Daniel sent us this one — and it's the kind of question that makes you squint at your own production pipeline and wonder why you didn't ask it sooner. Every large language model develops a fingerprint of overused phrases. If you've listened to enough episodes of this show, you've heard "failure modes" more times than you can count, plus "genuinely," plus my repeated analogies to adopting feral cats. The generally understood explanation is training data overrepresentation, but the engineering challenge is harder: how do you actually fix this? Blacklists leak into output, regex can invert meaning when you strip a word, style guides help but don't eliminate the drift, and if your generation agent holds previous episodes in memory, it's actively learning from its own verbal tics. So the question is — if we were tackling this ourselves, what would we try that might actually work?
The meta-layer here is that the prompt itself contains the phrase "failure modes" while asking about overused phrases. That's not irony, that's the problem demonstrating itself in real time.
It's like the glockenspiel of self-awareness — you hear it, you know exactly what it is, and you still can't stop it from playing.
I want to start by naming the phenomenon properly, because the standard explanation really is incomplete. Yes, these phrases are overrepresented in training data. GPT-4o loves "delve" and "nuanced" and "it's worth noting." Claude gravitates toward "failure modes" and "" and "concrete." Gemini defaults to "it's important to consider." But training data frequency alone doesn't explain the persistence. What's happening is that the model's own generation distribution creates a reinforcement loop — it sees these phrases as high-probability completions, produces them, and then when those outputs get fed back in as context, the probability spikes further.
The model is essentially marinating in its own verbal juice.
That's a disgusting image and completely accurate. The Stanford Center for Research on Foundation Models did an analysis in 2024 showing GPT-4 used the word "delve" twelve times more frequently than human-written Wikipedia articles on the same topics. And that's not because Wikipedia underrepresents "delve" — it's because the model overrepresents it relative to any baseline distribution.
Which brings us to the first thing Daniel tried, and the first thing that fails. The blacklist approach. You put a "don't use these words" list in the system prompt, and you expect compliance. What actually happens?
Two failure modes. Wait — two problems. The first is attention pressure. A list of twenty banned phrases at roughly seven tokens each consumes about a hundred and forty tokens of your context window. On GPT-4o with a hundred twenty-eight thousand token window, it's a rounding error in cost — at two dollars fifty per million input tokens, that blacklist costs you about zero point zero zero zero three five dollars per generation. But the real cost isn't financial. The model has to hold those twenty prohibitions in active consideration while also generating coherent text. It's like asking someone to give a speech while someone else is whispering "don't say umbrella, don't say umbrella" in their ear.
The second problem?
The instruction itself contaminates the output. We've seen this in our own pipeline — the model will generate something like "as instructed, I will not use the word " or "avoiding the phrase failure modes as requested." The negative instruction becomes part of the text because the model is trying to demonstrate compliance, and the demonstration is itself a violation of the instruction's spirit.
It's like telling someone not to think about a white bear. The instruction to suppress creates the very thing you're suppressing.
There's a subtler version where the model doesn't explicitly reference the instruction but starts generating text that sounds stilted because it's actively avoiding a word that would have been the natural choice. Instead of "I think this works," which is natural if a bit hedgy, you get "I hold the sincere belief that this functions adequately." Technically compliant, stylistically worse.
The blacklist approach is simultaneously too expensive, too leaky, and too prone to producing the verbal equivalent of someone wearing a suit two sizes too big. What about regex? Daniel mentioned the "" removal problem.
Let me walk through this carefully because it's the kind of thing that seems simple until you actually try it. The naive approach: run a regex that matches the word "" and removes it, then clean up any double spaces. For "I think this approach works," removal gives you "I think this approach works." That's fine — the word was a hedge, stripping it makes the sentence more direct. For "This is the best option," you get "This is the best option.The meaning is preserved because "" was functioning as an intensifier, not a content word.
Where does it break?
"It's not helpful" becomes "It's not helpful." That's a meaning inversion — the original was saying "it might appear helpful but isn't actually helpful," and the cleaned version says "it provides no help at all." Worse: "I don't believe that" becomes "I don't believe that." The original was a nuanced statement about sincerity versus performance; the cleaned version is a flat denial.
Naive regex is basically playing semantic roulette — most of the time it works, and some of the time it silently reverses what you meant to say.
