Docker, Inc’s Post

https://lnkd.in/gcZKF46R TLDR - "code generators" may complement, if not replace, low code platforms. “Coding will become almost entirely automated, and UX designers will become the de facto front-end developer. " “Instead of graphics tools like Figma to mock up UI, they will work with genAI tools to generate working UI prototypes in the company’s framework of choice.” I used to prescribe that "everyone is a 'coder', as long as we get the low code environment right". Perhaps the idea was before its time. 😅 #ai #digital #digitaltransformation

Figma Ai “Make Design” pulled down for more QA Make Design, Figma's flagship AI feature, was launched at the recently concluded Config24. During the keynote, the mention of this feature was met with enthusiastic applause from the audience. This feature is indeed a game-changer, helping designers bypass the blank canvas🤩. By simply describing your app in a prompt, you can watch as the UI is generated on the fly🚀 ⏰Shortly after its launch, Andy Allen, founder of Not Boring Software Ltd , discovered that using the Make Designs feature to create a 🌤️weather app repeatedly produced results nearly identical to the built-in iOS weather app. Allen posted on X, “Figma AI looks rather heavily trained on existing apps. This is a ‘weather app’ using the new Make Designs feature, and the results are basically Apple’s Weather app. Tried three times, same results.” This sparked conversations. During the opening session of Figma Config24, Dylan Field, Figma’s Chief Executive, mentioned that the AI would only learn from design files that users have consented to. Did they change gears? Field defended the tool, stating that the Make Design feature was not created by training an AI system on work done using the Figma app by other creators. “The Make Design feature is not trained on Figma content, community files, or app designs,” he said, calling claims that the service was trained on existing apps “false.” Instead of designing the app from scratch, the AI system built an app using an algorithmic toolkit put together by Figma’s own designers. However, this approach resulted in low variability, which Field acknowledged. I agree that Figma’s AI does not necessarily create designs from scratch but uses predefined toolkits and templates built by Figma’s designers. But if these toolkit and components look 👀 like existing apps wouldn’t it be the same thing? This issue could definitely be addressed in various ways, such as expanding the design toolkit, training on diverse data (our data), or introducing more randomization to increase variability. Currently, the feature has been pulled down. Interestingly, Field admitted that he is responsible for not insisting on a better quality assurance process and for pushing his team to meet a deadline for their annual conference, Config24. What are your thoughts?

Software development is complex. From the early stages of requirement gathering, UI/UX design, development, testing and then maintaining the final product. What would you say if a company told you they could automate 80% of the development and create nearly perfect code bases? Shreds.ai claims exactly this. With an example of generating 95% error free code that replicates the full Wordpress code base in 3 hours vs 91 human years of development. It's hard to validate this type of claim until the actual solution is released. In todays era of AI hype it's important to be skeptical but with an open mind, so I'm interested to see where this goes or if this is the one and only time we hear of them. #ai #softwaedevelopment https://lnkd.in/gYNHtQ-V

Are You a Startup Struggling with UI/UX For Production? Meet CodeParrot: An AI-Powered Tool that Transforms Figma Files to Production Ready Code https://lnkd.in/dkgsDehK CodeParrot AI: Transforming Figma Files to Production Ready Code Practical Solutions and Value CodeParrot AI offers super cool AI tools for designers and developers to streamline web development. It translates Figma designs into frontend components for React, Vue, and Angular, saving time and effort for coders. How CodeParrot Works CodeParrot simplifies the process by taking the URL of a Figma file and automatically generating the necessary code components, making it incredibly easy for developers. Features and Benefits CodeParrot can import Figma files and generate code, handle tedious tasks using AI-powered task allocation, and provide contextual search for troubleshooting. It ensures the resulting code uses pre-existing components and coding standards, improving the user experience with its vscode extension. Conclusion CodeParrot helps developers concentrate on essential features, reduces development time, and facilitates a seamless workflow with technology stacks like React and Vue.js. AI for Your Company Evolve with AI Stay competitive and transform your company with AI. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually for measurable impacts on business outcomes. AI KPI Management Connect with us at hello@itinai.com for AI KPI management advice and continuous insights into leveraging AI on Telegram t.me/itinainews or Twitter @itinaicom. Spotlight on AI Sales Bot Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom #CodeParrot #AI #Figma #WebDevelopment #ITINAI#artificialintelligence #ai #machinelearning #technology #datascience #python #deeplearning #programming #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #business #engineering #robot #datascientist #art #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer

