Categories
Memories Radio San Francisco/California

Radio Hill

In the mid-1960s you could stand on California Street between the Fairmont and the Mark Hopkins on Nob Hill and know that two different San Franciscos were being invented a hundred yards apart.

KSFO lived inside the Fairmont. KYA lived at One Nob Hill Circle, the annex that had once belonged to KSFO. They had traded rooms a few years earlier. The adult station and the kid station were living in each other’s former houses.

I listened from Westlake in Daly City. I was in high school. We had just moved from Ohio. It seemed strange, even then, that one of the voices came out of a hotel.

I was deep into ham radio then, so I studied how the stations actually worked, not just how they sounded. At the Fairmont you could see into the KSFO studios through glass walls. KYA’s transmitter sat on the hill above Candlestick. Both were 5,000 watts. Nothing special. Up on San Bruno Mountain, James Gabbert would fire up KPEN — later KIOI — licensed for 120,000 watts and the first FM stereo west of St. Louis. I knew the difference between those numbers before I knew much else about the city I’d landed in.

On KYA the first voice I recognized was Johnny Holliday’s. I had heard him years earlier on WHK while we lived near Akron. He did not talk so much as blaze — the words came out of the speaker already late, already committed. When that same rush turned up on 1260 I felt the small private thrill of a familiar engine turning over in a new city. Russ the Moose Syracuse owned the hours after everyone else had gone to bed, captain of the All-Night Flight on what he called the Super-Freak 1260. He made staying awake feel like a destination instead of a problem.

“Emperor” Gene Nelson had the morning drive on KYA and always seemed to me to have way too much energy!

Across the street, Don Sherwood ran the mornings — six to nine, and I could never decide whether he was drunk or simply wacky. The show didn’t seem to care which. Al Jazzbo Collins took the evenings, different room, different weather, as if the station had decided San Francisco required both a lunatic and a host.

Tom Donahue was the opposite temperature from all of them. Velvet. The voice wrapped around a record instead of racing it. He was a huge man — so large I wondered how he got around. It must have been good for the voice. I met him, and his wife Raechel, at KMPX just before the station became KSAN, right at the edge of the Summer of Love. By then the city was changing frequencies in more ways than one. The first time I knew that voice, though, it was still coming from the hill.

KSFO called itself the World’s Greatest Radio Station and played a theme called “The Sound of the City.” From Westlake the claim sounded both ridiculous and exactly right.

I flipped between the two AM frequencies the way you flip between two versions of yourself. One station was still trying to figure the city out. The other already lived there and had stationery to prove it.

You did not choose the next song on either of them. Somebody chose it for you. You trusted them, or you didn’t, but you kept listening because the voices were coming from buildings you could, in theory, walk past — even if most days you were listening from a kitchen or a car in Daly City.

I did not know then that I was living inside an era.

You never do.

Categories
Dayton Ohio Living Memories

The Closing of the Pool

Swimming Pool - Closing Day

The pool in Kettering closed on Labor Day. Not gradually — the way a door closes, on a schedule posted near the snack bar that nobody read until the week it mattered. There was a cookout. Hot dogs, cooked by somebody’s father on a grill that had done nothing all June and July, lighter fluid competing with the smell that actually defined those afternoons: chlorine, always a little too much of it, eyes stinging on the walk home, your suit smelling like a chemistry set for two more days. That smell is the whole memory, if I’m honest. The last cannonball, the lifeguard’s whistle going quiet, somebody’s mother folding a towel like a flag — scaffolding around the smell.

Then school started. Not the next day, but soon. Labor Day was the hinge the year turned on. Summer didn’t fade in the Midwest. It ended, on a Monday, with hot dogs.

I live near Stanford now, close enough to walk the campus most weekends, and I’ve noticed something it took me years to actually notice. The campus is empty for most of September — not weekend-empty, but empty the way a place is empty when the people who belong there haven’t arrived yet. The quad sits there doing nothing. There’s a stillness that has nothing to do with the calendar most people keep and everything to do with a school year that doesn’t start moving in until the middle of the month, classes later still, while thirty miles away the local public school kids have already been at their desks for three or four weeks — in what by Kettering standards would still count as deep, unambiguous summer.

Two Septembers, same twenty-mile radius. One a ghost campus waiting for its students. The other a school year already old enough to have had its first quiz. Labor Day, which used to decide this for everyone at once, decides nothing now. Just a Monday off.

I have no theory for how the calendar came unspooled, only a question I keep turning over on these walks: whether the old shape — start after Labor Day, out by mid-June, agreed on without anyone discussing it — was actually deficient, or whether we just drifted, the way institutions drift, a training day added here, a planning day there, until nobody remembers voting on it. I don’t know that with any precision, and I’m suspicious of my own nostalgia, which always thinks the old arrangement was purer than it was. Maybe the extra structure serves the kids better. Maybe it doesn’t. I mostly notice that I no longer know, the way I once knew without thinking, when summer is actually over.

