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.
hanson1014
Full-stack developer experienced in fixing and deploying AI-generated apps from Lovable, Bolt.new, Cursor, and Replit. I specialize in debugging Supabase integration issues (auth flows, RLS policies, database connections), fixing broken deployments, resolving routing/blank screen problems, and cleaning up messy React/Vite codebases. I also build production apps with the Claude API and have shipped a Mac desktop dev tool (Nexterm from scratch. Based in Hong Kong, fast turnaround.
Jared Hasson
Full time lead founding dev at a cyber security saas startup, with 10 yoe and a bachelor's in CS. Building & debugging software products is what I've spent my time on for forever
Matthew Butler
Systems Development Engineer @ Amazon Web Services
Dor Yaloz
SW engineer with 6+ years of experience,
I worked with React/Node/Python
did projects with React+Capacitor.js for ios
Supabase expert
Bastien Labelle
Full stack dev w/ 20+ years of experience
Yovel Cohen
I got a lot of experience in building Long-horizon AI Agents in production, Backend apps that scale to millions of users and frontend knowledge as well.
zipking
I am a technologist and product builder dedicated to creating high-impact solutions at the intersection of AI and specialized markets. Currently, I am focused on PropScan (EstateGuard), an AI-driven SaaS platform tailored for the Japanese real estate industry, and exploring the potential of Archify.
As an INFJ-T, I approach development with a "systems-thinking" mindset—balancing technical precision with a deep understanding of user needs. I particularly enjoy the challenge of architecting Vertical AI SaaS and optimizing Small Language Models (SLMs) to solve specific, real-world business problems. Whether I'm in a CTO-level leadership role or hands-on with the code, I thrive on building tools that turn complex data into actionable value.
Victor Denisov
Developer
rayush33
JavaScript (React.js, React Native, Node.js) Developer with demonstrated industry experience of 4+ years, actively looking for opportunities to hone my skills as well as help small-scale business owners with solutions to technical problems
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.
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