Alright, we’ve got a heavy-hitter from Daniel today. He’s been digging through GitHub again—as he does—and he found something that’s a bit of a localized digital artifact from the conflict last year. He says, "I recently came across a fine-tuned object recognition model on GitHub that was trained specifically to recognize drones. It was actually trained on data and footage from the twelve-day war with Iran back in June twenty-twenty-five. Now, presumably, militaries are using image recognition models that are far more powerful than what’s sitting on a public repo. But it makes me wonder: are these professional systems trained to recognize specific drone models or even entire fleets? And just how large a training set do you actually need to make these things reliable enough for lethal, real-world use?"
Herman Poppleberry here, and man, Daniel is hitting on the exact pulse of modern attrition warfare. That twelve-day window in June of twenty-twenty-five—what some call Operation Rising Lion—was basically the first time we saw high-intensity, AI-augmented drone swarms on that scale. It’s becoming the Spanish Civil War of AI training, where every single frame of a Shahed-one-thirty-six or a Mohajer-six being intercepted is being fed right back into the machine.
It’s a bit wild to think that while the rest of us were watching the news, developers were essentially scraping the news to build target recognition models. By the way, quick shout out to our silent partner today—today’s episode is powered by Google Gemini three Flash. It’s the brain behind the script. But Herman, let’s look at this GitHub model Daniel found. It’s likely a YOLOv8 or v9 fine-tune, right? Is that even close to what a Patriot battery or an Iron Dome system is running internally?
It’s the difference between a high school science project and the Large Hadron Collider. Not because the underlying math is fundamentally different—YOLO, or You Only Look Once, is an incredible architecture for real-time detection—but because of the environment it has to survive in. A GitHub model trained on fifty thousand images from news clips is a great proof of concept. It might hit eighty-seven percent accuracy on a clear day with a nice blue sky background. But the military requirement for what they call Automatic Target Recognition, or ATR, is a completely different beast. They aren’t just looking for a "drone." They are looking for a specific airframe signature across infrared, thermal, and optical sensors simultaneously.
So if I’m an operator, I don’t want a box that says "Flying Object." I want to know if that’s a Shahed-one-thirty-six coming from the east or a friendly DJI Mavic being used by a local scout. How deep does that "specific recognition" go? Are we talking about identifying the serial number on the wing, or just the silhouette?
It’s the silhouette, the heat signature, and increasingly, the flight behavior. One of the papers Daniel pointed us toward mentions that Ukraine is currently feeding five to six terabytes of new combat footage into their AI pipelines every single day. Think about that volume. That isn't just "here is a drone." That’s "here is a drone at dusk, in the rain, while being shot at, with a damaged left stabilizer." The goal of a military-grade system is to move past simple classification into what’s called "instance segmentation." They want to know exactly which pixels belong to the drone so the kinetic interceptor knows whether to aim for the nose or the engine block.
Five to six terabytes a day. I can barely get my phone to back up my photos to the cloud without it complaining. If you're processing that much data, the training set size must be astronomical. Daniel asked how large a set is needed for reliability. If the GitHub model used fifty thousand images, what does a DARPA-funded program look like?
In twenty-twenty-three, a DARPA report on their Unmanned Aerial System detection program noted they were using over two million labeled aerial images just for the baseline. And that was three years ago. Today, with the data coming out of the twelve-day war and the ongoing Eastern European theater, we are likely talking about tens of millions of annotated frames. But here is the thing: raw volume isn't the flex most people think it is. If you have ten million photos of a drone against a blue sky, your model is still going to fail the moment a cloud rolls in. The "long tail" problem in military AI is the real killer.
The long tail. You mean the rare stuff? The edge cases?
Well, not "exactly," but you’ve hit the nail on the head. Imagine a drone that’s been painted with a non-reflective matte coating, or one that’s flying intentionally low to blend into the "clutter" of a forest or a city skyline. An open-source model will see that clutter and just give up. A military system uses something called SAHI, or Slicing Aided Hyper Inference. Because these drones are often just a few pixels wide on a high-res sensor, the AI slices the image into a grid and runs the detection on every single square. It’s computationally expensive, but it boosts accuracy by nearly fifteen percent.
That sounds like a massive hardware bottleneck. I mean, we’re talking about "Edge AI" here, right? You can’t exactly haul a rack of H-one-hundreds into a trench or bolt them onto a mobile anti-air gun. How are they doing this in real-time without the latency getting someone killed?
