Computing efficiency is sometimes treated as late optimisation: the system exists, then engineers make it faster. Many of the largest gains come earlier from choosing the right problem, representation and architecture. Avoiding unnecessary work is usually more powerful than performing the same work with a small improvement in speed.
Define the useful outcome
A system cannot be efficient without a clear measure of value. Optimising requests per second may harm response quality, while minimising memory may increase repeated computation. The measure needs to reflect what users experience and which resources are genuinely constrained.
Targets should include realistic conditions. Average performance can conceal long delays for uncommon inputs or during busy periods. Measuring distributions and resource use reveals whether an improvement helps broadly or only in a convenient benchmark.
Find evidence before changing code
Intuition about bottlenecks is often wrong in layered systems. Profiling shows where time, memory and data movement are spent. It can reveal repeated database queries, oversized representations or work performed for results that are never used.
Measurement also protects maintainability. A complicated optimisation should earn its cost through a demonstrated improvement. If a simple design already meets the requirement, additional mechanisms can create more future work than present benefit.
Architecture determines the ceiling
Caching, streaming and incremental computation change how work is organised. A result can be reused instead of recalculated, processed in parts instead of held entirely in memory or updated from the difference instead of rebuilt from the beginning.
These techniques bring tradeoffs. Cached information can become stale, streaming can complicate error recovery and incremental state needs consistency. Efficiency remains a design judgment rather than a universal recipe.
Efficient computing respects both machines and people. It reduces infrastructure needs, improves responsiveness and can make capability more accessible. The discipline is to define the outcome, measure the real system and prefer the simplest architecture that avoids waste while preserving correctness and clarity.