[HN Gopher] Launch HN: OctaPulse (YC W26) - Robotics and compute...
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Launch HN: OctaPulse (YC W26) - Robotics and computer vision for
fish farming
Hi HN! My name is Rohan and, together with Paul, I'm the co-founder
of OctaPulse (https://www.tryoctapulse.com/). We're building a
robotics layer for seafood production, starting with automated fish
inspection. We are currently deployed at our first production site
with the largest trout producer in North America. You might be
wondering how the heck we got into this with no background in
aquaculture or the ocean industry. We are both from coastal
communities. I am from Goa, India and Paul is from Malta and Puerto
Rico. Seafood is deeply tied to both our cultures and communities.
We saw firsthand the damage being done to our oceans and how wild
fish stocks are being fished to near extinction. We also learned
that fish is the main protein source for almost 55% of the world's
population. Despite it not being huge consumption in America it is
massive globally. And then we found out that America imports 90% of
its seafood. What? That felt absurd. That was the initial
motivation for starting this company. Paul and I met at an
entrepreneurship happy hour at CMU. We met to talk about ocean
tech. It went on for three hours. I was drawn to building in the
ocean because it is one of the hardest engineering domains out
there. Paul had been researching aquaculture for months and kept
finding the same thing: a $350B global industry with less data
visibility than a warehouse. After that conversation we knew we
wanted to work on this together. Hatcheries, the early stage on-
land part of production, are full of labor intensive workflows that
are perfect candidates for automation. Farmers need to measure
their stock for feeding, breeding, and harvest decisions but fish
are underwater and get stressed when handled. Most farms still
sample manually. They net a few dozen fish, anesthetize them, place
them on a table to measure one by one, and extrapolate to
populations of hundreds of thousands. It takes about 5 minutes per
fish and the data is sparse. When we saw this process we were
baffled. There had to be a better way. This was the starting point
that really kicked us off. Here is the thing though. Most robots
are not built to handle humid and wet environments. Salt water is
the enemy of anything mechanical. Corrosion is such a pain to deal
with. Don't get me started on underwater computer vision which has
to parse through water turbidity and particles. Fish move
unpredictably and deform while swimming. Occlusion is constant.
Calibration is tricky in uncontrolled setups. Handling live fish
with robotics is another challenge that hasn't really been solved
before. Fish are slippery, fragile, and stress easily. All of this
is coupled with the requirement that all materials must be food
safe. On the vision side we are using Luxonis OAK cameras which
give us depth plus RGB in a compact form factor. The onboard Myriad
X VPU lets us run lightweight inference directly on the camera for
things like detection and tracking without needing to send raw
frames over USB constantly. For heavier workloads like segmentation
and keypoint extraction we bump up to Nvidia Jetsons. We have
tested on the Orin Nano and Orin NX depending on power and thermal
constraints at different sites. The models themselves are CNN and
transformer based architectures. We are running YOLO variants for
detection, custom segmentation heads for body outlines, and
keypoint models for anatomical landmarks. The tricky part is
getting these to run fast enough on edge hardware. We are using a
mix of TensorRT, OpenVINO, and ONNX Runtime depending on the
deployment target. Quantization has been a whole journey. INT8
quantization on TensorRT gives us the speed we need but you have to
be careful about accuracy degradation especially on the
segmentation outputs where boundary precision matters. We spent a
lot of time building calibration datasets that actually represent
the variance we see on farms. Lighting changes throughout the day,
water clarity shifts, fish density varies. Your calibration set
needs to capture all of that or your quantized model falls apart in
production. There is no wifi at most of these farms so we are
using Starlink for connectivity in remote or offshore locations.
