #KI #Chatgpt