I used o3 to profile myself from my saved Pocket links

LLM-based Profiling of People

• Multiple commenters describe using LLMs to “profile”:
  • Pocket archives, HN comment history, Reddit history, group chats, and music or YouTube history.
  • To detect trolls (especially “concern trolls”) by feeding in posts/subreddits, scores, and history patterns.
  • For curiosity about surprising HN takes, or for humorous “roast” profiles of HN accounts and users on internal platforms.
• Some note troll behavior like generic posts, then short-lived political replies that are later deleted, and possible “account farming” for manipulation campaigns.

Accuracy, Methods, and Limits

• Users report reasoning models giving surprisingly accurate guesses about age, city, politics, hobbies, but often misclassifying job roles.
• Others find this underwhelming given older, simpler ML that could already infer traits from social “likes.”
• Concerns about:
  • Long-context reliability (models may effectively use only a subset of data).
  • Need for hierarchical / iterative summarization pipelines.
  • Barnum/Forer effect: flattering, generic-but-plausible descriptions feel precise.
  • System prompts and training that push LLMs to be “nice” and engagement-maximizing.

Privacy, Surveillance, and Power

• Strong worries that chat histories and personal prompts provide highly detailed psychological profiles for advertisers, platforms, and governments.
• Several assume large platforms already run this type of profiling at scale (email, video, browsing data).
• Discussion of how cheap large-context calls are per user, but still favor big players for mass profiling.
• Emphasis on end-to-end encryption and local models, given rising value of “boring” conversations when combined with phishing and voice cloning.

Self-Understanding vs. Manipulation

• Many see value in self-analysis: surfacing blind spots, clarifying interests, contrasting “saved” vs. actually-read items, and compressing huge personal archives into something interpretable.
• Others liken it to “modern astrology” and caution against assuming you can reliably profile strangers from metadata alone.

Tools, Workflows, and Pocket Shutdown

• Ideas: using LLMs to classify and tag thousands of bookmarks; prune “read later” hoards; build personal world-models or knowledge graphs from all digital activity.
• Pocket’s shutdown drives interest in exports and alternatives (Wallabag, Linkwarden, Instapaper, Safari Reading List) and in third-party tools to rescue full archives, including article content, tags, and highlights.

Meta: Titles, UX, and LLM Style

• Several objected to the original post title as implying passive, secret profiling; they prefer agent-focused wording.
• Complaints about LLM “fluff” and recipe-like verbosity; some mitigate with prompts like “be concise,” noting that models like o3 already tend to be terser.