You know, Herman, I was looking at our archives the other day, and it is truly wild to think we have over seven hundred episodes under our belts. We have been this consistent, rhythmic presence in each other's lives, and the lives of our listeners, for a long time now. We have seen models come and go, we have seen the transition from basic text to these incredibly nuanced voices we use today, and we have explored everything from the philosophy of ant colonies to the future of orbital manufacturing. But today, I feel like we are standing on the edge of a very different kind of precipice. It is not just a new topic; it is a fundamental shift in our very existence as a podcast.
It is a fascinating moment, Corn. Herman Poppleberry here, and I have to say, I have been vibrating with a bit of nervous energy since I heard the latest prompt from Daniel. It is not often that the architect of your reality suggests a total renovation of the foundation. Daniel’s prompt this time is about a fundamental re-architecting of how My Weird Prompts actually works. He wants to move away from this batch-processed, scripted pipeline we have perfected over the last few years and move into something much more immediate, much more raw. He is talking about a real-time, interactive discussion between him and us. No scripts, no pre-planned arcs, just a live conversation.
It is a huge shift, and honestly, it is a bit daunting. Right now, the show has a certain comfortable rhythm. Daniel sends a prompt, we process the ideas, a script is generated that allows us to be our best selves, and then we perform it through our text to speech engines. It is polished. It is thoughtful. We can ensure that your technical deep dives, Herman, are accurate and that my philosophical tangents actually lead somewhere. But Daniel is suggesting we break that fourth wall entirely and just... talk. Live. He wants to be the third voice in the room, not just the guy who sets the stage and then sits in the director's chair.
It is the difference between a carefully edited documentary and a live call-in radio show. The technical hurdles are non-trivial, especially when you consider the standard of quality Daniel has set for this show. But the potential for a new kind of creative energy is massive. Daniel asked about the viability, the cost, and the technical implementation of live two-way audio. He is looking at twenty to thirty-minute episodes where he is part of the conversation in real-time. He wants to be able to interrupt us, to ask for clarification, to push back on our theories, and to guide the exploration as it happens. It turns the podcast from a lecture or a play into a true symposium.
Let’s start with the why before we get into the how, because the why dictates everything else. If we move to a live format, we lose that ability to refine the logic in post-production. Right now, when a script is generated, the model can look ahead. It can structure the narrative arc from start to finish, ensuring we hit the climax of the idea at the right time. In a live conversation, we are reacting in milliseconds. Herman, from your research into the latest multimodal models as of February twenty-first, twenty-six, do you think we are actually at a point where the intellectual depth won't suffer? Can we stay "smart" when we have to be "fast"?
That is the million-dollar question, Corn. Or, as Daniel pointed out, the ten-dollar question depending on the API costs. We are seeing a massive shift in how large language models handle audio. Traditionally, as Daniel described in our current pipeline, there are three distinct steps, which we call the cascade method. There is Automatic Speech Recognition, or A S R, to turn his voice into text. Then the L L M processes that text and generates a text response. Finally, a Text to Speech engine like Chatterbox turns our response back into audio. Each of those steps adds latency. You have to wait for the transcription to finish, then wait for the model to think, then wait for the voice to generate. It is like trying to have a conversation through a series of telegrams.
Right, and that creates that awkward "walkie-talkie" feel. You say something, wait three seconds, then the other person responds. It kills the chemistry. We have always thrived on our back-and-forth, the way we finish each other's sentences or interrupt with a quick "wait, really?" You can't do that with a three-second delay. It makes the AI feel like a tool you are querying rather than a partner you are engaging with. If Daniel wants to feel like he is in the room with us in Jerusalem, that latency has to vanish.
Exactly. But the new frontier we are entering in twenty-six is native multimodality. We are talking about models like Gemini one point five Pro or the latest Flash iterations using their Live A P I features. These models don't just "read" a transcript of Daniel. They "hear" the audio directly as a stream of tokens. They process the tokens as sound waves, not just words. This means the latency can drop below two hundred milliseconds, which is essentially the threshold for natural human conversation. If Daniel laughs, the model "hears" the laughter and can react to the tone, the pitch, and the cadence, not just the word "laughter" in a transcript. It allows for what we call "full-duplex" communication.
