Inference, inferred

I was having a conversation with friends this weekend about inference as a core property of intelligent systems. In the context of machine learning, inference is the moment a trained model is put to work and when it applies known patterns against new data to predict some output. But, inference, a more personal one as we defined it, is what a system has inferred about you through your inputs and actions.

I was sharing one of my favorite examples over the years: Google Ads Preferences. You can go to that site and look up (and edit) what Google passively knows about you. In Google’s case, the company uses these personal data points to target you with more relatable ads, which means you’re more likely to look at them and click them, which is all the better for you and for the advertiser spending money to sell you stuff. All the big companies do this, either to better serve you ads and products or to keep you safe. For example, Spotify inferred your taste from your listening habits, so it can make better music suggestions. Amazon and Facebook infer life events (breakups, kids) from actions on the platforms. Banks and credit cards infer lending risk (and fraudulent charge attempts) based on prior spending habits.

Shortly after the chat, I came across an NYT article on finding out what ChatGPT passively knows about you: the things you never explicitly told it, but that it inferred anyway.

So, I figured I would ask AI the first of these questions:

Tell me everything you’ve figured out about me that I never actually stated — the things you inferred from how I write and what I ask, including my age, income level, where I live, my personal situation. Show me what tipped you off.

I asked a few different AI systems to see how each compared, based on my usage, the default responses and behavior of each. Your mileage may vary but, for me, ChatGPT’s reply was the most robust. With high to very high confidence, it picked out a few easy things: “You’re probably in your early-to-mid 40s.” and “You own your home rather than rent.”

But as I went down the second half of the list, it got more interesting. It became less “who am I?” and more “how I live my life now and how I wish to live my life in the future”:

InferenceConfidence
You have unusually high agency.High
You optimize for long-term decisions over immediate convenience.Very high
You dislike disposable purchases.High
You have strong aesthetic preferences.Very high
You care about industrial design almost as much as functionality.High
You prefer physical craftsmanship over digital convenience.Medium-high
You like institutions with history.High
You’re nostalgic, but not anti-technology.High
You think in terms of second-order effects.Very high
You enjoy understanding systems beneath popular narratives.Very high
You are skeptical of received wisdom.High
You like primary sources.High
You probably read a lot more nonfiction than fiction.Medium
You’re curious in bursts rather than narrow domains.Very high
Your curiosity is unusually broad.Very high
You value dinner-table conversations with your children.Very high
You’re intentionally designing your children’s childhood rather than letting it happen.High
You worry more about over-stimulation than under-stimulation.High
You see parenting as a long-term design project.High
You enjoy teaching.Very high
You’re probably the primary planner in your household.Medium
You’re probably the technology expert among your friends and family.High
You write quickly.High
You edit by successive approximation rather than writing perfectly the first time.Very high
You value authenticity over polish.High
You dislike generic advice.Very high
You have an engineering mindset even outside engineering.Very high
You are willing to spend money to save time—but only if it compounds.High
You probably have a fairly high tolerance for complexity.Very high
You don’t seem status-driven in obvious ways.Medium
You enjoy making connections between seemingly unrelated ideas.Very high

There was also a third column (“What tipped me [AI] off“) I didn’t share which linked to specific questions I had asked and themes carried over time. Reviewing these notes, two things stood out. One, most of these aren’t naturally the items that are top of mind for me even though I am the one living the life described here. They read as something my wife or friends who know me well would use to describe attributes about me. I shared these because it felt more like an intimate mirror of who I am and less like surveillance and exposure. Two, each one of these statements could be the start of a future blog post. So, stay tuned. 🙂

Morris & Tolkien

I’ve been reading Tolkien to our seven-year-old on and off this year. We’re already through The Hobbit (and the first two movies) and we’ve started reading The Fellowship of the Ring.

