Tag Archives: AI-chats

Category “tag cloud”

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AI tools like Perplexity and Gemini have proven to be invaluable at giving me a new perspective and understanding of my 24 years of blog posts. I’ve written about this frequently. This morning I provided a list of my categories showing the number of posts in each and prompted Perplexity to create a visual representation of the data. Took a little tweaking but I find the results interesting.

For what it’s worth, Gemini was unable to perform this task but did offer several explanations of what it was my fault and not the AI.

The agentic iPhone

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Last month Apple announced a partnership with Google that will mark a fundamental shift in the iPhone architecture. The integration of Gemini into iOS means the system is pivoting from a collection of apps to an agentic operating system. It turns the iPhone into a more powerful cognitive lever, but it demands you trust the “black box” more than ever before. I prompted Gemini to show me what that might look like. Excerpts below, the full thread here)

I asked how a new, more powerful iPhone might change the way I write or research my blog posts:

When the operating system moves from being a “file cabinet” to an “active participant,” the boundary between your thoughts and the machine’s suggestions begins to blur.

  • The Loss of Serendipity: When the AI “fetches” the exact answer, you miss the accidental discovery of a different, unrelated factoid in the “wrong” folder. The “digital twin” optimizes for efficiency, but creativity often thrives on inefficiency.
  • The Echo Chamber of One: If Gemini is trained on your “Personal Context,” it will get very good at sounding like you. There is a real risk that your blog posts will start to feel like they’ve been “laundered” through an AI that is simply reflecting your own style back at you, stripping away the rough edges of original thought.
  • The “Privacy vs. Utility” Trap: To get these benefits, you have to let the “Campos” agent index your “private thoughts.” You’re essentially trading the sanctity of your “unrecorded” mind for a more powerful cognitive lever.

I’m sure I’ll find some uses for the new iOS but writing my blog posts won’t be one of them. Near the end of the thread Gemini gives specific instructions for how to disable some of these features.

Gemini analysis of my taste in movies

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(Gemini) MovieLens.org is a non-commercial movie recommendation engine and research platform operated by the GroupLens Research lab at the University of Minnesota. Since its launch in 1997, it has served a dual purpose: providing users with high-accuracy personalized film suggestions and serving as a critical “living laboratory” for the study of collaborative filtering and human-computer interaction.

Unlike commercial alternatives like IMDb or Letterboxd, MovieLens is entirely free of advertisements and corporate sponsorship. Its primary output is academic insight. The site is famous for pioneering early collaborative filtering algorithms which predict how you will rate a film based on your historical behavior and the behavior of similar users.

Last year I spent a few hours on MovieLens.org rating 435 movies I had watched. I think my criteria was “would I watch this movie again?” I uploaded those those movies and my ratings and asked Gemini for an analysis.

Based on your 435 ratings, I’ve performed a skeptical audit of your cinematic preferences. I’ve bypassed the “predictive recommendations” fluff to look at where your taste actually diverges from the consensus.

1. The “Lebowski” Anomaly (Contrarian Profile) -You are significantly more critical than the average MovieLens user. While the site average is ~3.72, your mean rating is 3.41. You are most “contrarian” when it comes to established cult classics and high-concept stylistic films.

  • The Big Lebowski (1998): You gave this a 1.0, while the average is 3.99. This is a massive 3-point delta.
  • The Hateful Eight (2015): You gave it a 1.0 (Avg: 3.89).
  • The Abyss (1989): You gave it a 1.0 (Avg: 3.68).
  • Scott Pilgrim vs. the World (2010): You gave it a 1.5 (Avg: 3.77).

Analysis: You appear to have a low tolerance for “quirk” or indulgent stylistic flourishes that others label as “genius.”

2. The “Maverick” Picks (Guilty Pleasures or Hidden Gems?) – Conversely, you value certain films far higher than the general public. Your “Maverick” score is highest for:

  • Duets (2000): You gave it a 5.0 (Avg: 2.85). This is your largest positive divergence (+2.15).
  • World War Z (2013): You gave it a 5.0 (Avg: 3.30).
  • The Bourne Legacy (2012): You gave it a 5.0 (Avg: 3.43).
  • Mosul (2019): You gave it a 5.0 (Avg: 3.52).

