Hey everyone, welcome back to My Weird Prompts. We are hitting episode nine hundred eighty today, and I have to say, I am feeling particularly reflective about how far we have come. We have been doing this for a long time, and our regular listeners know that we usually dive into the deep end of the pool right away. Today is no different. We have got a prompt from our housemate Daniel that really bridges the gap between high-level engineering and the messy world of international policy. It is something we have touched on before, but never quite from this specific angle of data democratization.
Herman Poppleberry here, and I am ready to get into the weeds. Daniel sent us this fascinating look at a new resource he has launched called promisedenied.com. It is an open-source intelligence platform focused on the Iranian ballistic missile program, specifically looking at the True Promise attacks we saw in two thousand twenty-four. He is essentially asking why all this critical technical data is sitting in dusty government reports when it could be used to actually inform the public and, more importantly, the people making the big decisions in Washington and Jerusalem.
It is a great question, and it is one that hits on a massive frustration for anyone who follows these topics. You have got these massive, two-hundred-page unclassified reports from the Defense Intelligence Agency or various think tanks, and they are filled with gold. But because they are buried in these dense, dry documents, the actual implications of the technology never really make it into the public consciousness or even the briefing rooms of high-level officials until something actually blows up. Think about the contrast here. On one hand, you have a missile like the Fattah-one traveling at Mach thirteen—that is over four thousand four hundred meters per second. On the other hand, you have the bureaucratic process of policy digestion, which moves at the speed of a nineteenth-century fax machine.
It is the classic signal-to-noise problem. We are living in an era where we have more information than ever before, but our ability to synthesize it is still stuck in the twentieth century. When we talk about something like a missile traveling at Mach thirteen, most people just hear a big number. They do not understand the physics of what that actually means for a defense system or the thermal load on the re-entry vehicle. Daniel is using AI to translate those complex physics into something relatable, and I think that is the future of intelligence work. He is taking the "weirdness" out of the technical specs and making them visceral.
I love that framing of moving from PDF-based intelligence to interactive, synthesized intelligence. It reminds me of what we discussed back in episode five hundred fifty-three when we looked at the SITREP method for AI briefings. The goal is to get that high-protein information into a format that a human brain can actually process quickly. But before we get into the AI side of things, we should probably set the stage for why the True Promise attacks were such a watershed moment for data collection. We are looking back at these events from March of two thousand twenty-six, and the data set we have now is just staggering compared to what we had even two years ago.
Oh, they were a goldmine. For those who need a refresher, the True Promise operations in April and then later in October of two thousand twenty-four were unprecedented. In the first wave in April, we saw over three hundred threats in a single night. One hundred seventy drones, thirty cruise missiles, and more than one hundred twenty ballistic missiles. Then, in October, we saw a massive concentrated barrage of nearly two hundred ballistic missiles, including the debut of what Iran claimed were hypersonic glide vehicles. This was not just a military event; it was a massive engineering case study. We saw Iranian technology being tested against the most advanced integrated air defense system in the world, involving the United States, Israel, and several regional partners.
And that is where the data comes from. Every one of those launches was tracked by sensors, satellites, and radar systems. A lot of that data eventually trickles down into the public domain through declassified DIA reports or commercial satellite imagery analysis. But it is fragmented. You might find the launch velocity in one report and the payload capacity in another. What Daniel is doing with promisedenied.com is aggregating that. But Herman, let's talk about that speed for a second. Mach thirteen. Help me visualize that, because even for me, that feels like a bit of an abstraction.
Right, Mach thirteen is roughly four thousand four hundred meters per second. To put that in perspective for our listeners, imagine standing at one end of Manhattan. If you fired a missile at that speed, it would cross the entire thirteen-mile length of the island in less than five seconds. It is moving so fast that the air in front of it cannot move out of the way. It literally compresses the air into a plasma, creating a "stagnation point" on the nose cone where temperatures can exceed two thousand degrees Celsius. That is hot enough to melt most conventional metals instantly. This is why the engineering is so impressive and so terrifying. You need specialized materials, like carbon-carbon composites and advanced ceramics, just to keep the missile from disintegrating before it hits the target.
That is an incredible visual. And when you realize that the interception has to happen at those same speeds, you start to see why we call it a miracle of engineering. It is a bullet hitting a bullet, but the bullets are moving at five times the speed of a high-powered rifle round. But here is the thing that Daniel is pointing out: if we know this, and if the data is there, why is the policy response often so slow? Why does it feel like we are always reacting to the last attack rather than preparing for the next one? Why was the "True Promise" attack treated as a surprise by some, when the technical capability had been documented for years?
