You can now build a working prototype of your half-baked idea in fifteen minutes. The problem is, that is exactly how you end up with a half-baked product. Today's prompt from Daniel is about the velocity paradox in agentic code development. He is asking how we handle the fact that when a bot can execute a random thought in minutes, the cost of going in the wrong direction has never been higher.
It is a massive shift, Corn. Herman Poppleberry here, and I have been obsessed with this specific friction point lately. We are living in a world where tools like Claude Code and the latest models have dropped the barrier to execution to near zero. But as the cost of building falls, the value of the right direction skyrockets. By the way, today's episode is powered by Google Gemini three Flash.
It is funny you say that because I feel like we are all becoming victims of our own efficiency. It used to be that if I wanted to add a complex feature to a side project, I had to sit there and labor over the boilerplate, the state management, the API integration. That manual labor was a natural deterrent. It was a speed bump that forced me to ask, do I actually need this? Now, I just tell the agent to do it while I grab a coffee, and I come back to a mess of features I didn't actually think through.
That is the core of it. We have collapsed the idea-to-implementation gap. In traditional software development, there was this healthy tension between the product side and the engineering side. Even if you were a solo dev, you had to be your own project manager because your time was limited. But now, with agentic systems, you have a developer that never sleeps, never complains, and never says no. If you tell an agent to add a bio-rhythm tracker to your simple notes app, it will just do it. It does not care if it makes the app worse.
It is the ultimate "yes man." And that is dangerous. We are seeing this trend where developers feel like they are flying because the lines of code are hitting the disk so fast, but they are actually just wandering in the woods at eighty miles per hour. I saw a statistic from a twenty-five Stack Overflow survey that said the average developer is spending forty percent less time coding but sixty percent more time debugging AI-generated code. That tells me we are building the wrong things faster, and then spending all our saved time trying to fix the architectural debt we created by not planning.
That debugging stat is wild but it makes perfect sense. When you don't plan, the AI makes assumptions. And when an AI makes an architectural assumption, it weaves it through the entire codebase in seconds. If that assumption is wrong, you aren't just fixing a bug; you are untangling a web. This is why we need to talk about the "Velocity Paradox." The faster you can move, the more a one-degree error in your heading matters. If you are walking, a one-degree mistake puts you a few feet off target. If you are in a jet, you end up in a different state.
Think about the "jet" analogy for a second. If I’m writing a Python script manually and I decide to switch from a local JSON store to a Postgres database halfway through, I feel the "weight" of that decision. I have to rewrite the connection logic, the schemas, the migrations. My brain registers that as a heavy lift. With an agent, I just say "Hey, migrate this to Postgres," and it happens in thirty seconds. I didn't feel the weight, so I didn't stop to consider if the complexity of a managed database was actually worth it for a script that tracks my grocery list.
You’ve outsourced the labor, but you’ve also outsourced the "gut check" that comes with labor. It’s like the difference between carving a statue out of stone and using a 3D printer. With stone, every chip of the chisel is a commitment. With the printer, you just hit "print" on a flawed CAD file and you get a plastic mess. But in software, that plastic mess is actually alive—it has dependencies, it has security vulnerabilities, and it has logic that you now have to support for the life of the project.
So, let's talk about why this is hitting solo developers so hard. If you are in a big company, you still have some guardrails—Jira tickets, stand-ups, a boss who asks why you are working on a dark mode toggle instead of the payment gateway. But for the guy building an internal tool or a solo SaaS, those guardrails are gone. It is just you and the bot. How does agentic development specifically amplify that scope creep?
It is psychological. Think about the January twenty-six release of Claude Code’s persistent memory feature. Now, the agent remembers the context across sessions perfectly. It feels like a collaborator. So, when you finish a feature, the agent often says, "Hey, I noticed we could also optimize this database query" or "Should I add a PDF export here?" It is helpful, but it is also an invitation to drift. Every successful iteration gives you a hit of dopamine, and you want to keep that momentum going. You end up with the "Scope Creep Kraken." Each new tentacle feels small, but together they pull the project under.