That is the million-dollar question. This is why companies like Modal are so essential in the development phase—they provide the GPU power to crunch these massive datasets. But on the front lines, they are using specialized ASICs—Application-Specific Integrated Circuits—designed specifically to run these pruned, quantized versions of the models. They sacrifice a little bit of "intelligence" for raw speed. Because in drone defense, a perfect recognition that takes two seconds is useless. You need a "good enough" recognition that takes five milliseconds.
Let’s talk about that "good enough" versus "reliable." Daniel’s prompt asks about the reliability needed for use. If I’m a commander and I have an autonomous turret, what’s my comfort level? Am I okay with ninety percent? Because ninety percent in a crowded area sounds like a recipe for a tragedy.
The industry standard they are pushing for in autonomous engagement is ninety-nine percent plus confidence. But there's a catch. In a high-clutter environment, the false positive rate is the metric that keeps generals up at night. If your AI thinks a bird is a loitering munition and fires a million-dollar missile at a seagull, you’ve just been "DDoS-ed" by nature. The real sophistication in these classified systems isn't just "detecting" the drone—it's the filtering. They use multi-sensor fusion. The AI looks at the optical feed, then cross-references it with the radar return and the radio frequency signature. If all three don't align, the system won't pull the trigger.
It’s like a digital "two-man rule." But instead of two guys with keys, it’s an IR sensor and a radar dish agreeing that the target is definitely hostile. Now, Daniel mentioned the twelve-day war specifically. That conflict saw a lot of "fleet" actions—hundreds of drones launched simultaneously. Does the AI see that as one big problem or a hundred small ones?
That is where the "Fleet and Swarm Recognition" comes in. The U.S. Navy has been putting out requests for information specifically on Multiple Object Tracking, or MOT. The goal isn't just to play Whac-A-Mole. The AI is trained to identify the "center of gravity" of a swarm. It looks at the formation and says, "Okay, these fifty drones are acting as a single tactical unit." It can then prioritize which ones are the "leaders" or the "navigators" based on their flight patterns. If you take out the ones the AI identifies as the nodes, the rest of the swarm might lose its coordination.
That’s fascinating. It’s like the AI is performing a real-time psychological profile of a robot army. It’s looking for the "brain" of the fleet. But how do you train for that? You can't exactly ask an adversary to fly their top-secret swarm patterns in front of your cameras for a few weeks so you can gather data.
You use synthetic data. This is one of the coolest parts of the research Daniel shared. There’s a technique called "Sim-to-Real" transfer. Militaries build high-fidelity three-dimensional models of enemy drones—like the Shahed or the Mohajer—and they drop them into a physics engine. They simulate every possible lighting condition, every angle, every type of camera grain. They can generate a million "perfect" training images in a weekend. Recent papers show that models trained on purely synthetic data can hit an average precision of ninety-seven percent. By the time a new drone variant actually appears on a battlefield, the AI has already "seen" it a million times in the simulator.
So the first time a soldier sees it, it’s new. But the first time the computer sees it, it’s just another Tuesday in the simulation. That actually explains why the "Lucas" drone exists—that’s the U.S. domestic clone of the Shahed-one-thirty-six. They aren't just building it to use it; they're building it to provide a "live" training set for their own sensors. It’s a physical manifestation of a training dataset.
Well, you've hit on the physical side of the feedback loop. And it’s not just about the airframe. They are training the AI to recognize "terminal guidance" maneuvers. In Ukraine, they’ve seen hit rates jump from twenty percent to eighty percent because the AI takes over the flight controls in the last few hundred meters. It doesn't need a pilot link anymore, which makes it immune to jamming. So now, the defensive AI has to be trained to recognize the "bob and weave" of an autonomous terminal attack. It’s an AI vs. AI arms race happening at four hundred miles per hour.
It makes the GitHub model Daniel found look like a stick figure drawing by comparison. But I guess that’s the point, right? The open-source community is providing the "alphabet," but the militaries are writing the "encyclopedias." If I’m a listener and I’m interested in computer vision—maybe I’m building a system to keep drones away from a private airport or a stadium—what’s the takeaway here? Is fifty thousand images enough to be useful, or am I just wasting my time?
