How Parsnip unlocks the tech tree of cooking
A tale of two pivots
Dear Friends of Parsnip,
Hello from Substack! It’s been a while since our last update, and this is a longer read than usual. It’s worth it, though, and includes a demo of our latest iOS app. 😊
In the spirit of “building in the open”, we’ll be writing this newsletter on Substack from now on. Parsnip is a universal product with a compelling story, and we have everything to gain by sharing our mission, vision, technology, culture and more with our users and future team. These memos also help our future investors invest in lines, not dots.
A bit of history
In our friends & family memo, we proposed building recipes that teach cooking skills as you go. The idea was to build a tech tree of cooking skills that shows you what you can learn and also tracks your progress as you go—essentially turning your cooking journey into a giant RPG-style game.
We implemented this approach in our beta targeting a demographic of 20 to 35 year olds (Gen Z and late millennial), with an interest in cooking who found it hard to learn. There are many of these folks in r/cookingforbeginners and similar subreddits, and we had a hypothesis that our value prop would resonate there. At least about that part, we proved to be right.
On July 6, 2021, we posted this thread in r/cookingforbeginners. It was upvoted to the top of the subreddit, stayed there for over 24 hours, and received over 100 comments. In the first week of the app, solely from this post, we saw over 1,000 non-trivial user sessions and 200 registered users. However, very few users actually sustained any sort of engagement with the app. In other words:
we identified a clear value proposition, and
we know where to find our users, but
we missed the mark with the product.
What we observed
In talking to our users, digging into data, and viewing LogRocket sessions1, we saw that people who spent the most time on the app were actually browsing through the learn pages and perusing cooking skills. But, this wasn’t a focus of our app, and there wasn’t an organized path to learn those skills.
More fundamentally, we realized that this app was asking users to do two difficult things at the same time:
learn to use a new app
learn some hard new skills
By asking new users to figure out our app while also trying to cook something2, we made it very hard to get into the app. Who is going to run out and buy groceries just to try out an app they just downloaded?
One user, bless his heart, cooked every recipe in Parsnip—and the red wine braised short rib four (yes, 4!!) times. He had never cooked before using Parsnip. But he was definitely a go-getter and an outlier, as Parsnip inspired him to consider a culinary career3. Here are some of his creations:
Charting a new direction
In this process, we experienced the common lesson that early-stage startups are not measured by how much product you’ve built, but how much you’ve learned about the problem you’re trying to solve. Fortunately, we learned a lot.
While our first arrow missed the mark, the response to our Reddit post (and many previous ones) made it abundantly clear that there are many, tech-savvy young people who are really trying to cook more but find systematic learning to be difficult. Don’t take our word for it: just consider the food shortages from viral cooking Tiktoks over the last year. These recipes are cooked millions of times because they’re easy and don’t require fancy skills or equipment—the opposite of food porn.
One of our inspirations is Duolingo, which teaches you bits of a new language without being immersed in it, or even having someone to practice with. You can spend as little as a few minutes a day, whenever you have time, with just the app on your phone. So, we began to wonder… what if we could separate learning to cook from actually cooking?
Long story short, we’ve continued down this path and developed an approach that:
is far simpler to use and to explain
can be used outside of the kitchen, whether on the couch or on the bus
provides a well-defined path of what to learn
surfaces your unknown unknowns and builds confidence (critical for beginners!)
Before continuing, I encourage you to download Parsnip and play with it for 5 minutes. This demo contextualizes the next section.
A eureka moment
This prototype you just played with is admittedly limited, so far. But if you found the core mechanism interesting at all, imagine with me for a moment.
Having a single type of question (e.g. multiple choice) is a bit monotonous, so let’s add different question types (in blue) to make it more interesting. For example:
multiple choice questions to build knowledge
picture and “match the pairs” questions to identify ingredients, spices, and tools
ordering questions to build up intuition about the sequencing of common techniques, recipes, and other kitchen tasks
Crucially, every type of interaction is simple to engage with, piques your curiosity, and teaches you as you do it.
Take these different types of questions and group them into levels (in yellow), e.g. Get to Know Eggs in the demo. Each level is topical, and may explore ingredients, techniques, food safety, food culture, and more, using different kinds of questions. These levels are designed to surface the 80% of knowledge that isn’t in recipes.
For the next release of Parsnip (the app you just tried), we’re focusing on teaching basic American cuisine to US-based beginners. So we’ll group sets of levels into some very simple units (in green) consisting groups of levels. If you complete the levels in a unit, you have the foundational knowledge to cook scrambled eggs, a burger, tacos, mac & cheese, etc.
At this point you can see an obvious problem: there’s no personalization. Everyone who uses Parsnip has go through the same set of units. And maybe that’s just a poor experience if you don’t like burgers—but it’s worse if you’re a vegan: learning about cooking meat may actually be repulsive. How do we fix this?