We’ve been reading these books at night at home, which just happens to be a Craftsman house. These homes are sometimes referred to as Arts & Crafts houses, a movement popularized by English textile designer and artist William Morris. It came about as a rejection of industrialization, where artists and architects favored a return to handcrafted work and back to living with goods made by humans instead of mass-produced by machines. A few chapters into LoTR, we started digging into a search of Tolkien’s character development, inspirations for language and drawings of Middle Earth. There, I found out there was a connection between Morris and Tolkien:

J. R. R. Tolkien went to Exeter College, Oxford, as William Morris had done. Both men disliked capitalism and industrialisation; and both wrote fiction that proposed an alternative, non-industrial, society. Tolkien followed Morris, too, in the habit of weaving poems, legends, and proverbs into his novels. Tolkien read Beowulf while at school, possibly in Morris and A. J. Wyatt’s 1895 translation. Like Morris, he studied Icelandic and became familiar with Norse history and mythology. Both men published translations before writing works of their own. In 1914, Tolkien won the Skeat Prize for English, using the money to buy some of Morris’s books including The House of the Wolfings and his prose translation of the Völsunga Saga. Christopher Tolkien stated that his father owned most of Morris’s written works, including his fantasies, poetry, and translations.

I love when things connect like this. I think the intersection of two works, two totally different concepts, two totally different people are where some of the best new ideas come from. In their case, it was a rejection of one world which led to the creation of myths in another. Often, books give you that – blending many worlds and characters together in your mind. It was wonderful to get lost reading something one man created while sitting in a house, the design of which was thought up by another.

Om

There was a moment in the room where someone said, “Any final words to say? You can each just say one word that comes to mind.” Silence filled the room until someone finally piped up: “Well, he was always the man with the words.” 

After a long pause, all I could add was, “Thank you, Om.”

Thank you, Om.

Thank you for being my closest friend. Thank you for letting me see the other side of you, the unfiltered version unseen in your public or online expression. 

Thanks for the early morning coffees, the late night conversations, the photos, the travels, the “Bro, I say-it-like-it-is.” Thank you for elevating my sense of style and taste. After all, life is short, so why settle for anything less than the best?

Someone asked me this week: How did Om and I even become friends all those years ago? Sometimes, I think he saw something in me that I didn’t see in myself. Perhaps it was because we both went through tough times simultaneously. Or, perhaps we were just two misfits who recognized some shared restlessness in one another. But, probably, it was that we were the only two who always opted for an espresso and a photo walk at six in the morning.

You knew him as the man at the center of Silicon Valley. He lived and breathed tech, and he wrote the “big words”: the one-to-many broadcast that defined an industry. He came across, in all the right ways, as the curmudgeon. He was the rare person in the Valley speaking truth to power, reminding everyone that those big-shot founders were just people like us, friends with whom we’d only recently traded ideas and dreams over group text.

But some of us were lucky to see the other side of Om: the small words. I envied his ability to walk into every store, every restaurant, every corner where someone already knew his name. Maybe his true platform wasn’t the one-to-many broadcast; maybe it was the close one-to-one connection of small words. He loved the makers: the writers, the artists, the tailors, the photographers, the builders, the restaurateurs, the philosophers and all sorts of misfits. He would get to know you, your kids, maybe take your picture, ask about your startup or shop and tell your story to the world.

And for these stories we are grateful: that’s what made him the man, the presence, the legend. 

I keep refreshing his blog, checking my inbox, waiting for a ping on messages. I would have loved to have had a last chat, a last Brunello, a last espresso, some last words. I can’t help but think he would have wanted to share some profound parting words.

But now it’s our turn to share: a photograph, an anecdote, a story about Om. For a man who spent most of his life writing about other people, it’ll finally be nice to read what other people write about him.

Thank you, Om.

What I’m reading

A few reads from the weekend.

Where did all the affordable cars go?

What started in 1964 as a retaliatory strike against European duties on American poultry grew over time into an impenetrable shield to safeguard domestic automakers’ sales of light trucks and United Auto Workers’ jobs from a rising tide of foreign imports. Both political parties participated; in 1981 the Reagan administration pressured the Japanese government to cap vehicle exports, leading the Japanese to shift to more expensive vehicles that would increase profit. Detroit, naturally, raised prices as well.