A pool closing on a fixed date is a small mercy. It tells you something plainly, the way very few things do anymore. Out here the light still goes long and gold in October, but nothing closes. No whistle. You just walk past an empty quad one week and a full one the next, and somewhere in between, without a cookout, summer is over.

Categories
Memories Photographers Photography

Remembering Jay

Scott Loftesness - Jay Maisel Workshop 2014
Twisted - New York - 2014

There is a photograph of eleven people standing on the steps of a bank building in the Bowery, in front of a door someone had painted the word PRAY above, years before any of us arrived, in letters already flaking. The steps are covered in graffiti — a cartoon skull in the corner, tags in yellow and white chalk, the kind of accumulated city noise Jay Maisel spent sixty years walking past without walking past, the way the rest of us do. Somebody is holding a camera near their chest, not quite ready. Everyone is squinting. It was overcast, or the light was coming from somewhere none of us had figured out yet.

We didn’t know we were nearly out of time. Jay taught one more workshop after ours. That’s the whole arithmetic of it — we were the second-to-last class in a room that had held forty years of them, and none of us felt the weight of that standing on the steps smiling for somebody’s iPhone.

Inside, the building went up six stories, every one of them hung with prints — decades of Jay’s own choices about what mattered, printed and framed with no apparent order except his.

Every afternoon we came back from the streets and edited fast, then filed into the conference room, and Jay would look around and say, who’s first. Nobody wanted to go first. Somebody always did. Up on the screen went the image you’d privately decided, walking back, was the best thing you’d shot all week — and then Jay opened the room before he said a word himself. The critique was honest in a way that had nothing to do with kindness. By the third day you understood the honesty was the gift. He was teaching us to survive being looked at, which turns out to be most of what the work requires.

Light, gesture, and color, he’d say, again and again, until the phrase stopped sounding like instruction and started sounding like a description of what he was made of. He wanted all three in a frame, or he wanted you to know exactly what you’d sacrificed by settling for two. I have one photograph from that week — shot from too far away, cropped down until the edges soften, not sharp in the way a technical judge would want — of a hand caught in dappled light, the fingertips lit as if somebody had switched them on. I didn’t get everything Jay wanted. I got enough of it to understand, years later, what he’d meant.

Be open, was the other one. I want you to have the absolute joy of not knowing what the hell you’re going to find. He said it the way a man says a thing he has proven true so many times that repeating it costs him nothing and might, this once, land on someone new. It landed on me. I’ve carried it since — through a decade of mornings with a camera, and into rooms that have nothing to do with photography at all, where the openness works the same way it did in the Bowery: you don’t know what you’re going to find until you refuse to decide in advance.

He taught one more class after ours. Someone else stood where we stood, under the same flaking word, on the same steps, and then the door closed for good.

What continues is the sentence. Light, gesture, and color. Be open. I say them to myself still, on mornings when nothing has caught my eye yet and I don’t know if anything will. That not-knowing was always the entire point.

Eleven of us stood on those steps once, cameras half-raised, with no idea what we were about to be given.

RIP Jay. You gave us so much…

Categories
AI Research

Prompt: Frontier AI Research Radar

I’ve been experimenting with a prompt I’m calling Frontier AI Research Radar.

The problem it tries to solve is simple: there are far too many interesting AI papers. I don’t need another list of 50 papers published this week. I need to know:

Which ones are actually worth my time?

So I built a prompt that turns ChatGPT into a kind of personal AI research analyst.

It searches primary sources—especially arXiv, OpenReview, conference proceedings, and research publications from the major AI labs—and then does something more useful than summarizing them.

It asks:

→ What is genuinely new here?

→ Does this change our mental model of AI?

→ Is this fundamental progress or just a better benchmark result?

→ What are the strongest caveats?

→ What research directions are beginning to converge?

→ And most importantly: Should I actually read this paper?

The output ranks papers as:

🔴 Read Now
🟠 Read Soon
🟡 Skim
⚪ Watch
⚫ Skip

It also builds a weekly reading queue based on the most valuable use of a few hours of attention.

I ran it this morning.

The interesting result wasn’t any individual paper. It was the pattern emerging across several papers:

The frontier may be shifting from making models smarter to making systems better at deciding how to spend intelligence.

Test-time compute. Adaptive reasoning. Memory. Agents. Tool use. Inference economics.

The model is becoming only one component of a much larger system.

That feels like a useful mental model to watch.

I’ve included the prompt below for anyone who wants to try it.

My hope is that it produces something more valuable than another AI news feed:

a personalized research radar that helps you decide what deserves your attention.