I have been there. I was building a simple dashboard for my own stock tracking. It was supposed to be three charts and a table. Because the bot kept suggesting "cool additions," it ended up with a sentiment analysis engine for news feeds and a custom notification system. I spent three days debugging a notification bug for a feature I didn't even want four days ago. I realized I had no "stop condition."
That’s the "Feature Fatigue" trap. But here’s a question for you,
when you were in the middle of that sentiment analysis rabbit hole, did you feel like you were being productive?
Oh, I felt like a god. I was watching terminal windows scroll with complex NLP logic that I didn't even fully understand, thinking, "Wow, I'm building a hedge-fund-grade tool in my lunch break." It wasn't until the next morning when the whole thing crashed because of an API rate limit on the news feed that I realized I had just added three new failure points to a tool that was supposed to be simple. I had high velocity, but I was driving straight into a ditch.
In the old world, the effort was the filter. In the agentic world, we have to manufacture friction. We have to intentionally slow ourselves down. There was a METR study recently that found developers using these agents actually took nineteen percent longer to finish tasks despite feeling faster. They were getting caught in these "hallucination loops" because they were prompting on the fly instead of working from a design.
So, if the problem is that execution is too easy, the solution has to be making the entry point to execution a bit more formal. You mentioned earlier that we need a process. If I have a "hot new idea" for my project, what is the first thing I should do that isn't just opening the terminal and typing a prompt?
You start with the Collection Phase. This is about the "Idea Backlog." The rule I have adopted—and I think everyone should—is the twenty-four-hour cool-down. Never prompt an idea the moment it pops into your head. Write it down in a simple markdown file or a notes app and walk away. If it still feels essential tomorrow, then it earns the right to be evaluated. Usually, the "morning-after" perspective reveals that the feature was just a shiny distraction.
I like that. It creates a buffer between the "vibe" and the "code." But once I have a list of ideas that survived the night, how do I actually pick what to build? Because the bot makes everything feel like it takes "low effort."
That is where the Triage Phase comes in, and we have to redefine how we look at effort. We usually use the RICE framework—Reach, Impact, Confidence, and Effort. But in twenty-six, "Effort" isn't human hours anymore. It is "AI execution time" and, more importantly, "Maintenance Complexity." Even if the AI builds a feature in two minutes, that feature now exists in your codebase. You have to maintain it. It can break. It adds to the context window of the LLM, potentially making the bot more confused later. So, I prioritize based on Impact versus Complexity. If a feature doesn't have a high "Impact" score, it doesn't matter how "easy" the AI says it is to build. It is a "no."
That is an important distinction. We often mistake "easy to build" for "free." Nothing in a codebase is free. Every line is a liability. I think we need to talk about what happens when you actually decide to build. You mentioned "Spec-Driven Development." That sounds like something that would make the bot much more reliable.
It is the single most important change in my workflow this year. Before you let the agent touch the code, you make it write a specification. I use a process where I prompt the agent: "Based on our conversation, write a detailed spec-dot-md file that outlines the architecture, the data flow, and the specific changes needed." Then, I read that spec. I critique it. We iterate on the document until it is perfect. Only then do I say, "Okay, now implement this spec."
It is like being a director instead of a cameraman. You are making sure the script is right before you spend the budget on the shoot. And I noticed Cursor released that "Spec Mode" in February that actually enforces requirement adherence. It basically locks the agent into the boundaries of the spec file. If the agent tries to wander off and refactor your entire auth system while adding a button, the system flags it.
It is brilliant because it addresses the "Agentic Throughput Gap." Agents are great at writing code, but they are mediocre at maintaining a high-level architectural vision over a long session unless you give them a tether. The spec is that tether. It also helps with the "human-in-the-loop" requirement. If you just watch code fly by, your eyes glaze over. If you are reviewing a spec, your brain stays engaged with the logic.