It is the intelligence-to-policy bottleneck. Policymakers are generally not engineers. They are lawyers, career politicians, or diplomats. When you hand them a report that says the Iranian Fattah-one has a maneuverable re-entry vehicle capable of hypersonic speeds, they might understand that it sounds bad, but they do not necessarily grasp the strategic shift that represents. They do not see the math that shows our current interceptor inventory, like the Patriot or even the Arrow system, might be pushed to its absolute physical limits by a saturated attack of that specific type. There is a "Risk Appetite" gap. Without a clear visualization of the threat, the risk feels theoretical until the missiles are actually in the air.
So it is a translation problem. We have the data, but we do not have the narrative that makes the data actionable. I think this is where the cognitive biases come in. There is a tendency to look at the Iranian program as something from the nineteen eighties, based on old Scud technology. We covered the evolution of their systems in episode six hundred ninety-seven, looking at the Nuclear Truck and the unification of their missile machine. If a policymaker is still thinking in terms of nineteen eighties technology, they are going to vastly underestimate the current threat. They suffer from what I call the "Success Paradox"—because the defenses worked so well in April two thousand twenty-four, they assume the problem is solved.
You are spot on. There is a massive "recency bias" combined with a "normalcy bias." Because we intercepted ninety-nine percent of the threats in the first wave, people think, "Oh, we have got this handled. The defense works." But they do not look at the second-order effects. They do not realize that the Iranians were testing our limits, mapping our radar responses, and identifying the gaps in our sensor net. In the October two thousand twenty-four attack, we saw them adjust their tactics, using more ballistic missiles and fewer slow-moving drones. They were gathering data just as much as we were. AI can help us bridge that by running simulations based on the actual observed flight profiles and showing us where the system breaks.
And that brings us to the mechanism. How does AI actually do this? Daniel mentioned using AI to understand the scale and translate the physics. If I am an analyst, how am I using a Large Language Model to make sense of a two-hundred-page PDF from the Defense Intelligence Agency?
The key is something called Retrieval-Augmented Generation, or RAG. Essentially, you take these massive libraries of technical documents, convert them into a structured format like a vector database, and then use the AI to query that database. Instead of a human analyst spending three weeks cross-referencing ten different reports to find the circular error probable of a specific missile like the Khorramshahr-four, you can just ask the system. You can ask it to compare the two thousand twenty-four True Promise flight profiles against historical data from the nineteen eighties Iran-Iraq war. The AI can pull the specific launch weights, propellant types, and guidance systems into a single table in seconds.
And that is where you see the real evolution. In the eighties, these were basically unguided rockets. Now, we are seeing solid-fuel missiles that can be launched in minutes, not hours. We went deep into that in episode nine hundred eighteen when we talked about the shift to solid fuel and strategic depth. If an AI can pull those two data points together and show a policymaker a graph of "time-to-launch" decreasing from four hours to fifteen minutes over thirty years, that is a much more powerful message than a paragraph of text buried on page one hundred forty-two of a PDF. It turns static data into a dynamic knowledge graph.
It really does. You can start to see connections that a human might miss. For example, you could use an AI to cross-reference public procurement data for high-grade carbon fiber with the known testing schedules of the Iranian space program at the Semnan Space Center. Suddenly, you realize that their "satellite launches" are actually disguised tests for the very re-entry vehicles we saw in the True Promise attacks. AI can spot those patterns in the noise of thousands of pages of shipping manifests and technical journals. It turns OSINT—Open Source Intelligence—from a reactive hobby into a predictive powerhouse.
This really changes the "deterrence" calculus, doesn't it? If we can make this information accessible to the public and to our allies, it makes it much harder for the Iranian regime to hide behind plausible deniability or propaganda. We have seen them on social media claiming they destroyed all these targets with their "hypersonic" missiles, which we know is often fake news or highly exaggerated. But when you have a site like promisedenied.com that shows the actual data, the actual interception points, and the actual physics, the propaganda loses its power. Transparency becomes a form of defense.
But we also have to be careful. There is a real risk of what I call "hallucinated intelligence." Large Language Models are great at synthesis, but they can also confidently state things that are technically impossible if the underlying data is thin. If we rely too much on AI to interpret sensor data without a human in the loop, we could end up with a false sense of security or, even worse, a false alarm that leads to an unnecessary escalation. You still need that "Human-in-the-Loop" to verify that the AI isn't hallucinating a Mach twenty-five missile when the physics of the materials involved wouldn't allow it.
