Let's talk optimal weight and optimal nutrition for backpacking food and resupplies

tl;dr: Eight years ago I posted a breakdown of cheapest backpacking calories by cost using government nutrition and purchase data. I've had some recent free time (while on parental leave), so decided to update things to be more helpful for optimizing food weight for resupply strategies. I have some general example/take away 4-day resupply lists below.

Introductory remarks

I'm aware this has come up a few times. Most recently the largest discussion on r/ultralight was based on some gear skeptic video about caloric density. But we can honestly do better than that! Let's use the vast quantity of data the government actually uses to inform nutrition and purchase decisions. With real data on hand, this becomes a relatively easy optimization problem to solve: how do you optimize your food nutrition and calorie density based on what you're trying to achieve (constraints)?

What is linear programming

Linear programming is an optimization technique used in economics (my main use of it), logistics, supply chain, finance, and other sorts of things that often benefit from optimization. Because it's linear, you can do matrix math super fast and efficiently to solve the problem! Basically: you take the derivative of the function, set it equal to zero, and that solves for the local maximum or minimum (the optimal solution).

The core idea is that you have a lot of decision variables (in this case, how many grams of each possible food in a supermarket you might want to pack), a linear objective function to minimize or maximize (e.g. minimize total weight -- so you sum across all possible food variables), and a set of linear constraints that the solution must satisfy (e.g. I don't want anything with chocolate).

For a resupply optimization, the formulation looks like this:

• Objective: minimize sum(grams of each food selected)
• Constraints:
  • Total calories must fall within ±3% of your daily target × days
  • For each nutrition target you care about: the sum of that nutrient across all foods must meet the minimum
  • For nutrients with safe upper limits (iron, calcium, vitamin A): sum must stay below the threshold
  • Each individual food is capped at some maximum fraction of total calories (the diversity control)
  • The total number of distinct foods selected must be greater than some food diversity threshold

You can then solve the system of liinear equations (matrix math) to find the combinations and quantities that satisfy all constraints at minimum total weight. This is different from just sorting foods by kcal/g, as sorting will only tell you the best single food (it's oil, obviously). Solving an LP matrix tells you the optimal combination when multiple constraints interact simultaneously!

The food and nutrition data

I used two USDA datasets:

USDA WWEIA (What We Eat in America) is a large nationally representative dietary survey. Respondents record everything they ate over 24 hours; USDA maps those responses to a food database with per-100g nutritional content for 26 macros and micronutrients. The database covers ~3,000 food categories -- generic types rather than branded products, so "Cookie, with peanut butter filling, chocolate-coated" rather than a specific brand.

USDA FoodAPS is a household food purchase survey with real transaction prices. I joined the two datasets to get estimates on actual purchase prices per 100g for foods you might consider for backpacking.

The combined dataset also provides information about where food is available based on purchase history (grocery store, gas station, etc.), whether it requires cooking, whether it's shelf-stable, and how long it lasts without refrigeration.

What an optimizer tool like this optimizes

I ran some optimizations for myself as a general baseline. All examples below assume: 134 lbs, 4-day resupply, 3,000 cal/day, grocery store, no-cook, shelf-stable only.

The two main controls that change the output significantly are:

1. Nutrition profile -- whether the solver must hit DRI targets. "None" means calories only are optimized; "optimal" activates targets for protein, fiber, calcium, iron, vitamins C/D, potassium, zinc, magnesium, folate, B12, and choline.
2. Diversity -- a minimum number of distinct foods the solver must select. Below I show ≥6 foods and ≥18 foods.

