Show HN: HN Wrapped 2025 - an LLM reviews your year on HN

Overall Reception

• Many found the project “hilarious,” “scary good,” and surprisingly on-point; several said it captured their year or personality better than they’d like to admit.
• A notable subset found it underwhelming or annoying, feeling the output was shallow, wrong on key points, or tonally off.
• Multiple people say they’d share their wrap on social/LinkedIn or adopt phrases from it as taglines.

Humor, Accuracy & Self-Reflection

• Users praise the roasts as witty, specific, and often uncomfortably accurate about obsessions (Rust, VAT, dark mode, GDP, LLM pricing, browser choices, etc.).
• The “vibe check” and custom labels (e.g., “contrarian/pedantic/helpful”) are widely liked and spur self-reflection.
• The fake “HN front page in 2035” and xkcd-style comics generate many laughs; some highlight particularly creative fake headlines.

Failures, Misfires, and Critiques

• Several report obvious misreads: assigning them strong views based on a single comment, confusing criticism of a position with advocacy, or extrapolating entire “lifestyles” from one thread.
• Many note a heavy recency bias and/or fixation on a few posts, making it feel less like a true “year in review.”
• Some roasts are seen as lazy stereotype riffing off keywords (e.g., “Haskell extremist”) rather than engaging with actual arguments.
• A few users were genuinely hurt: the system mocked unfinished PhDs, disability-related projects, and hearing loss, or misgendered them in comics. These are called “not cool” and “demeaning.”

Technical Behavior & Feature Requests

• Early issues: server overload, “not enough activity” errors, and incorrect comic caching; also case-sensitive usernames and speech-attribution errors in comics.
• The author reports iterative fixes: improved prompts, shuffling, two-pass pattern extraction, attempts to reduce recency bias.
• Suggestions include: better story-level weighting (so multiple comments on one thread aren’t overinterpreted), configurable year, saving/caching outputs, Open Graph previews, and more original company names in 2035 predictions.

Privacy & Ethical Concerns

• Some are unsettled by how easily an LLM can infer politics, hobbies, and personality from public comments and how this could be misused by states or bad actors.
• Others downplay this, noting HN profiles are already public and likely scraped.

Meta Reflections on LLMs & Satire

• Several observe the system illustrates inherent LLM flaws: over-indexing on a few salient items and “context rot.”
• There’s speculation that as AI satire becomes easier to produce, it will start to feel formulaic—much like current AI art.