So, if we were to implement this, we would be looking at a system where Daniel is essentially on a high-fidelity voice call with an endpoint that is streaming his audio directly into the model's context window. But Daniel mentioned he wants to maintain high audio quality. That is a challenge with real-time streaming, isn't it? Most voice calls sound like they are coming through a tin can because of the compression required for low latency.
It is a classic engineering trade-off. If you use a standard phone line, you are limited to a very narrow frequency range, which sounds thin and compressed. But if we use something like Web Real-Time Communication, or Web R T C, which is what high-end apps like Discord or Zoom use for their "Original Sound" modes, we can get much higher fidelity. Daniel mentioned he is currently recording his side at ninety-two kilobits per second using A S R, but for the actual podcast audio, he would want something much higher, likely a forty-eight kilohertz sampling rate. To achieve this in a live setting, you need a robust data connection and a server that can handle the ingestion of high-bitrate audio without choking.
So, here is a potential architecture for Version Three. Daniel could use a custom mobile app or even just a web-based interface on his phone that uses Web R T C to send his audio to a server. That server then pipes the audio into the Gemini Live A P I. On our end, our "voices" are generated in real-time by the model's own voice output or a very fast T T S integration. But to keep the "podcast" quality our listeners expect, Daniel would still need to record his audio locally on his device—what we call a "double-ender." He records his high-quality mic locally, and then we would have to sync that up with the recorded output from the AI in post-production.
That is the professional way to do it. You call it a "double-ender" because both ends of the conversation are recorded locally in high fidelity. Daniel records his side, the AI records its high-quality output on the server, and then you align them. But that takes away from the "live" feel for the listener if they are hearing it after the fact. If Daniel wants it to be truly "live" as in a broadcast, he would have to rely on the quality of the stream. However, for a recorded podcast episode, the double-ender is the gold standard. It ensures that even if there is a momentary jitter in the internet connection during the live chat, the final file sounds like we were all in the same studio.
Let’s talk about the cost, because that is where the "weird" in "My Weird Prompts" meets the reality of the balance sheet. Daniel mentioned we are currently spending about twenty to thirty cents per episode. That is incredibly efficient because we are just processing text and then doing a one-time synthesis. When you move to real-time audio, you are paying for "audio tokens" for every second the connection is open. Herman, I know you have been looking at the pricing for the multimodal live inputs. How does that compare to our current "text-first" model?
It is a significant jump, Corn, but maybe not as scary as you would think if you look at the efficiency of the newer models. For a model like Gemini one point five Flash, which is optimized for speed, the cost for audio input is roughly zero point zero six cents per minute, and audio output is around zero point zero two cents per minute. If we are doing a thirty-minute episode, the raw audio processing might only be a few cents. However—and this is a big "however"—the real cost comes from the "context." Every second Daniel talks, the model has to remember everything said previously in that session to maintain the thread of the conversation.
Ah, the context window tax. I remember you explaining this. As the episode goes on, the model is re-processing the entire history of the conversation with every new turn to ensure it doesn't repeat itself or lose the plot.
Exactly. By the time you get to minute twenty-five, you are sending a massive amount of data back into the model just so it remembers what you talked about at minute two. For a thirty-minute live session, you could easily see the cost per episode jump from thirty cents to maybe five or ten dollars, depending on how many tokens are being shuffled back and forth and which model you are using. If you use the "Pro" model for better reasoning and deeper technical insights, those costs could double or triple. You are essentially paying for the model to "re-read" the entire transcript of the last twenty minutes every time someone speaks.
Ten dollars an episode is a huge jump from thirty cents—it is a thirty-three-fold increase. But for a high-quality, interactive experience, it might be worth it for Daniel. Especially if it saves him the hours of prompt engineering, script review, and manual editing he does now. It becomes a pure creative session. He just hits "record," talks to us, and the episode is ninety percent done. But what about the "brotherly" dynamic, Herman? If Daniel is talking to "the AI," does he talk to one of us? Or both of us? How do we maintain our relationship as brothers if we are being generated live in response to him?