6+ foods, no nutrition constraints:

Food	Weight (g)	Calories	Cal/g
Walnuts	340	2,484	7.30
Chocolate candy, sugar free	274	1,620	5.91
Brazil nuts	303	2,000	6.60
Pine nuts	301	2,025	6.73
Macadamia nuts	279	2,000	7.16
Pecans	227	1,701	7.50
TOTAL	1,728 / 3.8 lbs	11,850	6.86

Nuts dominate. The calorie density ceiling on a shelf-stable 4-day no-cook carry is ~7.5 kcal/g (pecans), and the solver averages 6.86 kcal/g across the basket. One piece of sugar-free chocolate fills the 6th required slot (it's denser than most snacks at 5.91 kcal/g). 3.8 lbs total. Not bad, but I feel like I personally wouldn't be the happiest camper eating ONLY nuts the entire time, although I recently did pursue a similar strategy on an 8-day, 30+ mile water carry on the hayduke

6+ foods, optimal nutrition:

Food	Weight (g)	Calories	Cal/g
Walnuts	340	2,484	7.30
Pine nuts	301	2,025	6.73
Macadamia nuts	279	2,000	7.16
Pecans	227	1,701	7.50
Oatmeal cereal, baby food, dry, instant	227	894	3.94
Milk, dry, not reconstituted	219	792	3.62
Brazil nuts	167	1,103	6.60
Vegetable mixture, dried	70	228	3.29
Salmon, canned	68	93	1.36
Sweet potato chips	56	298	5.29
Potato chips, lightly salted	55	308	5.59
TOTAL	2,010 / 4.4 lbs	11,928	5.93

Adding full nutrition targets costs 0.6 lbs. The solver still anchors on nuts for base calories and adds lightweight nutrient carriers: baby oatmeal (fiber, about $1.50 a pouch and an actual thru-hiker staple! Hust ignore the 'baby' component. Not sure what USDA throws that on there, but they do!), dry milk powder (calcium, B12), canned salmon (vitamin D, which is apparently the hardest nutrient to hit although you'll be outdoors, so supplementation probably doesn't matter? But maybe with sunshirts...), dried vegetables (vitamin C, vitamin A), sweet potato chips (potassium). 4.4 lbs.

18+ foods, no nutrition constraints:

Food	Weight (g)	Calories	Cal/g
Pine nuts	120	810	6.73
Potato chips, lightly salted	119	667	5.59
Almonds, chocolate covered	119	666	5.60
Dark chocolate candy with nuts	119	667	5.60
Cookie, peanut butter filling, chocolate-coated	119	667	5.62
Cheese flavored corn snacks	118	667	5.67
Chocolate candy, sugar free	113	667	5.91
Mixed nuts, roasted	108	667	6.20
Filberts / hazelnuts	106	667	6.28
Almond butter	104	666	6.42
Peanut butter, reduced sugar	103	667	6.50
Brazil nuts	101	666	6.60
Macadamia nuts	93	666	7.16
Walnuts	91	667	7.30
Pork cracklings	91	519	5.69
Pecans	89	667	7.50
White potato chips, lightly salted	44	247	5.59
Peanut butter, reduced sodium and reduced sugar	10	60	6.19
TOTAL	1,884 / 4.2 lbs	14,515 (est)	6.18

Forcing 18 distinct items adds 0.4 lbs vs the 6-item version. With diversity constraints, the solver distributes calories more evenly across the 18 foods. You can see each item ranging from 89-120g. At 18+ foods the pool expands to include nut butters, chocolate-coated items, and pork cracklings (at ~5.7 kcal/g. Basically just dried pork fat? Don't ask me, I'm a vegetarian...). Still primarily a nut carry, though.

18+ foods, optimal nutrition:

Food	Weight (g)	Calories	Cal/g
Oatmeal cereal, baby food, dry, instant	227	894	3.94
Milk, dry, not reconstituted	184	666	3.62
Sweet potato chips	156	825	5.29
Pine nuts	120	810	6.73
Potato chips, lightly salted	119	667	5.59
Chocolate candy, sugar free	113	667	5.91
Mixed nuts, roasted	108	667	6.20
Filberts / hazelnuts	106	667	6.28
Almond butter	104	666	6.42
Peanut butter, reduced sugar	103	667	6.50
Brazil nuts	101	666	6.60
Macadamia nuts	93	666	7.16
Walnuts	91	667	7.30
Pork cracklings	91	519	5.69
Salmon, canned	90	122	1.36
Pecans	89	667	7.50
Crackers, butter	72	370	5.11
Vegetable mixture, dried	46	150	3.29
Oatmeal with bananas, baby food	41	160	3.92
Cheese flavored corn snacks	40	225	5.67
Salad dressing, NFS	20	87	4.30
Cashew butter	10	60	6.08
TOTAL	2,243 / 4.9 lbs	12,222	5.45

