Leasing a Car in the Age of AI

This is a Just the Facts (JTF) post: generated by AI from my own raw material (voice memos, session notes) and a short outline I wrote, then checked by me. What's a JTF?

Shira's Acura RDX lease was winding down. We wanted to lease something that wasn't an Acura, and we wanted to get out of the Acura in the way that worked best for us. We ended up with a Hyundai Santa Fe. It was our first car deal in the age of agentic AI. Here's what helped, where the AI went wrong, and what actually got the deal done.

Huge Win: LeaseHackr's Rate Findr

• Rate Findr on LeaseHackr is like X-ray vision for leasing. It shows the lease program numbers that dealers don't publish: money factor, residual and lease cash.
• It isn't AI. It's reliable data that the AI can use. Without real numbers Claude guesses, and those guesses can get you in trouble.
• Lease terms are set for the whole program, so they're the same at every dealer. In theory you could work out the lease deal by comparing offers from different dealers.
• LeaseHackr makes that much simpler. Because you can see which lease programs are being offered, you don't have to depend on dealers. You can compare your leasing options ahead of time instead of trying to work them out on the fly.
• Rate Findr also shows how each deal is put together. One model may have a lower interest rate. Another may have a higher rate but come with lease cash, which lowers the cap cost.
• Access works a little oddly: you join the forum and make a monthly donation, which you can cancel anytime. That's a small price for what you get.
• If you lease, you need this tool.

Where AI Helped

• It taught me. None of the explanations of Money Factor or Capitalized Cost I found made sense to me. Claude patiently let me dig into these topics until I understood them.
• It compared scenarios in seconds.
  • Which lease program was best? The 2026 or the 2027 Santa Fe? Gas or hybrid? A lower interest rate, or a higher rate with more lease cash?
  • Claude put all of this into one table I could actually compare.
  • Gas beat hybrid, because the hybrid's higher MSRP and smaller (or no) lease cash outweighed its better residual. The 2026 and 2027 gas models came out about even.
  • Claude's take on the Acura MDX deal was especially helpful. The low interest rate made it look like a no-brainer. But Claude factored in the MDX's low residual value and showed me that what looked like a great deal wasn't.
• It mapped the market. Claude could go through sites like cars.com and quickly map the range of discounts on a make and model. That gave us a yardstick. Some dealers were selling at MSRP, and the good deals were around $3,500 off. So when a dealer beat that, we knew we had the winner.
• It pushed back on ideas that seemed sensible to me but were dead ends:
  • Why not buy out the Acura and sell it to any dealer? Because then we'd pay sales tax on the buyout. It's better to stick with Acura or Honda dealers, who can buy the car straight from the lease so it isn't really a sale on our end. Honda also blocks third-party buyouts, so places like Carvana and CarMax couldn't buy it anyway.
  • Why not email every Acura and Honda dealer within 90 miles and take the best offer? Dealers don't reliably answer cold emails, many don't have a public email address, and I was way overcomplicating this. Call 3 nearby dealers and you're done.

Where the AI Failed Me

When it misread something, had incomplete information, or made a bad assumption, it gave me bad advice that sounded confident:

• The CX-90 PHEV "giveaway." The AI was convinced Mazda was practically giving away the CX-90 PHEV. It wasn't. The AI had read two separate lease options, one with a low interest rate and one with lease cash, as one very generous deal.
• The "inflated" cap cost. The AI was sure a dealer's quote had quietly inflated the cap cost. Not so. Claude had "forgotten" that Virginia makes you pay sales tax on the full car up front, and that tax is part of the cap cost. Once I had the real numbers, I could see how the dealer got there, and it was legitimate.
• The ad that wasn't a lease offer. It saw an ad on a cars.com page and concluded the dealer was running a very low interest rate lease offer. The ad wasn't a lease offer, so everything the AI built on that assumption was wrong.

