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
Mímir: A Python Memory System Built on 21 Neuroscience Mechanisms
Mímir is a Python memory system for AI agents that implements 21 cognitive science mechanisms like flashbulb memory and retrieval-induced forgetting. It uses a hybrid BM25 + semantic + date index and shows benchmark improvements including 13% higher tool accuracy on Mem2ActBench versus VividnessMem.
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.
LLM Matrix: Community-Voted Model Comparisons Built with Claude Code
A data scientist built llm-matrix.vercel.app to compare LLM scores across multiple dimensions simultaneously, with community votes shaping rankings. The site was developed entirely using Claude Code with two specific plugins.
Building CLIs for AI Agents: Design Principles from Google's gws CLI
Google's gws CLI demonstrates how to design command-line interfaces specifically for AI agents, prioritizing raw JSON payloads over human-friendly flags and implementing safety rails against hallucinations.