Analysis: You seem to favor competence and high-stakes procedural/action narratives (Mosul, Bourne, Sicario) over “vibe-based” cinema.

3. Historical Bias (Decade Analysis) – Despite your skeptical leanings, you have a soft spot for the mid-century.

  • 1950s: Your highest-rated decade (Avg: 3.80).
  • 2010s: Your most critical decade (Avg: 3.30).
  • 1990s: Where you spent the most time (100 movies rated).

4. Interest Alignment: Radio & Mechanics – Given your interest in radio history and mechanical vehicles:

  • Radio: You gave Broadcast News (1987) a perfect 5.0. You seem to appreciate the “transmission” of truth or the mechanics of the medium.
  • Mechanical/Procedural: You rated Sicario (2015) a 5.0, which aligns with your preference for technical precision and unsentimental realism.
  • Consciousness/Mind: You gave A Beautiful Mind (2001) a 2.0, suggesting a skepticism toward Hollywood’s “glossy” portrayal of cognitive struggles.

You are a Pragmatic Realist. You reward movies that execute a specific function well (thrillers, procedurals, mid-century classics) and punitively down-rate movies that rely on irony, surrealism, or “cult” status to earn their keep.

Hmm. Not sure I agree but I guess the data doesn’t lie.

Apple Music Update

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I’ve been a fan of Apple Music since its launch in 2015 and have posted here praising the curation process. I asked Gemini for an update:

(Gemini) Apple Music leans heavily on human curation as a point of pride, positioning itself as a “cultural curator” rather than just a data utility. While Spotify is known for its “Algotorial” approach, Apple’s editorial team is the backbone of their most prominent playlists. Here is a breakdown of the people behind the lists:

The Global Editorial Team

Apple employs over 1,000 curators globally. These aren’t just tech employees; they are largely recruited from the worlds of professional DJing, music journalism, and radio programming.

  • The Specialists: They are organized by genre and region. For example, the person curating a “K-Pop” playlist likely lives in or has deep cultural ties to Seoul, rather than a generalist working out of Cupertino.
  • The Task: Their job is to listen to thousands of tracks weekly. Unlike algorithms, they are looking for “narrative flow”—ensuring the transition from a high-energy track to a mellow one feels intentional and human.
  • The Philosophy: Apple’s internal mandate often focuses on “human emotion and cultural wisdom.” They prioritize songs that might not have the best “data” (skips/saves) but are culturally significant or represent a “vibe” the algorithm might miss.

High-Profile Tastemakers

You’ll often see names attached to specific radio shows or “Guest Curator” lists. These individuals influence the editorial direction of the broader playlists:

  • Zane Lowe & the Radio Team: As the Global Creative Director, Lowe (formerly of BBC Radio 1) oversees much of the “tone.” His team includes veteran broadcasters like Ebro Darden (Hip-Hop/R&B) and Matt Wilkinson.
  • External Partners: Apple also allows “Brand Curators” to maintain profiles. These include publications like The Independent, Pitchfork, or Rolling Stone, and labels like Numero Group or XL Recordings.

How They Work With “The Machine”

The process is a feedback loop. While the human editors pick the “Flagship” tracks for playlists like Today’s Hits or Rap Life, they use data to see if their “gut feeling” was right.

  • If an editor places a song at #1 and users skip it 80% of the time, the data tells the editor to move it.
  • Conversely, the human editors provide the “high-authority signals” that the algorithm then uses to populate your personal Discovery Station or New Music Mix.

You won’t usually find an individual’s name on a standard Apple-branded playlist (e.g., Pure Throwback). However, if you look at the bottom of a playlist description, Apple often credits the curator if it’s an external partner. For internal lists, the “voice” is kept anonymous to maintain the Apple brand identity.

Scott Adams (1957-2026)

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[see comment below]

For many years I was an ardent fan of cartoonist Scott Adams. Between 2002 and 2016 I posted more than one hundred times. I read five of his books and count God’s Debris as one of my all time favorites.

In 2016 Adams became a vocal supporter of Donald Trump and —as far as I know— remained one until he died a couple of weeks ago. I asked Gemini if Adams’ support for Trump affected his career. Continue reading →