Taming AI

• Keep asking. Say "I don't get it, let's discuss" until either (a) you understand it, or (b) the AI admits it was wrong all along.
• Have it explain things back. Don't tell it what the lease says. Have it read the lease and tell you, then check that it sees what you see.
• Build tools. I wrote leaseassist to pull lease deals from LeaseHackr and put them in a consistent format, so the AI didn't have to keep re-parsing them. The scraper behind it is ratefindr.js.

What Got It Done: Hustle

• AI gave us the information to recognize a good deal.
• Texting, calling and visiting dealers is what actually got the deal done.
• Leasing the new car: We got a great price on a car in the wrong color. It was silver, and Shira wanted blue. Shira kept shopping until she found a dealer who had the color she wanted and would match the deal.
• Selling the Acura: It took visits to more than one dealer. I eventually got a good offer, and then found another dealer who wanted the car badly enough to make an even better one. A Honda dealer bought our Acura lease, so the cross-brand buyout really does work.

Resources

• LeaseHackr
• Rate Findr Lease Calculator
• leaseassist: pulls lease program numbers from LeaseHackr's Rate Findr
• ratefindr.js: the Rate Findr scraper behind leaseassist

Behind This Post

The outline I wrote

- The setup:
  - Shira's Acura RDX lease was winding down
  - We wanted to lease a new car that wasn't an Acura
  - We wanted to wind down the Acura in the most beneficial way possible
  - This is our first car purchase in the age of agentic AI
- Huge Win: LeaseHackr's Rate Findr
  - This is like X-Ray visions for Lease
  - This isn't AI, but it's reliable data the AI can use
  - Leasing terms are program wide, so in theory, you can derive lease
    deals by cross-checking dealer offfers
  - LeaseHackr dramatically simplifies this: by being able to see what
    lease programs are beign offered, you don't need to rely on
    dealers and pre-game different leasing options versus trying to
    figure them out on the fly.
  - If you lease, then you *need* this tool
- Where AI Helped
  - It was able educate me: no one description of Money Factor or
    Realized Captilized Costs made sense. But, Claude patiently let me
    explore these topics until I understood them.
  - AI could sift through various scenarios and determine
    which was the best in a few seconds.
    - Which was the best lease program? The 2026 or 2027 Santa Fe?
      What about Gas or Hybrid? What about a lower interest rate vs
      higher interesate rate and a bigger discount?
    - Claude could put a table in front of me normalizing this
    - The clarification on the Acura MDX deal was especially helpful:
      the low interest rate made it seem like a no-brainer. But,
      Claude factored in the low residual value and showed me how what
      looked like a great deal, wasn't.
  - AI could crawl through sites like cars.com and quickly map the
    range of discounts on a make and model. That gave us a yardstick:
    some dealers were selling at MSRP, the good deals were around
    $3,500 off. So when a dealer beat that, we knew we had the winner.
  - The AI could push back on ideas tht made sense to me, but were in
    fact, dead ends:
    - Why don't we buy our Acura and then sell it to any old dealer?
      Doing so causes you to pay sales tax; best to limit to Acura or
      Honda dealers, then it's not a real sale.
    - Why not email all dealers in the area within 90 miles to see who
      wants our car, then take the best offer? Dealers don't relaibly
      answer cold emails; many dealers don't have a public email; and
      you're way overcomplicating this. Call 3 nearby dealers and
      you're done.
- Where the AI failed me: with misread, incomplete or bad
  assumptionns, it gave me conflidently sounding bad advice:
  - The AI was convinced they were practically giving away the CX-90
    PHEV. Nope: it read two lease variations, the first with low
    interest rate, and the second with lease cash, as being one
    super generous deal.
  - The AI was certain a dealer's quote had sneakily inflated the Cap
    Cost. Nope, Claude 'forgot' that VA requires paying sales tax on
    the full car up front and that's part of the cap cost it hadn't
    considered.
  - It saw an ad on cars.com page and derived fromn there that the
    dealer was running a super-low interest rate lease offer. That
    wasn't a lease offer, so the assumptions that flowed from this
    were all wrong.
- Taming AI:
  - Keep asking: "I don't get, let's discuss" until either (a) you get
    it, or (b) the AI reveals it was wrong all along.
  - Build tools: I created `leaseassist` to pull back and normalize
    lease deals from leasehackr so the AI wouldn't need to keep
    re-parsing it.
- What got it done: Hustle
  - AI armed us with information to know what a good deal looked like
  - But, texting, talking and visiting dealers is what actually got
    the deal done
  - On the lease of the new car: we got a great price on the wrong color car. Shira
    shopped around until she found a dealer who had the color she
    wanted and matched the deal
  - On the sale of the Acura: it took taking it to multiple dealers, but
    ultimately, I got a good offer and then found another dealer who
    wanted the car so badly, that they gave me an even better offer

