You rarely lose a system because of one obviously broken endpoint. You lose it because something subtle shifts. A new caching layer adds a tiny bit of overhead. A query
You budget for GPUs. You forecast token usage. You negotiate enterprise contracts for foundation models and pat yourself on the back for shaving five percent off inference costs. Then six
If you have ever watched an infrastructure curve bend the wrong way, you know the feeling. Latency climbs faster than traffic. Deployments slow down as headcount grows. Every new service
You already know the feeling. Everything works beautifully in your local environment. Your order service writes to Postgres. Your payment service talks to Stripe. Your inventory service decrements stock. Each
Most teams do not fail because they chose “the wrong tool.” They fail because they use the same communication tempo and ignore asynchronous communication for every kind of work. Synchronous
You have probably lived this moment. Traffic is calm. Dashboards look green. Then a campaign launches, a batch job overlaps with a product push, or a customer in another time
You have seen this movie before. A team hits a relevance problem, someone suggests semantic search, and the solution becomes “just add an embedding.” A vector database appears. A few
Most architectural lock-in does not start with a grand decision. It starts with a reasonable shortcut taken under pressure. A library added to hit a deadline. A deployment model was
You usually notice “scaling” is broken when a dashboard goes flat, a consumer lag graph turns into a ski slope, and someone asks the worst question in engineering: “Are we