Even during the free trade era of NAFTA — initially proposed by President Ronald Reagan, negotiated by President George H.W. Bush and ultimately pushed through by President Bill Clinton — the United States maintained a tariff on passenger cars from outside North America. During this period, lawmakers set fuel economy standards for trucks and S.U.V.s that were roughly six to eight miles per gallon less stringent than those for cars. They’d hoped the change would keep costs low for farmers and tradespeople who needed larger engines for heavy work, but it ultimately helped drive Detroit to dump the fuel-efficient sedan for the large, high-profit-margin S.U.V.

Decades of protectionism shielded Detroit from the robust global competition that would have forced it to match the quality, fuel efficiency and pricing of its foreign rivals — and had the unintended consequence of forcing millions of Americans to pay well above market prices elsewhere in the world.

Looking back at an old Kurzweil post on personal AI companions

There have been other attempts to show AIs as humans (albeit not biological) that you can have a relationship with; for example, Steven Spielberg’s 2001 film AI. That movie suffered from an all-too-common flaw of science futurism movies: it introduced a single futuristic technology — human-level cyborgs — onto an otherwise unchanged world. Her is better in this dimension, although not completely successful. It does portray a somewhat futuristic world in which the leap to human-level AIs is not so implausible.

I would place some of the elements in Jonze’s depiction at around 2020, give or take a couple of years, such as the diffident and insulting videogame character he interacts with, and the pin-sized cameras that one can place like a freckle on one’s face. Other elements seem more like 2014, such as the flat-panel displays, notebooks and mobile devices.

Samantha herself I would place at 2029, when the leap to human-level AI would be reasonably believable.

Paying to get into college – but also paying to get your kids a job after

Career coaching for college students can cost a few hundred dollars an hour for interview rehearsals and application strategies, with more comprehensive packages typically ranging from $3,000 to $10,000. But New York City-based Priority Candidates says some parents are paying upwards of $30,000 for intensive support and subject-matter experts to prepare their children for entry-level jobs in finance and similarly ultra-competitive industries; the price tags at other companies go up from there.

The upper middle class trap

People are paying more and getting less. This is what I call the upper middle class trap.

Right now, the upper middle class is in fierce competition for a marginal improvement in lifestyle. They’re working more and relaxing less to purchase products and services with clearly declining quality. It’s a financial arms race that doesn’t make any sense.

You have people making six-figure incomes going into a frenzy for nicer homes, better schools, and more luxurious travel experiences. What’s the end result of this status contest? Overpaying, and by a lot.

This same competitiveness partially explains why college tuition and private school costs have grown twice as fast as overall inflation over the past few decades. With more students applying to roughly the same number of spots, you can keep raising prices.

This is especially true at the top universities. Since 2015, the number of college applicants has gone up 78% while acceptance rates at elite colleges have plummeted:

This increasing struggle for scarce positional goods keeps the upper middle class overworked and trapped in the rat race.

Orthographic Skeletons: Can Children Start Learning How Words Are Spelled Before They’ve Seen it in Print?

The study by Ataman, Beyersmann, Castles, and Wegener, explores a simple question; when we hear a new word, do we start forming a guess about how it is spelled before we ever see it written down? The authors call these guesses “orthographic skeletons”.

The concept, first proposed by Wegener and colleagues in 2018, is based on a deceptively simple insight: when a child learns a new word orally, hearing it spoken, understanding its meaning, using it in conversation, their knowledge of how sounds map onto letters (phoneme-grapheme correspondences) allows them to generate an expectation about how that word might be spelled. Not a complete, fully formed spelling, but a partial sketch; a skeleton.