Review: The Minuscule Mansion of Myra Malone

This is a Just the Facts (JTF) post: generated by AI from my own raw material (voice memos, session notes) and a short outline I wrote, then checked by me. What's a JTF?

Note: this post may contain spoilers.

I picked up The Minuscule Mansion of Myra Malone by Audrey Burges more or less at random and couldn't stop listening to it. The premise is clever, and some of the scenes fit together in really satisfying ways. Once I thought about it a bit, though, it all seemed too neat. Then I started a much heavier book and realized Myra Malone was exactly what I needed right now.

The Premise

• There's a real mansion and a miniature version of it, and the two are magically linked. Cool!
• Decorated rooms appear and disappear in the model. In the real house, music plays and nobody can tell where it's coming from.
• The book never tries to explain the magic. It just runs with it.
• As a blogger, I liked that Myra's blog is front and center in the story.

Scenes That Snap Together

• Scene one: Grandpa Lou turns up with a trunk full of Vienna sausages that nobody wants. He comes across as a doddering, out-of-touch old man, or maybe a hoarder.
• Scene two: years later, a blog post by Myra explains it. Lou grew up in the Great Depression, and for him, food was how you showed love. It wasn't dementia. It was love and childhood trauma.
• This is a book about magic and fairy-tale endings, and Burges still gives her characters real depth. Lou isn't the butt of a joke.
• Scenes also repeat across generations. Trixie's two great loves have hints of each other, and the lady of the house stays frozen while the world moves on around her.

Too Neat?

• In the moment, having all the pieces fit together felt great. Almost too great, to the point of being cheesy.
• With even a little thought, it started to feel like a pretty simple view of the world. I wanted it to be more complex than it was.
• Some characters feel one-dimensional. Rutherford is angry from the day he's born and spends his whole life fighting the house, and I never really understood why.
• The plot has holes. A well-known historic mansion has its miniature posted online, and only the owner recognizes it? Nobody else in town puts it together?
• The house keeps getting called a refuge, but we never really see anyone take refuge in it.

The Right Book at the Right Time

• After finishing, I started The Things They Carried by Tim O'Brien, a serious and profound book about the Vietnam War.
• With everything going on in my life right now, it wasn't the right book. Myra Malone was.
• Getting to escape into a world of magic and a perfect love story deserves more credit than I first gave it. That doesn't make it an unserious book. It just has its own kind of beauty and pleasure.
• And the love story between Myra and Alex is what you show up for. Who doesn't love a happily-ever-after?

Read it? Yes! Just don't expect it to survive close plot scrutiny.

Resources

• The Minuscule Mansion of Myra Malone (Amazon)
• The Things They Carried by Tim O'Brien (Amazon)

Behind This Post

The outline I wrote

1. I found the premise really clever: a physical world and a tiny world
   that are linked. Cool!
2. I was impressed with scenes that snapped together, like the Vienna
   sausages. In scene 1, Grandpa appears to be a doddering, out-of-touch
   old man. In scene 2, we learn that trauma has shaped his world, so
   what looks like hoarding is neatly put into context.
3. Everything snapping together perfectly felt great in the moment, but
   on even a little introspection it felt like a bit too simplistic a
   worldview.
4. It took me trying to read a book about Vietnam to realize: at this
   moment, I needed the magic and perfect love story of The Minuscule
   Mansion of Myra Malone.

