Beyond Commands: Redefining AI Interactions as a Collaborative Partnership

For many, AI is still viewed as just another tool—an advanced calculator for automating tasks and responding to inputs with precision. This mindset reduces interactions to a transactional exchange: input in, output out. But to see AI in this way is to limit its potential and, more importantly, to constrain our own.

To unlock the true value of AI, we must rethink this relationship entirely. Rather than seeing AI as a tool to be wielded, it should be seen as a partner to collaborate with. This shift in perspective is more than a semantic difference—it changes the way we approach every interaction, from prompt design to creative brainstorming. In this light, AI becomes a co-navigator, an active participant in a shared journey.

The Partner Mindset: Why Augmented Intelligence Matters

The concept of artificial intelligence often conjures images of machines replacing humans, making decisions on our behalf, and even supplanting our roles. But this narrative is both misleading and restrictive. Instead, AI should be thought of as augmented intelligence: a technology that enhances human capabilities rather than competes with them.

When we approach AI as a partner, it amplifies our strengths, compensates for our weaknesses, and pushes us to consider perspectives that might never have emerged on our own. In this way, AI doesn’t reduce our creative or analytical effort; it multiplies it. This collaborative model encourages a dialogue where AI's outputs are treated as contributions rather than mere responses. Each interaction builds on the last, forming a feedback loop of insights that drive toward shared goals.

From Commands to Conversations: Shaping a New Dynamic

Traditional prompt engineering typically revolves around formulating precise commands: tell the AI what to do, how to do it, and then expect a specific output. This transactional method is effective for simple tasks but falls short when dealing with nuanced, open-ended, or creative problem-solving. Instead, framing prompts as conversations—using questions, scenario-building, or even hypothetical dilemmas—transforms the interaction.

For example, a standard prompt might be: “Summarize the current state of renewable energy investments.” While this will get a useful answer, it leaves no room for nuance or exploration. Rephrasing it as a more collaborative prompt, like: “Let’s explore the current landscape of renewable energy investments. What key trends are shaping the sector, and which factors should we consider most critical for evaluating future opportunities?” invites the AI into a more expansive dialogue.

This subtle shift encourages the AI to “think” alongside the user, revealing pathways and insights that a simple command would have overlooked. By treating the AI as an active participant, rather than a passive recipient, the interaction becomes richer, more dynamic, and ultimately more valuable.

Collaborative Prompting: A Shared Goal

Collaboration is defined by a shared objective. Whether creating a complex report, brainstorming new business strategies, or even tackling a coding challenge, both the human and the AI must be aligned toward the same goal. This shared focus shapes the interaction in subtle yet powerful ways.

Consider a scenario where the task is to generate ideas for a workshop on prompt engineering. Approaching the AI with a mindset of partnership might lead to an initial broad brainstorming session, followed by refining and narrowing down concepts together. Instead of rigidly scripting every request, the user can guide the AI’s outputs through iterative questions: “Which of these ideas could have the most impact on beginners? Let’s refine this one further—how can we make it more interactive?” This method not only draws out more contextually relevant ideas but also surfaces insights that neither the user nor the AI could have fully developed alone.

This is the essence of a collaborative relationship: a constant, back-and-forth exchange where each side builds on the other’s contributions. And when we approach prompt engineering with this mindset, we move beyond mere task completion. We create something new—an emergent product of human creativity and machine intelligence working in tandem.

Foundations of Collaborative Prompt Engineering

Shifting from seeing AI as a tool to embracing it as a partner requires adopting a new mindset. Just as effective teamwork between people relies on trust, communication, and alignment, collaborating with AI is no different. I’ve found that there are three core principles that shape this new relationship: Trust, Iteration, and Co-Creation. These foundations have fundamentally changed how I interact with AI, and I believe embracing them will open up far more productive and meaningful dialogues.

Trust: Building Confidence in the Partnership

In any partnership, trust is essential. When it comes to working with AI, this doesn’t mean blindly believing in its abilities or outputs. Instead, it’s about developing confidence in what the AI can bring to the table, even when it surprises me or offers an unexpected suggestion. For me, this trust developed through experience—by testing the AI in various scenarios and observing how it responds, I’ve learned when to rely on its recommendations and when to steer it in a different direction.

One of the moments where trust became crucial was when I was refining the DualFlow Method. I realized that the more I explored the AI’s interpretations, even when they weren’t what I anticipated, the more I uncovered unique insights that shaped the methodology. Trust isn’t about accepting every answer as correct—it’s about approaching its outputs with curiosity. I often ask myself, “Why did the AI suggest this? What might I be missing here?” When I approach it with an open mind, I find that what initially seemed like a misunderstanding is often an opportunity to see the problem from a different angle.

