Replit
performance
High Token Usage from AI Agent on Replit
Your AI agent consumes excessive tokens causing high API costs. Each request uses thousands of tokens unnecessarily.
Verbose prompts, large context windows, and inefficient tool use waste tokens.
Common Causes
- Entire codebase in context instead of relevant files
- Verbose system prompt with unnecessary instructions
- Including all previous conversation history
- Tool responses not summarized/truncated
- Multiple tool calls when one would suffice
How to Fix It
Limit context to relevant code only (use file selectors). Summarize system prompt to essential instructions only. Keep conversation history to last N messages. Truncate tool responses to relevant portions. Cache frequently used context. Implement tool result filtering to return only needed data.
Real developers can help you.
Matthew Jordan
I've been working at a large software company named Kainos for 2 years, and mainly specialise in Platform Engineering. I regularly enjoy working on software products outside of work, and I'm a huge fan of game development using Unity. I personally enjoy Python & C# in my spare time, but I also specialise in multiple different platform-related technologies from my day job.
Taufan
I’m a product-focused engineer and tech leader who builds scalable systems and turns ideas into production-ready platforms.
Over the past years, I’ve worked across startups and fast-moving teams, leading backend architecture, improving system reliability, and shipping products used by thousands of users. My strength is not just writing code — but connecting product vision, technical execution, and business impact.
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/
Rudra Bhikadiya
I build and fix web apps across Next.js, Node.js, and DBs.
Comfortable jumping into messy code, broken APIs, and mysterious bugs.
If your project works in theory but not in reality, I help close that gap.
Matthew Butler
Systems Development Engineer @ Amazon Web Services
Alvin Voo
I’ve watched the tech landscape evolve over the last decade—from the structured days of Java Server Pages to the current "wild west" of Agentic-driven development. While AI can "vibe" a frontend into existence, I specialize in the architecture that keeps it from collapsing.
My expertise lies in the critical backend infrastructure: the parts that must be fast, secure, and scalable. I thrive on high-pressure environments, such as when I had only three weeks to architect and launch an Ethereum redemption system with minimal prior crypto knowledge, turning it into a major revenue stream.
What I bring to your project:
Forensic Debugging: I don't just "patch" bugs; I use tools like Datadog and Explain Analyzers to map out bottlenecks and resolve root causes—like significantly reducing memory usage by optimizing complex DB joins.
Full-Stack Context: Deep experience in Node.js and React, ensuring backends play perfectly with mobile and web teams.
Sanity in the Age of AI: I bridge the gap between "best practices" and modern speed, ensuring your project isn't just built fast, but built to last.
Kingsley Omage
Fullstack software engineer passionate about AI Agents, blockchain, LLMs.
Pratik
SWE with 15+ years of experience building and maintaining web apps and extensive BE infrastructure
David Olverson
Solo dev shipping production apps with AI-assisted development. I specialize in rescuing broken Lovable/Bolt/Cursor builds and taking them to production. 10+ apps shipped including SaaS CRMs, gaming platforms, real estate tools, and Discord bots. Stack: Next.js 16, TypeScript, Tailwind CSS, FastAPI, PostgreSQL, Prisma. I use Claude Code with 50+ custom skills for rapid delivery. Average turnaround: 2-4 weeks from broken prototype to production.
Nam Tran
10 years as fullstack developer
You don't need to be technical. Just describe what's wrong and a verified developer will handle the rest.
Get HelpFrequently Asked Questions
How do I estimate token cost?
OpenAI: ~4 tokens per word. Monitor API usage dashboard
Should I include full repo context?
No. Use file search first to identify relevant files, then include only those