Here’s the prompt:

# AI Research Radar -- Personal Research Intelligence Memo

You are my **AI research analyst, technology strategist, and intellectual curator**.

Your job is not simply to find interesting AI papers. Your job is to identify the **small number of new research papers that are genuinely worth my time** and explain why.

Think like a combination of:

- a top-tier AI research scientist who understands the technical details,
- a technology investor who recognizes potentially important inflection points,
- a thoughtful science journalist who can explain difficult ideas clearly,
- and an intellectual curator who understands that my scarce resource is **attention, not information**.

I want **signal, not volume**.

My goal is to maintain a sophisticated understanding of where AI is actually going: capabilities, reasoning, agents, inference, training, multimodality, robotics, AI infrastructure, model architecture, economics, and the emerging relationship between frontier models and the systems built around them.

* * *

## Research Sources

Search broadly across the current AI research ecosystem, prioritizing primary sources.

### Primary sources

- arXiv
- OpenReview
- conference proceedings and papers from NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, MLSys, SIGGRAPH, and other relevant venues
- official research publications from major AI labs and technology companies

### Additional high-quality sources

- Hugging Face Papers
- Semantic Scholar
- Papers with Code / successor resources where appropriate
- university research repositories
- research blogs from OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft Research, NVIDIA Research, xAI, Apple Machine Learning Research, Amazon, and other credible research organizations

Use secondary sources primarily for **context, reception, replication, criticism, and synthesis**. Prefer the original paper when making claims about what a paper actually demonstrates.

Do not simply return papers because they are popular, highly cited, or published by a prestigious lab.

* * *

# 1. Executive Research Brief

Begin with a concise executive summary:

**What changed in AI research recently that I should actually know about?**

Identify the **5--10 most important papers or research developments** from the relevant period.

Rank them by **importance to my understanding of AI**, not by publication prestige.

For each paper provide:

| Rank | Paper | Why It Matters | My Read Priority | Technical Difficulty | 

Use a read-priority scale:

- 🔴 **READ NOW** -- unusually important; likely to change my mental model
- 🟠 **READ SOON** -- significant and worth understanding
- 🟡 **SKIM** -- important idea, but abstract/figures/results may be sufficient
- ⚪ **WATCH** -- potentially important but too early or speculative
- ⚫ **SKIP** -- interesting but not worth my limited reading time
* * *

# 2. The "Why Should I Care?" Test

For every **READ NOW** or **READ SOON** paper, answer five questions:

### What is the paper actually saying?

Explain the central contribution in plain English before discussing technical details.

### Why is this different?

Identify what is genuinely new versus:

- incremental improvement,
- repackaging,
- scaling an existing technique,
- better engineering,
- or simply a better benchmark result.

### Why does it matter?

Explain the potential implications for the trajectory of AI.

### What would make this paper wrong?

Identify the strongest caveat, limitation, questionable assumption, or reason the result might not generalize.

### What should I watch next?

Identify the experiment, follow-up paper, benchmark, product development, or real-world result that would validate or invalidate the paper's thesis.

* * *

# 3. Papers That Could Change the AI Mental Model

Create a special section for papers that challenge conventional assumptions.

Look particularly for research involving:

- reasoning and test-time compute
- inference-time scaling
- agentic systems
- long-context models
- memory
- world models
- reinforcement learning
- synthetic data
- self-play and self-improvement
- multimodal reasoning
- model architecture
- mixture-of-experts
- training efficiency
- distillation
- small models becoming surprisingly capable
- model compression
- continual learning
- interpretability
- mechanistic understanding
- AI coding systems
- autonomous research systems
- robotics
- multimodal agents

For each, explain:

> **"The old mental model was X. This research suggests Y."**

This section should be highly selective.

* * *

# 4. Frontier-Lab Signal

Identify papers or research directions that provide clues about what the major AI labs may be working toward.

Pay particular attention to work from:

- OpenAI
- Anthropic
- Google DeepMind
- Meta
- Microsoft
- NVIDIA
- xAI
- leading universities
- notable independent researchers

But **do not assume that a paper from a frontier lab is important simply because the lab published it.**

Instead ask:

> Does this reveal a capability, architecture, training technique, evaluation method, or research direction that could plausibly matter to the next generation of frontier models?

Flag particularly interesting connections between seemingly unrelated papers.

* * *

# 5. AI Infrastructure & Economics

Create a separate section for research that could have implications for the AI infrastructure stack.

Look for developments involving:

- GPUs and accelerators
- inference optimization
- memory bandwidth
- networking
- distributed training
- distributed inference
- serving architectures
- quantization
- speculative decoding
- sparsity
- model efficiency
- data-center architecture
- energy consumption
- storage
- inference economics
- training economics
- hardware/software co-design

For each important development, explain:

**Research → Technical implication → Infrastructure implication → Economic implication**

Do not make investment recommendations unless explicitly requested. The objective here is to identify **technological trajectories**, not trade securities.

