Categories
Interstate 280 San Francisco/California

The Freeway That Saved the Hills

A curving six-lane freeway follows a ridge beside a long reservoir, surrounded by rolling California hills covered in oak and chaparral, glowing in warm golden-hour sunlight.

Note: I’ve written here before about Interstate 280 on the San Francisco Peninsula. It’s a beautiful drive along an unusually natural and undeveloped section of land. This afternoon I went down a bit of a “rabbit hole” exploring more about the history of how the route the roadway takes was decided. Turns out it’s quite a story. One that’s unlikely to have happened were it being decided in today’s political climate.

Drive north on I-280 past Woodside and the view still startles: a multi-lane interstate running the rim of the San Andreas rift, and west of it an almost unbroken sweep of water, oak, chaparral, and ridge. No subdivisions. No office parks. No golf-course mansions stepping down to the lake. The land above Crystal Springs, and the country around Filoli, looks as if the postwar Peninsula never happened.

It almost did. The emptiness is not an accident of topography. It is the residue of a late-1960s bargain in which local activists pulled the U.S. Department of the Interior into a state highway fight, San Francisco traded development rights on its watershed for a relocated freeway, and federal money became the lever that shoved Interstate 280 east onto the ridge and away from the reservoir. A highway argument became, without much fanfare, a federal conservation instrument.

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Categories
AI

LLMs Reward Expertise — Key Takeaways

Core thesis: Sean Goedecke argues against the popular notion that “everyone talking to the same model gets the same results.” Instead, he claims domain expertise is the most important variable in LLM output quality — and that this gap will persist as models improve.

The Terence Tao example: Goedecke points to Tao’s public conversation with ChatGPT about a counterexample to the Jacobian Conjecture. Tao’s prompting style — short messages, pushback framed as “this looks more complex than expected” rather than direct correction, and rarely taking the model’s suggested next steps — produces dramatically better output than an amateur asking the same model about the same topic. The model shifts into “talking-to-mathematicians” mode simply by detecting Tao’s fluency.

Why expertise matters more than prompting technique: You can’t mimic Tao’s style without his math knowledge underneath it. The skill isn’t the phrasing — it’s knowing what “looks wrong,” which idea to extract from a wall of model output, and which alternate formulation to suggest. He draws a parallel to his own work as an engineer: familiarity with a specific codebase lets him say “don’t we already do X?” or “I think it could be simpler here,” pushing the LLM much harder than generic system-design knowledge would.

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Categories
Aging Illinois Memories Tracy Loftesness

Little Donuts

Midwestern State Fair Midway

Every August, when the Illinois State Fair came to Springfield, we took the kids.

We lived in Springfield in the late 1970s, and the fair belonged to summer the way certain things do — you didn’t plan for it, you just went.

The first thing I remember is the smell. Frying dough, grilled onions, hot dogs, popcorn, livestock, hay, dust, something sweet running underneath all of it. That combination doesn’t exist anywhere else. It’s the smell of summer vacation with a little livestock mixed in. The classic Midwestern state fair.

Then the sounds — the clatter of the rides, bells and whistles, barkers working the games, country music drifting in from somewhere, kids squealing, the low hum of a few thousand people wandering from one attraction to the next.

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Categories
Banking Markets

The Honesty of the Long Bond

Treasury Bond

There is a particular sound a fraud model makes right before someone silences it. Not an alarm, not a siren — a score. A number ticking upward on a screen, quietly, the way a fever climbs before anyone thinks to take the temperature. At Visa, in the years when the network was still teaching itself to smell trouble before trouble arrived, the worst mistake wasn’t missing the signal. It was seeing the signal and deciding, for reasons that felt reasonable in the room, to turn the threshold down. To make the number stop being inconvenient. The fraud didn’t go away when you did that. It just went un-priced for a while, and un-priced things have a way of arriving all at once, later, with interest.

I thought about that instinct — the turned-down threshold — reading Stanley Druckenmiller’s account of what the Treasury Department did on Aug. 19. The 30-year yield had touched a nineteen-year high. Within hours, Treasury announced it would double its long-dated bond buybacks, from two billion dollars a operation to at least four, running through early November. Yields fell. By the next afternoon they’d round-tripped back above where they started. The market had said its piece and gone back to saying it.

Druckenmiller’s point is not really about buybacks. Four billion dollars against a marketable debt stock nearing thirty trillion is a rounding error, and he says so. His point is about what a price is for. The long Treasury yield is the closest thing this country has to an incorruptible witness — a number nobody in Washington controls, that aggregates what millions of lenders actually believe about a borrower’s arithmetic, and reports back without spin. Inflation running above target since 2021. Unemployment low enough to call full employment by any definition. A deficit near six percent of GDP in peacetime, at full employment, which is not a thing this country has produced before. Interest payments outrunning the defense budget. The debt crossing forty trillion the same week Treasury decided the honest price of borrowing against all of that was too loud, and needed managing.

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Categories
Memories Radio San Francisco/California

Radio Hill

Radio Studio in Hotel

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.

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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.

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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.

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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.