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A tweet discusses Australia's policy on opting out of algorithmic feeds and expresses a similar desire for control over AI personalization to avoid biased or personalized shaping of responses.
The article discusses the ethical and practical distinctions between AI personalization in cold emails using publicly sourced data versus behavioral data, noting how these differences impact recipient reactions and complaint rates.
Social media platforms like Threads, Instagram, and TikTok are introducing tools that allow users to personalize their recommendation algorithms, giving users more control over their feeds.
Hungryroot offers 30% off first-week orders plus a free gift through June, using proprietary AI to personalize meal kits based on user preferences.
Google Labs has launched Dreambeans, an AI-powered app that curates personalized stories and recommendations by analyzing data from Google services like Gmail and Calendar. The app aims to surface relevant content tailored to user interests, cutting through digital noise.
This paper introduces representational accuracy and a Behavioral Specification as an interpretive layer for AI personalization, showing that it improves representational accuracy at about 25× less context cost compared to raw data retrieval, especially for interpretation-required questions.
A Twitter user shares a voice-dna.md file that configures Claude to adopt the user's personal writing style by setting rules, banned phrases, and requiring writing samples to pattern-match against.
The article questions whether AI products over-rely on chat history for personalization, noting its noisiness and suggesting that summaries, tags, and preference fields have shortcomings. It seeks alternative sources of truth for context without becoming intrusive.
The author introduces DRIFT, a local AI system built with Python and Ollama that features persistent memory, simulated somatic feedback, and Jungian psychological modeling to create a more grounded, sovereign AI interaction.