Massimiliano Marchesiello’s Post

The Future of Low-Code and AI: A Complementary Relationship Low-code development and Artificial Intelligence (AI) have emerged as powerful forces in the software development landscape. While they might seem like competitors, their synergy holds immense potential to accelerate innovation, reduce development time, and enhance application capabilities. Understanding Low-Code and AI - Low-Code: This approach abstracts away much of the complex coding, enabling both technical and non-technical users to build applications more rapidly. Visual interfaces and pre-built components significantly reduce development time and effort. - AI: AI has evolved to handle tasks once thought exclusive to humans. From natural language processing to machine learning, it can automate processes, improve efficiency, and generate complex code. The AI-Low-Code Synergy While some might question whether AI will replace low-code platforms, the reality is more nuanced. Rather than a competitive relationship, AI is poised to complement and enhance low-code capabilities. - Visual Abstraction vs. Code Generation: Low-code platforms provide a visual abstraction that is easier for non-technical users to understand. While AI can generate code, validating and understanding it often requires technical expertise. - Deployment Simplicity: Low-code platforms typically offer one-click deployment, simplifying the process. AI-generated code may require additional steps involving DevOps teams. - Real-Time Collaboration: Low-code platforms often facilitate real-time team collaboration, streamlining workflows and reducing handoffs. The future of software development lies in the harmonious integration of low-code and AI. While AI can automate tasks and generate code, low-code platforms provide a user-friendly environment for building and deploying applications. By leveraging the strengths of both, organizations can accelerate innovation, reduce costs, and create more sophisticated software solutions. The Axellero.io Edge:  Platforms like Axellero.io are leading the way in this exciting new era. Axellero.io offers a user-friendly, flexible, and scalable development environment for building innovative and efficient AI-driven applications. Axellero.io empowers businesses to: - Build enterprise-grade applications 10x faster with drag-and-drop tools and pre-built components. - Reduce development costs by 25% compared to traditional methods. - Empower non-technical users to participate in application development through visual interfaces. - Break free from complex software limitations with AI-powered design tools for workflows, databases, and future web/mobile app development. By embracing the synergy of low-code and AI, businesses can unlock a future of faster, more efficient, and intelligent application development. #Axellero #LowCode #AI #SoftwareDevelopment #Innovation

Agent Zero: A Dynamic Agentic Framework Leveraging the Operating System as a Tool for Task Completion AI assistants have the drawback of being rigid, pre-programmed for specific tasks, and in need of more flexibility. The limited utility of these systems stems from their inability to learn and adapt as they are used. Some AI frameworks include hidden features and processes that are difficult for users to access or modify. This lack of transparency makes it easier for users to modify the system to meet their requirements.   Specific AI assistants and frameworks that enable adaptability and customization are already available; however, many of these solutions rely heavily on pre-programmed commands or require programming knowledge to change. Although these tools can be useful, their adaptability, memory, and usability are often limited. To fully utilize the system’s capabilities, the user must often spend a significant amount of time learning its architecture or honing their technical skills.   These restrictions are addressed by a new framework called Agent Zero, which offers an organic, flexible AI system. Agent Zero does not come with pre-programmed tasks like other AI systems do. As it is used, it learns and develops into a versatile assistant. It is transparent to users, letting them see and change how it functions, and can be tailored to perform various tasks. This AI framework also makes task delegation and multi-agent cooperation possible, which lets agents create and collaborate with subordinate agents.  Its persistent memory aids in its retention of completed assignments, knowledge, and solutions, gradually increasing its efficiency. It can collaborate with other agent instances to accomplish complex tasks, write its code, and use the terminal. Because of its adaptability, it can operate with miniature models and ensure accurate tool usage without consuming significant computational power. Thanks to the real-time interaction feature, users can closely monitor the agent’s actions, step in when needed, and make adjustments on the fly. Users can easily follow the agent’s process thanks to the output’s color and readability, and session logs are automatically saved for later use.  Agent Zero is dynamic, adaptable, and simple, providing a novel approach to AI support. Traditional AI systems are rigid; using a transparent framework that expands with its users can avoid this. But it’s essential to use this tool carefully because, if not used properly, it can significantly alter a system.  The post Agent Zero: A Dynamic Agentic Framework Leveraging the Operating System as a Tool for Task Completion appeared first on MarkTechPost. via MarkTechPost https://ift.tt/Erhn5W6 September 17, 2024 at 01:00PM