This next part was quite a Eureka moment for us, and it might be for you too if you have a computer science background.
Taking inspiration from the idea of a tech tree4, break up the levels from a fixed arrangement and create a directed acyclic graph (DAG). Add nodes that represent recipes (in red). Recipes can depend on the knowledge encoded in levels, which can also depend on other levels. This graph is our tech tree. Can you see what happens next?
As a user, you no longer have a fixed path to travel. Instead you just choose what you’d like to learn.
By traversing the graph from these recipes, we find the necessary group of skills you need to learn (technically an induced subgraph). A topological sort of these levels gives you a personalized path of levels to help you cook what you want. If you change your goal recipes, your path also updates. This personalization is not based on machine learning, but graph algorithms.
… but of course, why not introduce some machine-learning based personalization if it makes things even better?
Now, instead of having fixed pre-defined recipes, we allow any recipe to be imported from the Internet5. Then, we have a killer feature: take any recipe from the internet and learn it with Parsnip.
Although this seems potentially tricky, the natural language processing (NLP) problem to pick out which levels are relevant to a given recipe is pretty simple6. In fact, it’s even easier than the skill prediction problem we outlined in our earlier memo, which required accurately determining which cooking skills were needed by the recipe. Not only is this an off-the-shelf classification problem, the application is also less sensitive to prediction error: it doesn’t matter if you maybe miss a level, or get an extra one that wasn’t quite relevant. You still learn something and it’s engaging as long as the core levels & questions are fun—content that we’re continually improving.
So that’s why I’m excited: products need to be insanely simple to engage and delight users—and both the questions and level path are easy to understand. But we found a way to encapsulate some badass computer science behind it. Sure, we’re going to be maintaining and traversing a moderately large graph, and doing some NLP-based classification. But for the user, you just pick what you want to cook and get a path that takes you there, Arthur Clarke style7.
In other words—for the nerds out there—we figured out how you can research the tech tree of cooking!
What’s next?
Launching this product is our plan for the rest of the year, and we believe it’s a strong beachhead to capture other parts of the cooking vertical. To summarize, Parsnip:
is easy, simple, and fun to engage with
offers a learning experience personalized to users’ goals
uses non-bullshit machine learning that is highly error-tolerant
has a unique value proposition that is invaluable to folks trying to learn to cook. Just imagine seeing this button when you find a recipe on a food blog:
What makes Parsnip different?
The food/food-tech space is crowded with many companies, and we’ll explore how Parsnip stands out in a future memo. In brief, here’s what distinguishes our approach:
Cooking fundamentals instead of recipes
Education rather than entertainment ("food porn")
Scaling built on software/data/ML instead of hardware (capital-intensive) or delivery (operations heavy)
A cooking product requires content—but by using cooking knowledge as a building block, we can create exponentially less content than a recipe app. Then, a little bit of AI connects us to millions of recipes already out there.
“Learn to make this with Parsnip” rather than "buy these groceries" or "order this food" or "watch this video" is a unique value prop found nowhere else
Asks
We appreciate your help as we continue to build! Please e-mail with any ideas or if you can help with the following:
Please sign up for a usability test if you can—your feedback will make the app sing. Otherwise, try the beta and poke us!
Know any food writers or content creators to help us build out the tech tree?
Have any space to share in NYC, or recommended incubators / co-working spaces as we start ramping up in-person work?
We need for some marketing help as we launch the app—someone who groks the blocking and tackling that comes with early stage products.
We need some old (but not too old) iPhones for testing. Got one? I’ll meet you in NYC and buy you lunch for it.
All the best to you and your family in 2022, and see you next time!
Andrew
LogRocket offers a great tool to “look over the shoulder” of your users for unmoderated usability testing. It was very insightful and their free tier is pretty generous.
In grad school, I once took a class in theoretical economics. It was common in proofs to represent small probabilities as ε (Greek letter epsilon). Sometimes we’d see an ε² term appear next to ε and it was usually safe to drop for approximation purposes—because the square of a small number was so small it didn’t matter. Getting adoption of a new product is already a hard ε-probability problem; asking a new user to learn our app and some new skills was likely an ε² proposition.
There’s precedent for this, as many renowned chefs are actually not professionally trained, but self-taught, including Thomas Keller, Ina Garten, Rachael Ray, and Jamie Oliver.
Tech trees seem very familiar in games, but they were a huge innovation when Sid Meier first introduced them in Civilization.
It really helps that Google has shepherded almost all recipe sites into a well-defined schema.
“Any sufficiently advanced technology is indistinguishable from magic.”
Sounds you have found something in a place either no one pays attention, or no one considers a problem, also good thinking and solutions on cooking and skill learning. Hope this site grows rapidly from now!