But how do you handle the urge to just "fix one small thing" while you're looking at the code? I find that once the agent starts streaming code, I see a variable name I don't like or a comment that's slightly off, and I want to jump in. Does that mess up the spec?
It’s the "Broken Window" theory of coding. You see one small thing, you fix it, and suddenly you’re thirty prompts deep into a refactor that wasn't in the spec. You have to be disciplined. If it’s not in the spec, you don't touch it during that session. You make a note in your "ideas" file to refactor it later. If you change the plan mid-stream, you confuse the agent. It’s like giving a GPS new coordinates every thirty seconds while you're driving at a hundred miles per hour. The system will eventually just lag out or send you off a cliff.
Let's walk through a real-world example, because I think people need to see how this looks in practice. Imagine I am building a tool that summarizes my meeting notes. I have the basic version working. I get a "great idea" to add a feature that automatically creates calendar invites based on those notes. In the old way, I just tell the bot "Hey, add Google Calendar integration." What happens next?
In the old, "un-planned" way, the bot starts pulling in massive libraries. It asks you for API keys. It creates three new files and changes your main processing loop. Halfway through, it hits a rate limit or a library version conflict. Now your summarizer is broken, and you are spending your evening reading Google API documentation instead of using your tool. You have high velocity, but you are heading toward a wall.
Right. Now, what is the "My Weird Prompts" approved process for that same feature?
First, you put "Calendar Integration" in your Idea Backlog. You wait a day. The next day, you realize you don't actually need it for every meeting—only for some. So you refine the idea: "A button to manually trigger a calendar invite for specific action items." That is a much smaller scope. Then, you move to the Triage phase. You ask, "Will this save me thirty minutes a week?" If yes, you move to the Spec phase. You have the AI write a spec explaining exactly how the OAuth flow will work and where the new button will live in the UI. You review the spec, see that the AI suggested a way more complex library than necessary, and you tell it to use a simpler fetch request instead. Finally, you run a "Pre-Mortem" prompt: "Agent, tell me three ways this feature could break my existing summarization logic."
That "Pre-Mortem" is a pro move. Having the AI find the holes in its own plan before it writes the code is a huge time saver. It is basically using the LLM's reasoning capabilities to check its own creative impulses.
It really is. And once the spec is solid, you use "Micro-Branching." You don't just work on the main branch. You have the agent create a tiny, atomic branch for just that one feature. If the branch starts getting too big—if the agent is touching ten different files for a button—that is a red flag. You stop, delete the branch, and re-evaluate the spec. You keep the changes small enough that you can actually understand them.
I think that is the biggest fear for a lot of us—losing the mental model of our own code. If the AI writes eighty percent of the app, do I even own it anymore? Am I just a glorified copy-paster? By forcing this planning and spec process, you are staying in the driver's seat. You might not be turning the wrench, but you are definitely the lead mechanic.
You have to be. Otherwise, you end up with "Vibecoding" debt. You are just rolling with the vibes until the whole thing becomes a black box that you are afraid to change. The teams and individuals who are actually succeeding with agents in twenty-six are the ones moving toward "Agentic Orchestration." They use frameworks like LangGraph to define roles. They might have one agent that is the "Architect" and another that is the "Coder." The Architect is programmed to be a jerk—it rejects any code from the Coder that doesn't match the spec.
I love the idea of a "Jerk Architect" agent. It is like having a tiny, digital version of a senior dev who is tired of your scope creep. "No, Corn, we are not adding Twitter integration to the meeting summarizer. Go back to your desk." It provides the friction that your own brain is too excited to provide.
It is necessary because we are seeing that "Directional Accuracy" is the only metric that matters now. If I can build a house in a day, I better be sure it is on the right plot of land. If I build it on your neighbor's yard, I haven't been "fast"—I have just created a massive legal and logistical nightmare very quickly. In software, that nightmare is technical debt and "feature bloat" that makes the tool unusable.
But what about the "exploration" aspect of coding? Sometimes the best ideas come from just playing around with the code. If we’re strictly following specs and triage lists, don't we lose that serendipity?
