Testing Muse Glimmer lead to some frustration, so I started the next prompt with something I wouldn't write professionally.
And this led to the question: how often do I do this? I would have guessed that 10% of my machine-human interactions contain some swear words.
But why guess when you can evaluate the data and produce hard facts?
Therefore I prepared as project on GitHub, which evaluates both your and logs and gives you on the CLI a short two liner. And additionally you receive an HTML page in nice green IBM 3279-style. All locally, no AI or models involved, just some Python and HTML.
It will reveal to you how many swearwords were used, their frequency, streaks without swearing, which hour of the day the tension rises high, etc ..
marcelpetrick•1h ago
Testing Muse Glimmer lead to some frustration, so I started the next prompt with something I wouldn't write professionally. And this led to the question: how often do I do this? I would have guessed that 10% of my machine-human interactions contain some swear words.
But why guess when you can evaluate the data and produce hard facts?
Therefore I prepared as project on GitHub, which evaluates both your and logs and gives you on the CLI a short two liner. And additionally you receive an HTML page in nice green IBM 3279-style. All locally, no AI or models involved, just some Python and HTML. It will reveal to you how many swearwords were used, their frequency, streaks without swearing, which hour of the day the tension rises high, etc ..