You’re Not Thinking Big Enough: Rebuilding Food Systems with AI Agents — Cody Menefee, Firecrawl

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>> All right. Hello everybody.

My name is Cody. Um

I put this picture up here because this

jacket so far has not actually landed as

well as I thought it would. No one gets

the joke. Um so this was to really put

it in front of your face. I don't just

enjoy wearing heavily branded letterman

jacket. We intentionally tried to

play into the bit a little bit. Um so,

my name is Cody. I'm on the growth team

at Firecracker. Um and today I'm here to

tell you you're not thinking big enough.

Um but before I actually get into that,

I have to address a bit of an elephant

in the room which is Theo stole my talk.

Uh Theo put out a video about a month

ago uh called uh you need to think

bigger. But I would like to say I

submitted the name for this talk a month

before Theo put his video out. I didn't

steal his talk. He stole my talk. So,

um

Today we're actually going to talk about

farming. And yes, I actually mean

farming. Uh more specifically, I mean

livestock farming. And even more

specifically, I mean automating pasture

rotation for grass-fed livestock

systems.

Um I have a feeling most of you did not

expect to learn about cows and grass and

farming today, but I'm here so you're

going to.

Uh little bit of background on who I am

and why maybe you should listen to me.

Uh the short version is I actually have

no credentials that qualify me for this

talk, but nonetheless I'm going to do my

best to give it. Uh I grew up in

Kentucky. Uh have a background of blue

collar work. I was a bartender, a

mechanic, a uh um

a server, uh a whole bunch of things. Uh

never actually a farmer though.

Um and then I sort of found my way into

engineering, uh software development,

etc. I actually don't really like taking

the title of software engineer. Um I'm

pretty averse to that. Feels like stolen

valor because I am the vibe coder most

of you all are scared of.

I use AI agents all day long. I don't

have any syntax memorized. I am not

proficient in any particular coding

language, but I will crank out some

stuff on a weekend.

But today I actually work at Firecrawl

where we're building context for AI

agents. We have a series of

web data APIs to give your agents access

to the web. Again, I said I'm on the

growth team, but today we're actually

getting into some more farming stuff.

But first a bit of credential I do have

is this is a real picture of me hauling

turkeys on top of my Tesla and I do

still have a crack in that glass ceiling

because of it.

This was in Nashville where I live in

the middle of a residential neighborhood

where you are not allowed to raise

turkeys,

but I raised 10 turkeys in my backyard

cuz I wanted to know what it was like to

actually

raise livestock myself that I would eat.

It's a very mentally

difficult process to be mentally honest,

but this was me loading them up onto the

roof of my Tesla and then I drove for 3

hours with them to the processor. Had to

stop at a supercharger on the way

and lots of people were taking pictures

and what I can tell you is if your range

is sufficiently decreased when you have

a giant windbreak full of turkeys on top

of the roof.

So that was

quite the anxious drive I can tell you.

I also almost left engineering to be a

farmer. I really wanted to raise

chickens. This is a real product image

that I came up with. I wanted to wrap

turkeys in white wrapping and literally

just slapped the word eat me on top of

it. I thought it was provocative. I

thought it would get you to buy

chickens,

but I realized it's actually really hard

to make any money farming.

Surprise, surprise.

And I have a wife. I have two kids. It

didn't feel

right to ask them to give up the lives

they had so that I could go cosplay as a

farmer and raise chickens. So

I decided to pivot and see if there were

ways that we could scale farming itself

and the the types of systems that I'm

interested in when it comes to livestock

agriculture. So, three things I want to

get accomplished in this talk is one,

convince you all to pursue bigger ideas.

Um I think a lot of these talks, a lot

of these conferences, a lot of us

individually

uh spend a lot of time talking about

building software for people who build

software for people who build software,

so on and so forth. And I really am just

here to challenge you that there are

other problems to solve than just

another MCP for another SaaS solution at

another company. Um but also, I'm just

really trying to take advantage of a

captive audience. Uh if you corner me

anywhere at any time, there's a good

chance I will talk to you about farming.