At first glance, evaluating AI-generated code seems similar to assessing AI-generated review suggestions. When AI generates code, we first have to ask ourselves whether the code is correct and meets the necessary quality standards for use. However, the context and expectations of this evaluation differ significantly when we talk about writing code with AI or reviewing code with AI. When you generate code with AI, you are either unable to write the code yourself or you’re looking to speed up the process. In case one, when you can't write the code yourself, any working code that moves you closer to solving your problem is valuable. Ideally, the AI would produce perfect code that adheres to best practices, but this is rarely the case. Even so, imperfect code can serve as a helpful starting point. You expect to run, refine, and test the solution through multiple iterations, and this iterative process is a natural part of the workflow. When you know how to solve the problem but want to save time, the challenge of evaluating AI-generated code seems minimal. Yet, research on large language models like Copilot shows that many developers often struggle to fully understand, change, or debug code generated by LLMs. Thus, evaluating (and changing) the proposed AI solutions can still be challenging, even for experienced developers who know how to solve the problem. The scenario for AI-reviewed code is fundamentally different. In most cases, you’re reviewing existing code written by another developer, who considered it good enough for production. Here, the expectations for quality are higher because the code is already functional, and AI suggestions are intended to improve it further. Again, we can envision two scenarios. One in which the reviewer completely understands the AI-generated suggestion, and one case when the reviewer is confronted with unfamiliar suggestions. In case one, if you could have made the same recommendations yourself, it’s easy to evaluate and apply them.That's the best-case scenario. AI works as a memory aid and helps us not to overlook problems. Yet, if AI proposes unfamiliar changes, the process becomes more challenging. Thoroughly researching every suggestion, or even validating them through testing, is time-consuming and can detract from the efficiency of the review process. This additional work is also not part of the usual code review workflow. I find AI review perfect for self-review during development. In this scenario, you can iterate over AI suggestions, apply them if you see fit, or research and evaluate suggestions if you have time (aka learn and grow). Yet, uncertainty in AI review suggestions is a real problem in a peer-review scenario. Developers can either apply them and hope for the best (a risky, not advisable strategy), investigate a significant amount of time to evaluate the suggestion or ask another developer to review the AI review suggestion. What's your experience with AI review?

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐂𝐨𝐝𝐢𝐧𝐠: 𝐭𝐡𝐞 𝐆𝐨𝐨𝐝, 𝐭𝐡𝐞 𝐁𝐚𝐝, 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐔𝐠𝐥𝐲… Generative AI is the shiny new toy everyone's buzzing about. Promises of faster development, fewer resources, and higher quality–sounds like an employer’s dream, right? It’s not all rainbows and unicorns as you’ll see: 👍 𝐓𝐡𝐞 𝐆𝐨𝐨𝐝 👍 • Increased Productivity: Generative AI can automate repetitive coding tasks, freeing up developers to focus on more complex and creative aspects of software development. This can significantly improve development speed and efficiency. • Reduced Errors: AI can analyze code patterns and identify potential bugs that human developers might miss. This can lead to more robust and reliable software. • Explore New Ideas: Generative AI can suggest different code solutions or approaches that developers might not have considered. This can spark innovation and lead to more efficient or elegant solutions. • Prompt Engineering: When asked the right question, Gen AI tools can consolidate ideas across multiple forums to provide a quick relevant solution especially in concepts that are new to a developer.   👎 𝐓𝐡𝐞 𝐁𝐚𝐝 👎 • Copycat, not Creator: Great at automating existing code, but don't expect AI to write the next groundbreaking algorithm. • Garbage In, Garbage Out: AI models are trained on existing code, so biases and errors can creep in. Test relentlessly, folks! • Mystery Machine: AI-generated code can be cryptic, making future maintenance a developer's worst nightmare. • Overly Confident: Depending on the tool you use, the solutions provided may look correct but might be incomplete or factually incorrect to the context of your application. 👺 𝐓𝐡𝐞 𝐔𝐠𝐥𝐲 👺 • Brittle Architecture: AI-generated code has limitations when it comes to non-functional requirements and design constraints necessary for a well-architected and scalable system. This can lead to poor performance, instability, and software fragility. ⚖️ 𝐓𝐡𝐞 𝐕𝐞𝐫𝐝𝐢𝐜𝐭 ⚖️  Generative AI is a powerful tool, but it's not a silver bullet. Use it wisely, understand its limitations, and most importantly, perform peer reviews by human experts in software development and architecture. Let Hamelin Tech’s extensive experience help you reap the benefits and avoid the pitfalls. Reach out to us today to see how we can help build a custom solution that spearheads your AI journey. What are your thoughts on generative AI in coding? Let's discuss in the comments! #GenerativeAI #Coding #FutureOfWork