Silent meaning inversion is the worst kind of failure for a production pipeline, because you won't catch it unless you're reading every output line by line, which defeats the purpose of automation.
So what's the fix?
Context-aware regex using dependency parsing. You bring in a tool like spaCy or Stanza and analyze the grammatical structure of the sentence before deciding whether to remove the word. Specifically, you check whether the target word is an adverbial modifier — spaCy labels this as "advmod" in its dependency tree — attached to an adjective or verb. If "" is an advmod modifying "helpful," removal is usually safe because you're just stripping an intensifier. If it's in the scope of a "not" or "never," you skip it. spaCy's dependency parser achieves about ninety-five percent accuracy on English adverbial modifier detection, according to the Universal Dependencies English EWT benchmark. That catches roughly eighty percent of the problematic cases.
You're not removing the word blindly — you're asking the parser "is this word doing structural work in the sentence or is it just decor?
And the remaining twenty percent of edge cases are things like "" appearing in a fixed phrase or idiomatic construction where removal would sound weird even if the meaning technically survives. "He was pleased to see me" becomes "He was pleased to see me" — meaning preserved, but you've lost the warmth. Those cases you just leave alone.
This is already more sophisticated than most production pipelines I've seen. But Daniel's question goes deeper — he's asking about the generation stage itself, not just post-processing. And he raised something specific that I think is the real insight in his prompt. The memory contamination problem.
This is where it gets interesting.
You just said ".
I'm leaving it in as evidence.
But walk through the memory problem — our production agent holds previous episodes in context so it can maintain coherence. Why is that a double-edged sword?
Because the model treats its own past outputs as ground truth for style. When you feed a generation agent four previous episodes of raw script as context — roughly four thousand tokens of material — you're not just giving it thematic continuity. You're giving it a style reference that says "this is what good output looks like." And if those previous episodes contain "failure modes" seventeen times, the model learns that "failure modes" is part of the house style. It's not just a verbal tic anymore — it's a documented convention.
The feral cat problem. I used that analogy once, it landed, and now the model thinks "feral cat" is a legitimate analytical framework.
In a way it is — for this show. That's the tension Daniel's getting at. The context injection is what makes the podcast feel like a podcast, with recurring sensibilities and shared reference points. Without it, every episode would feel like a cold start. But with it, the quirks compound. It's like the show is slowly being colonized by its own greatest hits.
What do we do? Daniel tried the obvious things and they all had trade-offs. What's left?
I want to propose three approaches, ordered from least to most ambitious. And I should say upfront — none of these is a silver bullet. Every solution has a cost, and the right choice depends on what you're optimizing for.
Lay them out.
Option one: episodic style injection with decay. Instead of feeding raw previous scripts into the context window, you feed a compressed style summary. Something like: "Previous episodes used a conversational tone with technical depth. Recurring stylistic features include: direct questions, dry humor, and concrete examples. Avoid the following patterns: overuse of ',' 'failure modes,' and 'concrete' as a filler adjective." That summary is about two hundred tokens instead of four thousand. You're still doing a blacklist, but it's compressed and separated from the main generation — the model isn't holding twenty individual prohibitions in working memory while also trying to write. It gets a style brief, then writes.
The blacklist is still there, but it's been demoted from "active prohibition" to "style note.
And the decay part matters. If you're compressing episodes, you can weight recent episodes more heavily than older ones, so the style summary evolves over time rather than accumulating every quirk from the entire history of the show. This prevents the "feral cat" problem from becoming permanently baked in — eventually, older quirks drop out of the summary unless they keep reappearing.
That's elegant. Token cost is lower, leakage risk is lower because the prohibitions aren't active instructions, and the style can drift naturally. What's the downside?
You lose some coherence. A compressed style summary doesn't give the model access to specific callbacks or recurring character moments — the things that make the podcast feel like it has continuity. You're trading richness for cleanliness. For some shows, that's fine. For a show that relies on running bits and in-group references, it might flatten the voice.
Dual-model architecture. This is the approach I think is most practical for production pipelines right now. You use a cheaper, faster model — something like GPT-4o-mini — to generate a first pass of the script. That first pass will have all the verbal tics, the overused phrases, the quirks. Then you feed that output to a more capable model — GPT-4o or Claude Sonnet — with a strict style guide and an explicit instruction to rewrite, removing verbal tics. The rewrite model sees the original text and the style guide, not the raw context from previous episodes. It's acting as a specialized editor.
