Claude Code performance

Inefficient Algorithm Causing Request Timeouts

An endpoint that processes data times out when dataset size increases. The algorithm works correctly for small datasets but degrades exponentially with larger inputs. Response time jumps from 100ms to 10+ seconds as data volume grows.

The algorithm is correct but has poor time complexity that wasn't apparent at scale.

Error Messages You Might See

Request timeout after 30 seconds Response time increases exponentially with data size 504 Gateway Timeout with larger datasets
Request timeout after 30 secondsResponse time increases exponentially with data size504 Gateway Timeout with larger datasets

Common Causes

  1. Nested loops creating O(n²) or O(n³) complexity (sorted list checking for each item)
  2. Inefficient search: linear search where binary search should be used
  3. Unnecessary array copying in loop creating O(n²) memory usage
  4. Recursive algorithm without memoization, recalculating same values
  5. Sorting inside loops instead of once before loop

How to Fix It

Profile the slow endpoint with realistic dataset size. Look for nested loops and recursive calls. Use appropriate data structures: HashSet for O(1) lookup, sorted array for binary search. Avoid creating new objects in tight loops. Memoize/cache expensive calculations. Consider pagination: process in batches instead of all at once.

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Jacek Rozanski Jacek Rozanski Senior PHP/Symfony developer and DevOps engineer with 20+ years of professional experience, running opcode.pl (web development agency, est. 2004). Day job: I'm the sole backend developer at merketing company where I own and maintain 11 PHP/Symfony microservices on AWS (ECS Fargate, RDS, S3, CloudFront), handle the full CI/CD pipeline (Bitbucket Pipelines, Docker), and manage monitoring with Sentry and CloudWatch. These services handle high request volumes in production every month. What I bring to AI-built apps: - I audit and fix security issues (OWASP methodology), performance bottlenecks, and architectural problems in codebases generated by Cursor, Claude Code, Lovable, Bolt, and v0 - I refactor AI-generated prototypes into production-grade applications with proper error handling, testing, and clean architecture (SOLID, DDD, hexagonal architecture) - I set up the infrastructure AI tools don't touch: AWS hosting, CI/CD pipelines, automated deployments, database optimization, monitoring, and alerting - I integrate external services: payment providers, email systems, partner APIs, SSO/auth Tech stack: PHP 8.x, Symfony, React, Next.js, PostgreSQL, MySQL, Docker, AWS (ECS, RDS, S3, SQS/SNS, CloudFront), Terraform, Supabase. I also use AI tools daily (Claude Code, Cursor) in my own workflow, so I understand both the strengths and the gaps in AI-generated code. Based in Poland (CET timezone). Available for async work and calls during EU/US business hours. 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I'm also comfortable working across the stack when needed, integrating APIs, handling business logic, and helping transform prototypes into production-ready systems. My goal is always to deliver solutions that are **simple, efficient, and genuinely useful for the people using them.** Sage Fulcher Sage Fulcher Hey I'm Sage! Im a Boston area software engineer who grew up in South Florida. Ive worked at a ton of cool places like a telehealth kidney care startup that took part in a billion dollar merger (Cricket health/Interwell health), a boutique design agency where I got to work on a ton of exciting startups including a photography education app, a collegiate Esports league and more (Philosophie), a data analytics as a service startup in Cambridge (MA) as well as at Phillips and MIT Lincoln Lab where I designed and developed novel network security visualizations and analytics. I've been writing code and furiously devoted to using computers to make people’s lives easier for about 17 years. My degree is in making computers make pretty lights and sounds. Outside of work I love hip hop, the Celtics, professional wrestling, magic the gathering, photography, drumming, and guitars (both making and playing them) legrab legrab I'll fill this later Matthew Butler Matthew Butler Systems Development Engineer @ Amazon Web Services Jaime Orts-Caroff Jaime Orts-Caroff I'm a Senior Android developer, open to work in various fields Vlad Temian Vlad Temian 15+ years shipping production infrastructure for startups. Former CTO at qed.builders (acquired by The Sandbox). Cursor ambassador and agentic tooling builder. I've scaled systems, automated deployments, and built observability tools for AI coding workflows. I specialize in taking vibe-coded apps from broken prototype to production-ready: fixing Supabase auth/RLS, Stripe integrations, deployment pipelines, and cleaning up AI-generated spaghetti. I build tools in this space (agentprobe, claudebin, micode) and understand both sides: how AI generates code and why it breaks. https://blog.vtemian.com/ Simon A. Simon A. I'm a backend developer building APIs, emulators, and interactive game systems. Professionally, I've developed Java/Spring reporting solutions, managed relational and NoSQL databases, and implemented CI/CD workflows. Richard McSorley Richard McSorley Full-Stack Software Engineer with 8+ years building high-performance applications for enterprise clients. Shipped production systems at Walmart (4,000+ stores), Cigna (20M+ users), and Arkansas Blue Cross. 5 patents in retail/supply chain tech. Currently focused on AI integrations, automation tools, and TypeScript-first architectures.

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Frequently Asked Questions

How to identify O(n²) problems?

Double the input size. If time increases by 4x, likely O(n²). If increases by 2x, likely O(n). If no change, probably O(1).

When should binary search be used?

When searching sorted array/list. O(log n) instead of O(n). If unsorted, sort first (O(n log n)) then binary search.

How to optimize recursive algorithms?

Add memoization (cache results). Fibonacci: instead of recalculating fib(5) multiple times, cache it. Or use iterative approach.

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