4.9 lbs. Adding nutrition to the 18-item list costs 0.7 lbs. The nutrition constraints pull in oatmeal and milk powder up front (they're binding on fiber and calcium), expand sweet potato chips (potassium), and tuck in canned salmon and dried vegetables for vitamin D and C. Also note the 20g of salad dressing, which I guess is needed because of choline.

Summary:

Scenario	Weight	Cal/g
6+ foods, no nutrition	1,728g / 3.8 lbs	6.86
18+ foods, no nutrition	1,884g / 4.2 lbs	6.18
6+ foods, full DRI nutrition	2,010g / 4.4 lbs	5.93
18+ foods, full DRI nutrition	2,243g / 4.9 lbs	5.45

The weight spread across all four scenarios is 1.1 lbs. Adding full DRI nutrition targets costs 0.6-0.7 lbs; going from 6 to 18 required foods costs 0.4-0.7 lbs. The drivers are similar in magnitude and small overall.

The dominant variable that impacts the optimal resupply opportunity is which food group the solver can access. Nuts at 6.6-7.5 kcal/g are the calorie-density ceiling for shelf-stable grocery foods and they're also nutritionally dense! Walnuts have choline, vitamin E, omega-3s; brazil nuts are high in selenium and magnesium; pine nuts carry decent iron. I'll be honest, though -- I'm not sure how much nut diversity I run into in various places, as I mostly can choose between different flavors of peanuts, cashews, and almonds at gas station resupplies, but I'll let you be the judge!

For a 4-day resupply, if you run it with full nutrition constraints on, the penalty is 0.6 lbs and you get an actual balanced diet! The optimal backpacking food list appears to be: mostly nuts, with a few ounces of oatmeal, milk powder, and salmon to cover gaps.

The web tool

I've moved the optimization model into a web interface at trailpeaches.com/lightest-backpacking-food-optimizer/.

You can constrain the optimization by store type, no-cook requirement, dietary resitrctions, etc. Slide the diversity control to set a minimum food count. Set a daily calorie target and trip length. Toggle the nutrition profile from none to reduced to full DRI. Block individual foods you won't eat. Click any food in the results to cap its weight and force the solver to compensate with other foods. Export the optimized list to CSV.

The underlying model allows you to solve the same thing described above. It's the LP running on the server and returning results through a browser interface. If there's specific additional kinds of constraints or other things you think I should correct/change, let me know!

There are certainly some data categorization/cleaning errors in the FoodAPS and WWEIA data set that I likely missed, so point 'em out =)

Happy hunting and gathering out there!

tl;dr: The Strava Heatmap is one of the most invaluable topo layers I use. It takes some effort to setup, but I think it's worth it.

Whenever I end up hanging around and hiking with other hikers in an area with off-trail navigation or questionable trail conditions where trails vanish into California's Chaparral, I'll find myself making a statement like: "oh, there's a Strava track over there."

Basically, I rely on the Strava Heatmap layer as one source of beta for traversing areas that aren't necessarily "the most beaten track." Maybe the heatmap makes me lazy when it comes to planning some things in advance, but one of the things I cherish most about hiking and long distance trails is the freedom and flexibility that comes from being able to make changes on the fly. Give me worthwhile alts over "the red line" any day.

Example of the value of the strava heat map for route planning

I mean, maybe there's an interesting thing you just learned about that you want to check out? Maybe trail conditions have turned to shit and you need to bail? (OMG, so many times!) Maybe you want to spend an extra day in the backcountry and add a 30 mile loop? Whatever the case, it's worthwhile to have a handful of tools at your disposal to make informed decisions. The Strava Heatmap is one of them that I realize very, very few hikers know how to make use of.