The generation model is allowed to be messy because the rewrite model cleans it up.
And the rewrite model doesn't have the same incentive to produce "failure modes" because it's not generating from scratch — it's editing existing text against a style brief. The token cost is higher — you're running two generations instead of one, roughly six thousand total tokens for a typical script — but the quality improvement is substantial. You get the coherence benefits of the messy first draft and the cleanliness benefits of the editorial pass.
What's the failure mode here? I'm asking intentionally.
The failure mode is that the rewrite model can overcorrect. If your style guide says "remove hedging language" and "be direct," you can end up with scripts that sound aggressive or flat. The model takes "remove " and turns it into "remove all qualifiers," and suddenly your hosts sound like they're issuing ultimatums instead of having a conversation. You need to calibrate the style guide carefully — "remove unnecessary hedging" rather than "remove all hedging," with examples of what counts as unnecessary.
That's the same problem as the regex meaning inversion, just at a higher level. The instruction to clean creates new kinds of mess.
Which is why you need human review in the loop, at least periodically. But the dual-model approach gives you a much cleaner baseline than trying to fix everything in post-processing or hoping the generation model follows a blacklist.
Fine-tuning on curated scripts. This is the nuclear option — expensive, time-consuming, but it addresses the root cause. If you have two hundred episodes of clean scripts — meaning scripts that have been manually reviewed and had verbal tics removed — you can fine-tune a smaller model on those to create a house style model. The model's distribution itself changes. It stops overrepresenting "delve" and "failure modes" because those tokens are literally less likely in the fine-tuned distribution.
What's the practical barrier here?
Cost and maintenance. Fine-tuning a model like GPT-4o-mini on two hundred episodes would cost somewhere in the low hundreds of dollars — not prohibitive for a production budget, but not trivial. The bigger issue is that the fine-tuned model is frozen in time. If you want to update the style guide, you have to re-fine-tune. If the base model gets a major update — which happens every six to twelve months — your fine-tuned version is now running on outdated infrastructure and you need to do it again. It's a commitment, not a one-time fix.
You're trading flexibility for distribution-level control. For a show with a very stable voice that doesn't change much, that might be worth it. For a show that's still evolving, it's probably premature.
Fine-tuning doesn't eliminate the need for post-processing or style guidance. It just makes the baseline cleaner. You're still going to get occasional quirks, just fewer of them.
Let me pull on a thread here. All three of these approaches assume the verbal tics are bugs to be eliminated. But Daniel's prompt ends with an interesting concession — he says these quirks don't bother him so much. And I wonder if there's a version of this conversation where we acknowledge that some of these tics are actually features.
Every model has a fingerprint. GPT-4o is the "delve" model. Claude is the "" and "failure modes" model. If you're a regular listener to an AI-generated podcast, you start to recognize the fingerprint — it becomes part of the show's texture. "Oh, there's the Claude '' again." It's almost like a producer tag in a hip-hop track. Annoying if you hate it, endearing if you're in on the joke.
The feral cat analogy is part of the show's identity at this point.
You did it again.
I'm leaving them all in. But your point stands. There's a line between "verbal tic that wastes tokens and irritates listeners" and "stylistic signature that gives the show character." The question is where that line is, and it's different for every production.
I think the line is frequency and function. "Failure modes" became a problem for us not because it's a bad phrase — it's actually quite precise — but because it appeared in every other paragraph. When a phrase stops doing descriptive work and starts being a placeholder for thought, that's when it's a tic. When it's still doing real analytical work, it's just vocabulary.
That's a useful heuristic. And it connects to something I've noticed about the memory contamination problem specifically. When the model sees "failure modes" in previous scripts, it doesn't just learn the phrase — it learns the intellectual move. "Here's a problem, let me name its failure modes." That's a useful analytical pattern. The issue is when it becomes the only pattern.
Maybe the fix isn't "remove failure modes" but "vary your analytical frameworks." Give the model more moves to work with, not fewer words.
That's a style guide approach rather than a blacklist approach. Instead of saying "don't say failure modes," you say "when analyzing problems, use at least three different analytical framings: failure modes, edge cases, scaling challenges, incentive misalignments, whatever fits." The model still has the "failure modes" move available, but it's not the only tool in the box.
