// Topics / Optimization

Optimization

Definition

Optimization coverage in this archive spans 6 posts from Aug 2017 to Feb 2026 and deals with structural tradeoffs: coupling, failure boundaries, and long-term change cost. The strongest adjacent threads are performance, cost, and ai. Recurring title motifs include ai, cost, go, and trends.

What the archive argues

  • Most pieces recommend choosing the simplest architecture that can be operated confidently.
  • Early posts lean on stop and guessing, while newer posts lean on ai and cost as constraints shifted.
  • This topic repeatedly intersects with performance, cost, and ai, so design choices here rarely stand alone.

Execution checklist

  • Define failure domains and data boundaries before introducing additional services or protocols.
  • Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
  • When boundary questions appear, cross-read performance and cost before committing implementation details.

Common failure modes

  • Breaking systems into many parts without clear ownership of cross-service behavior.
  • Choosing architecture for trend alignment rather than workload constraints.
  • Applying guidance from 2017 to 2026 without revisiting assumptions as context changed.

Suggested reading path

References

6 entries tagged “Optimization”

    AI Inference Cost Trends 2026: Model Pricing and Token Costs · 11 min AI inference costs are falling, but durable savings come from routing, caching, context control, and cost per outcome. cost ai economics AI Cost Benchmarking: What Your Bill Actually Tells You · 5 min Price-per-token is the least useful number on your AI bill. Real cost benchmarking starts with your workload, not a provider's pricing page. ai cost benchmarking LLM Prompt Caching in Go: Cut Costs Without Breaking Things · 6 min Caching LLM responses is the highest-leverage optimization most teams are not doing. Here is how I implement it in Go, with real patterns for keys, invalidation, and safety. llm caching go PostgreSQL Performance: Measure First, Tune Second · 6 min Most Postgres performance problems are indexing problems. The rest are vacuum problems. Here's how to find and fix both. postgresql databases performance Making Go Services Fast: What Actually Matters · 7 min Practical patterns for squeezing performance out of Go services — profiling, allocation control, bounded concurrency, and HTTP/DB tuning from real production work. go performance backend Stop Guessing: How I Fix Slow Databases · 7 min The repeatable process I use at the fintech startup to diagnose and fix database performance problems instead of throwing random indexes at the wall. databases performance postgresql
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