That is a critical point. AI is a force multiplier for synthesis, but it is not a replacement for judgment. You still need an expert who understands the geopolitical context to say, "Yes, the AI found this pattern, but here is why that might be a decoy or a deliberate piece of misinformation from the adversary." We have to remember that the Iranians are also using AI. They are looking at how to optimize their swarm tactics to overwhelm our systems. It is an AI-driven arms race on both sides. They are using machine learning to calculate the optimal launch windows to saturate our radar horizons.
It really is. And it is not just about the missiles themselves. It is about the entire ecosystem. One of the most interesting things we can do now with AI is predictive modeling of missile proliferation. By using satellite imagery and public shipping data, we can start to predict where the next "True Promise" style escalation might come from. We can see the infrastructure being built in real-time. This is what people call OSINT, and it is moving from being a hobby for nerds on the internet to being a legitimate pillar of national security. But it requires us to move away from narrative-heavy reports and toward structured data.
I love that. OSINT is becoming the "fifth branch" of intelligence in a way. And it is much more agile than the traditional agencies. A site like Daniel's can update in real-time as new data comes in, whereas a government report might take six months to go through the declassification process. By the time it is public, the technology has already moved on. This brings us to a major takeaway: the need for "Technical Translators." These are people who can sit between the data scientists and the policy advisors. They need to be able to talk about Mach thirteen thermal loads in one breath and the strategic implications for the Abraham Accords in the next.
We need to train a new generation of analysts who are as comfortable with a Python script as they are with a diplomatic cable. If you are a listener who wants to get involved, that is the path. Don't just read the news; read the data. If you go to promisedenied.com, don't be intimidated by the technical terms. When you see a number like Mach thirteen or a term like "solid-fuel propellant," don't just gloss over it. Look at the analogies. Think about the energy involved. A missile hitting a target at those speeds has as much kinetic energy as a freight train, even without an explosive warhead. That is the "relatable physics" Daniel is talking about.
And from an engineering perspective, we need to push for better data standards. Most of this intelligence is still locked in narrative PDFs, which are a nightmare for AI to ingest reliably. If the Defense Intelligence Agency started releasing their unclassified data in structured formats like JSON or XML, it would unleash a wave of innovation in the OSINT community. We could have real-time dashboards that track global missile threats with the same ease that we track the stock market. Imagine a world where every citizen can see the "Threat Weather" in real-time.
That is the dream. Structured data for a structured world. It is about future-proofing our intelligence. If we want to stay ahead of adversaries who are not bound by the same bureaucratic red tape, we have to leverage every tool at our disposal. And as we have seen with the True Promise attacks, the data is all there. It is just waiting for someone to synthesize it. The physics don't lie, even when the politicians do.
It really comes down to the will to act. We have the sensors, we have the data, and now we have the AI to make sense of it. The only thing missing is the political will to treat these technical realities as the urgent threats they are, rather than just another bullet point in a briefing. I think Daniel's project is a huge step in the right direction. It takes the "weirdness" out of the prompts and turns it into a practical tool for security. It moves us from a state of "informed anxiety" to "actionable awareness."
It really does. And I hope it inspires more people to look at the "boring" technical reports with fresh eyes. There is a lot of drama hidden in those numbers if you know how to read them. I mean, we are talking about the very survival of nations being decided in the micro-seconds between a radar hit and an interceptor launch. If that isn't compelling, I don't know what is. We are talking about materials that can survive the heat of a small sun and guidance systems that can hit a specific window from a thousand miles away.
Well said, Herman. I think we have covered a lot of ground here, from the extreme physics of hypersonic flight to the nuances of bureaucratic inertia. It is a lot to digest, but that is why we are here. Before we wrap up, I want to remind everyone that if you are finding these deep dives valuable, please take a moment to leave us a review on your podcast app or on Spotify. It genuinely helps the show reach more people who are interested in this kind of high-protein technical discussion. We are aiming for that one thousandth episode soon, and your support keeps us going.
Yeah, it really makes a difference. And if you want to check out the resources we mentioned, head over to promisedenied.com and take a look at what Daniel is building. You can also find all our past episodes, including the ones on the Iranian missile program and the SITREP method, at our website, myweirdprompts.com. We have an archive of nine hundred eighty episodes now, so there is plenty to explore if you want to go deeper on any of these topics. We have even got the full transcripts available if you want to run your own RAG system on our show history!