Everything runs locally first and syncs when connection is
available. We are not streaming video to the cloud. All inference
happens on device. Behind the scenes we have been building our own
internal tooling for labeling, task assignment, and model
management. Early on we tried existing labeling platforms but they
did not fit our workflow. We needed tight integration between
labeling, training pipelines, and deployment. So we built our own
system where we can assign labeling tasks to annotators, track
progress, version datasets, and push models to edge devices with a
single command. It is not fancy but it keeps everything under our
control and makes iteration fast. When you are trying to close the
loop between data collection on farm, labeling, training,
quantization, and deployment you cannot afford to have fragmented
tooling. We needed one system that handles all of it. On the
robotics side we are building custom enclosures around off the
shelf components and modifying delta robots with soft robotics
grippers for handling. Vacuum and typical gripper actuation will
not work in this environment so we are using compliant grippers
that can safely handle fish without damaging them. We started with
the Delta X S as our test platform and are evaluating whether to
move to industrial delta robots or build our own from scratch once
we validate the kinematics and payload requirements in wet and
humid environments. The end effector design is still evolving. Fish
come in different sizes and body shapes depending on species and
life stage so we need grippers that can adapt. Right now we are
focused on operations outside the water. Hatchery phenotyping,
sorting, quality inspection. These are more accessible than full
underwater deployment and cheaper to start with. The idea is that
if we can combine genetics data, environmental data, and phenotypic
imagery we can help farms identify which fish to breed and which to
cull. This is where selective breeding starts. Something that
surprised us early on: only a tiny fraction of farmed fish species
have been through genetic improvement programs. Chickens grow 4x
faster than they did in 1950 because of decades of selective
breeding. But most farmed fish are essentially wild genetics. The
opportunity to improve aquaculture genetics is massive but it is
completely bottlenecked on measurement. You cannot improve what you
cannot measure, and farms can barely measure anything at scale so
far. The industry moves on trust though. We are dealing with live
animals and farms are cautious about who they let near their stock.
Coming from outside aquaculture, that trust had to be earned. Paul
was already a Future Leader with the Coalition for Sustainable
Aquaculture but the real turning point was attending World
Aquaculture Society, the largest conference in the US. Through a
connection of a connection he met the incoming lead geneticist at
what became our first customer. That relationship turned into a
paid pilot with the largest trout producer in North America. I
previously worked at ASML, Nvidia, Tesla, and Toyota. Paul worked
at Bloomberg. We met at CMU and immediately knew that we wanted to
tackle this problem and put our life's work into this. We would
love feedback from any of you who have worked on computer vision in
harsh or unpredictable environments, edge deployment on constrained
hardware, or gentle and appropriate handling of live animals with
robotics. If you are running inference on Jetsons or OAK cameras
and have opinions on quantization workflows we would love to hear
what has worked for you. If you have aquaculture experience we are
curious what problems we should be thinking about that we haven't
encountered yet. Dang told us you're all used to demo videos but
unfortunately we can't share them due to NDAs. But here's a photo
of us building our initial dataset for phenotyping and morphometric
analysis:
https://drive.google.com/file/d/1z3oSlB8ed9hanrybzP24XTfjDJE....
This is a weird industry to be building in and we are learning
something new every week. If you have experience with edge
deployment, robotics in wet environments, or aquaculture itself we
would love to hear your perspective. And if you just have questions
about fish or the tech we are happy to go deep in the comments.
Excited to hear what this community thinks.
Author : rohxnsxngh
Score : 53 points
Date : 2026-03-02 16:39 UTC (6 hours ago)
| chadash wrote:
| The fish cursor is cute, but extremely annoying.
| m_w_ wrote:
| ^ no way this was tested by anyone with eyes before it was
| deployed
| chfritz wrote:
| Agreed. Feels icky. Made me want to leave the page as quickly
| as possible again.
| rohxnsxngh wrote:
| We got a little too excited about the fish theme. Noted for
| the next iteration.
| ginkgotree wrote:
| I am a fan of the fish cursor. We should make the internet
| quirky again. This is like a modern take on Geocities websites,
| and they should do things like fish cursors now while they
| still can before a board of VCs comes in and makes them remove
| the fish cursor.
| rohxnsxngh wrote:
| Ha, thank you. We figured if we are building robots for fish
| we might as well commit to the bit. Enjoy it while it lasts.
| darkhorse13 wrote:
| I like it, but it should just be visual. It jacks my default
| scrollpad/mouse behavior, which is the annoying part.
| rohxnsxngh wrote:
| yup I got similar feedback from other batchmatches, gonna fix
| this.