Generative AI and the impact on APIs and software development: Generative AI has captured the world’s imagination. Computers have surpassed humans in many ways in the past, but for the most part, they have excelled at repetitive and deterministic tasks Generative AI, also known as creative AI, is a branch of artificial intelligence that focuses on using machine learning algorithms to generate new content or creative outputs such as images, videos, music, and even text. This technology has the potential to significantly impact APIs (Application Programming Interfaces) and software development in several ways: 1. Content Generation 2. Personalization 3. Automation 4. Innovation 5. Integration with Existing System. Impact on APIs Let’s look at the possible impact on people and companies respectively. For people: 1.API debugging and API testing will become even more important to get right. These processes will be augmented by generative AI and will decrease the amount of time it takes for developers to get productive. 2.API design and architecture will still very much be in the realm of skilled humans. Writing code will increasingly become more commoditized, but picking how to combine the right components for an integrated experience will be a key differentiating skill for developers. 3.Collaborative API workspaces, used in conjunction with an AI-driven bot, will become a powerful way to work with APIs. Static documentation experiences and developer portals will feel even more outdated, and AI technology will only hasten their demise. 4.API integration will become easier. Code-driven, point-to-point integrations or clunky integrations will feel rigid. AI-driven integrations will be able to incorporate new APIs—and heal faster if they break. Finally, generative AI will lower the barrier to entry for non-developers to build APIs. For companies: 1.Companies will start leveraging AI-powered software tools to drive productivity gains. 2.Companies that don’t have APIs will be invisible to AI and will therefore fall further behind in the API economy. Companies that don’t have APIs but do expose data over the internet will not be able to capture the value of their data. 3.Companies that have poor APIs will need to design better ones if they want AI models to interact with their data and actions in the right way. Companies will need to take an inventory of their known and unknown APIs. If they are exposing data that they want to harness later on, they need to act now. 4.Companies will need to get smarter about identity and verification mechanisms for their APIs. As bots could be driving transactions, companies will need to ensure that they have put the right governance and security measures in place. Overall, generative AI has the potential to revolutionize API-driven software development by automating tasks, personalizing experiences for users, and enabling innovative solutions across various domains #generativeai #api #testing

Generative AI tools are getting multi-file editing capability. It recently got introduced to GitHub Copilot. It's already in Cursor and my current favorite, Cline. It can implement a feature across multiple files, but it's still bound to the token limits that the LLM it's using has. Adding a parameter to an API response and modifying a few files to pass it along is cool, but forget about building the whole application from a prompt. (For all but the most simple apps) The limiting factor is my architecture, but this is for me, also the benefit. It forces me to think about the design of my application. If the AI is having trouble making a simple change, because it needs to rewrite 6 files, perhaps its context is not to blame, but my poorly designed, tightly coupled code spaghetti bolognese. 🧢 I ask AI to implement features. Complexity grows. AI starts struggling. 🎩 Let's analyze the architecture of the app. What's a better approach? This way, I'm not just theory-crafting the perfect architecture, but I actually build it. Fast. And see the limitations. Every time I hit a roadblock and I'm forced to refactor, I learn something about my application and find a better approach. Sure, this is the same process that I've been doing for years. Implement it in a naive way, see it work and then improve. But with AI writing the boilerplate, that process is much faster. Writing boilerplate and CRUD calls is no longer the thing that takes the most time. It's improving the architecture while the robot does the mundane work. This will only get better in the future and I look forward to just having a conversation about how I want the app to behave and what a good structure and separation of concerns would be. Of course, these are early days, so every once in a while it still runs in a loop trying to fix a bug it can't find. That is annoying. But I suspect there will be less and less manual intervention as the tooling improves. In the last week I got something up the ground that would take me a month to build before. I could build and compare two approaches fast. And I have a bunch more experiments to run on actual code. It's so satisfying to have a question and just build it out and see. The danger is to not get too carried away, just because I can, but keep focus on the experiments that are important for making the one-way door decisions I have to make at the start of a project. 💡 Get more insights into adopting GenAI tools in my daily newsletter. 📌 The link's in the comments.👇🏻