Um so, here I am. Uh and hopefully, I

can convince you to come work at Fire

Crull.

So, uh first things first, I believe

livestock belongs on pasture. I think

animals should live on grass. Um I think

it's better for the animal, the

consumer, the farmer, the ecosystem. I

can give you a whole TED Talk on each of

those if I need to. You can find me

later if you need me to tell you why

it's better for animals to be on the

grass, but I don't have enough time to

get into all of that. Take my word for

it. Let's start there. The assumption is

animals should be on grass.

Um this is the goal I want to hit. I am

not actually anti-containment farming. I

think there's a reason we needed to do

that, but 97% of cows are still

currently uh finished on feedlots. 3%

are raised on pasture. My opinion here,

more animals could be on grass. I want

to try to figure out how we get more

animals on grass. The question is, why

aren't they on grass? And that is labor

is the bottleneck. It is a pain in the

ass to actually raise animals on grass.

Uh pasture done right actually means

moving animals constantly. And that

takes a lot of work. Um if you think

about grass-fed uh beef, you might think

of I have 100 cows, 100 acres. I put 100

cows on 100 acres. They eat grass. I got

beef at the end of the year. That's not

quite how it works. Um Um, will

uh very rapidly decrease the quality of

your pasture if you just let cows graze

where they want cuz they'll graze their

favorite things, ignore things that they

shouldn't, trample areas consistently,

so on and so forth. So, the solution to

that is rotational grazing. What this

means is you break up your pasture into

individual paddocks where the animals

have enough uh food for one one day and

then you move them every single day. Um,

this allows certain areas to rest and

other areas to be grazed and over time

will increase the efficacy of your

pasture. But,

this takes a whole whole whole lot of

work. Um, this means you have to move

fences, animals, water, and keep track

of it every single day in order to

uh appropriately move the animals as

often as they need to.

There are some solutions actually trying

to work on this problem. Uh, you may

have seen a company called Halter uh in

the news recently. Peter Thiel invested

at a $2 billion valuation.

Uh, No Fence is another company. What

these companies do is provide collars

for the animals connected to the GPS

satellites that allow you to draw

virtual boundaries where you can move

the animals uh remotely. Um, I think

this is a great step in the direction of

trying to

uh expand labor. Um, but this has a

problem which is you have to know where

to move the animals. This is not a

science to actually be honest with you.

You can't just move them in a straight

line across the pasture routinely every

single day to the same part of land. Um,

the reason is is grass doesn't grow the

same every single day. Uh, there are

drought conditions, rainfall, uh how

much impact a particular section of the

paddock has had. And the way that this

is solved today is actually farmers

going out on pasture, putting eyeballs

on the grass, and making intuitive

decisions about where the next best move

should be. So, the question is how do we

replace the farmers' eyes on pasture so

that they can remotely make educated

decisions on where to move their virtual

fences.

Uh there's a bit more that actually goes

into this as well, and that is you can't

just you have to also know how tall the

grass is. Um grass has a growing cycle.

If you grow it way too short, it takes

really long time to come back. If you

let it go too long, it becomes old and

bitter and the animals don't like it.

There's this juvenile sweet spot that

you want to keep the grass in. You want

to cut it before it gets too tall, but

then you also don't want to cut it too

short. So you need to keep the animals

moving and then constantly coming back

to the same pasture so that your grass

stays at the most optimal growing age

and constantly has uh the most

productivity possible.

So, a couple of ways that we can do

this. Um these are things These are my

solutions. This is something I've

actually been working on thinking about

how we can do this. Um a couple of

options that I have are drone

orthomosaic maps. If we could find a way

to automate drone flights, we could go

flying around our pasture, take a whole

bunch of pictures, get some very high

fidelity high resolution images of the

grass that farmers could analyze. The

problem with this is uh there's a lot of

uh skill upgrade you need to do with the

farmers to teach them how to fly drones,

a lot of regulatory issues with keeping

the drones in sight, and ideally this

would be autonomous and there currently

isn't a jurisdiction in the world that

has approved autonomous drones for these

types of applications. So this is really

big bottleneck. Um I think it has pretty

high fidelity in the quality of imagery,

but is going to be a hard problem to

solve in terms of actually getting all

those hurdles accomplished. Uh

satellites is my most favorite option

today. There's a really cool company

called Planet out there taking pictures

of the entire globe every single day

um with a uh 1 by 1 m resolution. Um but

they're still

those satellites are really high up in

the sky and uh it's hard to tell some of

the things you need to tell uh to

actually make those educated decisions.