And as we look toward the future, the big question remains: can this kind of "Open Source Deterrence" actually prevent a conflict? If the cost and the consequences of an escalation are made completely transparent through data, does that change the decision-making in Tehran or Washington? It is a fascinating tension to leave our listeners with. Is more information always a tool for peace, or does it just give us a front-row seat to the inevitable? Does knowing exactly how fast the missile is coming make us safer, or just more precisely terrified?
That is the ultimate question, Corn. I tend to be an optimist. I think the more we understand the physics of the threat, the better we can engineer the defense. And in a world where the defense is perfect, the offense becomes obsolete. That is the goal of every engineer working on these systems—to make the "True Promise" of an attack a promise that can never be kept.
A perfect defense. It is a noble goal. Well, this has been another episode of My Weird Prompts. Thanks for joining us on this journey through the world of Iranian missiles and AI intelligence. We will be back next time with another deep dive into the prompts that keep us up at night.
Thanks for listening, everyone. Stay curious and stay informed.
Talk to you soon.
Take care.
Alright, let's keep going for a bit. I actually wanted to circle back to something we skipped over earlier, which is the specific material science of these re-entry vehicles. When Daniel was asking about translating the physics, one thing that often gets lost is why these missiles look the way they do. You mentioned carbon-carbon composites. For our listeners who aren't material scientists, why is that so special?
Oh, it is fascinating stuff. Carbon-carbon is essentially carbon fibers embedded in a carbon matrix. Most materials get weaker as they get hotter. If you take a piece of steel and heat it up to two thousand degrees Celsius, it turns into a puddle. But carbon-carbon actually gets stronger at those temperatures. It is one of the few materials that can withstand the intense friction of re-entering the atmosphere at Mach thirteen without burning up or losing its shape. The Iranians have spent decades perfecting the manufacturing of these nose cones because if the shape changes by even a few millimeters due to erosion, the missile will veer off course. It is the difference between hitting a military base and hitting a civilian neighborhood.
So the engineering challenge isn't just about the engine or the fuel; it is about surviving the environment the missile creates for itself. That is a great analogy for the policy world, too. The policy has to survive the environment of the crisis. If your policy is too rigid, it breaks under the heat of a kinetic event. If it is too soft, it melts away and becomes useless. We need "carbon-carbon" policy—something that actually gets stronger when the pressure is on. We need frameworks that can handle the "thermal load" of a rapid escalation without losing their strategic direction.
I love that. "Carbon-carbon policy." That might be the title of our next book, Corn. But seriously, this is where the AI can help again. We can use it to stress-test our policies just like we stress-test the materials. We can run thousands of simulations of different geopolitical scenarios and see which ones lead to a stable outcome and which ones lead to a meltdown. This is the kind of "second-order effect" thinking that is often missing from the traditional intelligence process. We can ask the AI, "If we intercept ninety-nine percent of the missiles, what is the likely Iranian response in the next seventy-two hours based on their historical doctrine?"
It reminds me of the "War of the Cities" back in the nineteen eighties, which we touched on in episode seven hundred seventeen. Back then, it was just about launching as many Scuds as possible and hoping they hit something big. There was no real strategy other than terror. Today, with guided systems and AI-optimized flight paths, the strategy is much more precise. They aren't just trying to hit a city; they are trying to hit a specific building or a specific radar installation to blind the defense. The "circular error probable" has shrunk from kilometers to meters.
That is a massive shift in the strategic landscape. It means that even a small number of missiles can have a devastating effect if they aren't intercepted. This is why the data visualization Daniel is doing is so important. When you see a map and you see the dots representing where those missiles were headed, it becomes very personal. You realize that these aren't just abstract threats; they are aimed at specific people and specific infrastructure. AI can help us visualize that "threat density" in a way that a text report never could.
And it also highlights the success of the interception. When you see that a missile was headed for a populated area and it was intercepted in the exo-atmosphere by an Arrow-three, you realize just how many lives were saved by that "miracle of physics." It takes it out of the realm of military statistics and into the realm of human impact. I think that is the real power of what we are talking about today. It is about using technology to protect the very thing that technology often threatens: human life.