Is AI going to replace us all?! Should we be worried or just keep coding? From my adventures with Copilot and Claude, AI tools are like superhero sidekicks—strong and helpful, but let’s be real, they’re not the ones saving the day! They don’t deliver flawless code, and they often require tweaks and adjustments. As of now, AI can’t create and deploy entire projects to production because of the many intricate processes involved. Even if we reach a point where a tool could handle this, defining what you want in a prompt and making necessary adjustments is still a challenge. The dream of many entrepreneurs is to eliminate the “middleman” (i.e., the developer) to save resources, but I don’t think AI will replace programmers anytime soon. The tools I use can generate specific functions or SQL queries, but a programming project is much more than that. It often involves UI/UX design and implementation, architecture design, server setup with API definitions, cloud deployment, and sometimes ML model training—all of which are beyond the current capabilities of AI. You might consider using multiple AI agents, each with a different role, but they still need to be synchronized. A good software engineer writes code that’s future-proof, maintainable, reliable, reusable and readable. They also have a broad understanding of the system and the product, and they know how their work contributes to the product’s success—traits that AI agents currently lack. When I started using Copilot, I was thrilled by how it could write code snippets for me, seemingly knowing exactly what I wanted to write. But over time, I’ve come to see its hallucinations and realize that it’s not always accurate (even though it sounds convincing). In one instance, it wrote a unique sorting algorithm that seemed efficient at first glance, but after testing, I found that it didn’t account for edge cases, which led to incorrect outputs. Maybe I’m naive (and a little biased 😉), but I don’t think programming is under threat. It’s a highly creative job that requires mental flexibility and spans various disciplines. However, I do believe AI is speeding up the development process, and in the right hands, it is an incredibly powerful tool for boosting productivity. For now, AI is just another tool in the developer’s toolbox, not the developer itself.. yet.. #AI #Programming #SoftwareEngineering #TechThoughts

Unlock the Power of Generative AI for Software Engineering: 3 Key Strategies to Master Prompt Engineering In today’s AI-driven world, prompt engineering has become a game-changing skill. Whether you're a developer, software architect, tester, or simply an AI enthusiast, crafting well-structured prompts can amplify your productivity and unlock the full potential of generative AI systems. While numerous tips and tricks are often suggested, I’ve found that three essential strategies are enough to elevate your prompt engineering skills: 1. Start with Clear Context: Clarity is everything. The more specific and detailed your prompt, the more relevant and accurate the response. Generic prompts lead to generic answers. Always provide enough context for the AI to "understand" the task. Don’t worry about prompt length—modern models can handle it: - DeepSeek R1: Up to 128k (~100,000 words) - GPT-3.5: Up to 4k tokens (~3,000 words) - GPT-4: Up to 8k tokens (~6,000 words), with extended versions supporting 32k tokens. - Claude: A staggering 100k tokens (~75,000 words)! When drafting a prompt, think of it as a conversation starter: Be specific, set the stage, and communicate your needs clearly. 2. Assign a Role or Persona Assigning a role to the AI is a powerful way to tailor responses. For example: - “Act as an expert software developer” for code generation. - "Be a software architect" for system design and architecture discussions. - “You are a witty and humorous storyteller” for creative writing or storytelling tasks. Assigning roles helps the AI adopt the right “mindset.” You can even assign multiple roles to tackle complex problems! For more specialized tasks, a precise role is essential, while for general queries, assigning a persona can add creativity and depth. 3. Iterate and Refine Prompt engineering is not a “one and done” process—it’s iterative! Think of it as a dialogue with the AI: - Review the response. - Refine your prompt. - Add feedback or ask follow-up questions. - Test different phrasings to get closer to the desired result. This feedback loop can transform vague outputs into tailored, high-quality results. Bonus Tips: - Set Boundaries and Constraints: For example, “Answer in no more than 200 words.” - Provide Examples: If the task is complex, include sample inputs/outputs for clarity. - Break Down Complex Tasks: Use step-by-step instructions to guide the AI through multi-step processes. By mastering these strategies, you’ll turn generative AI into a superpower for your work, enabling smarter coding, better design, and more productive workflows. What is your favorite trick? #AI #PromptEngineering #SoftwareEngineering #Productivity #genAI #LLM https://lnkd.in/dWQa99wq

Great news to increase value of generative AI. CodiumAI leverages this technology to automate code generation, testing and validation with increased effectiveness thanks to integrated RAGS capabilities. It should be particularly impactful in large organizations and/or regulated industries where code creation and QA processes may be quite complex and sensitive. "At the heart of CodiumAI’s enterprise solution is its advanced Retrieval Augmented Generation (RAG) capabilities. This powerful technology indexes an organization’s full codebase, allowing CodiumAI’s tools to gain a deep understanding of the unique context of each company’s code. By leveraging this comprehensive knowledge, the platform can provide more accurate and relevant suggestions, ensuring that AI-generated code aligns seamlessly with existing codebases and practices. Complementing this deep understanding of code context, CodiumAI’s enterprise platform also features a sophisticated database of best practices."