Developers tackle AI token cost monitoring and grapple with frontend specialization

One developer built a local Token Monitor to track LLM token usage and associated costs during code generation. The tool scans project directories, visualises consumption over time, breaks down usage by model (e.g., Gemini, GPT‑4o, Claude) and applies configurable pricing to estimate expenses. It is fully open‑source on GitHub and aims to help individual engineers manage AI API budgets.

Another software engineer reflected on a four‑year career shift toward front‑end work after initially working full‑stack. Frequent placement in front‑end roles has limited exposure to back‑end architecture, databases, and business logic, leading to feelings of reduced relevance, especially as AI tools emphasize visual output over code quality.