d copy of the response here. It's extremely detailed! Here are a few notes that
caught my eye.

    From the "Assistant Response Preferences" section:

        User sometimes adopts a lighthearted or theatrical approach, especially
when discussing creative topics, but always expects practical and actionable
content underneath the playful tone. They request entertaining personas (e.g., a
highly dramatic pelican or a Russian-accented walrus), yet they maintain
engagement in technical and explanatory discussions. [...]

        User frequently cross-validates information, particularly in
research-heavy topics like emissions estimates, pricing comparisons, and
political events. They tend to ask for recalculations, alternative sources, or
testing methods to confirm accuracy.

    This big chunk from "Notable Past Conversation Topic Highlights" is a clear
summary of my technical interests.

        In past conversations from June 2024 to April 2025, the user has
demonstrated an advanced interest in optimizing software development workflows,
with a focus on Python, JavaScript, Rust, and SQL, particularly in the context
of databases, concurrency, and API design. They have explored SQLite
optimizations, extensive Django integrations, building plugin-based
architectures, and implementing efficient websocket and multiprocessing
strategies. Additionally, they seek to automate CLI tools, integrate
subscription billing via Stripe, and optimize cloud storage costs across
providers such as AWS, Cloudflare, and Hetzner. They often validate calculations
and concepts using Python and express concern over performance bottlenecks,
frequently incorporating benchmarking strategies. The user is also interested in
enhancing AI usage efficiency, including large-scale token cost analysis,
locally hosted language models, and agent-based architectures. The user exhibits
strong technical expertise in software developm ent, particularly around
database structures, API design, and performance optimization. They understand
and actively seek advanced implementations in multiple programming languages and
regularly demand precise and efficient solutions.

    And my ongoing interest in the energy usage of AI models:

        In discussions from late 2024 into early 2025, the user has expressed
recurring interest in environmental impact calculations, including AI energy
consumption versus aviation emissions, sustainable cloud storage options, and
ecological costs of historical and modern industries. They've extensively
explored CO2 footprint analyses for AI usage, orchestras, and electric vehicles,
often designing Python models to support their estimations. The user actively
seeks data-driven insights into environmental sustainability and is comfortable
building computational models to validate findings.

    (Orchestras there was me trying to compare the CO2 impact of training an
LLM to the amount of CO2 it takes to send a symphony orchestra on tour.)

    Then from "Helpful User Insights":

        User is based in Half Moon Bay, California. Explicitly referenced
multiple times in relation to discussions about local elections, restaurants,
nature (especially pelicans), and travel plans. Mentioned from June 2024 to
October 2024. [...]

        User is an avid birdwatcher with a particular fondness for pelicans.
Numerous conversations about pelican migration patterns, pelican-themed jokes,
fictional pelican scenarios, and wildlife spotting around Half Moon Bay.
Discussed between June 2024 and October 2024.

    Yeah, it picked up on the pelican thing. I have other interests though!

        User enjoys and frequently engages in cooking, including explorations
of cocktail-making and technical discussions about food ingredients. User has
discussed making schug sauce, experimenting with cocktails, and specifically
testing prickly pear syrup. Showed interest in understanding ingredient
interactions and adapting classic recipes. Topics frequently came up between
June 2024 and October 2024.

    Plenty of other stuff is very on brand for me:

        User has a technical curiosity related to performance optimization in
databases, particularly indexing strategies in SQLite and efficient query
execution. Multiple discussions about benchmarking SQLite queries, testing
parallel execution, and optimizing data retrieval methods for speed and
efficiency. Topics were discussed between June 2024 and October 2024.

    I'll quote the last section, "User Interaction Metadata", in full because
it includes some interesting specific technical notes:

    [Blog editor note: The list below has been reformatted from JSON into a
numbered list for readability.]

            User is currently in United States. This may be inaccurate if, for
example, the user is using a VPN.
            User is currently using ChatGPT in the native app on an iOS device.
            User's average conversation depth is 2.5.
            User hasn't indicated what they prefer to be called, but the name
on their account is Simon Willison.
            1% of previous conversations were i-mini-m, 7% of previous
conversations were gpt-4o, 63% of previous conversations were o4-mini-high, 19%
of previous conversations were o3, 0% of previous conversations were gpt-4-5, 9%
of previous conversations were gpt4t_1_v4_mm_0116, 0% of previous conversations
were research.
            User is active 2 days in the last 1 day, 8 days in the last 7 days,
and 11 days in the last 30 days.
            User's local hour is currently 6.
            User's account is 237 weeks old.
            User is currently using the following user agent:
ChatGPT/1.2025.112 (iOS 18.5; iPhone17,2; build 14675947174).
            User's average message length is 3957.0.
            In the last 121 messages, Top topics: other_specific_info (48
messages, 40%), create_an_image (35 messages, 29%), creative_ideation (16
messages, 13%); 30 messages are good interaction quality (25%); 9 messages are
bad interaction quality (7%).
            User is currently on a ChatGPT Plus plan.

    "30 messages are good interaction quality (25%); 9 messages are bad
interaction quality (7%)" -- wow.

    This is an extraordinary amount of detail for the model to have accumulated
by me... and ChatGPT isn't even my daily driver! I spend more of my LLM time
with Claude.

    Has there ever been a consumer product that's this capable of building up a
human-readable profile of its users? Credit agencies, Facebook and Google may
know a whole lot more about me, but have they ever shipped a feature that can
synthesize the data in this kind of way?

He's right. That's an extraordinary amount of information, organized in human
understandable ways. Yes, it will occasionally get things wrong, but LLMs are
going to open a whole new world of intimate surveillance.

** *** ***** ******* *********** *************
House of Representatives Bans WhatsApp

[2025.06.26] Reuters is reporting that the US House of Representatives has
banned WhatsApp on all employee devices:

    The notice said the "Office of Cybersecurity has deemed WhatsApp a high
risk to users due to the lack of transparency in

--- BBBS/LiR v4.10 Toy-7
 * Origin: TCOB1: https/binkd/telnet binkd.rima.ie (618:500/1)