That might be because, unfortunately, it's not trivial to get the Strava Heatmap into a usable form on your phone. I mainly use Gaia GPS to implement, although you can also load it into CalTopo or any map app that supports custom tile layers.

Setting Up the Strava Heatmap

What you'll need

• A Strava account (free; you don't even need any uploaded activities)
• A GitHub account (free; this is where the proxy code lives)
• A Cloudflare account (free; this is what actually runs the proxy)

Why a proxy at all?

Strava's heatmap tiles require authentication for use. Map apps like Gaia can't handle that natively. The solution is to create a small proxy server that sits between the mapping app and Strava, handles the authentication, and serves the map tiles as if they were a public map tile source. The proxy server can be setup to run for free as a Cloudflare Worker. Because the amount of output is limited from the free version, it makes sense to set it up yourself for your own use, rather than paying a bunch of money to host it publicly for everyone.

The setup at a high level:

1. You Fork my strava-heatmap-proxy repo to your own GitHub account (one click, no code. Original credit to Erik Price for pulling together the proxy code and figuring this out)
2. You copy and paste your Strava athlete ID from your profile URL, and your session cookie from your browser's dev tools while logged in to strava.com
3. You create a Cloudflare API token and a small key-value store (KV namespace) for caching your strava credentials on Cloudflare
4. You add a handful of copy-pasted login and credential information fields to your GitHub repo: your Cloudflare credentials, Strava ID, session cookie, and KV namespace ID
5. You click the "Deploy to Cloudflare" GitHub Actions workflow in Github
6. Add the resulting tile URL as a custom map source you can add to Caltopo or Gaia GPS at about 50% opacity

Because the subreddit is no good for a full walkthrough with screenshots (images are trickier), I've posted the full guide with screenshots and every step on my website: https://trailpeaches.com/strava-heatmap-gaia/

One thing to know going in: the Strava session cookie expires periodically (every month or two). When it does, the tiles go blank and you repeat step 2 through 5 to refresh it. Minor annoyance, not a dealbreaker, and should definitely be done before any sort of long hike.

Once you've gotten the tile server running, you get the full global heatmap as a toggleable layer. I typically keep it at 40-50% opacity over a topolayer in Gaia, but may drop it down or up depending on the situation. For off-trail route-finding, even a faint trace through an otherwise blank cliff band tells you the terrain probably goes and someone's been there and recorded it. Not perfect, but genuinely useful.

Happy to answer questions on the setup. The instructions in the guide have screenshots for every step, but it is a little more invovled than I personally would prefer.

I scraped 233 r/ultralight lighterpack URLs made up of shakedown requests, trip reports, and gear discussions then ran an analysis. I supplemented the analysis with 88 community wiki reference lists for a point of comparison. The parsing and categorization isn't perfect, but the overall takeaways seem fairly robust.

Median base weight across 233 scraped posts is 11.6 lb / 5.2 kg. Only 33% of lists met the traditional UL threshold (<10 lb / 4.5 kg).

Tier	Threshold	Lists	%
SUL	< 5 lb / 2.3 kg	4	1.7%
UL	< 10 lb / 4.5 kg	73	31.3%
Light	< 20 lb / 9.1 kg	146	62.7%
Traditional	20 lb +	10	4.3%

Interquartile range: 9.2 to 14.2 lb — most packs cluster solidly in the light-but-not-quite-ultralight zone.

The wiki reference lists are noticeably lighter: 8.7 lb / 4.0 kg median, 66% hitting the UL threshold. The gap between the wiki's curated showcase and actual subreddit posts is about 3 lb.

What's actually in these packs?

Frequency of specific items across the 233 scraped lists (unique lists, not total item mentions):

Item	Lists	%
Extra socks (any)	200	85.8%
Insulation layer (down/puffy/fleece)	175	75.1%
Toothbrush	169	72.5%
Rain jacket	149	63.9%
BIC Mini lighter	120	51.5%
iPhone (any model)	109	46.8%
SmartWater 1L bottle	103	44.2%
Garmin inReach Mini	81	34.8%
NeoAir XLite sleeping pad	81	34.8%
Darn Tough socks	80	34.3%
Sawyer Squeeze	70	30.0%
Durston X-Mid	65	27.9%
TOAKS 750ml Ti pot	56	24.0%
BRS-3000T stove	48	20.6%

Quilts outnumber sleeping bags roughly 2:1 (50% vs 28% of lists). Quilt median: 607g / 21.4 oz. Sleeping bag median: 756g / 26.7 oz. Sleeping pad median: 369g / 13.0 oz.