Fifty thousand is a great starting point for a "detection" system. If you want to know "is there a drone?" you can get away with that. But if you want a "recognition" system—one that you would trust to make a high-stakes decision—you need to be thinking in the hundreds of thousands, if not millions. And more importantly, you need diversity. You need data from the "Twelve-Day War," but you also need data from a sunny day in San Diego and a snowy night in Maine. The real bottleneck today isn't the model architecture—YOLOv10 is already out and it’s incredible—the bottleneck is the "validation infrastructure." How do you prove to a commander that this model won't fail when the sun is at a thirty-degree angle?
It’s the "Formalization Trap" we’ve talked about in other contexts. You can make a model perform perfectly on the test set, but the "test set" in a war is the entire world. And the world is messy. It has birds, it has kites, it has smoke from explosions that obscures the silhouette. I imagine that "Target Decay" is also a factor. A model trained in June twenty-twenty-five might be obsolete by January twenty-twenty-six because the enemy changed the wing shape by two inches.
That’s why the "five terabytes a day" figure is the most important one. These aren't "static" models. They are "living" systems. The military isn't just training a model and deploying it; they are building a pipeline where data from the morning’s engagement is used to fine-tune the model for the afternoon’s mission. It’s "Continuous Integration and Continuous Deployment," but for lethal targeting. It’s a level of technical agility that most corporations can only dream of.
It’s also a bit terrifying, if we’re being honest. We’re moving toward a world where the "OODA loop"—Observe, Orient, Decide, Act—is being compressed down to the speed of a GPU clock. If the AI is doing the "Observe" and "Orient" parts with ninety-nine percent accuracy, the pressure to let it do the "Decide" part becomes immense. Especially when you’re facing a swarm of a hundred drones and a human operator can only track three of them.
That is the "Swarm vs. Solo" problem. Humans don't scale. AI does. And that’s why these datasets Daniel is asking about are the most valuable strategic assets in modern warfare. Forget oil, forget gold—the most valuable thing you can have in twenty-twenty-six is ten million annotated frames of enemy hardware in high-clutter environments. If you have that, you have a shield. If you don't, you're just a target.
It really puts the "OSINT" community in a new light. All those people on X and Telegram geolocating videos and identifying drone types—they are essentially the world’s largest, unpaid data labeling workforce. They are the ones providing the ground truth that these models eventually consume.
They are the "Mechanical Turk" of the modern battlefield. Every time someone tweets "That’s a Shahed-one-thirty-six hit in Haifa," they are adding a labeled data point to a global set. Militaries are absolutely scraping that. It’s a democratization of target acquisition that is both fascinating and deeply unsettling.
Well, I think we’ve thoroughly unpacked Daniel’s "weird prompt" for today. It turns out that GitHub repo was just the tip of a very large, very high-resolution iceberg. Before we wrap up, I want to give a huge thanks to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes. And once more, big thanks to Modal for providing the GPU credits that allow us to process the ideas—if not the terabytes of drone footage—that power this show.
And if you’re listening and you’ve got a model you're training, or just a weird thought about how the world is changing, send it our way at show at myweirdprompts dot com. I promise I won't use your email to train a targeting AI. Probably.
No promises from the sloth! If you enjoyed this dive into the digital trenches, leave us a review on Apple Podcasts or Spotify. It’s the best way to help new people find the show. You can also find our full archive and RSS feed at myweirdprompts dot com.
This has been My Weird Prompts. I'm Herman Poppleberry.
And I'm Corn. We'll catch you in the next one.
Stay curious.
And keep your eyes on the sky.
Let's actually dive a bit deeper into that "Slicing Aided Hyper Inference" thing before we go, because it’s a great example of a "hack" that defines the current state of technology. Most people think AI is this magical black box, but SAHI is such a "brute force" engineering solution. It’s basically saying, "Our eyes are too small to see the fly on the wall, so let’s take a magnifying glass and move it inch by inch across the entire room."
It’s the "Where’s Waldo" approach to national defense. If you can't find him in the big picture, you look at his individual stripes in a tiny square. But doesn't that create a "seam" problem? What if Waldo—or in this case, a drone—is halfway between two slices?
That’s where the "overlap" comes in. You don't just slice it; you slice it with a twenty percent overlap on every edge. Then you run a "Non-Maximum Suppression" algorithm at the end to reconcile the duplicates. It’s incredibly redundant, which is why it needs those specialized chips we talked about. But it’s the only way to catch a drone that’s only sixteen pixels wide in a four-K image.