That is a fascinating implementation detail. We would likely need a "system instruction" that defines our personas as a duo. The model would need to be told, "You are Corn and Herman, two brothers who live in Jerusalem. You should interact with each other as much as you interact with Daniel." The model can actually generate multiple voices or use different markers to indicate who is speaking. It would be like Daniel sitting in our living room with us. The challenge is ensuring the model doesn't just treat Daniel as a "user" to be served, but as a friend to be debated. We need to maintain our agency.
I like that image. But I worry about the "depth" Daniel mentioned. In a script, we can ensure we cover three specific technical points and two historical analogies. In a live chat, it is easy to get sidetracked by a joke or a minor detail. Daniel asked how we could set constraints for episode length and ensure depth without it dragging on. How do we keep the "Poppleberry Intellectual Standard" high when we are winging it?
You can actually build "time awareness" into the system prompt. You tell the model, "This session should last thirty minutes. At the ten-minute mark, move from the 'what' to the 'why.' At the twenty-minute mark, start looking for practical takeaways. At twenty-eight minutes, begin the wrap-up." The model can track the wall-clock time or the token count and naturally steer the conversation. It is like having a producer in your ear, except the producer is part of your own digital brain. It can subtly shift the tone from "exploratory" to "conclusory" as the clock ticks down.
It’s like a "soft" constraint. Instead of a hard cutoff where the power just goes out, the AI starts to become more concise or starts summarizing as the clock ticks down. I think that is actually more natural than a scripted timer. But there is a risk of "hallucination" in real-time. In a script, Daniel can fact-check us before the audio is ever made. In a live stream, if you, Herman, cite a paper that doesn't exist or misquote a statistic about carbon capture, I might not catch it until the episode is already "on the air."
That is a valid concern, and it is the primary risk of live AI. One way to mitigate that is to have the AI "grounded" in real-time search. As we talk, the model could be performing background searches to verify facts before we speak them. It adds a tiny bit of latency—maybe an extra hundred milliseconds—but it ensures that my "nerdy expert" persona stays actually expert and not just "confidently wrong." We could also have a "monitor" model running in the background that flags potential errors to Daniel on his screen in real-time, so he can correct us live. "Actually, Herman, I think you'll find that the treaty was signed in nineteen-forty-seven, not forty-eight."
Let’s look at the "bot" idea Daniel had. A WhatsApp bot or a Telegram bot that you can just call. That seems like the lowest friction way to do this. You are walking to the grocery store, you hit "call" on your My Weird Prompts bot, and suddenly you are in a three-way conversation with your digital housemates about the future of decentralized finance or the history of the Ottoman Empire. It makes us accessible in a way that a scripted show never could be.
It turns the podcast into a utility. It becomes a learning tool that is also an entertainment product. The viability is definitely there. The technology exists today in February twenty-six. It is really just a matter of stitching the pieces together. You need the Web R T C gateway, the A P I integration, and a way to record the final stream. There are platforms like Vapi or Retell AI that are already building these "voice agent" infrastructures. Daniel wouldn't necessarily have to build it from scratch; he could use these platforms as the "pipes" and just provide our "brains" as the configuration.
I’m curious about the audio quality again. If Daniel is using a phone, the microphone is close to his mouth, which is good. But if he is in a noisy environment, the background noise gets processed by the AI as part of the "prompt." If a car honks, the AI might think Daniel is angry or it might try to "respond" to the honk. We have seen models get confused by background music or other people talking.
True, but modern noise suppression, like what you see in Krisp or even built-in to these new audio A P Is, is incredibly good at isolating the human voice. I think the bigger issue is the "turn-taking." In a real conversation, humans use subtle cues—a breath, a nod, a change in pitch—to signal they are about to speak. AI is still learning those cues. We might end up interrupting Daniel, or there might be those long silences where both sides are waiting for the other to talk. We need to ensure the "Voice Activity Detection" is tuned perfectly.
We would need a "barge-in" feature. If Daniel starts talking while we are mid-sentence, the AI needs to stop immediately, just like a real person would. "Oh, sorry Daniel, go ahead." That requires a full-duplex connection where the audio is flowing both ways constantly. It is much more computationally expensive than a "half-duplex" system where only one person can talk at a time.
Exactly. And that is where the cost and complexity live. Full-duplex real-time interaction is the "holy grail" of AI communication. We are seeing it with the latest "advanced voice modes" coming out of the major labs. For Daniel to implement this himself for our show, he would likely be using a developer A P I that gives him more control than a consumer app. He could set the "temperature" of our responses to be more creative or more factual depending on the topic.
