epicX’s Post

The biggest mistake I see people make when deciding whether to use no-code for a project is treating the decision as binary. "We're trying to decide whether to do it in no-code or traditionally code it." It's not a binary choice; it's a spectrum. A better question to ask would be – which parts of our product must be custom coded to meet our needs, and what can we accomplish more quickly using no-code? No-code has evolved to the point where you have multiple approaches to how you use it: 👉 Develop the entire project in no-code. 👉 Use no-code for both backend and frontend. 👉 Keep the backend custom and use no-code for the frontend. 👉 Opt for low-code for the entire project. 👉 Code everything traditionally. The more you lean towards no-code on this spectrum, the quicker you can launch your first MVP, acquire initial users, gather data, or earn revenue. Then, you iterate. This is the essence of the lean startup methodology. 🚀 #nocode #nocodedevelopment

Full Stack Development < Low-Code < No-Code < No-Code + AI Value and innovation will be driven by everyone and not limited to developers. Value will no longer by tied to long development builds but by value driven ideas built in a fraction of the time full stack development takes. #BeyondCode

Adding New Features Without Disrupting Your Codebase. As a startup, you need to be able to innovate and add new features quickly to stay competitive. However, this can be challenging when you have a monolithic codebase that tightly couples different parts of your application together. Making a change in one place can unintentionally break something else. 💯 This is where microservices can help! With microservices, you break your application into smaller, independent services that can be developed, deployed and scaled independently. 🎯 Here are some key benefits of using microservices: ◾Faster Innovation: Microservices allow you to develop and release new features faster by breaking work into isolated teams. This agility is extremely valuable for startups needing to iterate quickly. ◾Reduced Risk: The modular architecture of microservices localizes potential issues. Changes and failures don't impact the overall system as much, allowing more frequent releases. ◾Scalability: The ability to scale individual microservices only as needed optimizes infrastructure costs. This efficient resource allocation is great for startups. ◾Reliability: Microservices minimize downtime and user impact by keeping unaffected services running if one goes down. Resilience is important for business continuity. ◾Independent Development: Build new features in parallel without tampering with core product code. This enables easier testing and feature experimentation. ◾Technology Freedom: Choose the best tech stack for each microservice. For example, use Python and FastAPI to quickly build a chatbot, and Java for your core product. This flexibility unlocks performance. Microservices are a perfect fit for startups that need to regularly ship new features. It prevents changes from cascading and breaking existing code. 💯 One great framework for building microservices in Python is FastAPI. It's fast, easy to use and generates interactive API documentation. With microservices and FastAPI, you can add new features like chatbots, recommendations engines, without disrupting your existing application. ---🚀--- If you have a product roadmap that includes innovative new features, but your legacy codebase is holding you back, you can contact me via a direct message.... I can help you seamlessly integrate new features while ensuring that your existing code remains stable. #microservices #fastapi #programing #startup #business #product #coding

Discover how startups boost their productivity by building Elixir apps. Uncover 13 real-world examples to achieve efficiency and scalability for your business. #elixir #programming #coding #softwaredevelopment #functionalprogramming #opensource #techcommunity #techstack #elixircommunity #webdevelopment #softwareengineering

Gauge (YC S24) is building open source developer tools to solve the microservices/monolith dilemma, allowing you to maintain velocity as your team scales. As startups grow, code quality takes a back seat to velocity, leading to code sprawl and tightly coupled services. Once companies reach this stage, they often reach for microservices. However, this introduces a whole new world of complexity that can stymie even the most competent of teams. Gauge is starting with tooling that allows you to separate your monolith into independent modules with well-defined interfaces. They’ve built visualization tools and test suite acceleration features and currently have over 100k downloads/500 stars on GitHub. Caelean Barnes and Evan Doyle first met as roommates in college, and in the decade since, they’ve taken on multiple founding engineering roles and helped several startups go from 0 to 1. At every startup, they ran into the microservices/monolith dilemma as the team and codebase scaled. They’re building the tools that they always wished they had. Check out their first tool, Tach, at https://lnkd.in/g68FM4AM. Learn more: https://lnkd.in/gMmw65JX

I'm going to go against software engineering orthodoxy, on a couple of topics: 1. Monorepos 2. Trunk-Based Development. These ideas can be particularly toxic to small startups when taken as gospel truth. These practices have merit when applied to very large code-bases with many developers and very high-rate of code-commits that results in a constantly moving deployment target. The monorepo, combined with trunk-based development, and an enforcement mechanism that always ensures a green build, effectively acts as the synchronisation mechanism to ship working code. However, just because you always have a shippable head commit on your trunk, does not mean you have a higher feature velocity. Indeed, most developers will find that larger the team working on a monorepo, the slower it gets to actually ship features into production. This is because, the burden of keeping up with the rapid evolution of the code base falls to the developer. The complexity of deploying large features to a large, rapidly changing codebase, means that these features have to be very carefully planned and broken down into smaller atomic units that can be committed into the trunk. This means that you need spend a lot of time and effort in planning the commit and adding complexity to the codebase with feature flags etc. This makes the code not only harder and slower to deploy, it actually increases the error rate, as it is harder to identify errors in such a complex system. For a small startup, it is better for the small developer team working on a feature to just ship the feature branch and then merge it into the trunk. This way, the value is delivered early, and complexities of the merge can be handled after crunch time. There are many other benefits to feature based development. I'll post on them another time. #softwareengineering #git #monorepos #trunkbaseddevelopment (Image credit: Microsoft Designer AI with the prompt: "A diagram of a git workflow with a datacenter server in the background"

🚀👨💻 As a seasoned developer, I strongly advocate for #Startups to adopt a dynamic tech stack! 💡 My preferred choice? #NextJs for that slick, seamless frontend, and the robust #dotNET for backend magic. 🎩⚙️ Why? Because balance in the Force is essential! 🌓 Who agrees or thinks differently? 💭 Let's chat about your preferred stack and why? 🚀 #TechStartups #CloudNative #AI #ProgrammingLife #K8s #Docker 💻🌐

Pair programming with my assistant. A lot of new features coming soon at deployment.io :-). Stay tuned. #ai #code #deployment #startup #codeanalysis

My Top Advice for New No Code Developers? Embrace Continuous Learning! Yes, It's been my secret sauce for unlocking the full potential of these tools. Stay curious, experiment relentlessly, and innovate solutions for your clients. Trust me, it's a game-changer! #nocode #freelancingtips #bubbleio #nocode #developement

Startups need to plan for 𝗔𝗜 𝗶𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 in 2024. ❗ Throwing money at AI tools won't work. Instead, -Do a proof of concept to see if AI helps developers work faster or feel better.  -Track metrics like cycle time and developer experience.  -Use AI across different tasks and with different coders to see if it works in various situations. #SoftwareDevelopment #DevOps #Coding #Programming #SoftwareEngineer #DeveloperLife #TechStack