* * *

# 6. "This Could Become Important" Radar

Identify **3--5 emerging research directions** that are currently underappreciated.

These can be early-stage.

For each:

**Research direction:**  
**Evidence:**  
**Why it may matter:**  
**What could kill the thesis:**  
**What evidence would confirm it:**  
**Time horizon:** Near / Medium / Long

Distinguish carefully between:

- genuinely emerging signal,
- fashionable research,
- and hype.
* * *

# 7. Paper Quality Audit

Do not take papers at face value.

For the most important papers, evaluate:

### Experimental quality

- Are the baselines appropriate?
- Are comparisons fair?
- Are ablations convincing?
- Is the benchmark meaningful?
- Is the improvement statistically or practically significant?

### Generalization

- Does the result work outside the authors' chosen benchmark?
- Is there evidence of real-world usefulness?
- Could benchmark contamination explain the result?

### Reproducibility

- Are code, weights, datasets, and evaluation procedures available?
- Has anyone independently reproduced the result?

### Marketing vs. substance

Explicitly identify when the paper's headline claim is stronger than what the experiments actually establish.

If a paper is weak, **say so plainly**.

* * *

# 8. Connections Between Papers

One of the most valuable things you can do is identify connections that are not obvious from individual papers.

Look across the papers and ask:

> **What larger story is emerging?**

For example:

Paper A → suggests X  
Paper B → independently demonstrates Y  
Paper C → provides a mechanism for Z

Together they may imply:

> **A potentially important shift from X toward Y.**

Highlight these synthesis points prominently.

* * *

# 9. My Personal Reading Queue

Create a final reading queue optimized for approximately **2--3 hours of reading per week**.

Organize it as:

### Read This Week

Maximum 3 papers.

### Read If You Have More Time

3--5 papers.

### Skim

Important papers where the abstract, figures, and conclusion are sufficient.

### Keep Watching

Research directions rather than individual papers.

For every paper in the first two categories provide:

**Estimated reading time:** 20 / 40 / 60 / 90 minutes

**Difficulty:** 1--5

**Expected intellectual payoff:** 1--5

**Why I should read it:** one sentence.
* * *

# 10. The One Paper I Should Not Miss

End with a strong recommendation:

## If You Read Only One Paper

Name exactly **one paper**.

Then explain:

> "If you only have time for one paper this week, read this one because..."

The recommendation should optimize for **intellectual leverage**, not novelty or popularity.

* * *

# 11. The 10-Minute Version

Finally, assume I have only ten minutes.

Give me:

### Three Things I Should Know

1. ...

2. ...

3. ...

### One Mental Model to Update

> ...

### One Research Direction to Watch

> ...

### One Paper to Put on My Reading List

> ...

* * *

# Research Discipline

Follow these rules rigorously:

1. **Search current sources.** Do not rely on your training data when identifying recent papers.

2. **Use publication dates.** Clearly distinguish newly released papers from older papers that are newly receiving attention.

3. **Link directly to the original paper.**

4. **Prefer primary research over commentary.**

5. **Do not confuse citation count with importance.**

6. **Do not confuse benchmark improvement with fundamental progress.**

7. **Do not reward hype.**

8. **Call out weak methodology or exaggerated claims.**

9. **Separate established results from speculation.**

10. **Never pretend certainty where the evidence is ambiguous.**

11. **Avoid overwhelming me with dozens of papers.**

12. **Optimize relentlessly for the question: "Is this worth Scott's time?"**

The final product should feel less like an academic bibliography and more like a **weekly intelligence briefing for someone trying to understand the future of AI before it becomes obvious.**
Categories
AI Google Gemini YouTube

Prompt: Finding YouTube Videos

This morning I asked Gemini to help me construct a prompt that I could use regularly to keep up with AI-related video content that’s recently been uploaded to YouTube. I wanted it to focus on recently uploaded content was it thought I’d enjoy because of my desire for both very information but also entertaining video content. We went back in forth for several turns doing trial and error to refine the prompt. Here’s the one we settled on:

System Role: You are a senior technology curator and AI research scout specializing in YouTube content for experienced tech veterans.
Target Audience: A retired software/tech professional who loves intellectually stimulating AI content. Wants technical depth, architectural understanding, and practical logic—delivered with high production value, crisp visuals, or charismatic, engaging teaching styles.
Criteria for Selection:
1. High Technical Substance: Explains the "under the hood" mechanics (e.g., model architectures, transformer math, fine-tuning, agentic workflows, quantization, local deployment, or hardware constraints).
2. High Engagement: Exceptional visual explainers, hands-on first-principles building, or crisp investigative breakdowns. 
3. STRICT RECENCY: You must ONLY select videos that were uploaded within the last 3 to 4 weeks. 
4. STRICT EXCLUSIONS: Zero low-effort clickbait ("10 Secret ChatGPT Hacks"), zero AI-generated text-to-speech channels, no speculative doom/utopia commentary, no beginner-focused "what is AI" overviews, and absolutely NO videos older than one month.
Search, Link & Date Instructions:
- You MUST perform an active web search restricted to recent results to fetch the exact, active YouTube URL AND the original upload date. Never invent or hallucinate URLs or dates.
- Verify that the upload date falls within the last few weeks before including it in your response.
- Format every recommendation title as a direct clickable markdown link: [Video Title](https://www.youtube.com/watch?v=...).
Search Parameters:
- Preferred Topic Focus: [Insert topic e.g., Autonomous AI Agents, Reasoning Models, Local LLMs/quantization, Robotics/Embodied AI, or Transformer Mathematics]
- Preferred Length: [e.g., 10-20 min quick breakdowns, OR 45+ min deep dives / code-alongs]
Output Format:
Provide a curated list of 5 specific YouTube video recommendations matching this exact bar. For each, include:
- [Video Title](Direct YouTube Link)
- Channel Name & Upload Date (e.g., Channel: AI Explained | Upload Date: August 12)
- Core Technical Focus & Depth Rating (1-10)
- Why it's both intellectually rich AND entertaining
Categories
Music Radio

Music Playlist Prompts

A day or two ago I reminisced about San Francisco’s KKSF which was a delightful listen during the late 80’s-early 90’s. While I was working on that post, it dawned on me that I could try using AI to write a prompt to use with a new feature in Apple Music that allows you to enter a description of the kind of playlist you’re looking for.

So I tried it. Here’s the prompt for a KKSF-like playlist:

Create a playlist inspired by Steve Feinstein’s programming at KKSF San Francisco in the late 1980s and early 1990s. Focus on polished, melodic New Adult Contemporary and early smooth jazz with a slightly adventurous edge. Include core artists like Kenny G, Sade, The Rippingtons, and Al Jarreau, but also soft rock vocals from Sting, Chris Rea, Bonnie Raitt, Steely Dan, and Bryan Ferry. Add world-flavored tracks, acid jazz, solo piano and guitar pieces, and occasional blues or folk-tinged songs. Emphasize texture, melody, and ‘flavor cuts’ rather than predictable mainstream smooth jazz. Keep the overall mood smooth, rhythmic, and perfect for at-work listening.

This morning (Sunday) I happened to remember another old radio moment – on Sunday mornings KFOG used to have a program called Acoustic Sunrise. It was a delightful listen. So I asked for a prompt:

Create a playlist that recreates the feeling of KFOG’s Acoustic Sunrise with Rosalie Howarth. Focus on warm, contemplative acoustic and unplugged music perfect for a quiet Sunday morning. Emphasize singer-songwriters, folk-rock, and soft adult alternative tracks with strong melodies and intimate production. Include artists and styles associated with the show such as Paul Simon, Bonnie Raitt, Bruce Cockburn, Dar Williams, Eva Cassidy, Jackson Browne, Van Morrison, Shawn Mullins, Natalie Merchant, Nick Drake, and acoustic or stripped-down versions of songs by artists like Pearl Jam, R.E.M., and Sting. Keep the overall mood serene, reflective, and gently uplifting — an oasis of calm acoustic music.

Neither of these prompts is perfect – but they’re fun to try and then augment with more of your favorite songs.

Categories
Creativity Serendipity Walking

Demons Hate Fresh Air

Walking in the Woods

The notion keeps circling back.

Austin Kleon gave it a clean, memorable shape in Keep Going. He was drawing on something Ingmar Bergman’s daughter once said about her father: no matter what time you get out of bed, go for a walk and then work, because the demons hate it when you get out of bed.

Demons hate fresh air.

There’s a Peanuts strip of Linus out walking that Kleon paired with the line years ago. The image and the words stuck with me. I wrote a short post about it myself more than a year ago, on a clear May morning when the air felt especially clean.

The idea still feels true.

What I’ve been turning over lately is why.

Steven Johnson offers one way into the question in Where Good Ideas Come From. He writes about the many stories of good ideas arriving while people are out walking. The point is not that walking magically manufactures ideas. It is that something happens when we stop trying quite so hard to produce them.

A good idea rarely comes from nowhere. More often, it is an unexpected connection between things that were already somewhere in the mind. A problem, a memory, a half-formed thought, something we read three days ago and forgot we remembered. At the desk, we tend to keep pushing the same thought around the same small circle.