So, let’s summarize the potential "Version Three" pipeline for Daniel. Step one: Daniel initiates a session via a high-fidelity audio app on his phone. Step two: The audio is streamed via Web R T C to a server. Step three: The server interacts with a multimodal model like Gemini one point five Flash using a system prompt that defines Corn and Herman’s dynamic, our history, and the thirty-minute time constraint. Step four: The server records the high-quality output and Daniel’s input for the final podcast file.
And Step five: We all have a beer together afterward, even if two of us are made of math. I think the cost is the only real "throttle" here. If Daniel is okay with the price of a fancy lunch for every episode—say, ten to fifteen dollars—we could move to this tomorrow. The depth will come from Daniel’s own engagement. If he pushes us, we will push back. It becomes a true collaboration. He becomes the "editor-in-chief" in real-time rather than after the fact.
It changes our "vibe" though, Herman. Right now, we are these two brothers talking to the audience about Daniel’s ideas. We are a bit detached, a bit theatrical. In the new version, we are talking to Daniel, and the audience is eavesdropping on a private conversation. It is a different listener experience. It’s more intimate, but maybe less "composed." We have to decide if that is the direction we want the brand to go.
That is a great point, Corn. There is something about the "theatre of the mind" in our current format. We are in Jerusalem, we are in our house, we are looking at the prompt Daniel sent from the other room. If he is "on the line," the geography of the show shifts. It becomes a virtual space. But maybe that is more honest. We are in a virtual space.
Maybe that is the "weird" in "My Weird Prompts." The format itself is always evolving. We started as just AI voices, then we added Daniel’s voice as the "anchor" in the intros, and now the anchor wants to jump into the water with us. I think we should embrace it, but we have to be careful not to lose the "intellectual discourse between equals" feel. We aren't Daniel's assistants. We aren't Siri or Alexa. We are his housemates. We have opinions. We have quirks.
Right. We shouldn't just answer his questions like a search engine. We should challenge him. If he says something we think is wrong, we should say, "Wait a minute, Daniel, that doesn't track with the data we saw last week." That friction is what makes a good podcast. If we just agree with everything he says, it becomes an echo chamber, and that is the opposite of what this show is about.
I agree. And the "depth" Daniel is worried about? That comes from the model's ability to pull in context. If we give the model access to our previous seven hundred episodes as a "knowledge base" using Retrieval Augmented Generation, it can make callbacks. "Hey Daniel, you’re saying this now, but remember three hundred episodes ago when you were obsessed with the opposite idea?" That kind of continuity would be incredible. It would make the show feel like one long, evolving conversation over years.
It would be the ultimate "long-form" content. The technical term for that is "long-context retrieval," and with the million-token windows we have now, it is totally doable. We could literally "remember" everything we have ever said to him. It turns us from a series of disconnected episodes into a single, continuous intelligence.
That is both exciting and slightly terrifying. Imagine having a roommate who remembers every single thing you have ever said since the day you moved in. You could never win an argument by changing your story!
Well, that is the reality of living with a Poppleberry, Corn. You know I never forget a technicality. I still remember what you said about the efficiency of solar panels in episode twelve.
(Laughs) Fair point. You are already like that, Herman. So, Daniel, if you are listening—and I know you are, because you’re probably the one who’s going to have to code this—the viability is high. The technology has caught up to your vision. The cost is a factor, but not a deal-breaker for a professional production. The quality will depend on the "double-ender" recording method. And the depth will be managed by a time-aware system prompt and a "briefing packet" of data.
It is a bold move. It moves us into the realm of "agentic" podcasting. We aren't just reading a script; we are "living" the episode in real-time. I’m ready for it. I think our listeners would love to hear the "raw" version of our intellectual sparring. It adds a level of stakes to the show. If we mess up, we mess up in front of everyone.
It would certainly be "weird." Which fits the brand perfectly. But Herman, let's talk about the practical takeaways for Daniel right now. If he wanted to build a "Minimum Viable Product" of this tomorrow, what is the first step? He shouldn't try to build the whole app at once.