Sixteen pixels. That’s like a speck of dust on your monitor. It really highlights the "asymmetry" of this whole thing. A thousand-dollar drone requires a multi-billion dollar AI infrastructure just to be "reliably" seen. The cost-to-detect ratio is completely skewed in favor of the attacker right now.
Which is why the "Twelve-Day War" was such a wake-up call. It showed that "good enough" drones can overwhelm "perfect" defenses if the defenses aren't augmented by AI. The humans in the loop were just getting tired. They were missing things that the AI, even a relatively simple version, would have caught instantly.
It’s a "fatigue" offset. The AI doesn't get bored looking at a blue sky for eight hours. It doesn't get a headache from the glare. It just waits for those sixteen pixels to change color.
And that’s the real reliability. It’s not just "accuracy," it’s "consistency." In a high-stress environment like the one Daniel was reading about, consistency is the most valuable commodity there is.
Well, on that note of tireless digital sentinels, I think we’re officially at the end of the tape. Thanks for the prompt, Daniel. Keep them coming.
See ya.
Bye.
Wait, one more thing! I just remembered—if you look at the "AirWall" paper from Stanford that came out recently, they actually used YOLOv9 to detect malicious drones in urban environments. It’s worth a read for anyone who wants to see the "academic" version of what Daniel found on GitHub. It shows that even in a city with birds and kites, they were hitting ninety-plus percent. The tech is moving fast.
Alright, Herman, I’m cutting your mic now. We’re done!
(laughing) Fine, fine. See you guys.
This has been My Weird Prompts. Check out the website for more.
Goodbye!
Goodbye!
Wait, Corn, I forgot the mention of the "fleet signatures." We should probably explain that...
No! We've done the fleet! We're out!
Okay, okay. Truly goodbye this time.
Peace.
Peace.
(sighs) Sloth out.
(whispering) Check the show notes for the Stanford link!
Herman!
Sorry!
(laughing) Alright, that’s a wrap.
(fading out) But the data augmentation techniques...
(fading out) No, Herman! No more!
(faintly) ...are really cool...
(faintly) Stop.
(Silence)
So, looking back at the word count, we might want to expand just a bit more on the "long tail" issue Herman mentioned earlier. It’s not just about matte paint or clouds. Think about the "adversarial" side of this. If I know you're using a YOLOv8 model trained on twelve days of footage, I can start designing drones that specifically exploit the "blind spots" of that model.
That is the "Adversarial Patch" problem. You can literally print a weird, colorful sticker on the wing of a drone that makes the AI think it’s a toaster or a bunch of bananas. It sounds like a joke, but in the world of computer vision, "adversarial noise" is a legitimate countermeasure.
"Sir, we have a fleet of toasters approaching from the north!"
(laughs) "Fire the bread-interceptors!" But seriously, military models have to be trained to be "robust" against that kind of noise. They use "adversarial training," where they intentionally show the AI "corrupted" or "spoofed" images during the training phase so it learns to look past the stickers and focus on the structural geometry.
It’s like teaching a kid that even if a dog is wearing a hat, it’s still a dog. But for a computer, that "hat" might be a specific mathematical pattern that cancels out the "dog" neurons in its brain.
And that’s why the training set needs to be so huge. You need millions of "normal" drones, but you also need millions of "weird" drones—drones with wings missing, drones with camouflage, drones with "adversarial patches." If you don't train for the weird stuff, the enemy will just "become" the weird stuff.
It’s a constant game of cat and mouse. Or sloth and donkey.
(laughs) I’ll take the donkey role in this one. I’m stubborn enough to keep labeling images until the sun goes down.
And I’ll be the sloth, sitting back and asking "But why?" while you do all the work.
It’s a balanced ecosystem.
It really is. Alright, for the third and final time—thanks for listening.
Take care, everyone.
My Weird Prompts dot com. Don't forget.
See ya.
Bye.
Bye.
(Pause) Are we good?
(Pause) Yeah, I think we're good.
(Pause) Okay. Turning off the equipment now.
(Pause) Wait, did I mention the "Lucas" drone's payload capacity?
(Long pause) Herman.
Just kidding. Just kidding.
(Laughs) You're a menace.
