Codex logging bug may write TBs to local SSDs

Original Article ↗ Hacker News Discussion ↗

Logging bug and impact

  • Codex’s trace-level logging writes continuously to a local SQLite DB, with WAL files growing to tens of GB and potentially TBs over time.
  • Users report idle sessions still hammering disks; some discover the issue only via tools like iotop or when machines run out of space or hang.
  • Similar heavy logging behavior is noted for other AI tools (e.g., Claude Code, ChatGPT macOS app), sometimes mitigated by symlinking logs to tmpfs.
  • A fix has been committed in the Codex repo and is expected in the next release.

Workarounds and mitigations

  • Suggested mitigations include:
    • SQLite trigger to ignore all inserts into the logs table.
    • Running VACUUM FULL to shrink oversized DB files.
    • Deleting WAL files via scripts or moving logs to a RAM-backed filesystem.
  • Some users simply uninstall Codex or switch tools due to fear of SSD wear.

Vibe coding, slopware, and engineering process

  • Many attribute the bug to “vibe coding”: AI-generated code shipped with minimal human review or design.
  • Commenters highlight that AI often produces plausible-looking but subtly wrong code, making review harder and encouraging over-trust.
  • There’s frustration that flagship AI companies, while claiming to “solve coding,” ship fragile, opaque Electron/Rust apps with severe regressions.

Testing, QA, and accountability

  • Several ask why basic QA or integration tests didn’t catch unbounded logging or resource usage.
  • Others argue such bugs are easy to miss unless you explicitly test for long-running usage and resource ceilings.
  • Strong pushback against developers blaming AI: humans are still responsible for code they ship, regardless of tooling.

User experience and resource usage

  • Broad complaints about Codex, Claude Code, Cursor, and other AI tools: high CPU/GPU use, memory leaks, laggy UIs, background VMs, and unconfigurable behaviors.
  • A minority say these tools work “well enough” for them and prioritize features over polish.

Broader AI and industry implications

  • The incident is used to question claims that AI has made programmers obsolete or that “coding is solved.”
  • Some see this as emblematic of a wider software-quality crisis and hype-driven management pressure to be “10x with AI.”
  • Others note that humans also ship catastrophic bugs; the issue is inadequate process and incentives, not just AI.