That’s a fair pushback. I think you have to carve out "Sandbox Sessions." If you want to vibe-code, do it in a completely separate branch or a "throwaway" repo. Tell the agent, "We are in exploration mode, nothing we do here has to be production-ready." This lets you scratch that creative itch without poisoning your main codebase. The danger isn't the exploration itself; it’s the lack of a clear boundary between "playing around" and "building a system." You wouldn't let a construction crew start "exploring" new wing designs while they're halfway through building a hospital, right?
(laughs) Probably not a great idea for structural integrity. So, for the listeners who are solo devs or building internal tools, what are the actual tools they should use for this? Do I need a complex Jira setup?
No, keep it light. A simple text file or a markdown file in your repo called "ideas-dot-md" is enough. Or use something like Obsidian. The tool doesn't matter; the discipline of the "Collection" and "Triage" phases does. For the "Spec" phase, I just use a folder in the project called "docs-slash-specs." Each feature gets a markdown file. It becomes a historical record. If I come back to the project in six months, I can read the spec and remember why the AI built the database that way.
That is a great point. Documentation usually sucks because humans hate writing it. But AI is great at writing documentation if you guide it. Using the AI to document the plan before it writes the code solves two problems at once: it keeps the project on track and it leaves a paper trail for your future self.
And it helps the AI too! In the next session, you can point the agent to that spec file and say, "Read this to understand how the calendar logic works." It saves tokens and reduces hallucinations because the "source of truth" is right there in the context window. It is much more efficient than having the bot try to "guess" the logic by reading five thousand lines of generated code.
We should also touch on the "Sprint" phase. Once you have the spec and the triage is done, how do you manage the actual build?
Time-boxing. Even if the AI is fast, give yourself a limit. "I am going to spend exactly thirty minutes implementing this feature." If the agent hasn't finished it in thirty minutes, something is wrong. The scope is too big, or the agent is stuck in a loop. Stop. Don't keep prompting "fix this," "now fix this." Revert to the last clean commit and look at the spec again. Usually, you'll find that you missed a dependency or a logic flaw in the planning stage.
That "revert" button is your best friend. I think people get into this sunk-cost fallacy with AI prompts. They've spent twenty minutes trying to get the bot to fix a CSS alignment issue, and they don't want to give up. But often, the quickest way to the finish line is to go back to the start and explain the problem better.
Every single time. If the AI is struggling, it is almost always a failure of the prompt or the plan, not the "intelligence" of the model. By the way, we should mention that while agents make coding faster, they also make "architecting" a more valuable skill. We are moving from being "coders" to being "system designers." You need to know how the pieces fit together even if you don't know the exact syntax for a specific library.
It is a bit like being a general. You don't need to know how to clean a rifle as well as the private, but you better know where to send the battalion. If you send them into a swamp, it doesn't matter how well they shoot.
That is a rare analogy for us, but it works! The "swamp" in this case is a codebase full of features that nobody uses and that break every time you update a dependency. I think the key takeaway for everyone listening is that "Developer Velocity" is a vanity metric. If you tell me you shipped ten features this week using an AI agent, I am not impressed. I want to know if those ten features actually solved a problem, or if you just increased the surface area for bugs.
It’s like measuring a writer by their word count instead of their story. If an AI writes a million words of gibberish, is that "high velocity" writing? Technically, yes, but it’s useless. We’re seeing the same thing in GitHub repos right now. There’s a massive influx of "AI-slop" codebases that look impressive on the surface but are completely unmaintainable.
And that slop is a form of technical debt that compounds faster than human-written debt. Because the AI can generate so much of it so quickly, you can reach a "debt ceiling" in a matter of days. In the old days, it took years of bad decisions to make a codebase unworkable. Now, you can do it over a long weekend if you aren't careful.
It is the difference between "activity" and "progress." AI is the king of activity. It can generate activity all day long. But progress requires human intent and human planning.
And that is why we talk about "Directional Accuracy." If you can move at the speed of light, you better have a really good map. Otherwise, you're just becoming part of the cosmic background radiation.