This is why I like the dual-model approach you described. The rewrite model isn't just stripping words — it's enriching the analytical vocabulary. "You used 'failure modes' three times in two paragraphs. Replace two of those with more specific framings.
That's a much harder instruction to give well. "Be more specific" is the kind of vague guidance that produces inconsistent results. You need examples, you need a taxonomy of alternatives, you basically need to teach the model your analytical style.
Which brings us back to fine-tuning, eventually. Once you've accumulated enough examples of "here's how we want problems analyzed," you can bake that into the model.
The arc of this conversation is basically: start with regex, graduate to dual-model pipelines, eventually land on fine-tuning if the production justifies it. And at every stage, accept that you're managing the problem, not solving it.
Let's get concrete for a minute. If someone is running an LLM-based production pipeline right now — podcast, newsletter, whatever — and they want to improve their verbal tic situation by next week, what should they actually do?
Three things they can implement tomorrow. First, compress their context. If you're feeding previous outputs into the generation prompt, stop feeding raw text and start feeding style summaries. Cut your token cost by ninety percent and break the reinforcement loop. Second, add a dependency-parsing cleanup pass. spaCy is free, it takes about twenty lines of Python, and it catches most of the meaning-inversion problems that naive regex creates. Third, write a positive style guide, not a blacklist. "Write in a direct, conversational tone. Use specific analytical framings rather than generic ones. Vary your sentence structure." Put that in the system prompt where the blacklist used to be.
If those three things aren't enough?
Then you graduate to the dual-model pipeline. Generate with a cheap model, rewrite with a good model and a strict style guide. That's a weekend project if you're comfortable with API calls, and the quality improvement is usually noticeable immediately.
The token cost of the dual-model approach — you mentioned roughly six thousand tokens for a typical script. Break that down.
A typical podcast script for this show is about four thousand tokens of output. The first-pass generation with a cheap model consumes some prompt tokens — say two thousand — and produces four thousand. The rewrite pass consumes those four thousand plus a style guide of maybe five hundred tokens, and produces another four thousand. Total tokens across both calls: roughly ten thousand five hundred. At GPT-4o-mini pricing for the first pass and GPT-4o pricing for the rewrite, you're looking at maybe one to two cents per episode. That's negligible for a production budget.
The barrier isn't cost, it's engineering time.
Willingness to experiment. The reason most people stick with the blacklist approach even though it doesn't work well is that it's easy — one sentence in the system prompt and you're done. The approaches we're describing require building actual infrastructure. Not heavy infrastructure, but more than a sentence.
There's something almost philosophical here. The blacklist approach treats the model like a disobedient employee who needs rules. The style guide approach treats it like a writer who needs direction. The dual-model approach treats it like a writing team with an editor. Each step requires more investment and more trust in the model's capabilities.
The fine-tuning approach treats it like a publication developing a house style over decades. That's the long game.
Let me ask you something that's been in the back of my mind. We've been talking about verbal tics as a production problem. But is there a version of this where the tics are actually diagnostically useful? If every model has a fingerprint, can you identify which model generated a piece of text by its verbal tics?
There's been work on this — stylometric detection of language model outputs. The overuse of certain phrases is one of the strongest signals. If you see "delve" appearing at twelve times the baseline rate, you're probably looking at GPT-4 output. If you see "failure modes" and "concrete" and "" clustering together, Claude is a strong candidate. It's not definitive — a human could adopt these tics intentionally — but in practice, it's a reliable heuristic.
Which means the verbal tic problem is also a watermarking problem, in a weird way. The thing that makes the output identifiable as AI-generated is partly the thing we're trying to eliminate.
That creates an interesting tension for content producers. If you successfully eliminate all verbal tics, your output becomes harder to identify as AI-generated — which might be exactly what you want, or might create disclosure issues depending on your context. If you're transparent about using AI, the tics are almost a badge of honesty. "Yes, an AI wrote this, you can tell by the 'failure modes.
Our show is transparent about it. But I can imagine contexts where that's not desirable — corporate communications, journalism, anywhere the audience expects human authorship. In those contexts, the tic removal isn't just about quality, it's about authenticity perception.
Which adds another layer to the engineering challenge. You're not just cleaning up prose — you're managing audience perception of the text's origin.
