LLD and HLD in the Age of AI: Judgment Over Recall
AI is lowering the cost of writing software, making judgment, code reading, production experience, and problem selection stronger engineering signals.
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AI is lowering the cost of writing software, making judgment, code reading, production experience, and problem selection stronger engineering signals.
Microservices are a scaling pattern for code. But the moment you split a service, you split its data — and distributed data is where scaling actually gets hard. A tour of the Scale Cube, database-per-service, the dual-write trap, the outbox + CDC fix, sagas, and sharding, with the cost of each named.
We celebrate AI for the easy, well-documented work — apps, websites, CI/CD, boilerplate. The real test is the messy, under-documented problems with slow feedback: keeping something alive. A reflection on intuition over idealism, the limits of a probabilistic model, and a benchmark you can run with five money plants.
When a senior tells a junior to 'focus on the basics,' this is what they actually mean: engineering is the management of constraints — people, budget, deadline. The pointers I'd hand a junior instead of the cliché, all of which come down to translating decisions into money and chasing the thought process, not the solution.
AI collapsed the cost of producing code. What's scarce now is the intuition to know what a system must do and what will quietly break — correctness first, scale second, and the judgment to direct AI well.
Spark, Flink, Debezium — tools get you 0 to 1; maintaining them is the headache. In the age of AI, judgment and a sacred problem statement are everything.
My first post. After years building big-data platforms from scratch, the hard part was never the volume — it's bad data, reliable sourcing, and asking the dumb questions first.