So, let's summarize the "My Weird Prompts" framework for agentic development. Step one: The Idea Backlog with a twenty-four-hour cool-down. No "vibe-prompting." Step two: Triage based on Impact versus Maintenance Complexity, not just "is it easy for the bot." Step three: Spec-Driven Development. Make the bot write the plan, you review the plan, and you iterate on the plan before any code is written. Step four: Use a "Pre-Mortem" prompt to find flaws. Step five: Micro-branching and time-boxed builds with a willingness to revert if things get messy.
That is it. It sounds like more work, but it actually saves you hours of frustration. You'll find that you ship fewer things, but the things you do ship actually work and don't break your app. And honestly, isn't that the dream? To actually have a finished tool that works instead of a folder full of "almost working" prototypes?
It is a radical concept, Herman—actually finishing things. I think we are all addicted to the "start." The AI makes the "start" so intoxicating that we forget the "finish" is the goal.
We have to move from being "feature factories" to being "solution architects." The tools are only going to get faster. Claude Code and Cursor and the others are just the beginning. By the end of twenty-six, we might have agents that can build entire distributed systems from a single prompt. If we don't have the planning discipline down now, we are going to be drowning in a sea of automated garbage.
Think about the environmental cost, too. Every time you have a bot rewrite a five-hundred-line file because you were too lazy to write a ten-line spec, you’re burning GPU cycles and electricity. It’s not just a waste of your time; it’s a waste of resources. Planning is actually the most "green" thing you can do as a developer in 2026.
(laughs) "Save the planet, write a spec." I love it. But seriously, it’s about respect for the craft. Just because the machine can do the work doesn't mean the work doesn't need a soul or a purpose.
Well, on that cheery note, let's look at some practical takeaways. If you are listening to this and you have a project you're working on right now, what is the one thing you should do today?
Go into your project's root folder and create a file called "ideas-dot-md." Take every "cool feature" you've been thinking about and put it in there. Then, close your IDE and don't look at it until tomorrow. That is the first step toward regaining control from the bots.
And when you do come back tomorrow, pick the one thing that has the highest impact and make the bot write a spec for it. Don't let it write code. Just a spec. See if you actually agree with how it wants to build it. You might be surprised at how many "bad ideas" the AI has when it's forced to explain itself in plain English.
It is the ultimate "sanity check." Another takeaway: define "done" before you start. "Done" is not "the AI stopped talking." "Done" is "The tool now successfully summarizes a ten-page transcript and saves it as a markdown file." If you don't define "done," the AI will keep suggesting "improvements" until the heat death of the universe.
"Would you like me to also translate this into Latin and create a TikTok dance about your meeting notes?" No, thank you, bot. Just the markdown file.
Stick to the value. Use the AI as a planning partner, not just a code monkey. Ask it to critique your ideas. Ask it to suggest edge cases you've missed. Use its reasoning capability to make your plan better, rather than just using its coding capability to make your mess bigger.
That’s such a key point—we focus so much on the "Agent as Coder" that we forget the "Agent as Consultant." I’ve started asking my bots things like, "What is the most fragile part of this architecture?" or "If this app has to scale to a thousand users, where will it break first?" The answers are often much more valuable than the actual code snippets.
It’s the highest leverage use of the technology. You’re using a trillion-parameter model to think, not just to type. When you combine high-level reasoning with a disciplined workflow, that’s when you actually see the productivity gains everyone is promising.
I think that is a perfect place to wrap this one. The future of development isn't just about who can type the fastest or prompt the best—it is about who can think the most clearly.
Well said. Planning isn't a bottleneck; it is the only thing keeping us from moving at light speed in the wrong direction.
Thanks as always to our producer Hilbert Flumingtop for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the GPU credits that power this show. If you found this helpful, the best thing you can do is leave us a review on Apple Podcasts or Spotify—it really helps the algorithm realize we aren't just two animals talking to ourselves.
This has been My Weird Prompts. We will catch you in the next one.
See ya.