Uh the middle photo here is actually

from a friend of mine out in Missouri

working as a research grad assistant at

the Missouri Lincoln University. And

this idea is just putting a trail cam

next to a tree and some measuring

apparatus that camera can look at and

just figuring out how tall is the grass

in relation to that particular object.

Just so that we have some sort of

reference point that we can use to see

how well the grass is growing back.

If we can solve this problem along with

the

the the collar situation, I think

there's a world here where we can drop

an LLM in the middle of this loop and

start to work on autonomous grazing

operations. And so what this would mean

is an LLM essentially making the next

best decision on where the animal should

be any given day. But this isn't

multivariate analysis. This requires the

LLM to have several data inputs

including where the animals are in GPS

location, where they were yesterday,

where they might go tomorrow, what the

drought condition is in the area, how

tall the grass is across the entire

farm. And actually it has to make this

decision not just on day-to-day basis,

but in varying

degrees of relation. So where's the best

next place for a particular cow to be,

but where's the best next place for the

herd to be in relationship to the

pasture itself, in relationship to the

farm as a whole, and then more broadly

the ecosystem at large. There's

all of these components feed back into

each other and if you can optimize this

entire picture, you have a more

productive farm where you can actually

have more animals on fewer acres, which

is how we end up actually scaling to

compete with the feedlot style where you

can actually have more cows on fewer

grass.

How do we solve this problem? There's a

couple of components. There's three main

blockers that I think need to exist in

order for us to actually create this

system. The first one is building a

knowledge base and this is primarily

what I'm working on at Fire Corral and

then an open source project I have

called Open Pasture. The idea here is a

lot of the knowledge on when to move,

why where how to move,

the benefits of moving, etc. is all

locked up in primarily YouTube videos.

There's a bunch of really cool farmers

out there. I can give you a whole bunch

of channels you can go down rabbit holes

on of just good old guys out in

Missouri, Tennessee, Kentucky trying to

move their animals every single day

telling you what they're learning,

telling you what species are best for

this,

what lagoons are you want to aim for in

the biodiversity in your pasture.

There's a whole bunch of things that go

into this and we need to build that

knowledge base.

Fire fall is a toolkit that I used

actually collect this data going out

scraping those YouTube videos, scraping

research papers out to archive building

this knowledge base up and open pasture

is the actual repository I put this

information in to make it available to

any farmer I think that might be able to

use it.

The next thing to solve is the actual

visualization layer. There's a lot of

components we need to know about the

grass that the farmers primarily getting

out of the intuition from looking at the

pasture. The two main things worth

figuring out about the pasture both

where the animals are, where they should

go and where you want them to be is what

is the biomass, how much foliage

actually is available for them to

consume and then long-term what is the

biodiversity of that particular pasture.

If they overgraze sections too heavily

they'll start to over index on different

types of cool season, warm season

grasses, lagoons, etc. and ideally you

want a really rounded, really diverse

pasture over time to make sure that the

cattle are getting the nutrients they

need so you don't have to supplement

with things like hay, copper, aluminum,

etc.

Ideally they get all of the

macronutrients and micronutrients from

the grass itself which becomes an

entirely ideally hands-off system.

And then the third one is

those geofence companies. So no fence,

Halter. While I appreciate the

technology they're trying to push

forward I have a pretty strong

disagreement with them which is in order

to use their software they require you

buy their collars, and you can't plug

your own software into their collars.

From a business standpoint, I get why

this is. From an industry standpoint, I

think it's really a pain in the ass. Um

I would like to innovate on the software

layer. I would like to push GPS

locations to these collars that my LLM

can predict. I don't want to have to

rely on their software to do this

because I don't think it's as good, or I

think I can make it better, if I'm being

totally honest with you. Um so, a bit of

the purpose of this talk is actually a

call to action for you all in the

audience. I need someone to make me a

collar.