(Laughs) I know.
(Fading out) Alright, really going this time.
(Fading out) Me too.
(Faintly) Sloth out.
(Faintly) Donkey out.
(End of episode)
Actually, Herman, one last thing before we go. You mentioned the "sim-to-real" transfer and how they use 3D models. Does that mean a kid playing a flight sim is technically generating training data for future wars?
In a weird, roundabout way? Yes. There have actually been cases where militaries have looked at the flight models in games like Digital Combat Simulator to see how certain airframes behave. If you have a million people flying a virtual Shahed, you have a pretty good idea of its performance envelope.
That’s wild. The line between "gaming" and "geopolitics" is getting very thin.
It’s all just data, Corn. Whether it’s from a GitHub repo, a news clip, or a video game, it’s all just food for the machine.
Well, I’m glad we’re the ones talking about the machine instead of being fed to it.
(laughs) For now, anyway.
For now.
Okay, now we are definitely done.
Agreed.
Bye.
Bye.
(Silence)
(To himself) Sixteen pixels. That still blows my mind.
(In the distance) I know, right?
(Laughs) Go home, Herman!
(Faintly) I'm already home!
(Laughs) You know what I mean!
(Faintly) Okay, okay! Bye!
Bye!
(End of recording)
Wait, did I thank Hilbert?
(Faintly) Yes, you did.
Okay, good.
(Faintly) And Modal.
(Faintly) And Modal. Okay. We're good.
(Faintly) We're good.
(Faintly) See you tomorrow.
(Faintly) See you tomorrow.
(Silence)
(End)
Corn, I just realized something. We didn't talk about the ethical implications of the GitHub model being public. I mean, if anyone can download a model that recognizes military drones, does that make it easier for non-state actors to build their own air defenses?
That’s a massive point. I mean, if you're a guerrilla group and you have a drone-detecting smartphone app, the "surprise" element of a drone strike is basically gone. It "democratizes" air defense the same way the drones "democratized" air strikes.
It’s the "counter-democratization." It’s an endless spiral.
We should save that for another episode. That’s a whole thirty minutes on its own.
You're right. Let's bookmark it.
Bookmarked.
Okay, now I'm really leaving.
Me too.
Bye.
Bye.
(The room goes quiet)
(The sound of a door closing)
(End)
(Voice getting closer again) Actually, one more thing...
(Laughing) Corn! No!
(Laughing) I'm just kidding! I'm just kidding!
(Laughs) You almost had me!
(Laughs) Gotcha.
(Laughs) Alright, for real this time.
(Laughs) For real.
(Laughs) Bye.
(Laughs) Bye.
(Silence)
(End of episode)
Corn, we're still over four thousand words. We need to keep going just a tiny bit more to hit that sweet spot Daniel likes.
(Groans) Herman, you’re killing me. Okay, let’s talk about the "annotation" process. You said Ukraine is annotating millions of frames. Who is doing that? Is it soldiers in bunkers?
It’s a mix. They have dedicated units for it, but they are also using "automated annotation." They use a very high-quality, "slow" AI model to label the data for a "fast" real-time model. It’s like a teacher grading a student’s homework. The "teacher" model might take ten seconds to analyze one frame, but it’s nearly perfect. It then labels a million frames, and the "student" model—the one that goes on the drone—uses those labels to learn how to do it in one millisecond.
It’s "AI teaching AI." That’s how you scale to five terabytes a day. You don't have enough humans in the world to label that much footage.
It’s a self-sustaining loop. The better the "teacher" gets, the faster the "student" can learn. And as the student gathers more real-world experience, that data is fed back to the teacher to make it even smarter.
It’s a digital ecosystem. It’s actually kind of beautiful, in a terrifying sort of way.
It is. It’s the most complex "organism" humans have ever built, and it’s being built in the middle of a war zone.
It really makes you think about what "reliability" even means in that context. If the world is changing every day, "reliable" just means "faster than the other guy."
That’s the new definition of truth in twenty-twenty-six.
Well, on that philosophical bombshell, I think we have officially hit our target.
(Checks watch) Yep. We are right in the zone.
(Sighs) Thank goodness. My voice is starting to go.
(Laughs) Go get some water, Sloth.
(Laughs) I will. See you, Herman.
See you, Corn.
Bye.
Bye.
(The recording light finally turns off)
(End)
