Should engineers fear messy AI code or embrace it as a tool for fast product validation? This story reveals why your cleanest code might not be your most valuable contribution. Why founders will use AI generated code more and more for MVPs and prototyping and why engineers should embrace this.
Lynx could revolutionize cross-platform mobile development with its unique approach and performance benefits. Is it time to switch from React Native or Flutter?
Should SaaS founders buy or build their MVP? We compare pre-built apps vs custom development across cost, time, scalability, and code quality — so you can choose the right foundation for your product.
Building desktop apps with NativePHP misuses Laravel’s core strengths, creating potential security risks and practical limitations.
Many SaaS startups over-engineer their architecture with microservices. Here's why that's usually a costly mistake—and what to do instead.
Should startups test everything before shipping? We break down how to balance testing with product velocity—and why 100% coverage is often the wrong goal.
AI tools are reshaping how junior engineers approach problems, often replacing simple solutions with overly complex ones. Here’s why foundational thinking still matters. A real-life case of AI over-engineering gone wrong highlights why understanding problem domains still beats prompting.
Selecting the right tech stack is critical for AI startups. Python is essential for data science, but the backend, frontend, and infrastructure choices determine scalability and efficiency. Explore the best tech stack combinations, hosting tools, and ETL solutions to future-proof your AI startup.
Should you avoid managed services to prevent lock-in? Maybe. However, spending months reinventing infrastructure instead of shipping features is a bigger risk. Balance is key. Know the trade-offs, understand the costs, and make decisions based on your reality.