wtf is blackbar-nano?

Blackbar-nano is a Python tool that finds and hides personal information in text, and it does this work entirely on your own computer. It recognizes 35 categories of sensitive data, including names, phone numbers, email addresses, Social Security numbers, credit card details, and medical records. The program replaces each piece of personal information with a label, so a sentence like "Call Sarah at 503-555-0142" becomes "Call [FIRST_NAME] at [PHONE_NUMBER]". This makes it useful for cleaning text before sending it to an online service. The main idea behind the project is that redaction tools should not send your text to a remote server, since that would expose the very information you want to protect. It is designed to run fast enough to use in real time, about 38 milliseconds per message on an M1 Mac, rather than only as an overnight background task. A standard processor is the tested default, but it can also use an NVIDIA graphics card if available, though performance on that setup has not been measured for this release. The tool works by scanning text for spans that might contain personal information, then labeling each one. Two adjustable settings control how aggressive the detection is. The defaults lean toward flagging too much rather than missing something, which suits redaction work. You can raise the thresholds if you prefer fewer false alarms and accept a higher chance of missing items. Long documents are split into overlapping pieces automatically. The project includes evaluation numbers comparing it against another model called GLiNER2-PII. On a test of 3,895 rows, blackbar-nano covered 96.9% of sensitive items with a 10.9% false alarm rate, and it was much faster. The author provides scripts so you can run the checks yourself on an included 400-row sample. The limitations section is honest. The tool misses about two thirds of URLs, works only on English, and sometimes splits a name like "John Smith" into two pieces. It cannot create custom entity types. The full README is longer than what was shown.