Age of agents

We’ve all been looking at AI agents for a while now: probably since before OpenClaw, but it really accelerated this year after OpenClaw and its derivatives came out.
I’m not sure why it took nearly six to eight months after OpenClaw, but we’re finally seeing all sorts of personal AI agents come out: agents that you can just sign into and use out of the box. Maybe it’s just that the models truly are that good now. Maybe having small features like a bridge to iMessage did the trick to convince more users to try them.
Last week, it was Instinct – everyone you knew was talking about it non-stop. Today, it’s Muse from Meta. (The way this rolled out immediately after Instinct went public makes me wonder if Mark approached the company to drop his tried-and-true tactic: “Sell to us now or we’ll clone you next week.”
Instinct was fine. Like other off-the-shelf agents, it didn’t do that much more than our own family agent at home was already doing (We rolled our own, Robin – @roundrobin, a couple of months ago. More on that in another post.). But Muse is really good.
Muse has a lot of things going for it over all the others: just take a look at Assistant Benchmark. For all you want to hate on Facebook and the fact that they have fallen behind in models and being seen as the platform of choice for AI, never bet against them. Muse can:
- be free longer than Instinct (and others) can probably stay alive before needing to raise another round of financing,
- use all of Meta’s compute capabilities,
- monetize with ads (already a core strength of the company),
- monetize with commerce (see Collison’s tweet about Link integration),
- use Meta’s experience on how to distribute software and to cross-sell it across the other apps in their portfolio,
- seamlessly tie in data from other Meta apps you’ve been using for years (see my Instagram example in the picture).
The ads bit might be the most interesting: they already have the engine for it. The only way you’re going to get to a billion users is if the product is free*. Therefore, at some point, after you’re done spending your go-to market cash, you’re going to need ads or some other layer that makes money without the user directly paying for it.
A “pro”, besides the cost (free), might be that big players like Meta and Google are the only ones that can make an agent like this. You don’t want to sit around linking up all your accounts to a brand new startup. You have to deal with more points of friction, the biggest being their lack of knowledge and past context about you. The downside, of course, is that you have to choose which giant company you want handling all your data. Ultimately, it’s going to come down to whether you trust Facebook (or Google or Apple) with all your stuff and all the connectors to all your other stuff they don’t already have (e.g., Resy, OpenTable, airline logins, insurance, cars, Airbnbs, campgrounds, schools and anything else where you’d like an agent to log in on your behalf).
I’m looking forward to seeing what Apple will announce tomorrow and in the coming months on this front. Personally, I’ve split my “cloud” across a few platforms – email on one, photos on another, social (or whatever that means) on smaller networks, logins on another and so on. If there was any company I’d trust to have wider access to even more of my data with the intention of making my life easier with agents, it would be Apple.
* Or, say, 90% of the product is free to 90% of users; 10% can pay for some sort of premium access.
skills
I had a few skills scattered around, so I collected them into one repo.
There are three four so far:
- clipboard — Copies what the agent just wrote straight to the clipboard, formatted so it pastes cleanly into other spots. But it has a few tricks:
- /clipboard — copies the most recent thing the LLM generated (a blurb, message, snippet, or commit message), stripped to clean plain text for pasting into other apps.
- /clipboard commit — copies a git commit message, fully dedented (zero leading whitespace), no fences or prose — ready to drop into
git commit. - /clipboard raw — copies the last message verbatim with markdown intact (for GitHub/Notion/docs), minus code-block indentation.
- /clipboard — targets something specific, e.g.
/clipboard the bash command aboveor/clipboard the second option.
- recap — Reads a project’s git history and docs and tells me where things stand. This is particularly useful when I come back to a small project after a week or two.
- (update): land — Finish a workspace cleanly — the bookend to recap. Verify tests pass, generate the commit and PR body from the diff, push, open a PR against origin/main, and drop a one-line summary into .context/ so sibling agents (parallel Conductor worktrees) see it landed.
- web-minimal — Builds plain monospace pages in a simple style I prefer for all things (as you can tell with the various sites on this domain).
Install them into pretty much any coding agent with one line:
npx skills add naveen/skills
Or, if you’re on Claude Code, add the marketplace and pick what you want:
/plugin marketplace add naveen/skills
/plugin install recap@skills
It’s open source (github.com/naveen/skills) and will eventually show up on skills.sh. I’ll keep adding to the list as I make more.
An OmFest Reminder
A reminder to register for OmFest coming up in just under a month on September 29th. From Matt’s post:
Om loved putting on a good conference, and I’d like to celebrate his life with an awesome event on September 29, 2026 (his 60th) in San Francisco, like an OmFest.
(Update: Sign up here!)I’ll find a space where every community from the many facets of Om can come together. In the spirit of Open Source and co-creation, we can have some booths, flash talks, a gallery of his photography, pen showcase, and whatever other fun ideas people want to contribute. I can’t wait for the beautiful collision of his tech / journalism / Indian party planner / pen / coffee / shoes / photography circles, and probably some niches I couldn’t even imagine.
You can sign up for the event here: Luma link.
We’ll update that with a location and other details as we get closer to the day.
Secondhand lives

When Michael Crichton wrote these words in 1988 in Travels, it might have seemed like a niche opinion. It certainly doesn’t come across as prophetic. And, given he is a medical doctor, writer and lover of scientific narratives, he’s not exactly anti-technology. He just saw how one such technology, TV, had started warping our thoughts:
My own sense is that the acquisition of self knowledge has become more difficult by the modern world. More and more human beings live in vast urban environments, surrounded by other human beings and the creations of human beings. The natural world, the traditional source of self-interest, is increasingly absent.
Furthermore, within the last century, we have come to live increasingly in a compelling world defined by electronic media.
These media have evolved at a pace that is utterly alien to our true natures. It is bewildering to live in a world of ten-second spots, each one urging us to buy something, to do something, or to think something. Human beings in the past were not so assaulted. And, I think that this constant assault has made us pliable in a certain unhealthy way. Cut off from direct experience, cut off from our own feelings and sometimes our own sensations. We are only too ready to adopt a viewpoint or perspective that is handed to us. And is not our own.
I was reading his book a few weeks ago and it felt like something he could’ve written today. The only difference is the state of technology assaults is much worse than he could have ever imagined: advertisements, breaking news, chat notifications, tweets, stories, reels, iPhone pings in our pockets…the list is endless.
The ten-second assaults now are leading to something I’m calling secondhand lives. We’re living not our lives, but someone else’s – ten seconds at a time.
All that to say, I’m going to take a bit of a break: vacation calls. Time for some direct experiences.
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”:
| Inference | Confidence |
| 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.
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.
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.