| gus_massa wrote:
| Perhaps add a hook below the usual mouse real pointer and
| have a fish that is just a decoration that slowly swims to
| it.
| chadash wrote:
| ^ this
| rohxnsxngh wrote:
| interesting, I like the idea
| donalbrecht wrote:
| This is an awesome concept. Thanks for sharing.
|
| Have you had any issues with turbidity so far?
| rohxnsxngh wrote:
| Thanks! Yes turbidity has been one of our bigger challenges.
| Water clarity can shift dramatically throughout the day
| depending on feeding, fish activity, and weather. We have had
| to build our calibration datasets to capture that variance
| otherwise the quantized models degrade fast in production. We
| are also experimenting with different lighting setups to cut
| through particulate but it is still a work in progress :)
| dogclaw wrote:
| Shinkei was for the rich; you guys make it for all
| Pgrech wrote:
| Shinkei definitely has cool tech! Aquaculture has already
| surpassed commercial fishing in terms of production and has
| become the cheapest source of protein in many countries. We are
| excited to help the industry grow even further.
| cameron_b wrote:
| As a home aquaponics grower, I am really interested in the
| opportunity to develop tools that help this industry grow
| smarter. The impact to open-water fisheries can be undone if the
| markets can be affected to appreciate farm-raised fish _for their
| quality._
|
| I think there is such an incredible opportunity in the sector,
| and it probably looks a lot like any of the other sectors that
| have been augmented by data - gather giant piles of any
| measurable detail, and hope that after filtering you see a
| pattern that doesn't depend on your production environment
| running as many sensors ( or tensors ).
|
| Last Thought: Fish transfer pumps are not only a thing, but one
| of the best ways to have the whole pond population march past
| your camera in a lighting environment where you have more
| control.
|
| https://www.miprcorp.com/fish-pumping/ - just one example with
| decent pictures
| rohxnsxngh wrote:
| This is a great comment. You are absolutely right about the
| data opportunity. The industry is so data sparse right now that
| even basic measurements at scale would be a step change. We are
| seeing that firsthand with our customer. They went from
| sampling a few dozen fish by hand to continuous measurement and
| the insights are already compounding.
|
| Thank you for the fish pump link. We have looked at pump based
| systems as a way to create controlled measurement environments.
| You get consistent lighting, predictable fish orientation, and
| the fish are already moving through a constrained path. The
| challenge is you are still dealing with water turbidity,
| particulates, and bubbles in the flow which can mess with
| imaging. It is better than open water but not a free pass on
| the vision problems.
|
| We have also been looking at pescalators which use an
| Archimedes screw design to lift fish out of the water. Some
| setups combine this with anesthetization for operations that
| require handling. The tradeoff is you are adding stress and
| complexity but you get a much cleaner imaging environment.
| There is no single right answer here and the best approach
| depends on the species, life stage, and what you are trying to
| measure. This is definitely technology that will develop over
| time as the industry matures.
|
| What species are you working with in your aquaponics setup?
| cameron_b wrote:
| Tilapia, because the grow-out plan is very well documented.
| I'd happily sacrifice growth rate for a fish with higher
| "desirability" factor, and perhaps a lower optimal
| temperature. I previously tried Bluegill and lost them, I
| think, due to stress from temperature variation. I'd like to
| try them again or go with Catfish. Catfish are the top
| species (for food, by weight) produced in the US, and they
| seem nearly as durable as Tilapia in small systems.
|
| The pescalators sound great. There are so many tools like
| that where the application specifics ( species, system, life
| stage ) _could_ make room for a scalpel-precise optimization
| of some tool, but the benefits would have to come from scale,
| and there just haven 't been many first-movers ( or they keep
| quiet and defend the moat ) who seem poised to raise the tide
| for the whole industry. It is very ripe for the work you are
| doing to help the downstream gains over generations of
| stocks.
|
| Cheers to you guys!
| Pgrech wrote:
| Tilapia is a great species and the resilience is
| impressive. We have not started working with tilapia yet
| but love that it is one of the best species being grown in
| developing countries due to ability to thrive in warm and
| turbid water.
| lcnlvrz wrote:
| Great product!
|
| I wonder how do you manage data labeling? Do you outsource it by
| using data label vendors or do you have something in-house?
