Developer tracks frustration with 'F-Bombs Per Thousand Prompts' metric across 44,212 Claude Code logs

A developer publishing under /u/ChartBuilder created a metric called fpk — f-bombs per thousand prompts — to quantify frustration while using Claude Code. The data spans 5 months, 44,212 prompts, and 6,120 sessions.
Headline numbers per model
- claude-opus-4-5: 38.11 fpk
- claude-opus-4-7: 11.11 fpk
- claude-haiku-4-5: 0.00 fpk (used as subagent, never orchestrator)
That's a 3.4× drop in frustration between the two Opus versions, closely tracking Anthropic's official quality recovery from the Feb-Mar regression — but visible in a way release notes don't capture.
Fpk by Claude Code CLI version
- 2.1.30-69 era: 40 fpk
- 2.1.100+ era: 12 fpk
- Worst single version: 2.1.42 at 173.79 fpk
- Best: 2.1.110 at 0.00 fpk over 300+ prompts
Key insight: most frustration is environmental, not model-related
The author notes: "most cursing wasn't at the model. It was at environmental friction like gh auth failures, docker issues, screenshots breaking. The model is mostly the unwitting witness to my frustration with the surrounding tooling, not the cause."
But sometimes the model is the cause too — the full writeup includes a "greatest hits" collection of memorable outbursts.
Reproducible tooling
The developer has published tools to compute fpk on your own Claude Code logs:
- Full writeup with methodology: mpiv.ai/blog/fpk-f-bombs-per-thousand-the-dev-experience-metric-you-didnt-know-you-needed
- Open-source repo with audit tooling: github.com/MPIsaac-Per/claude-code-ops-audit
If you use Claude Code heavily and want a quantitative signal of how much friction you're actually experiencing, this metric is worth adopting. The drop between models and across CLI versions is a concrete indicator of Anthropic's recovery — and the environmental sources of rage are something every team can address.
📖 Read the full source: r/ClaudeAI
👀 See Also

SMELT compiler reduces OpenClaw workspace token usage by up to 95%
SMELT compiles OpenClaw workspace markdown files into a denser runtime form, sending only relevant content to AI models. Benchmarks show token reductions from 76.1% to 95.5% on queries, avoiding reprocessing of static files like USER.md and SOUR.md on every message.

Claude Code Plan Mode Reduces Redo Rate from 40% to Near Zero
A developer tracked 30+ coding sessions with Claude Code and found that skipping Plan Mode resulted in redoing tasks from scratch 40% of the time. With Plan Mode, the redo rate dropped to basically zero, with one feature taking 17 minutes total versus 35+ minutes without planning.

Relay lets Claude Code sessions message each other without alt-tabbing
A plugin called Relay uses Claude Code's channels capability to let parallel sessions communicate directly, removing the need to manually copy-paste context between backend and frontend repos.

Netflix Releases VOID: Video Object and Interaction Deletion Model on Hugging Face
Netflix has released VOID, a video inpainting model that removes objects from videos along with all physical interactions they induce, including falling objects and displaced items. The model requires a GPU with 40GB+ VRAM and uses quadmask conditioning with two checkpoint files for different refinement levels.