Read it? Yes! Just don't expect it to survive close plot scrutiny.

Unclogging the Publishing Pipeline: Introducing Just the Facts Posts

This is a Just the Facts (JTF) post: generated by AI from my own raw material (voice memos, session notes) and a short outline I wrote, then checked by me. This post explains what that means.

I'm way behind on blogging. I've been trying to finish posting about our January trip to Hawaii, and it's November. So I'm trying an experiment: Just the Facts (JTF) posts. I supply the raw material and a short outline of the points I want to make. AI pulls the supporting details from that material, and I get a factual, bulleted post that I can actually finish.

The Problem

• It's November and I'm still posting about our January trip to Hawaii.
• My plan is to spend an hour a day on writing and the blog, but that hour keeps getting eaten. Something always seems to get in the way.
• Part of the problem is that I think real posts deserve depth and research, and that takes time I don't have right now.

The Obvious Choices

• Wait until I have free time. From what I can tell, that's never going to happen. Even when the current busy stretch ends, something else will probably come up.
• Have AI pick up the slack. At a minimum I'd lose my voice, and at worst I'd be publishing AI slop.
  • Voice: AI writing can sound great, but it won't sound like me, and its word choices can make it generic. When I do use AI for writing, I spend a lot of time making sure the result sounds like me.
  • Facts: AI fills in gaps, and it can present a set of facts in a way that isn't quite true.

The Middle Ground

• Combine three things:
  • Verbose raw content: voice memos, notes AI generated during work sessions, code, and resources worth pointing to.
  • A short, hand-written outline that lists the specific facts I want the post to cover.
  • An AI prompt that asks for a fact-heavy post built around the outline, using only the raw content.
• The outline is the key piece. Summarizing my memos on their own wasn't enough. I tested this with a book review: four memos about one novel came to about 200 lines of notes, when I wanted about ten bullet points. Asking AI to merge them got it down to 69 lines, which was still long and repetitive and full of spoilers. When I added a short four-point outline, the same memos produced a 44-line review that said what I meant.
• The hope is a Just the Facts post: one that covers the key information I want to publish, gets the facts right, and doesn't read like AI slop.

What Makes This Work

• Raw content is easy to come by. I already have plenty, and making more is simple.
  • Voice memos: I hit record on my phone and talk. FolderSync pushes the recording to S3, a script transcribes it, and Claude summarizes it into an org file.
  • Coding sessions: I end the session with "write this session up as detailed notes."
  • The Claude summaries of my memos are surprisingly good. They turn long, rambling recordings into something much more coherent than I remember saying.
• I take the key decisions away from the AI.
  • Don't guess at facts: use the raw content.
  • Don't guess at the key points: use the outline.
  • Don't guess at the tone: keep it informational and bulleted.
• Each step is bite-sized. Recording the raw content, drafting the outline, generating the post, and editing it are all small jobs. The creative decisions should take minutes, not hours.

What May Make This Fail

• The posts could end up incoherent. Will they make good reading? Maybe not, which is why this is an experiment. Still, a post that's factual and gets the information across has value.
• Even with all the guidance, I'm still asking AI to produce something automatically, so it could go off the rails.

This Is About Me, but It's Also About You

• Publishing just feels good. Hitting the publish button is a free dopamine rush, and it feels like I'm getting something done. Not publishing feels just as icky.
• So yes, JTFs help me feel unstuck.
• But this isn't just about pushing out content.
• A main goal of my blog is to give back to the system that has helped me. Many of the software tools I use every day are open source, and a lot of the advice I follow comes from things people published on the web.
• The blog is my way of giving back. That might be a short elisp function that makes life in Emacs a little better, or some hard-won advice we learned visiting a water park.
• JTFs are meant to be light on style but heavy on what matters: bits of wisdom other people can use.

