the expensive part of vibe coding isn't the retries, it's the context you drag into each one
Summary
A developer reveals that the real cost driver in AI-assisted debugging sessions is the accumulated context per retry, not the number of retries, and introduces an open-source tool called codeburn to analyze session costs.
Similar Articles
Am I the only one starting to get 'Vibe Coding' fatigue ?
A developer shares their fatigue with 'vibe coding,' noting that while AI agents speed up initial creation, they introduce significant architectural debugging challenges and technical debt in complex repositories.
anyone else getting burned out by the "vibe coding" loop?
A developer shares frustration with the 'vibe coding' loop—spending more time managing AI prompts than writing code—and asks how others stay productive with tools like Cursor and Claude.
@dedene: Vibe coding looks easy. Zero upfront design. Zero structure. The reality is: Every broken iteration burns tokens. Every…
A tweet highlights the challenges of vibe coding and promotes a free 50-page guide on transitioning to agentic engineering, covering a new AI-driven software development life cycle.
@IntuitMachine: Your AI coding agent just burned $2 on a single bug fix. You thought it was "cheap automation." Here's what 16,000 prod…
An analysis of AI coding agent costs reveals that agentic workflows can use up to 3,500x more tokens than a simple ChatGPT call, with most waste coming from redundant context loading. The article suggests tracking repeated file actions and using efficient models to cut costs.
The most expensive part of running AI agents isn't the tokens. It's the time figuring out why they did something.
Building AI agents reveals that the major cost is debugging—spending weeks chasing issues like upstream API changes—not just token or model inference costs.