AI in Development, Seamless front-end UX, Kubernetes ☁️ Just a few of the big questions explored in our recent Chapter Syncs! And this month’s Chapter Sync didn’t disappoint. Highlights include: 🌐 Seamless front-end UX: At our recent Chapter Sync, Max Taylor walked us through an exciting technique for front-end development, focusing on creating seamless transitions in both single-page and multi-page applications. What stood out? The simplicity. Using a new API, Max demonstrated how this approach reduces the amount of code required while offering flexible CSS animation options—making it easier for developers to deliver smooth, professional results. 🤖 AI’s evolution in dev: AI in development has been a hot topic for good reason, and it took center stage during our recent Chapter Sync discussion. The team dug into the practical applications of AI—like streamlining documentation, automating code completion, and improving operational efficiency. It wasn’t all smooth sailing, though. The conversation touched on some critical challenges: ethical considerations, potential impacts on job roles, and compliance risks. We also reflected on AI’s current limitations and how it might evolve to reshape programming practices in the future. It’s clear that while AI offers exciting possibilities, it also comes with a lot to think about. But we also wonder, where do you see the biggest opportunities—or challenges—for AI in development? Let’s discuss. 🎥 Watch the session highlights and join the conversation: What’s your take on AI’s role in development? 🔗

🌟 Embracing the Power of AI in Frontend Software Development 🌟 As frontend developers, we constantly seek tools to create intuitive, efficient, and visually stunning user experiences. In this evolving tech landscape, one game-changer stands out: Artificial Intelligence (AI). Here’s why AI is transforming frontend development and why we should embrace it: 🔹 Automated Efficiency: AI-powered tools like code assistants streamline development processes by suggesting code snippets, identifying bugs, and automating repetitive tasks. This means faster delivery and fewer errors. 🔹 Enhanced Personalization: AI enables real-time data analysis to create personalized user experiences. From dynamic interfaces to tailored content, the potential for innovation is limitless. 🔹 Accessibility Improvements: AI helps us design inclusive web experiences, generating alt text, transcriptions, and even accessibility audits effortlessly. 🔹 Better UI/UX Design: AI tools integrate seamlessly with platforms like Figma, offering smart design suggestions, wireframe generation, and prototyping enhancements. 🔹 Upskilling Opportunities: Adopting AI encourages us to learn new technologies, from integrating APIs to utilizing machine learning for frontend analytics. The integration of AI isn’t just a trend—it’s the future. By embracing AI, we expand our creative horizons, build smarter systems, and stay ahead in this competitive industry. Let's use AI not to replace our creativity but to amplify it. The question is not if but how fast we adapt! 🚀 💡 What are your thoughts on AI in frontend development? Have you integrated AI into your workflow yet? Let’s discuss! #FrontendDevelopment #ArtificialIntelligence #WebDevelopment #Innovation

🚀 How AI is Becoming the Secret Weapon for Frontend Developers 🎨💻 For frontend developers, the workflow is changing fast. Today, AI-powered tools are stepping in to handle repetitive tasks, speed up design processes, and even enhance user experience—all of which free up developers to focus on what really matters: crafting beautiful, functional interfaces. Here’s how AI is transforming frontend development: 🎨 Automated Design-to-Code Conversion Imagine designing a layout in Figma or Sketch, and with a single click, AI generates clean, production-ready code. Tools like DeepCode and Uizard are making this a reality, helping developers move from design to deployment faster than ever before. No more translating designs into code line by line—AI takes care of it. 🔍 Real-Time Accessibility Checks Accessibility is crucial, but it can be time-consuming to get it right. AI now runs continuous accessibility checks, flagging issues like contrast errors, alt text gaps, and keyboard navigation problems as you code. This ensures your designs are compliant and user-friendly from the start. ⚙️ Personalized User Experiences By analyzing user data, AI can dynamically adjust components like recommended content, layouts, and interactive elements based on real-time behavior. For instance, an AI tool might rearrange elements or adjust colors to suit each user’s preferences, creating a personalized experience that keeps users engaged. 🚀 Intelligent Debugging and Code Review AI-powered debugging tools like TabNine and Kite are spotting errors before you even hit run. These tools provide suggestions, optimize your code, and catch potential issues in real time, saving hours of testing and revision time. Think of it as an extra set of eyes on every line of code you write. 💡 Frontend Optimization on the Fly AI can analyze loading times, performance metrics, and user interactions, suggesting improvements for faster, more efficient interfaces. It’s like having a performance analyst for every page you build, ensuring each element is optimized for the best user experience. When I started using AI tools in my own projects, the difference has been incredible. From automated code suggestions to real-time accessibility checks, AI has streamlined my workflow and helped me focus more on innovation. It’s a new era for frontend developers—and those who embrace AI are already a step ahead. Curious about how AI can elevate your frontend game? Let’s connect and dive into the future of development. #FrontendDevelopment #AI #WebDevelopment #UserExperience #Automation #Coding #Innovation

