
During February 2026, Anirudh Chicheruku developed a targeted feature for the browser-use/browser-use repository, focusing on improving large language model safety and developer experience with sensitive data placeholders. He implemented Sensitive Data Placeholder Guidance, introducing a visible SENSITIVE DATA header, a bulleted list of placeholders, explicit usage instructions, and a concrete example to clarify correct handling. Using Python and backend development skills, Anirudh updated the _get_sensitive_data_description() function to ensure placeholders are wrapped in <secret> tags, addressing previous issues where literal names were emitted. This work reduced LLM handling errors and enhanced documentation, demonstrating thoughtful prompt engineering and code refinement.
February 2026 monthly summary for browser-use/browser-use focused on improving LLM safety and developer UX around sensitive data placeholders. Delivered a targeted feature: Sensitive Data Placeholder Guidance for LLM Usage, including the addition of a prominent SENSITIVE DATA header, formatting improvements (bulleted placeholder list), an explicit MUST instruction, and a concrete example using the first actual placeholder name. Implemented changes in _get_sensitive_data_description() to ensure placeholders are wrapped in <secret> tags and to clarify that the system will replace tags with actual values. This work reduces LLM handling errors and clarifies proper usage for users.
February 2026 monthly summary for browser-use/browser-use focused on improving LLM safety and developer UX around sensitive data placeholders. Delivered a targeted feature: Sensitive Data Placeholder Guidance for LLM Usage, including the addition of a prominent SENSITIVE DATA header, formatting improvements (bulleted placeholder list), an explicit MUST instruction, and a concrete example using the first actual placeholder name. Implemented changes in _get_sensitive_data_description() to ensure placeholders are wrapped in <secret> tags and to clarify that the system will replace tags with actual values. This work reduces LLM handling errors and clarifies proper usage for users.

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