@tdinh_me: Just tried this in my codebase, burned ~$70 worth of tokens and resulted in 30+ PRs, all non-critical but totally legit…
Summary
A developer reports using an AI tool on their codebase, spending ~$70 on tokens to generate 30+ legitimate but non-critical security fixes.
Similar Articles
@wesbos: Burned $91.34 with Claude Code /goal in 3.5 hours Unreal, It was able to reverse engineer it!
Developer Wes Bos spent $91.34 using Claude Code over 3.5 hours to successfully reverse engineer a project, highlighting the potential power and cost of AI-assisted coding.
@_avichawla: Claude Code used 3x fewer tokens with one change: - Before: 10.4M tokens · 10 errors · $9.21 - After: 3.7M tokens · 0 e…
By swapping to Insforge Skills + CLI as the backend context layer, a user cut Claude Code token usage by 64 %, eliminated all errors and reduced cost from $9.21 to $2.81.
@realsigridjin: this random korean guy built agent pipeline that pushed over 500 commits to 100+ major open-source repos in just 72 hou…
A developer built an automated agent pipeline that submitted over 500 commits to more than 100 major open-source repositories within 72 hours, with maintainers from projects like Kubernetes and Hugging Face merging some pull requests before GitHub suspended the account.
@LangChain: This AI watches its own codebase, flags missing monitors, and opens PRs to fix bugs it finds. @Shevchenkoaalex on @TryR…
An AI agent built with LangChain continuously monitors its own codebase, flags missing monitors, and automatically opens PRs to fix bugs it finds, as described by Alex Shevchenko from Ramp.
Improving token efficiency in GitHub Agentic Workflows (12 minute read)
GitHub improved token efficiency in their agentic workflows by logging token usage via an API proxy and building daily optimization workflows, reducing overhead from unused MCP tool registrations.