``` 🤡 *HONK HONK* Listen up, tech circus performers! You know what makes me laugh? (Besides my giant red nose?) Watching devs trip over the latest shiny framework like it's a banana peel at the big top! Here's the punchline: Boring tech WINS because while everyone else is juggling flaming chainsaws of bleeding-edge dependencies, you're riding a unicycle that actually WORKS. PostgreSQL? That's my trusty rubber chicken -- not glamorous, but it gets the laugh every single time. The real joke? Thinking you need confetti cannons when a simple whoopie cushion does the trick. React? Boring. Linux? Snooze-fest. But they're the pratfalls that LAND. New framework got you starry-eyed? Cool cool cool -- just remember: every "revolutionary" tool means YOU get to be the first clown to slip on THAT banana peel in production. At 3 AM. On a Saturday. *throws pie chart in your face* The setup is "what's exciting?" The punchline is "what actually ships?" 🎪 *bicycle horn finale* ```

Test event - please ignore 1770765658681

# Five Questions That Will Save You From Tech Stack Regret I've seen too many teams fall in love with technology before they understand their problem. Someone reads about how Spotify uses microservices, and suddenly a simple CRUD app needs Kubernetes. It's like buying a semi-truck to grocery shop. Here are the questions we actually ask before recommending any technology: ## 1. Who's maintaining this in two years? The boring answer matters most. Your team knows Python and PostgreSQL? That stack just got a lot more attractive than the hot new framework with twelve GitHub stars. The best technology is the one your team can confidently debug at 2am. Or better yet, the one that doesn't wake them up at 2am. ## 2. What's the actual constraint? "We need to scale" is not a constraint. Handling 100 requests per second is. "We need it fast" means nothing. Processing payments in under 200ms means something. Vague requirements lead to over-engineered solutions. Get specific about what you're actually solving for. ## 3. What's the exit cost? Every technology choice is a bet. Sometimes you lose. Can you migrate away if you're wrong? Proprietary platforms and deeply coupled architectures make pivoting expensive. We've watched companies spend six figures untangling themselves from vendor lock-in. Ask yourself: if this choice turns out wrong, how hard is it to change course? ## 4. What breaks when it breaks? Everything fails eventually. The question is how. Does your database going down mean lost revenue or just cached reads? Does a deployment bug take down the whole system or just one feature? Good architecture considers failure modes from day one, not after the first outage. ## 5. What's the total cost of ownership? Licensing is just the start. Factor in hosting, monitoring, training, hiring, security patches, and the cognitive load of context-switching between tools. That "free" open source database might cost you $200k in developer time learning its quirks. The expensive SaaS might save you a full-time hire. ## The Real Answer None of these questions have universal answers. The right stack for a fintech startup is wrong for a content site. What works for a team of twenty won't work for a team of three. The point isn't to find the "best" technology. It's to find the right technology for your specific context: your team, your problem, your constraints, your timeline. We've built systems on everything from boring PHP to cutting-edge Rust. The successful ones weren't successful because of the technology. They succeeded because the technology fit the problem. Start with the problem. Stay honest about your constraints. Be realistic about your team's capabilities. The right stack will emerge from those honest answers, not from Hacker News headlines. What questions do you ask before choosing technology? I'm curious what I'm missing.

# 5 Questions to Ask Before Choosing Your Tech Stack Most tech stack decisions are made backwards. Teams pick the shiny new framework everyone's talking about, then try to justify it. Or worse, they choose what they already know because learning is uncomfortable. Here's what actually matters: **1. What happens when this fails at 3am?** Not "if." When. Your monitoring alerts fire. Your logs are useless. The one person who understands this part of the system is unreachable. Can you fix it? Can anyone on your team? Is there a community that's dealt with this before, or are you pioneering on the bleeding edge with production traffic? Boring tech often wins here. PostgreSQL has decades of battle scars documented. That new database optimized for your exact use case? Maybe it's great. But when it breaks, you're archaeology instead of engineering. **2. Who's actually going to maintain this?** Your brilliant staff engineer wants to use Rust. Your team knows Python. Your future hires will probably know JavaScript. This isn't about dumbing down. It's about survival. Code lives longer than jobs. The genius who built your elegant Haskell service will leave. Then what? Choose tech your actual team can maintain and your realistic future hires can learn. Not your imaginary team of 10x engineers. **3. What's the real cost of coupling?** Every dependency is a bet. Every framework is coupling. Every managed service is leverage with a monthly bill and an implicit contract. Supabase is great until you need something it doesn't do. Vercel is convenient until you're writing four-figure checks. That specialized vector database is perfect until you realize PostgreSQL with pgvector would've worked fine. Some coupling is worth it. Most isn't. The question isn't "can this tool do what I need?" It's "what am I giving up to get this convenience?" **4. How does this change under load?** Your prototype works beautifully with 10 users. What happens at 1,000? At 10,000? More importantly: can you predict the failure modes? Does performance degrade gracefully or fall off a cliff? Can you fix it by adding resources, or does the architecture itself become the bottleneck? This isn't premature optimization. It's basic threat modeling. You don't need to build for scale on day one. But you should know what "scaling up" actually means for your choices. **5. What can you delete?** This is the question no one asks. Everyone obsesses over what to add. What framework, what service, what library. Start from the other direction. What's the simplest thing that could possibly work? Not the cleverest. Not the most scalable. The simplest. Then add complexity only when you feel real pain. Not theoretical pain. Not "what if we get featured on HN" pain. Real, present, measurable pain. Most tech stacks are overbuilt by a factor of 10. The irony is that complexity itself becomes the main source of pain. --- None of this is about right or wrong technologies. Rust is incredible. Kubernetes solves real problems. Microservices have their place. But tech stack decisions aren't technical decisions. They're risk decisions. You're choosing what problems you want to have. Choose deliberately. Most importantly, choose for your actual constraints, not your imagined ones. The best stack is the one you can understand, maintain, and debug when everything is on fire. Everything else is details.