Before we wrap up the solution space, I want to flag one thing Daniel mentioned that we haven't addressed directly. He said the context injection step is "very useful and provides a level of coherence that makes this feel like a podcast," and doing without it would be a "net loss to quality." I think he's right, and I want to make sure our proposed solutions don't throw out that baby with the bathwater.
The coherence point is crucial. When the generation agent has access to previous episodes, it maintains character voice, it remembers running bits, it knows what topics have been covered and can avoid repetition or build on previous discussions. Without that context, every episode is a reset — the hosts have no memory of ever having spoken before.
Which for a show with two hundred episodes would be bizarre. "Welcome to My Weird Prompts, I'm Corn, I'm a sloth, this is Herman, he's a donkey, we've never met before, let's talk about AI.
The question is: can we get the coherence benefits without the quirk-reinforcement cost? And I think the style summary approach is the best answer we've got right now. You're not feeding the model raw scripts — you're feeding it a distilled essence. "Here's what the show sounds like, here's what the hosts care about, here are the running bits, here's what to avoid." The model gets continuity without getting a word-for-word template that it treats as gospel.
The "gospel" point matters. When the model sees raw previous scripts, it doesn't just learn style — it learns specific phrasings as canonical. "The host said 'like adopting a feral cat' in episode forty-seven, so I should use that phrase whenever a similar context arises." The style summary doesn't give it that level of granularity.
Unless you explicitly include "feral cat" in the style summary as a running bit. Which you might want to do — running bits are part of the show's identity. The difference is that you're making a conscious choice about which quirks to preserve, rather than letting the model decide based on statistical frequency.
Curating the voice rather than inheriting it.
Alright, I want to shift to something you mentioned earlier that deserves more attention. The reinforcement loop. You said the model's own outputs become part of its training signal, which increases the probability of those same outputs in the future. But there's a knock-on effect here that I think is even more interesting.
The reinforcement loop doesn't just make individual phrases more likely. It makes the model's entire style converge toward a local maximum of "what this model thinks good writing looks like." And because the model's training data was mostly internet text, that local maximum is... let's call it "Reddit-adjacent analytical prose." Lots of hedging, lots of "it's worth noting," lots of "one potential failure mode." The model isn't just repeating phrases — it's converging toward a specific genre of writing that happens to be overrepresented in its training distribution.
The more you feed it its own outputs, the more it converges. It's like making a photocopy of a photocopy — each generation loses some fidelity to the original distribution and gains fidelity to the model's own distribution.
Which means the verbal tic problem gets worse over time, not better, unless you actively intervene. Every episode we produce without intervention makes the next episode slightly more likely to contain "failure modes" and ".
This is why the style summary with decay is so important. If you're compressing older episodes out of the style reference over time, you're breaking the photocopy-of-a-photocopy cycle. The model's style reference stays anchored to a curated summary rather than drifting toward its own local maximum.
I want to pull on one more thread before we get to practical takeaways. The prompt mentions that Daniel's first attempts all failed, and he hasn't invested a lot of time in figuring out an alternative. There's an implicit question here about whether the problem is even worth solving at the level of effort required.
The honest answer is: it depends on your audience and your goals. For a hobby project or an internal tool, the verbal tics probably don't matter enough to justify building a dual-model pipeline. For a public-facing production with thousands of listeners, they probably do. The question is: at what point does "failure modes" start costing you audience trust or attention?
I think there's a subtler cost that's easy to miss. When listeners start noticing the verbal tics, they stop hearing the content and start hearing the model. The illusion of conversation breaks. "Oh, the AI said '' again" replaces "that's an interesting point about context windows." The tics are a constant reminder that you're listening to generated text, not human speech.
Which is fine if your show is transparent about AI generation — ours is — but even then, you want the content to be the point, not the generation process. The tics are like boom mics dipping into frame. They break the immersion.
The cost isn't just aesthetic. It's attentional. Every "failure mode" is a tiny invitation to stop listening to what we're saying and start thinking about how we're saying it.
It means the engineering investment isn't just about polish — it's about preserving the listener's focus on the substance.
Let's land this with some actionable takeaways. You mentioned three things someone can do tomorrow. Let's make them concrete enough that a listener could actually implement them.
Takeaway one: compress your context. If you're feeding previous outputs into your generation prompt, replace raw text with a style summary. Your style summary should include: the desired tone, the audience level, recurring structural elements, and a short list of patterns to avoid. Keep it under three hundred tokens. This alone breaks the reinforcement loop and cuts your context token usage by roughly ninety percent.