Where the weight is vs. a reference UL benchmark:

Category	Community median	Benchmark	% of lists heavier than bechmark
Hygiene	179g / 6.3 oz	100g	77%
Sleep system	~1,123g / 39.6 oz	900g	70%
Shelter	~850g / 30.0 oz	600g	70%
Clothing + rain	666g / 23.5 oz	650g	51%
Pack	790g / 27.9 oz	800g	49%
Cooking	293g / 10.3 oz	300g	49%
Electronics	423g / 14.9 oz	450g	45%

Pack and cooking are the categories where r/ultralight has actually figured it out. Sleep system and shelter are where the most room remains.

Methodology: Pulled lighterpack URLs from r/ultralight's RSS feed. Base weight uses lighterpack's worn and consumable flags (worn weight = shoes + on-body clothing, excluded). Brand and item counts search item name + description fields; each list counted once per list.

Full write-up with per-category breakdowns, additional brand frequencies, and the full, not-parsed lighterpack dataset for download, plus much, much much better content presentation: https://trailpeaches.com/2026/08/11/lighterpackdata/

Happy to dig into any category or answer methodology questions.

TL;DR: I did 200 miles of the Hayduke in 2025 with a friend, went back and finished the rest this spring as a LASH. I think it's probably the most beautiful long-distance route in the country. Some notes below for anyone planning a trip. I'm also making an app to consolidate the resources I leaned on for the HDT into something that I find to be a little better for water planning and on-trail info lookup. It's available for beta testing to those that are curious(details at the bottom).

The basics

I took two seasons to finish it. In 2025 I hiked the first 200 miles from Moab to Hanksville with Physics (we met on the CDT). I didn't have time to take off to do the full thing in '25. This spring I picked up where we left off and went the rest of the way with Tangent (+ a few fun add-ons e.g. 80 miles across the Paria Plateau), who I'd suffered through the Grand Canyon with on a different trip in 2023.

The route is best described as a sequence of deep canyons and imposing rock walls loosely connected by jeep trails, or what someone could optimistically refer to as roads.

2025 -- Arches to Hanksville (10 days)

The trail starts at Arches. We flew into Canyonlands Field Airport outside of Moab and walked off the runway. For all its grandeur, Arches was somehow one of the least spectacular sections of the Hayduke. Sandy washes steal energy with every step. You crest out of them onto solid rock and stare out into endless fields of erosion, never quite sure if what's ahead of you is a 300-foot cliff or the ledge system you're actually looking for.

The Colorado River corridor after Moab looks great on a map. The river is several hundred feet below you the whole time, accessible maybe every 30 miles. It's a dry endeavor.

Dark Canyon is the reward. No forks for miles. The cliffs cast shade in all the right places, water is plentiful, and the toads race you from bank to bank. After everything before it, Dark Canyon felt like the trail saying sorry. This section is one of the top highlights of the HDT, and worthy of some dedicated backpacking trips.

2026 -- Hanksville to Zion (36 days)

I jumped back on in late March, this time with my friend Tangent (who started the trail this year a couple weeks earlier). From Hanksville it's Capitol Reef, walking the Escalante River, Bryce, Paria Plateau, Jacob Lake, the Grand Canyon, and a 30-mile road walk into Colorado City before the final push to Zion.

Some things that happened along the way:

A bison charged by us in a slot canyon on the Henry Mountains Low Route (I did the low route to get into better tail shape for the start). I hear there's a herd of only around 200-or-so in the Henrys. So this was both spectacular and terrifying.