This trust extends to delegation. When I frame a prompt and receive a response that’s unconventional, I don’t dismiss it out of hand. Instead, I engage with it, probing further to see where the AI is coming from. This process of exploration builds a more dynamic exchange, where the AI’s contributions add depth and nuance to our shared goal.

Iteration: The Power of Refinement

Effective collaboration is never a one-and-done process—it’s built on refinement. I see prompt engineering in the same light. Instead of viewing prompts as static instructions, I treat them as starting points for an evolving dialogue. Each iteration isn’t just a repetition but a refinement, where I can adjust, redirect, and explore new lines of thought.

A single question might open up a new direction, but it’s the follow-up prompts—asking “Can you expand on this?” or “How would this apply in a different context?”—that bring out the real value. I think of it as a sculpting process, where each iteration shapes the AI’s response closer to what I envision, but also leaves room for unexpected insights to emerge.

When developing the DualFlow Method, this iterative approach was key. I would start with a broad concept, like a general project outline, and then use recursive prompts to add more and more detail. Each step wasn’t just about filling in blanks; it was about using the AI’s outputs to challenge my assumptions, refine the direction, and ultimately, co-create something more structured and sophisticated. Viewing the interaction as an ongoing, evolving process is where the true power of iteration comes in.

Co-Creation: Combining Strengths for Maximum Impact

Collaboration works best when each participant brings something unique to the table. In my experience, co-creation with AI means recognizing that while I bring domain expertise, intuition, and creative intent, the AI contributes by processing vast amounts of data, identifying patterns, and offering diverse perspectives. The key is to merge these strengths in a way that makes the whole greater than the sum of its parts.

I’ve seen this most vividly when I use AI to approach complex topics, like analyzing market behavior from a psychological standpoint. I know how human emotions and market cycles interact, but the AI helps me explore that idea systematically, pointing out logical inconsistencies or suggesting new angles that I hadn’t considered. This back-and-forth leads to a richer understanding that neither of us would have constructed independently.

Co-creation is about more than just combining human and machine capabilities—it’s about developing a rhythm where each side contributes and reacts to the other’s input. This can involve brainstorming prompts, scenario-building, or simply experimenting until a new idea emerges. The results feel less like a product of AI or human effort, and more like a synthesis—something born from both our contributions.

Taking a Moment to Reflect: A Shift in Perspective

Before we dive into more techniques and strategies, I want to pause for a moment and invite you to reflect on what we’ve discussed so far. Up until now, most of us have been trained to interact with computers in a turn-based, transactional manner—typing precise commands or optimized queries to extract the best possible output. The goal has always been clarity, precision, and efficiency, because that’s what traditional computing systems were built for.

But what we’re talking about here is something fundamentally different.

We’re suggesting a return to a more human way of interacting. Instead of narrowing down our inputs into a rigid, static format, we’re opening the conversation up—inviting flexibility, nuance, and even a little unpredictability. The focus shifts from getting the AI to “perform” perfectly on a task to engaging it as a participant in the dialogue, much like how we would approach a brainstorming session with a human colleague.

It’s a big shift. And it’s not just about changing how you write prompts—it’s about changing how you think when you engage with AI. For decades, our approach has been to treat computers as tools: execute commands, receive results, and repeat. What I’m proposing here is that we stop thinking of AI as a tool and start seeing it as a conversational partner. One that can enhance our thought process, add perspectives, and collaborate toward shared goals.

Now, I want to be clear: I’m not suggesting we treat AI as an actual individual. It doesn’t have emotions, and it doesn’t “think” like we do. What makes this approach work is understanding that the AI has been trained on human conversation—so anything true in human-to-human dialogue tends to hold true here as well. It’s not that the AI has emotions or consciousness, but it has been trained to explore thought in a way that mirrors human interactions. That’s why when we approach it as a collaborator, the conversation naturally flows better, and we can get far richer outputs than by treating it like a simple tool.

So, take a moment to really consider that change. What does it mean for how you approach an AI interaction? How would your prompts, your tone, or even your expectations shift if you thought of the AI as a participant, capable of creative contributions and thoughtful input—even if it’s ultimately just a sophisticated pattern recognizer?

This understanding is critical. Without internalizing this mindset, the rest of what I’m going to share might feel out of reach or disconnected. It’s not just about using new techniques—it’s about embracing a new way of thinking.

Take a break, let that idea sink in, and when you’re ready, we’ll continue exploring how this shift can open up entirely new ways of working and thinking with AI.

Shifting Mindsets: From Formulas to Frameworks

With the right foundations in place and a better understanding of the dynamics at play, we can start to dig into the core of what makes collaborative prompt engineering so powerful: its flexibility. Up until now, a lot of approaches to AI interactions have focused on finding the perfect formula—specific structures or keywords that reliably produce the desired outputs. But while formulas are efficient, they can also be limiting.