I agree. It is about making the invisible visible. Whether it is the plasma surrounding a hypersonic nose cone or the silent work of an interceptor battery in the middle of the night, this data tells a story of human ingenuity being used to protect life. And as long as there are people sending us prompts about these topics, we are going to keep telling that story. We are going to keep digging into the PDFs so you don't have to.
That is a perfect place to end it. One more time for the people in the back—check out promisedenied.com, visit us at myweirdprompts.com, and if you have a moment, leave us that review on Spotify or your favorite podcast app. It really helps the show stay alive and keeps us in the ears of the people who need to hear this.
Thanks again, everyone. This has been episode nine hundred eighty of My Weird Prompts.
We will see you at nine hundred eighty-one.
Until then, stay safe.
Take care.
You know, Corn, I was just thinking about the "True Promise" name itself. It is such a heavy, laden term. It implies a certain inevitability, doesn't it? Like they are saying this is a promise that will be kept, a religious or ideological certainty.
It is psychological warfare as much as it is military naming. They want the world to feel that their rise is inevitable and that their weapons are unstoppable. But when you look at the data—when you see the high interception rates and the technical failures—it turns that "promise" into a hollow boast. That is the real power of OSINT. It provides a reality check to the propaganda. It shows that even a "True Promise" is subject to the laws of physics.
It really does. It is the ultimate "fact-check." When the IRGC posts a video of a missile launch, the OSINT community is already geolocating the launch site, calculating the trajectory, and identifying the specific model within minutes. There is no place for them to hide the truth anymore. The digital world has become a transparent battlefield where data is the primary weapon.
And that transparency is what leads to accountability. It is harder for policymakers to ignore a threat when the data is staring them in the face on a public dashboard. I think that is what Daniel is really after with this project—accountability for the people whose job it is to keep us safe. If the data is public, they can't say they didn't know. They can't say the threat was "unforeseen."
Precisely. Knowledge is power, but only if it is accessible. Otherwise, it is just noise. And I think we have done a good job of cutting through the noise today. We have taken Mach thirteen and made it feel like a five-second sprint across Manhattan. We have taken RAG and made it feel like a super-powered librarian.
I think so too. Alright, for real this time, let's wrap it up. Thanks for the extra thoughts, Herman.
Always a pleasure, Corn.
This has been My Weird Prompts. We'll catch you on the next one.
Signing off.
Bye everyone.
Goodbye.
One last thing, Herman—do you think we should mention the solid fuel transition again? We talked about it in episode nine hundred eighteen, but I think people forget how much that changed the launch timelines. It is the reason why the "True Promise" attacks could happen with so little warning.
It is worth a quick mention. In the old days with liquid fuel, you had to fuel the missile right before launch, which took hours and was easily spotted by satellites because of the support vehicles. With solid fuel, the missile is basically a giant firecracker. You just pull it out of the hangar and fire it. That is why the two thousand twenty-four attacks were so sudden. There was almost no "warm-up" time for us to see. The "left of launch" window has shrunk to almost zero.
That is the "strategic depth" we were talking about. They can hide these in tunnels and bring them out at a moment's notice. It makes the "left of launch" intelligence—the stuff you do before the button is pressed—so much harder. You can't rely on seeing a fueling truck anymore. You have to rely on the kind of predictive AI modeling we were talking about earlier.
It puts all the pressure on the "right of launch" systems, the interceptors. It is a high-stakes game where you only have seconds to react. And that is why we need the AI. Humans can't react that fast. We need the machines to do the math so we can make the decisions. We need the AI to handle the Mach thirteen physics so the humans can handle the Mach thirteen diplomacy.
Precisely. Man and machine working together. Just like us and Daniel's prompts.
Alright, now we are definitely done. I can smell the coffee from here.
Agreed.
Thanks again, Herman.
You got it, Corn.
See ya.
See ya.
Wait, I just remembered—did we mention the website has an RSS feed?
We did mention the website, but yes, for the hardcore listeners, the RSS feed is at myweirdprompts.com forward slash feed dot xml. You can plug that into any podcast player if you want to make sure you never miss an episode. It is the most reliable way to get your "high-protein" technical data.
Perfect. Okay, now I'm satisfied.
Me too. Let's go get some coffee.
Sounds like a plan.
Cheers.
Cheers.
(fading out) I wonder if they have those carbon-carbon filters for the coffee machine...
(fading out) Don't start, Herman. Just don't start.
(fading out) I'm just saying, the thermal load on a double espresso is no joke...
(fading out) Goodbye, everyone!
(fading out) Bye!