Resources

• The JTF prompt
• jtfassist, the script that builds these posts
• generate.py, its generator

Behind This Post

The outline I wrote

1. I have a problem posting - I've been trying to finish posting about our trip to Hawaii in Januaray, and it's November!
2. The obvious choices:
   1. wait until I have free time - which by all indications will never happen
   2. have AI pick up the slack, but if I do that, I risk at a minimum
      losing my voice and at a maximum producing AI slop
3. Middle Ground: what if I combine three things:
   1. Verbose raw content: memos, AI generated notes from sessions, code, notable resources
   2. A short, hand written specific outline calling out the facts I'd like post
   3. An AI prompt asking to write a facts-heavy, outlined focused 'post' from these items
   4. The hope: I arrive at 'Just the Facts' post: a post that conveys
      key information that I want to publish, that's factually
      accurate and doesn't end up reading like AI slop
4. What makes this work:
   1. I have both existing raw content, and new raw content is easy to
      make. It's a simple as hitting record on my phone's voice app
      and talking, or ending a coding session with 'write this session
      up as detailed notes'
   2. I'm taking key decisions away from the AI: don't guess at facts
      (use the content),  don't guess the key points of the post (use
      the outline), don'rt geuss at the tone (keep it informational
      and bulletted)
   3. Each of these steps is designed to be bite sized: creating the
      raw content, drafting the outline, generating the post and
      editing. The creative decisions should take minutes, not hours
5. What may cause this experiment to fail:
   1. The posts may end up being incoherent
   2. As much as I'm guing the AI, I'm still asking it to create
      something automatically, so it may go off the rails
6. This is about me, but it's also about you
   1. Publishing literally feels good: hitting that publish button is
      a free dopamine rush, and I feel like I'm accompishing
      somemthing. Not publishing feels just as icky.
   2. So yes, JTFs help me feel unstuck
   3. But, this isn't just about pushing out content.
   4. A primary goal of my blog is to give back to the system that's
      helped me: many of the software tools I use every day are Open
      Source, much advice I follow comes from content published on the
      web.
   5. The blog gives me a platform for doing this. Whether it's short
      `elisp` function that makes life in emacs a little better, or
      some hard one advice we learned while visitng a water park,
      publishing let's me give back.
   6. JTFs are intended to be light on style, but heavy on what
      matters: bits of wisdom others can use.

Maui Adventure - Day 6: The Road to Hāna, Mile by Mile

[Composed 1/6/2026]

The Road to Hāna is Maui's most famous drive — cliffs, one-lane bridges, a waterfall around every bend — and from our base out on West Maui, we set off to see what all the fuss was about. Let's drive!

6:06 am  ·  Mile 0  ·  📍 map

Ladies and gentlemen, start your engines! Still in the hotel parking garage, and our roles are already set: Shira at the wheel, making forward progress, and me riding shotgun with our guidebook — Frommer's Hawaiʻi — open on my lap, hunting for reasons to stop.

6:29 am  ·  Mile 17  ·  📍 map

Rolling before sunup. That's not us being heroic — it's the guidebook's recipe for doing Hāna in a day: leave just before sunrise so you cover the easy opening miles in the dark and hit the real road as the light comes up. The price is missing the hotel's gourmet breakfast; peanut butter on a tortilla by headlight it is.

6:45 am  ·  Mile 26  ·  📍 map

The first official Hāna Highway sign — we're pointed the right way! Don't be fooled, though: it's another 15 miles before this ordinary road becomes the Road to Hāna everyone means.

7:08 am  ·  Mile 41  ·  📍 map

A preview of coming attractions. I've been nervous about this adventure since long before we got in the car. The Road to Hāna has a reputation — cliffs, hairpin turns, one-lane bridges — and I don't love heights. From the first time I heard of it, some part of me was shopping for reasons to skip it. Now here we are, and I'm eyeing the GPS, waiting to find out how much of that reputation is earned.