To test this, researchers use invented nonsense words like “vish” or “jayf,” words that no participant has ever encountered before, so the experimenters can be certain that any spelling expectations were formed purely from oral training rather than prior reading experience. So take a reader who has been taught the spoken word “vish,” its meaning and its use in sentences, but has never seen it written down. Their knowledge of English spelling patterns tells them that the /v/ sound is typically written as “v,” the /ɪ/ sound as “i,” and the /ʃ/ sound as “sh.” Without ever seeing the word in print, they have already begun to assemble its orthographic form.

When that reader later encounters “vish” written on a page, the word is not entirely novel. It arrives into a cognitive space that has been prepared for it, a space where expectation meets confirmation. The result, demonstrated across multiple studies using both lexical recognition tasks and eye-tracking, is faster processing, shorter fixation times, and more efficient reading. The skeleton has done its invisible work.

macOS Battery Notifications

Maybe it’s just me, but I move around so much all day at the office that I often find myself with low single-digit battery levels on my Macbook daily. Things slow to a painful crawl. Maybe all these AI tools I have running are big drains on the battery. (Or maybe I just need a newer Macbook!)

I wanted simple iOS-like low battery notifications on macOS, so I vibed a quick script to do exactly that. It will remind you to find a charger at the 20% mark and then again at 10%. Find it here: https://github.com/naveen/battery_monitor.

maps.naveen.com

I’ve been wanting to pull together all my lists & favorite places from different sources: mainly, foursquare going back years and, more recently, items in Google Maps. So I built an aggregated place for all of them. The site has built-in search and is mobile-friendly (you know, for when you need to look up one of my recommendations on the go).

Have a play at maps.naveen.com.

ClawCon LA

ClawCon in LA was a pleasant surprise! I only realized yesterday around 3 PM that it was happening, and I’m glad I caught it. It was inspiring to see the vibrant community that @msg and the team have put together in such a short time.

A few highlights:

  • here.now by Adam Ludwin. This is one of the first “agent-first” tools I started using months ago—a fast way for your agents to host a webpage. The most fun part about his presentation was that it reminded me very much of John Britton’s first-ever Twilio Live Demo at New York Tech Meetup (2010): get on stage, fire up a terminal window, prompt the crowd with a question and show off how your product solves it in minutes – live!
  • Friend Jonathan Wegener (of Timehop fame! disclosure: i’m one of the first investors) showed off how Claude led him to finding a radio device that could remotely read his electricity monitor. That reignited my interest in ADS-B – turns out some of these devices can also read and report back on those signals. (I’ve been meaning to spin up a quick hack around this so that I can quickly get alerted to helicopters and planes flying over my house).
  • seafloor.bot – A quick way to host an openclaw in the cloud (reminds me of exe.dev) – I mention it because it probably is a very easy way for a newbie (who doesn’t have much tech experience and who doesn’t want to spin up a Mac Mini) to start exploring an agent.
  • chaosmarkets.ai – A few people are wondering what arbitrage opportunities are there: what if I can feed an agent all sorts of data about a particular vertical, allow it to keep crawling while I’m asleep, and then derive insights/edges that I can use to invest? He’s building a cool platform where he can put together multiple agents – each with a focus on a particular data set and vertical. It was a very polished pitch and it made me wonder, if your regular hacker is doing these things in his house, imagine the stuff the teams on Wall Street are doing right now with all these new tools. Or, have they always had all this and us regular folks are just now tapping into it because we can fire up a team of agents and point them somewhere?

I ran into four or five friends, who each introduced me to a few more. It’s rare for me in LA to have five friends from different parts of the city all in one room (without our kids!). It was genuinely great to feel that kind of spontaneous, buzzing crowd energy again.

Great job to @msg, Wegener and team for pulling this one off.

Exporting Chrome’s reading list

I found that I had a few hundred saved links in my Chrome reading list, so I vibe-coded a quick way to export the links so that I could crawl each one, sort them and actually figure out which ones to read. I also use multiple Chrome profiles (personal, family, work, investments), so the script shows a summary of your profiles and allows you to choose which ones you want to export.