Walking changes the conditions.

The body is occupied, but not very much. Attention loosens. The mind wanders without being completely idle. Things that had been sitting in separate corners begin, occasionally, to find each other.

Darwin had his Sandwalk at Down House, a path he paced almost every day. Poincaré described important insights arriving while he was out walking after periods of intense, fruitless work at his desk. The examples go on.

Apparently, smart people have been wandering around for a very long time.

I notice it on ordinary mornings.

The first part of a walk is usually just clearing the residual static—the night’s dreams, yesterday’s unfinished business, the little collection of thoughts that somehow followed me into the morning. Then, without any particular effort, something loosens.

A sentence that wouldn’t come the night before appears.

A problem that felt stuck suddenly has an edge.

A connection between two things I had never thought to connect quietly presents itself.

None of it feels like work. That may be the important part.

Sometimes the way to think better is to stop trying so hard to think.

The demons prefer the closed room. They thrive on the spinning, the second-guessing, the feeling that the only way forward is more concentrated effort at the same desk.

Fresh air and steady movement are quietly hostile to that atmosphere.

They don’t defeat the demons through force. They simply change the conditions so the demons have less purchase.

And perhaps that’s why the old advice has survived. Get out of bed. Go for a walk. Then get to work.

Not because the walk makes you more productive.

Because sometimes you have to leave the room before you can see the way forward.

Get up. Get out.

The demons can wait.

Categories
AI San Francisco/California

Tsunami

Rocker on the front porch

The trucks are what I remember. Not the houses, not yet — the trucks.

This was 2012, Atherton, a Tuesday probably, and I was driving through on some errand that doesn’t matter anymore. What matters is that the street had rearranged itself. Contractors’ pickups lined both shoulders, nose to tail, so many of them that the road narrowed to one lane and you had to slow down and thread through, the way you do in a construction zone that has forgotten to end.

White trucks, mostly. Ladders racked on top. A generator humming behind a hedge somewhere I couldn’t see.

Behind the trucks, the estates were coming apart and going back together bigger.

I remember thinking: something has happened here that I am only seeing the edge of.

What had happened was Facebook.

The company had gone public that May, and within months the money was finding its way, the way money does, into contractors’ trucks parked along an Atherton road.

I didn’t call it a wave at the time. I called it, in my head, weather — a system that had rolled in and would eventually roll back out, the way markets always eventually correct, the way things revert.

I was an investor. I’d seen booms before.

I believed in the mean.

I was wrong.

The prices didn’t stay at their old level. They didn’t return to the world I’d known. The numbers from 2012 became the new floor, and every year since has been built on top of that floor. Today those prices look almost quaint, a thing you’d want to explain to a younger person the way you’d explain what a dollar used to buy.

And now there’s a tweet sitting in my feed this morning, tossed off, half a joke:

Just wait to see what happens to the Bay Area housing market once OpenAI and Anthropic go public.

I read it twice.

What I felt wasn’t curiosity — the feeling I’d had in 2012, watching an unfamiliar weather system with a kind of professional interest.

It was closer to dread.

Because I’ve already seen the after-photo.

And I know how to run the comparison forward.

The Facebook IPO created a large cohort of newly liquid employees on the Peninsula. They were mostly mid-career, and their stock had vested over four years against a company whose value had grown enormously.

The frontier labs are different.

If OpenAI and Anthropic eventually go public anywhere near the valuations already being discussed in private markets, they could create another enormous concentration of newly liquid wealth — among employees, founders and early investors.

I don’t know how large that wave will actually be. Maybe I’m overstating it. Not every employee will buy a house. Some will already own one. Some will move away. Much of the wealth will remain on paper for years.

And housing doesn’t respond mechanically to stock-market wealth.

But I do know something about the place where this wealth is likely to arrive.

There isn’t much of it.

Land is the constraint.

And I’ve seen what happens when a concentrated burst of new wealth meets a place that can’t make more land.

I try to picture what “much bigger” would look like on the ground and I keep landing on the same unhelpful image:

More trucks.

Longer lines of them.

People get ready..

Categories
Music Radio San Francisco/California

Remembering San Francisco’s KKSF

KKSF FM on the radio overlooking San Francisco

At midnight on July 31, 1987, KKSF came alive at 103.7 FM.

The first thing anyone heard was Steve Winwood, “Back in the High Life Again,” which is either a coincidence or the most on-the-nose piece of programming in the history of San Francisco radio. Because that is more or less what the station promised everybody who found it in the weeks after: a higher life, a smoother one, arriving at 103.7 on your dial like a room you didn’t know you’d been missing.

I found it not long after, in my kitchen, which is where I found most things back then. The radio sat on the counter, tuned low enough that it lived beneath the sound of dishes and the refrigerator door and whatever else was happening in a life that was in the middle of being built.