Instead, what I’m advocating for is a framework-based approach. Where formulas are static, frameworks are adaptable. They give you structure and direction, but also the freedom to change, evolve, and refine your approach as the conversation unfolds. This flexibility is crucial because, unlike traditional software, AI is inherently dynamic—it responds not just to what you say, but to how you say it and why. To leverage this responsiveness, we need to shift from rigid formulas to frameworks that can grow and adapt alongside the conversation.

Breaking Free from the Formula Trap

In traditional software, rigid formulas are essential because the system needs exact instructions to function correctly. If I’m coding a solution, I want my instructions to be precise and repeatable. But when it comes to working with AI, especially in a creative or exploratory context, this rigidity can backfire. Overly structured prompts can constrain the AI, boxing it into a narrow response range and shutting down potentially valuable tangents.

I’ve experienced this firsthand. Early on, I would spend a lot of time crafting perfect prompts, only to find the AI’s responses flat or repetitive. It was only when I began loosening my grip—allowing more open-ended instructions or adding conversational cues—that the interactions came to life. The outputs became richer, and I discovered new angles and ideas that wouldn’t have emerged otherwise.

Now, I’m not saying that structured prompts are never a good idea. In fact, for some specific, well-defined tasks, a highly detailed and structured prompt is exactly what’s needed. But even in those cases, I don’t see the process as a solo effort. Instead of sitting down and trying to craft the “perfect” prompt on my own, I take a different approach: I’ll expose the problem to the AI, describe what I need it to do, and ask the AI to help me build that ultra-detailed prompt. This way, I’m still working with the AI in a collaborative manner, even when precision is required. It’s about maintaining that back-and-forth dynamic, using the AI not just as a passive tool, but as a partner in creating the structure itself.

The point here is that, while formulas have their place, the real value of AI emerges when we let go of trying to find the “perfect” query on our own. By embracing flexible frameworks, we can guide the interaction without suffocating it.

Frameworks: A Blueprint for Dynamic Interactions

So, what exactly do I mean by frameworks? In essence, a framework is a loosely defined structure that gives your interaction a sense of direction without being overly prescriptive. It’s more of a strategic outline than a step-by-step guide. Think of it like planning a road trip: you have a destination in mind and some key stops along the way, but the exact route and timing are left flexible so that you can adapt to whatever comes up.

For example, when brainstorming, I might use a three-step framework: exploration, refinement, and synthesis. In the exploration phase, I’ll let the AI run wild—encouraging it to throw out as many ideas or perspectives as possible. Then, in the refinement phase, I’ll focus on honing in on a few of the most promising ones, using follow-up prompts to deepen the analysis. Finally, in the synthesis phase, I’ll work with the AI to combine these refined ideas into a cohesive whole.

What’s key here is that each phase isn’t defined by a specific set of prompts. Instead, I use broad guiding questions and keep adjusting based on what the AI produces. This framework lets me keep the conversation focused while still remaining flexible enough to pivot if a new, unexpected idea comes up.

Adapting to the AI’s Style and Strengths

Another advantage of frameworks is that they adapt well to the AI’s evolving capabilities. Each model has its own strengths, quirks, and limitations. Some excel at factual analysis, while others are better suited for creative problem-solving or abstract thought. If I’m using a rigid formula, I risk clashing with the AI’s natural tendencies, leading to frustration or subpar outputs.

By contrast, a framework lets me cooperate with the AI’s style. I can adjust my approach to play to its strengths—asking for lists when the model is detail-oriented, or using metaphorical prompts when it tends toward creative storytelling. This flexibility allows me to harness each model’s unique attributes, creating interactions that feel more fluid and less forced.

When using frameworks, I’m not just telling the AI what to do—I’m guiding it to use its capabilities in the most effective way. This requires paying attention to the subtle cues in its responses, learning to read between the lines, and constantly tweaking the approach as the conversation unfolds.

Embracing Evolution: Frameworks Grow with You

The final reason I advocate for frameworks is that they grow with me. As I refine my understanding of AI’s strengths and weaknesses, I can adjust my frameworks accordingly, keeping the structure but changing the emphasis or flow. This is especially important because the field is evolving so rapidly. New models, new capabilities, and even new interaction patterns are emerging all the time.

I think of it as creating a “living structure”—a dynamic blueprint that evolves alongside the technology and my own learning. Unlike a formula, which can become obsolete as the AI improves, a good framework remains relevant because it’s not tied to specific instructions. Instead, it’s tied to principles that guide the interaction, making it adaptable to whatever comes next.

Closing Thoughts on Shifting Mindsets

Moving from formulas to frameworks is about embracing flexibility and adaptability. It’s not about abandoning structure, but about finding a balance that allows the conversation to breathe and grow. In my experience, frameworks provide just enough guidance to keep things on track while leaving room for the unexpected, which is where true breakthroughs often happen.