| rohxnsxngh wrote:
| Great question. We are building our entire labeling and data
| management system in house. Early on we tried existing
| platforms but they did not fit our workflow. We have a lot of
| video data and need custom labeling for things like keypoints,
| body outlines, and deformity classification that off the shelf
| tools do not handle well. Building it ourselves is cheaper at
| our scale, gives us tighter integration between labeling,
| training pipelines, and deployment, and lets us iterate faster.
| We can assign tasks to annotators, version datasets, and push
| models to edge devices from one system. When you are trying to
| close the loop between data collection on farm and deployment
| you cannot afford fragmented tooling.
| bahmboo wrote:
| Have you familiarized yourself with Whooshh Innovations? They
| have been operating in this space for over a decade and have
| solved many of these problems. It is an interesting space for
| sure! Best of luck!
| Pgrech wrote:
| Thank you!
|
| Whoosh has really interesting tech more focused on the fish
| transport side with products that move fish from tank to tank
| while performing some operations.
|
| Our initial focus with inspection is taking high quality images
| of fish to pull insights needed for maximizing efficiency and
| improving breeding programs. We have designed our system to
| easily drop-in to the current operations so it is seamless.
| Serginusa wrote:
| Really impressive stack -- especially the quantization workflow
| with TensorRT/INT8 on Jetsons. We've been dealing with similar
| tradeoffs (speed vs segmentation accuracy) in other domain:
|
| Curious -- how many labeled fish images did you need before the
| quantized models stopped falling apart in production?
|
| (Also, for anyone tracking W26, we've got OctaPulse on our
| prediction market: ingene.win/?utm_source=hn_comment&utm_medium=s
| ocial&utm_campaign=mar2026)
| rohxnsxngh wrote:
| Thanks! The quantization tradeoffs have been a grind. We do not
| have an exact number but we found that a few thousand images
| was not enough once you account for the variance on farm.
| Lighting changes throughout the day, water clarity shifts
| between feedings, fish density varies by tank. Early on our
| calibration sets were too homogenous and the INT8 models would
| work great in testing and then fall apart when conditions
| shifted.
|
| We also found that segmentation required significantly fewer
| images compared to keypoint pose detection models. Segmentation
| generalizes faster since you are just finding body boundaries.
| Keypoints are more finicky because anatomical landmarks vary a
| lot more across species, life stages, and body deformation
| while swimming. We had to be much more intentional about
| diversity in the keypoint training data. What made the
| difference overall was building calibration sets that
| intentionally captured edge cases. Low light, high turbidity,
| dense occlusion, different life stages. We also started
| stratifying by time of day and tank conditions rather than just
| grabbing random frames. It is still not perfect but the models
| are much more stable now.
| setgree wrote:
| > Something that surprised us early on: only a tiny fraction of
| farmed fish species have been through genetic improvement
| programs. Chickens grow 4x faster than they did in 1950 because
| of decades of selective breeding.
|
| I agree that there is an opportunity here for getting more
| calories per fish (and especially per input of feed, which is
| really what decades of chicken optimization are about). But the
| consequences of these changes for chicken welfare have been
| disastrous [0] and we're seeing a concerted effort to move to
| higher-welfare breeds (though still more efficient than ancestral
| breeds). Likewise, intensive salmon farming has led to widespread
| '"environmental dewilding," or the process of modifying natural
| water bodies with artificial infrastructure -- in this case, fish
| farm pens and cages -- and polluting them' [1]. It sounds like
| there are lots of ways in which using more robots can make
| monitoring less-invasive, and therefore less stressful for fish.
| I certainly hope to see _those_ attributes, rather than the
| potentially disastrous ones, emphasized as you move forward.
|
| [0] https://www.ciwf.org/programmes/better-chicken/
|
| [1] https://www.vox.com/future-perfect/468348/atlantic-salmon-
| fa...
| myroon5 wrote:
| > The math of feeding 10 billion people only works if we farm the
| ocean
|
| Even for marketing puffery, "only" seems reductive when most
| resource usage seems specific to a few animal products like cows
| and lamb: https://ourworldindata.org/land-use-diets
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