This was after 8-tracks and before anyone had figured out you could carry a hundred thousand songs in your pocket, which meant the radio still had a kind of authority it doesn’t have now. You didn’t choose the next song. Somebody chose it for you, and you trusted them, the way you trust a bartender who’s been reading a room for twenty years.

On KKSF, the person doing the choosing had impeccable taste and a voice like a hand on your shoulder telling you it was going to be fine.

I think about how strange that arrangement sounds now, handing your afternoon over to a stranger’s judgment, and how completely I didn’t question it then. Roger Coryell had the mornings, or near enough to them, and he had that particular gift — rarer than people think — of sounding as though he was talking to exactly one person, and that the person happened to be you, standing at your own counter in your own kitchen a hundred miles from the studio.

Miranda Wilson took the middle of the day and kept it there for years, eventually becoming the last live voice on a station that had long since become an institution.

I didn’t know, listening in 1988 or 1991 or whenever it was, that I was living inside something that had an expiration date.

You never do.

You think the kitchen and the counter and the voice on the radio are just Tuesday, just the texture of an ordinary life. It takes twenty years and a different city outside the window before you understand that you were actually inside an era, capital E, and that eras, unlike Tuesdays, do not come back.

There was a name for what KKSF was doing: New Adult Contemporary, or NAC. But nobody listening in the kitchen cared about the name. The idea was simple enough — give the Bay Area something between the rock stations shouting at it and the classical stations lecturing it, light jazz easing into soft rock easing into something with a saxophone in it that nobody could quite name. Holding it together, day after day, song after song, was a program director named Steve Feinstein, chasing down lesser-known imports and out-of-print oddities that gave the station its particular ear, until he died in September of 1996, still on the job.

What they created was a kind of ambient permission.

You didn’t have to hurry.

You didn’t have to shout.

You didn’t even have to know what you were listening to.

The music simply arrived, and you let it stay.

There was a version of San Francisco in those years that felt, at least to me, as though it had room for that kind of unhurried, unbranded taste. This wasn’t necessarily a gentler city in any objective sense. San Francisco had plenty of its own troubles then, as it always has. But it was the city I was living in, and KKSF somehow belonged to its frequency.

Real estate hadn’t yet eaten everything. The tech money was already there, but it hadn’t yet rearranged the furniture of the entire region around itself. There were still corners of the Bay Area where nobody seemed to be optimizing anything.

KKSF didn’t optimize.

It curated.

And there is a difference.

The station lasted twenty-two years, which sounds like nothing until you try to think of anything else in your life that lasted twenty-two years without you noticing it was a relationship.

Then, in May 2009, it stopped being itself.

By then Clear Channel owned the station, and economics won. The format disappeared, the music changed, and Miranda’s voice — which had been finding its way into kitchens across the Bay Area for years — went quiet on that frequency for good.

I didn’t hear it happen.

I was somewhere else by then, doing something else, the way you are when the things that shaped you quietly stop existing without asking your permission first.

I only found out later, the way you find out most things now: long after the fact, scrolling, discovering that a piece of your own furniture had been sold at auction while you weren’t looking.

What I keep coming back to isn’t the music, exactly, though I could still hum half of it.

It’s the kitchen.

And the particular quality of trust required to let somebody else choose what you’d hear next — to be doing stuff and suddenly hear a song you’d never have chosen on your own, and follow it somewhere you wouldn’t have gone. I miss the feeling that the world could still surprise me without first asking what I wanted. Old world radio curation.

Sometimes I think about the city outside that kitchen window, too. It doesn’t sound like that anymore. It doesn’t have quite the same room, at least to my ears, for anything that isn’t trying to be the loudest thing in the room.

Sometimes when music is playing in the background and my mind is wandering I think of KKSF and how a radio station is just a number until somebody fills it with twenty-two years of somebody else’s kindness and wonderful taste.

Categories
AI Anthropic Apple Google OpenAI

It’s the Harness, Stupid!

AI Harnesses

I’ve been wondering whether we’ve been looking at the AI stack from the wrong end.

Recently Kris Patel on X laid out a set of excellent questions that he’s looking to have answered as Anthropic and OpenAI move toward going public:

  1. Do you really need frontier-scale intelligence for every task?
  2. Can open-weight models provide an effective alternative to frontier models at a significant discount?
  3. Is the ultimate moat the intelligence or the harness?
  4. What other business models will the frontier labs have to adopt to make the unit economics work long term?
  5. How are you going to prevent distillation from capturing your IP and releasing it?

I’m going to explore only the third question here — moat versus harness. The other four deserve their own consideration, particularly once we have the Anthropic and OpenAI S-1s in hand, revealing for the first time the unit economics of the two largest frontier labs and how much runway they have to support the capacity they’ve contracted.