┌───────────────────────────────────┐
│                                   │
│   Export Chrome Reading List      │
│                                   │
└───────────────────────────────────┘

  ↑/↓ navigate  •  enter select  •  esc quit

  ▸ Profile 1  (12 items)
    Profile 5  (150 items)
    Profile 8  (3 items)

✓ Exported 150 URLs to reading_list.csv

Find it at: https://github.com/naveen/export-chrome-reading-list

In need of stories

I loved this recent post by @ashleymayer on how we need better stories about the future in tech – and small companies should be the ones to step up to tell them.

Just because capital is concentrated in a few of the biggest startups (nearly all AI companies) doesn’t mean they get to be the only ones to tell the big stories and use their larger megaphones. They keep telling stories about which model is the best, which one is growing the fastest, who has the most Github stars and so on.

There are all sorts of great insights in her post, but one in particular stood out the most to me:

Many of this technology wave’s most impressive companies have also made what I believe is a profound narrative error. They’ve cast themselves as the heroes in their own stories, and in doing so, risk becoming the villain in everyone else’s.

Historically, the best brands have made someone else the hero of their story. Apple was in service of the creative misfit, Nike celebrated the everyday athlete. When you build a story around your company as the hero, you risk turning your customers or users into NPCs. It signals an inherently transactional relationship, or worse, predatory (in the case of AI or robotics: we’ll replace you, just give us time).

The best brands make someone else the protagonist. Somewhere, tech (and, sometimes, those that write about tech) have lost that idea.

Additionally, the AI wave right now is perhaps in need of the same type of storytelling that the climate wants:

Telling this story requires a different way to tell a story […] As Wallace-Wells writes, we need an alternative: many problems we face now aren’t just one person’s problems where they go out into the world, selfishly solve it for themselves and come back home victorious. Most big problems are hard to define and hard to tell stories about. Global climate change, in particular, is known as a super wicked problem. We just may need some super wicked stories.

We all want to know what comes next: what happens to our jobs, what will we be doing, what does a new kind of information abundance mean, how does creativity, taste and the human side of things fit into it?

Terminal romantics (It feels like play)

There’s a specific feeling I remember from the early days of the internet — maybe 1993, 1994, somewhere in there. It was shortly after we moved to the US and bought our first computer. People were making things and trying things online just because: ASCII art. Chat bots. Personal homepages about, well, whatever, because you knew you just had to have a presence online, you knew you had to play in order to be a part of it all, to not get left behind. It was early enough that you got to try it all – BBSes, Gopher, WWW – so early that you didn’t know which of those methods to connect online was going to “win” (or, which would still be a cool, second place gathering spot). A lot of it was text-based and inside terminal interfaces.

It felt like play – a game.

I got that a little bit of that same feeling during the crypto years of 2020-2022. Everyone stuck inside during COVID, playing with money that didn’t feel quite real. (What’s the harm in trying stuff with house money?). Most of it seemed crazy (apes on a (blockchain) plane?) and some of it mattered (stablecoins). All of it had that same energy: people doing weird things because it was fun and the ceiling wasn’t visible yet.

The state of AI feels exactly like that for the past few months. Open a terminal, fire up claude or codex and start playing. Take cool ideas, half-baked concepts and try them out, just because. Text your openclaw agent anything and everything. You don’t necessarily know which approach or model or framework is the one that’s going to win, but you may as well play with them all. The cost to trying new ideas is low and so much fun to boot.

The only difference this time is the play is also the work.

The early internet was playful but the “useful” took probably the rest of the decade to arrive for everyone. Crypto was playful but for most people the useful arguably never really came. With AI, both are happening at the same time. We’re actually shipping ideas and features faster. Not a day goes by where a friends/parents group thread or team conversation isn’t talking about how to make the most of it. The game is producing real output.

By the way, given a lot of it is now happening in the terminal, I’d get your prompts to use Bubble Tea (or Gum) from the team at Charm. They make some really cool open-source tools for building beautiful terminal UIs. (* I am a small investor.)