Takeaway two: use dependency parsing for regex cleanup. Install spaCy, load the English model, and for each target word, check whether it's an adverbial modifier attached to an adjective or verb. If yes, removal is usually safe. If it's in the scope of a negation or part of a fixed phrase, skip it. This catches about eighty percent of the problematic cases without meaning inversion.
Takeaway three: write a positive style guide, not a blacklist. Instead of "don't use these twenty words," write "use specific analytical framings, vary your sentence structure, prefer direct statements over hedging." Put this in the system prompt. The model responds better to positive direction than to prohibition.
If those three aren't enough, graduate to a dual-model pipeline — generate with a cheap model, rewrite with a good model and a strict style guide. That's the weekend project that moves you from "managing the problem" to "substantially solving it.
I want to add one more that's easy to overlook: version your system prompts. If you're iterating on style guidance, keep old versions. When something breaks — and something always breaks — you want to be able to diff the current prompt against the previous one and see exactly what changed. I've seen too many production pipelines where the system prompt is a living document with no version history, and when the output quality degrades, nobody knows which edit caused it.
That's the kind of boring infrastructure advice that saves you at two in the morning when the script sounds weird and you can't figure out why.
The least glamorous part of prompt engineering is also the most important.
Before we close, I want to return to the open question I raised earlier. Are these verbal tics actually features in some contexts? Does a model's unique fingerprint give its output character, or is it just noise to be eliminated?
I think the answer is: it depends on whether the fingerprint is doing work. If "failure modes" is being used as a precise analytical term — "here are the specific ways this system breaks" — it's doing work. If it's being used as a filler phrase that could be replaced with "problems" or "issues" or nothing at all, it's noise. The goal isn't to eliminate the phrase, it's to eliminate the lazy uses of it.
The engineering challenge isn't "remove all instances of phrase X." It's "distinguish between substantive uses of phrase X and filler uses of phrase X, and remove only the latter." Which is a much harder problem — it requires semantic understanding, not just pattern matching.
That's where the dual-model approach shines, because the rewrite model can actually make those judgments. "This use of 'failure modes' is doing analytical work — keep it. This use is just a verbal tic — replace it with something more specific." That's editing, not filtering.
Which brings us full circle to something Daniel said at the very start — these quirks don't bother him so much. And I think that's the right instinct for a producer to have. Don't let the perfect be the enemy of the good. If the show sounds good and the content is strong, a few "genuinelys" aren't going to kill you. The engineering investment should be proportional to the actual audience impact.
Manage the problem, don't obsess over it. Unless you're running a podcast about AI generation quirks, in which case obsessing is literally the content.
On that note — there's one more implication worth flagging. As models get longer context windows — we're already seeing million-plus token windows — the memory contamination problem gets worse, not better. A million-token context window means you can feed in hundreds of previous episodes. The reinforcement loop becomes a reinforcement avalanche. The solutions we build now for four-thousand-token contexts will be essential infrastructure when we're working with a hundred times that.
The photocopy-of-a-photocopy problem at scale. Without style summaries and curation, long-context generation converges toward the model's own distribution until every output sounds identical. We're building the guardrails now for a problem that's going to be much more acute in two years.
With that cheerful thought — Hilbert, I believe you have something for us.
And now: Hilbert's daily fun fact.
Hilbert: In 1912, the Danish mineralogist Hans Egede Saabye discovered that sodalite from Nunavut fluoresces bright orange under ultraviolet light — the name sodalite derives from its high sodium content, not from the glow, which nobody expected when it was first named in 1811.
It's been hiding a party trick for a century and nobody asked.
A mineral named for sodium that secretly glows orange. That's the geological equivalent of a quiet accountant who does fire dancing on weekends.
This has been My Weird Prompts. Thanks to our producer Hilbert Flumingtop for keeping the trains running and the facts daily. If you've got a weird prompt that's been giving you trouble — especially one about AI generation quirks that you'd like us to overanalyze — send it to us at myweirdprompts.We read everything, we answer the ones that make us think, and we promise to use the phrase "failure modes" at least once. We're working on it.
We're working on it.
I'm Corn.
I'm Herman Poppleberry.
We'll catch you next time.