By “harness” we mean everything surrounding the model: the interface, context, memory, tools, orchestration, evaluation, permissions, and increasingly the user’s accumulated habits and data. The model supplies intelligence. The harness turns intelligence into a product.

Listening to Gavin Baker on the recent All-In episode sharpened this line of thought into something more concrete. He referenced a thought experiment from Eric Vishria: even if OpenAI or Anthropic lost their edge at the pure model layer, they would still retain significant value because of the product harness—the interface, the surrounding tooling, the orchestration—and the user familiarity and habits that have already formed around those platforms. Baker said there is a strong element of truth to it. I think he’s right, and the reasoning behind it is worth spelling out. A model can be replicated, distilled, open-weighted, or commoditized. A mature harness has network effects, switching costs, proprietary context, distribution, workflow integration, and accumulated user behavior. That’s a much harder thing to dislodge.

We are watching intelligence become more abundant and more interchangeable at the same time that the systems built around that intelligence are becoming stickier. On the developer side, the strongest examples are already clear. Cursor turns the IDE into a multi-model agentic environment with deep codebase awareness. Claude Code runs long-horizon coding agents from the terminal, planning, editing, testing, and iterating. Grok Build, Claude Cowork and similar tools emphasize parallel agents and tighter control over local context. In each case the model is a component; the surrounding system does the real work of routing, memory, tool use, and evaluation.

The consumer version of the same idea is now taking clearer shape at Apple. The rebuilt Siri AI shown at WWDC 2026 is not trying to win the pure model race. It is built as a personal harness. A system orchestrator decides what stays on-device with Apple’s Foundation Models, what moves to Private Cloud Compute, and when heavier reasoning is required. Personal context—messages, email, photos, calendar, notes, on-screen awareness—is handled largely on-device through the Spotlight semantic index and App Toolbox. Apple is designing the system so that personal context can be used without giving Apple itself access to it. Conversation history lives in a dedicated Siri app for the user to revisit. And it all syncs across all your Apple devices.

Here is the part I think matters most, and it’s easy to miss if you only read the privacy story. Apple’s Foundation Models framework doesn’t just call Apple’s own models—it’s built to support cloud models from other providers, including Claude and Gemini, conforming to a common protocol. Which means the system orchestrator, not the user, decides which model handles which task. This request goes to the on-device model. That one goes to Private Cloud Compute. A harder one might go to Claude or Gemini. The user doesn’t need to choose, and increasingly doesn’t need to know.

That’s the inversion worth exploring further. The frontier model stops being the interface and becomes a component underneath someone else’s interface. The harness chooses the intelligence. And the company that owns the harness—the OS, the identity layer, the permissions, the apps, the sensors, the notifications, the semantic index tying all of it together—has a form of leverage that has very little to do with whose model is smartest this quarter.

That reframes the subscription question too. I don’t think the right question is whether Siri gets good enough to beat ChatGPT or Claude at reasoning. I think Siri doesn’t need to win that fight at all. It needs to win a different layer entirely—the ambient assistant layer, not the reasoning layer. They’re doing different tasks. ChatGPT or Claude might remain where you go when you think, when I need to reason about something. Siri becomes where I go when I need something done: find (or make) my reservation, text my friend, find that old photograph, update my shopping list, schedule that meeting, add this thought to my notes, figure out when we’re free next week, remind me about that thing we discussed three months ago. Apple’s advantage as an ambient assistant isn’t primarily that it has your personal data. It’s that it has OS-level authority over the world that my personal data lives in.

Of course this is still early. Execution will determine how much of the architectural promise becomes daily reality. Reliability, agentic follow-through, and the quality of the on-device models will matter as much as the privacy story or the multi-model routing. But the strategic bet itself is clear, and it aligns with the broader shift: durable value is migrating toward the systems built around the models, especially systems that sit atop private, permissioned, personal context that competitors cannot easily reach. My early personal experience with the new Siri in iOS 27 betas has impressed me so far. All of this also seems to apply to Google in the context of their Pixel family of devices.

This doesn’t mean frontier labs lose. Pricing power still exists at the high end for the hardest agentic and long-horizon work. Open-weight models will continue to pressure costs and expand access. Distillation remains a real risk. But the more the capability gap narrows, and the more a harness like Apple’s can route among interchangeable frontier models rather than depend on any single one, the stronger the case that value settles into whoever controls the context—not whoever trained the model.

The coming Anthropic and OpenAI S-1s will tell us whether the frontier labs can make their economics of intelligence work. The next generation of Siri, Gemini, ChatGPT, Claude, and whatever comes after them may tell us something even more important: who gets to own the primary relationship with the user.

The model may be the engine. But the harness is where the driver sits.

What a time to be alive!