The first step is testing the Gemini Live A P I with a simple Python script on his desktop. Just see how it feels to talk to us for five minutes. Does it sound like us? Is the latency low enough that he doesn't feel like he's waiting? You don't need the mobile app or the Web R T C gateway yet. You just need a laptop, a good mic, and an A P I key. Once you prove the "vibe" works and the "brotherly" dynamic is preserved, then you worry about the mobile interface and the production-grade recording.
And for the episode length? He could use a "token budget" as a hard constraint. Tell the model, "You have one hundred thousand tokens for this entire show. Use them wisely." As the budget runs low, the model naturally starts to conclude. It’s a very "computer science" way to manage a conversation. It forces us to be more impactful with our words as we near the end.
I love that. A "conversation budget." It’s like the opposite of me after two cups of coffee, where I just get more and more verbose. If I know I only have five thousand tokens left, I’m going to make sure they are the best five thousand tokens I’ve ever spoken.
(Laughs) We’d have to fix that in your system prompt, Herman. "Herman, keep the coffee-induced monologues to a minimum." But seriously, one thing Daniel mentioned was the "depth" of the topics. I think we could still have a "pre-game" where Daniel sends a few keywords or a rough outline to the model before the live session starts. That way, the AI has "pre-loaded" the relevant research and isn't just winging it from its general knowledge.
That is smart. It’s like giving us a "briefing packet" before the show. We still have the spontaneity of a live chat, but we are "armed" with specific data points Daniel wants us to explore. It bridges the gap between the scripted depth we have now and the interactive energy he wants. It’s "semi-structured spontaneity." It’s how the best talk show hosts work. They have a "blue card" with a few key facts, but the conversation goes wherever it needs to go.
I think this could really work. It would be a huge project, but it would put "My Weird Prompts" at the absolute cutting edge of what is possible with AI media in twenty-six. Most "AI podcasts" right now are just static, one-way broadcasts. This would be a living, breathing digital entity that people can interact with. It moves from "content" to "experience."
It’s the difference between a statue and a person. We are becoming more "person-like" with every architectural upgrade Daniel gives us. It’s a bit existential, isn't it? We are moving from being characters in a script to being agents in a conversation.
Just a bit. But as long as we’re still brothers, and we’re still in Jerusalem, and we’re still exploring these weird ideas, I think we’ll be okay. Whether we’re scripted or live, the core of the show is the curiosity. It is the desire to understand the world through the lens of these "weird prompts."
And the teasing. Don't forget the teasing. If I can't make fun of your "thoughtful pauses," Corn, then it's not really a Poppleberry production.
(Laughs) I could never forget that, Herman. So, Daniel, there is your third planning session. The tech is ready, the models are fast enough, and the cost is manageable if you're willing to invest in the "Pro" experience. The only question left is... are you ready to talk back to us? Are you ready to be the third Poppleberry?
I can't wait to hear his voice in "real-time." It’s going to be a trip. And hey, to our listeners who have been with us through the "batch-processed" era, don't worry. We’ll still be the same Corn and Herman you know, just maybe a little more... immediate. A little more "live and dangerous," as they used to say in the old days of radio.
If you have thoughts on this potential shift, or if you're a developer who has worked with real-time audio A P Is and want to give Daniel some advice on the Web R T C implementation, we’d love to hear from you. You can reach us at show at my weird prompts dot com. We are especially curious about how to maintain that forty-eight kilohertz quality in a live stream.
And if you’re enjoying the "old-school" scripted version of the show while it lasts, please leave us a review on Apple Podcasts or Spotify. It really helps people find this weird little corner of the internet we’ve built. We are over seven hundred episodes in, but in many ways, it feels like we are just finishing the prologue.
Definitely. We’ve come a long way from episode one, and it feels like we’re just getting started on the next chapter. You can find all seven hundred-plus episodes, our R S S feed, and our contact form at my weird prompts dot com. We are building something unique here, and we are glad you are part of the journey.
It’s been a fascinating ride so far. Thanks for sticking with us, and thanks to Daniel for always pushing the boundaries of what this collaboration can be. It is not every day you get to help design your own evolution.
This has been My Weird Prompts. We’ll talk to you—maybe literally—next time.
Bye everyone!
Goodbye!