[cbr] Here is a ChatBot UX MVP that uses HTML forms to interact with users instead of text. This has enormous potential to make the user experience much smoother and really starts to move the LLM's role into the 'Business Layer,' which is where I think it needs to be. This is an evolution of the MVP showed here https://lnkd.in/ef2XtF_H which uses text to collect the answers from the user (that MVP has a more detailed set of instructions on what to ask) This is an idea I had ages ago, and only now do I have the CI Pipeline and coding infrastructure to implement it :) In this (very rough) MVP, I: 1️⃣ Customized the ChatBot Web Components I created (which are open-sourced here: https://lnkd.in/e6siEc6z) to support HTML outputs (which is both a feature and a vulnerability). 2️⃣ Created a system prompt that asks the LLM to output HTML code for the questions to ask the user, and then at the end, create a review. 3️⃣ Added some custom JavaScript to submit the answers collected. 4️⃣ Note that I'm using Groq with Meta's Llama3, which not only is free to use but also works quite nicely. And it works really well :) There are lots of areas to improve: 1️⃣ UX is not well aligned (I couldn't get Llama3 to avoid using lots of <br> tags). 2️⃣ Although Groq is super fast, because this version fetches the LLM response in real-time, it takes a little bit to show and is not the best user experience (I'm not using streaming for these, so we have to wait for the full answer to be received from the LLM before displaying it). 3️⃣ My thinking is that these forms could be pre-generated either before the user gets to the page or while the user is filling in the data. 4️⃣ As an evolution, this shouldn't be using HTML for forms like this. It will be much better to use the LLM's function calling to create the form and let FastAPI or the JS UI be the one that creates the form and processes its events. 5️⃣ This last point should also help with capturing and displaying the submitted data. That said, I'm very happy with this evolution since this is the first time that I have an LLM workflow that is not text-based, which is pretty cool, right?

Great blog post about how we leverage LLM internally to accelerate SWEs. Smart Paste is truly amazing and feels like it can read my mind sometimes. The authors outline especially how much thought and consideration went into the UX of Smart Paste. And as a user it feels very natural and intuitive.

End to end (#e2e) driven development is the way towards effective User experience (#UX). I released a couple of opinionated production ready #Quasar #Playwright #E2E #POC, one using #typescript ( https://lnkd.in/dDwkcGPa ) and a second using python ( https://lnkd.in/drk-KGez ) which test login, user management, roles management, profile management, and extra menu options like the usual reports module. All this running in a 230 lines of fully #idempotent #typescript code and 263 lines of fully idempotent #python code. Note how on purpose I do not use any kind of custom reusable exports. Dependencies that often change are one of the major reasons for team slowness due to unnecessary regressions. You will notice the usage of logging up front because we want to follow up on what the test is doing at all times. This in combination with screenshots and videos from the failing tests gives us speed to fix issues. End to End (#e2e) Driven Development (#EDD) not only ensures no regressions, but in addition it ensures an effective user experience (UX). How EDD helps UX? 1. Applications that do not blur while loading or updating data and/or tests that do not assert the presence of blurring: An e2e test will not pass idempotently unless the app blurs and a user will be confused by apps that do not blur. 2. Applications that do not provide informative messages and/or tests that do not assert the presence of the informative messages: An e2e test will not pass idempotently unless it checks the results of its actions, and users will be confused if there are no informative messages. 3. Applications that do not handle Logoff and/or tests that do not assert that the user has not let anything behind: An e2e test will not pass idempotently unless it ensures to check the current URL and the expected content being seen and users will not have to perform unnecessary page refreshes. 4. Applications that are too slow and/or tests that do not assert at each step of their way: An e2e test will not pass idempotently if they do not assert the result of each interaction and users will face less app "hiccups". How you write your e2e specifications (#e2espec or e2e-spec) is paramount. You must constantly assert not only to ensure that functional requirements are met, but also that non-functional requirements are met as well. UI/UX requirements are functional, but some implicit requirements are not stated as functional at all; as it is the case of blurring and messaging. A software engineer should write the e2e-spec and as they implement the requirements, then tune such specs, ensuring that the specs themselves are written in the e2e implementation code. If you code e2e implementations this way you will spend way less time troubleshooting idempotency issues in your code and most importantly you will deliver faster and with higher quality y a very predictable way.