As we were trudging through the Escalante River nearing sunset, Tangent suggested scrambling up the steep route to camp on top of Stevens Arch where "there are defiitely campsites." We had ~20 minutes until sunset to do close to 1000 feet of upward scrambling. We ended up on a pretty slanted bivy site for the night surrounded by prickly vegetation. Absolutely stunning view of the arch at night, though.

In Kanab we ended up with an extra day compared to an original plan which had been to do some fun stuff through Buckskin Gulch. Due to some chest-deep cesspools in Buckskin, making the slot canyon route ill-advised unless we wanted to grab dry bags and deal with a bunch of additional logistics, we decided a nice Zero in Kanab sounded perfect. However, we applied to the Coyote Buttes North lottery on a whim -- the permit for "The Wave," which runs a daily lottery at $6 per application. Tangent had won it once before, roughly a decade ago. I won it on my first try this year! Instead of a Zero, we "had" to hit Coyote Buttes North. This also meant that what would have been a relaxing morning in town was instead spent running around Kanab finishing chores at speed. Coyote Buttes North is absolutely worth the hype! Some of the most magical rock I've seen.

The Paria Plateau stretch we added instead of the super chill trail to Jacob Lake from State Line was long, dry, and exposed. Beautiful in a desolate way. We also got snowed on! Yay! The descent down the Vermilion Cliffs leads to what "trail" markers refer to as the Honeymoon Trail -- a route so thoroughly reclaimed by desert that the most prominent features were BLM signs announcing that motor vehicle use was prohibited. If ever a trail needed a motor vehicle, this was it. Absolutely beautiful views of the Vermilion Cliffs along this "trail," though!

The Grand Canyon

We had a fun 12-degree Fahrenheit night before descending into the canyon. We thought it was going to be a cold night, but DAMN! Gotta say, though, that my Borah Bivy and Katabatic Alsek performed comfortably!

I love the Grand Canyon, and really feel like this is a great centerpiece for the trail, although I know a lot of folks feel pretty differently because it's so different from the rest of Utah. We dropped into the canyon via the typical route from Nankoweap Trail. It's technically Marble Canyon until the Little Colorado River confluence. The first day in the Canyon we found a New Zealand raft crew parked on a beach who fed us beer and produce and generally made us feel like the hike was going fine.

From the river the trail climbs onto the Tonto Platform, a cliff band roughly 1400 feet above the Colorado that contours around the side canyons and then the side canyons of those side canyons for about 90 miles. You might travel 15 trail miles to make 3 river miles of progress. It is slow, spectacular, and completely absorbing!

It turns out every time I hike in the Grand Canyon the North Kaibab trail is closed. Which meant that rather than taking that trail, we had a lovely 8-day food carry across the Tonto to South Bass, then a river hitch, and then up North Bass. Fortunately, we had an actual Zero on the South Rim before jumping back into the Canyon!

Unfortunately, the South Rim had no water, so we couldn't shower, or do laundry. There was also wind storms contributing to a public safety power shutoff during our zero, so there also wasn't power in the campground and we couldn't send packages, or buy refrigerated food from the store in the village. Helluva zero!

Crossing the Colorado the second time (to get to the North Bass Trail) required shouting at raft groups from a ridge until someone agreed to pick us up downstream. It took a few tries. A crew motored over, handed us life jackets like picking up hitchhikers on a float trip was routine, and dropped us upstream (thus the need for the motor) a handful of minutes later. We then hiked 30 trail miles up and then through Saddle Canyon. The river rafters kept pace with us perfectly by floating around 10 river miles downstream, and we ran into each other again over near Deer Creek Falls. The river folks were great, and thankfully contributed more calories to my well-being which was greatly appreciated.

It turns out I had accidentally left 4000 calories of my resupply in one of the Ammo boxes in Mather Campground and hadn't noticed until Day 2. This would have otherwise been a pretty intensely miserable 8-day food carry with not enough food. A completely different raft group, passing on the river, tossed four beers to us from their raft without stopping. Somehow I caught all of them ;)

Saddle Canyon on the north side is some of the most technically demanding hiking on the Hayduke. It's harder than all the other sections, and not even that many miles! But there's some 30-foot dry falls with high-exposure bypasses, requiring contouring around and bushwhacking on hillsides of loose sand and gravel. Eventually you reach a series of waist-deep plunge pools with 20-foot sandstone water slides, which are not optional. While not necessarily fun, they aren't too bad and they go! And that's awesome!