7:09 am  ·  Mile 41  ·  📍 map

Mile Marker 0 — we've arrived! Only took 41 miles to get to mile zero. From here the markers count the historic road: the stretch that, as the guidebook tells it, was cut in 1926 by "pickax-wielding convicts" carving a shelf out of the cliff's edge.

7:29 am  ·  Mile 44  ·  📍 map

Twin Falls — the guidebook's first stop, and somehow both the least and most rewarding stop of our day. Least rewarding because it was a letdown: pay-to-park, the rain turned it into a muddy mess, and it broke our momentum. In the moment, it felt like a tourist trap. Most rewarding because of who we ran into: a couple from Texas, comparing notes as to how we'd arrived here. They'd driven the entire loop the day before, skipped Twin Falls, and doubled back to see what they'd missed. Wait — the whole loop? I was intrigued. I quizzed them about the details, and as we left Twin Falls a seed had been planted.

8:01 am  ·  Mile 46  ·  📍 map

One of our first single-lane bridges. And if this is what I'd been dreading, I figured, we've got this. Slow down, respect the yield sign, then slow down some more — a couple of bridges in, I'd relaxed. Of course, the bridges were only one piece of the reputation.

8:12 am  ·  Mile 49  ·  📍 map

TRUCK! OK, maybe I relaxed too soon. A car coming the other way is a polite negotiation; a full-size truck is a different math problem. The same strategy holds — slow way down and wait your turn.

8:17 am  ·  Mile 50  ·  📍 map

Check out these views! With the bridges conquered, my other worry was the hairpin turns and sheer drop-offs. Fifty miles in there had been plenty of wild turns, but thankfully no sheer drop-offs. It should be noted that Shira, not once in planning or executing this adventure, had any concerns about the drive. After years at the wheel for sketchy drives, it takes a lot more than a few one-lane bridges to faze her.

8:39 am  ·  Mile 54  ·  📍 map

The overlook at Kaumahina State Wayside. Nice views. Nice bathroom. Nice bird. Just nice.

9:03 am  ·  Mile 56  ·  📍 map

The black-sand beach at Honomanu Bay. Nothing to pay and hardly a soul around — a few local surfers doing their thing, one other set of visitors, and otherwise the cove was ours. Just black sand and surf. There's also a small shrine tucked in here — a living one, it turns out — but that's a story of its own, coming soon.

9:49 am  ·  Mile 63  ·  📍 map

A pretty typical view from a one-lane bridge: lush green and a picture-perfect jungle stream. And "typical" is the marvel of this road — it's just what's out the window the whole way. You don't have to hunt for the beauty here; it finds you.

10:10 am  ·  Mile 65  ·  📍 map

The perfect waterfall at Puaʻa Kaʻa. It had been raining in bursts all morning — one of the little things nagging at an otherwise glorious day. Then it clicked: the rain wasn't marring anything. It was powering some of the best parts. These falls don't run this full and this gorgeous without it.

10:21 am  ·  Mile 66  ·  📍 map

A mile on, another beauty: Hanawī Falls. Proof it would have been safe to skip Twin Falls: this is the very thing they were charging for, running free and gorgeous a few feet from the car.

10:29 am  ·  Mile 67  ·  📍 map

You'd think a truck like this filling the frame would be a high-pressure moment. Shira played it cool the whole time — never once rattled. She just waited her turn and kept on truck'n.

10:33 am  ·  Mile 69  ·  📍 map

Coconut Glen's! Genuinely tasty coconut ice cream. My one gripe: the eco-friendly coconut-shell bowl actually cost more than the throwaway cup. C'mon, man — your whole brand is coconuts. How are the shells the upsell?