Um I need it to be open the APIs need to

be open. Ideally, it's an off-the-shelf

solution. Uh some of the component parts

that we can slap together. Uh farmers

are pretty scrappy and like to heal

their own things. There's a lot of uh

animosity towards John Deere in this

sort of like right to repair. Um so, my

ask to anyone maybe looking at this

problem is design me a collar where the

patent can be open and the APIs are open

so that we can compete on software

>> and optimize this solution.

>> Uh the next thing I'd like to maybe

tease you about is this actually goes

beyond just ruminants. Uh so, ruminant

is a type of animal, cows, sheep, goats.

Uh those are all ruminant animals. They

chew grass. They uh digest it in the

rumen in uh which is an organ that's

what they're called ruminants. Um but

there's actually an additional benefit

we get where we can stack species on

these pasture rotations. This is a

company called Pasture Bird. Uh they

were a big catalyst for me to really get

in obsessed with this idea. What they

did is took their normal chicken house,

put it up on big wheels, and automated

the movement so it creeps its width

every 24 hours across pasture. The

reason you can do it this scientifically

with chickens is cuz they don't actually

get most of their nutrients from the

grass. You have to supplement them with

grain feed uh because chickens are

omnivores, not quite just herbivores. Um

so, you can just inch this coop across

the grass uh giving them a uh uh uh land

to grow on. Uh the nitrogen from their

droppings actually help uh

as a manure or as a as a fertilizer for

for the the grass itself. And there's an

added benefit here. When you stack the

ruminants with the chickens, if you run

your ruminants first, they sort of cut

off the top of the grass, and then

chickens come behind them and peck out

the parasites from their droppings, and

it reduces the parasite load overall

across your farm, which reduces the

medication expense you have to actually

pay to keep your animals healthy. And

over time you have a more robust uh

uh seed stock or uh breeding stock so

that you can have stronger animals over

time that require fewer interventions

and can be left alone to just eat grass

and uh turn into meat eventually.

Um so, the three things I really hope

you all take away from this talk is one,

I think we need to find big, real-world

physical problems that we can solve um

that require multivariate analysis and

not quite uh yes or no decisions. Um

there are lots of problems out there

that don't actually have deterministic

solutions. I hear a lot of engineers

talk about how we turn LLMs into

deterministic processes, and my

contention is actually there's a lot of

problems that you can't solve with

deterministic uh algorithms. Um this is

one of them. It's a multivariate

analysis. There isn't a next best

paddock to move to. There's just your

best guess on where you think they

should go. Um and I think if we can take

systems like that, these uh multi-data

input systems, and drop an LLM in the

center to actually reason over the data

and at least make a suggestion that the

human can confirm or deny, uh we can

really start to scale systems like this

that are very much uh restricted by the

farmer's ability to scale their own

labor, their own decision-making power,

um and really give them the tools that

they need to grow their operations to

hopefully, I think uh all animals could

be raised on grass if we uh solve these

problems. And then the last one is is

maybe you can come help me solve some of

these problems. Um the number one

problem is actually just giving the

agents the context they need, gathering

the data, packaging that data, and then

presenting it in a way that the LLM can

reason over. And that's what we do over

at Firecrawl. Uh, so you're not thinking

big enough. Firecrawl is where we're

building the context layer for AI, and I

hope you can come build it with us. Uh,

we're hiring. Uh, so here's all the job

postings we currently have. Go to our

website and maybe find one that works

out for you. Uh, reach out to me. We'd

love to have more people trying to

figure out how we get data off the web

to solve some of these complex problems

and present that that data as context

uh, to these AI agents, and perhaps

we can make the world a better place.

My name's Cody. Uh, Open Pastures is my

open source project. Firecrawl is where

I do my day-to-day life, and uh, these

are my socials. I'll hang around for a

little bit. I love to chat more about

animals, cows, birds, uh, all the alike.

Uh, thank you very much for coming.

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