Water

Definitely worth having a carrying capacity of 8 liters for this thing! I had a handful of nicely placed caches, so never needed more than 6 liters, but also was able to hike most of the trail in a pretty mild year, pretty early season.

The Willow Springs source in the stretch between Kanab Creek and Hack Reservoir is radioactive -- not a figure of speech, so is probably the only "you need to cache on the Hayduke" point. While Mac at HalfwayAnyhwere says you don't need to cache, I think this one point is the exception to that statement.

Set up a cache at Hack Reservoir (~ 36.61942, -112.84387) before your hike. It's 18 miles from Kanab Creek to the cache and another 20 to the next reliable water after that. This is the one stretch I thought where getting the math wrong can have considerable consequences because there's poor tree cover and it's quite hot all through this section with few (if any) folks around to yogi water from. There's also some long water carriers near Lockhart Road, but that's harder to cache for.

End of the line

Colorado City at the finish has a complicated reputation. The FLDS seem to be a very, very tiny minority (like 0.5% to maybe 1%). Instead, the people are kind and the supermarket (Bee's) has a salad bar! There's a hardware store, a thrift shop, and a ton of poverty alongside wealth in this little town. After 700 miles of desert, the salad bar at Bee's felt like something my body had been craving for weeks. Also, shout out to the worker at the Subway in town that sold me a $4.80 footlong Veggie sub! I haven't had a 5-dollar-footlong in over a decade?

The Hayduke's reputation makes it sound punishing. It's not as bad as its reputation? Or at least, it isn't more than any serious off-trail desert route that requires some planning and logistics. The navigation requires genuine attention, the water carries require planning, and the Grand Canyon earns its reputation on its own terms. But it's also genuinely joyful out there in a way that's hard to explain until you've spent a few weeks moving through canyon country under your own power.

I'd go back tomorrow.

Post Trail Melancholy

I loved the Hayduke! It's not my favorite long trail (that's the CDT), but I think this might be the most beautiful long thing I've hiked (although the AZT is also fricken awesome and way less involved for planning).

Since getting back from trail, I've been able to spend some time pulling my photos, writing, and trail notes together into a little app that others may also find useful. I'm glad I've been able to relive this trail a tiny bit by going through my thousands of photos and fixing up my route data.

While planning and hiking this thing over the past couple years, I kept wishing the resources I was juggling lived in one place. Jupiter's guide, Jamal Green's water data (AcrossUtah), the guide book, skurka's notes, my own notes, mileage tracking, all of it. So I started building an app to pull it together rather than the janky spreadsheets and far too many waypoints and overlapping tracks that had an unexpected tendancy to occasionally crash Gaia. This thing, to me, is a preferred way to bring together offline topo maps, water sources with condition reports, waypoints, alternates, elevation profiles, and a way to actually keep tabs on distances to the next water or camp without doing mental math or google sheets math against three PDFs, or annoyingly tracing gaia GPS tracks to the right waypoints.

To be clear, this is a nice-to-have, not a necessity or replacement for planning or the power of Gaia's layers. People have hiked the Hayduke for decades without the amount of Hayduke data that's out there and will keep doing so. But I would have liked having something like this for the water planning and the on-trail "how far to the next source" lookups, and I suspect some of you would feel the same.

The app is in "beta" right now, and may stay that way forever. Some of my tracks may still feel a little rough (more cleaning!), although I've sunk a bunch of hours into cleaning up most things. If you want to check it out and flag issues, cool! I'm looking for testers, especially anyone planning a Hayduke trip or just curious to poke at it. It is absolutely not ready to be your sole navigation or planning resource on a real HDT hike, but it's a solid planning companion and every bit of feedback helps. iOS is on TestFlight, Android is through Google Play testing. Links to check it out can be found here.