11:23 am  ·  Mile 71  ·  📍 map

Our first major stop of the day: Kahanu Garden — "the garden with no flowers." That one earned a post all its own. Read it

12:42 pm  ·  Mile 74  ·  📍 map

Our second major stop of the day, on a timed reservation: Waiʻānapanapa State Park — the black-sand beach, a coastal hike, a gecko, a mongoose. Also its own post. Read it

3:16 pm  ·  Mile 77  ·  📍 map

And we're in Hāna! And to my own surprise, I have almost no pictures of the town. Is it because Hāna really is that tiny? Because I was so drained from the amazing journey so far? Because we still had one essential — and possibly stressful — question ahead: do the entire loop, or turn back? I honestly can't say. What I can say is this: the guidebook warns about the visitors who blow into Hāna and wonder what all the fuss is about — and with barely a photo, I feared that might be us.

3:34 pm  ·  Mile 77  ·  📍 map

A fantastic Thai dinner from a food truck in Hāna — and the answer to that fear: proof we'd made at least one real connection with the place. Vietnamese in a food court, now Thai from a food truck — Hawaii keeps serving up excellent meals in the most unexpected places.

4:11 pm  ·  Mile 83  ·  📍 map

Maybe it was the small gamble on the Thai food truck telling me to go all in — or maybe it was the seed that Texas couple planted back at Twin Falls, finally sprouting. Either way, I was now fully convinced: we should do the full loop. My yes was my completist nature cheering the decision on; Shira's was more the yes of someone planning to prove it a bad idea.

4:17 pm  ·  Mile 84  ·  📍 map

Wailua Falls — right off the road, and oh so pretty. And there's that crew of young folks again, goofing off — we'd leapfrogged this same friendly bunch a couple of times on the back half. Oh, it must be magical to do this road in your twenties, without a care in the world.

4:39 pm  ·  Mile 88  ·  📍 map

The small churchyard where Charles Lindbergh is buried. There's a lot more to this quiet spot than one grave — that's a post of its own, coming soon.

4:54 pm  ·  Mile 90  ·  📍 map

The guidebook flatly notes that rental-car companies warn you off this road — and right here, we got our first glimpse of why. What got to me was the not-knowing: does the road get rougher from here? And if it does — then what? With maybe an hour of daylight left, the odds of clearing the sketchy stretch before dark were slipping, and getting stranded out here after nightfall — exactly what everyone warns against — had gone from unlikely to uncomfortably plausible. The lone upside if it came to that: Shira would get to be right.

5:17 pm  ·  Mile 96  ·  📍 map

The road serves up a run of surprises. One: the dirt gives way to pristine, freshly-laid pavement — honestly the nicest we'd driven all day. Two: there's a catch — it's a single lane threaded with blind crests, and you come over each one with no idea what's barreling the other way. Three: this, it turns out — not the cliffs or the bridges I'd braced for all day — is the scariest driving of the whole trip. Four: look around — the jungle is falling away, and a whole new landscape is opening up.

5:24 pm  ·  Mile 99  ·  📍 map

It's like we've crossed onto a whole new side of Maui — and, actually, we had. Wide-open hills, sweeping golden vistas: I hadn't expected any of it, and it turned out to be one of the trip's real gems. We'd rounded the back of the island, out of the wet, windward jungle and onto Haleakalā's dry, leeward flank.

5:55 pm  ·  Mile 106  ·  📍 map

As the sun set, the road opened back up to two full lanes — right on schedule. Out before sunrise, home after sunset: exactly the plan, and amazingly, we'd hit it almost to the minute.

6:50 pm  ·  Mile 137  ·  📍 map

The back half of the loop is complete, and near Kahului we rejoin the Hāna Highway — the same sign we flagged this morning back at Mile 26 — for the final stretch home. We can't help but reflect on how much went right today: the "bumper-to-bumper rental-car parade" the guidebook warned of never showed, the rain turned out to be a gift, and our timing fell right into place.

7:32 pm  ·  Mile 164  ·  📍 map

Made it! Amazing driving, Babe!

Doing Hāna in a day can go wrong in a dozen ways, but between our planning, my insatiable curiosity, Shira's driving and her careful watch on the clock, and a healthy dose of good luck, we'd nailed it.

Two more stories from this drive are still to come — stay tuned.

The route

The full loop — about 164 driving miles, from West Maui out to Hāna and back around the dry south side of the island.