Category: Uncategorized

  • AI agents to fight back against high-friction systems

    Yesterday on LinkedIn I wrote about how so many parents are building ‘agents’ to sit on chat groups, emails, social media, and keep the calendar and todo lists up to date.

    There is a huge amount of administrative work that every parent ends up doing, and it only gets more complicated if you have more than one child or more than one school or sports situation.

    I find this interesting because it’s an example of AI used to shift the power dynamics of a shitty system in the favor of a person, instead of the other way around.

    We all have to exist within and use systems that are not optimized for us, and that we can’t change. These agents are one tool that we can use to reface complex and cruel systems into something that works for us, in the ways that we need.

    If I were a VC (I am not!) I would propose a category here and call it consumer-empowering enterprise software or something along those lines; and look for the markets where:

    1. the consumer must use the software but the provider has no incentive to care about their experience, with high switching costs (you won’t change to another school because you don’t like the way they share announcements, you won’t quit your job because HR uses a bad software system),
    2. this often happens in ecosystems where the full experience isn’t owned by any single entity or platform (cough, healthcare, cough), and
    3. where the core need is information management and consolidation, and there are real consequences for failing to perform and
    4. where the system benefits from friction, or at minimum doesn’t care about you experiencing it (insurance claims, appeals, warranty support, etc.) and
    5. where the ability to have an agent ultimately take action for you would be an absolute cherry on the sundae cake and provide a ton of value. (ie, handle the customer support bot to get the warranty form, fill in the form, follow up after six weeks when there’s been no response, write the note to the credit card company before the deadline, etc.)

    Given that framework, there are lots of opportunity spaces to build in here, for yourself, or as a tool for others:

    • healthcare coordination — if you have even mildly interesting health considerations, coordinating information across providers, making sure follow-ups happen, and meds are organized, that questions are answered
    • Insurance and individual benefits management — help optimize your plan selections and make sure you track, file, and use your benefits, and get the care you’re entitled to
    • Personal tax information management — if your taxes are even mildly interesting, record and file the receipts and docs, and highlight best strategies to prep the package for your accountant (or more)!

    And so on with:

    • Home ownership
    • Estate settlement
    • Vehicle acquisition and maintenance
    • Eldercare
    • Any project where you might hire a general contractor
    • Customer support, and getting service your warranties entitle you to
    • Managing parking tickets and other straightforward legal matters
    • Group travel planning and on the ground coordination

    So look for the spaces with severe fragmentation, high coordination cost, long-lived state and needs to follow up repeatedly, lots of documents/messages/information to manage, and enough economic or emotional value and high enough consequences for mistakes that just collecting that information is hugely beneficial.

    It’s also worth saying out loud that the people who can build these agents themselves today are privileged enough to have knowledge and access to do so; we should focus on making the tools open and accessible for everyone who would benefit from taking some of the power back from these systems.

    PS. If you want to play with building this sort of thing yourself, please be very thoughtful about the security and risks, use a system (like nanoclaw) that helps manage those things, and consider if a local LLM is necessary to keep information private!


    Originally published on Medium.

  • Welcome back!

    Welcome back!

    hilarymason.com lives again! I’ve migrated to hosted wordpress, so keeping the server up is no longer my problem. 😁

    I’d like this site to help you figure out who I am and what I do, share what I’m working on now, let people get in touch, and be a place for me to share some personal thoughts and works-in-progress.

    If you have suggestions, I’d love to hear them.

  • Data Driven: Creating a Data Culture

    data-driven-cover

    I’m excited that my short book, Data Driven: Creating a Data Culture, co-authored with DJ Patil, is out in the world!

    We talk about processes and qualities of strong data teams and how to design for these cultural practices in an organization.

    The book is available for free on O’Reilly’s site, and soon on Amazon. I hope you enjoy it.


  • I found myself in Google Street View!

    google street view wave

    Something fun to start off the new year — I found myself in Google Street View!

    (A quick search of tumblr posts with streetview in the URL leads to a lot of fun, related stuff.)


  • Play with your food!

    Play with your food!

    I spent a few minutes this week putting together a quick script to pull data from the Locu API. Locu has done the hard work of gathering and parsing menus from around the US and has a lot of interesting data (and a good data team).

    The API is easy to query by menu item (like “cheeseburger”, my favorite) and by running my little script I quickly had data for the prices of cheeseburgers in my set of zip codes (the 100 most populated metro areas in the US).

    pizza_by_zip

    burger_by_zip

    I’m a big fan of Pete Warden’s OpenHeatMap tool for making quick map visualizations, and was able to come up with the following:

    The blue map is the average price of a cheeseburger by zip, with the red one showing the average price of pizza. The most expensive average cheeseburger can be found in Santa Clara, CA, ironically the city currently hosting the Strata data science conference this week. Have fun with those $18 cheeseburgers, colleagues!

    You can also see some fun words in the pizza topping options:

    pizza_topping_dispersion

     

    In this plot, the x-axis is roughly geographic (ordered by zip code) and the y-axis is in order of popularity, with pepperoni being the most popular common pizza topping, and anchovies among the least.

    This is just a quick look at some data, but hopefully it’ll encourage you to play with your food (data)!