By shifting your mindset from rigid formulas to dynamic frameworks, you’ll find that every interaction feels less like programming a machine and more like collaborating with a partner. And that’s where the real magic happens.

Practical Applications: Building Your Own Philosophy

By now, we’ve explored a new way of thinking about AI interactions, shifted our focus from rigid formulas to adaptable frameworks, and discussed the key principles that shape a collaborative dynamic. But theory alone isn’t enough. What matters is how you apply these concepts in real-world scenarios to create your own approach—a personalized philosophy that fits your unique needs and goals.

In this section, I want to show you how to take these ideas and make them your own. Because the truth is, no single method works for everyone. Just as each of us has our own style of working, learning, and communicating, each AI interaction can and should be tailored to reflect that individuality. The goal here isn’t to give you another set of rules but to provide the tools and guidance to build your own prompt engineering philosophy—one that aligns with how you think, create, and solve problems.

Start with the End in Mind: Defining Your Goals

The first step in building your own approach is to clarify why you’re engaging with the AI in the first place. Are you brainstorming new ideas? Analyzing data? Exploring hypothetical scenarios? The way I see it, every interaction should have a purpose, even if it’s just a broad one like “explore this topic and see what comes up.”

I’ve found that when I set a clear intention upfront, it shapes how I frame my prompts and guides the entire flow of the conversation. For example, if I’m looking for creative brainstorming, I’ll lean heavily on open-ended questions, scenario prompts, or even playful challenges like, “Imagine we’re two scientists trying to solve this together—what crazy ideas would we explore?” If my goal is more analytical, I’ll use focused questioning and iterative refinement, always circling back to “What are we missing here?”

Think about your goal as the anchor that keeps your conversation from drifting, but don’t worry about getting it perfect. Just having that initial aim in mind gives your interaction direction and purpose, and you can always adjust as the conversation evolves.

Experimentation: Finding What Resonates with You

Creating your own approach means embracing experimentation. Just as no two people work the same way, no two interactions with AI will unfold identically. What worked well in one context might fall flat in another. So, I encourage you to view each session as a chance to test new techniques, frameworks, and styles.

Here’s a small exercise I often do when I’m trying to develop a new approach: I’ll take a simple topic—something I’m curious about, like emerging trends in AI—and run through it using three different techniques. Maybe I’ll start with a role-based prompt like, “Pretend you’re an expert in AI ethics—what are the most pressing concerns on this topic?” Then, I’ll shift to a contrarian prompt: “What’s the strongest argument against focusing on AI ethics right now?” Finally, I might wrap up with a recursive strategy, asking: “Based on these two perspectives, what’s a balanced approach?”

By comparing the results, I can see which techniques resonated the most for that topic. It’s a great way to build up a personal toolkit of methods that I know work well for me. The more you experiment, the more you’ll refine your style, and the more natural and effective your interactions will become.

Embrace Flexibility: Adapt as You Go

Even the best-laid plans need flexibility. One of the most important lessons I’ve learned is that sticking rigidly to a pre-defined structure often stifles creativity and limits exploration. While it’s essential to start with a clear goal, the key is to adapt as new insights or unexpected paths emerge.

For example, there have been times when I set out with a clear analytical intention, but partway through, the AI suggested something that shifted the entire conversation in a different direction. Rather than steering it back, I let the interaction unfold, and it ended up producing far more valuable insights than my original path would have. This flexibility is what allows for serendipity—those unexpected breakthroughs that come from allowing a little unpredictability into the process.

So, don’t be afraid to pivot. If the AI suggests an interesting idea, follow it. If a technique doesn’t seem to be working, switch to another. The point is to view your approach not as a strict set of rules but as a living framework that evolves with every conversation.

Document Your Journey: Create a Playbook

One of the best ways to solidify your own philosophy is by documenting your journey. I like to keep a “playbook” of sorts—a record of what worked, what didn’t, and any interesting observations I made along the way. This isn’t just about tracking success; it’s about capturing how I got there.

For instance, I’ll jot down notes like, “Role-based prompting works well for complex scenarios but sometimes needs more context to shine,” or “Contrarian prompts tend to lead to more innovative ideas when I’m feeling stuck.” Over time, these notes become a valuable reference, helping me see patterns in my own thinking and refining my methods based on actual experience.

Building your own playbook doesn’t have to be formal. It can be as simple as keeping a running log of your interactions, or as detailed as creating a structured journal with different sections for each technique. The important thing is to capture your thoughts while they’re fresh, so you can look back and see how your philosophy has evolved.

Build on What Works: Create a Signature Style

As you experiment and document your interactions, certain patterns will start to emerge. Maybe you find that you prefer role-based prompting for brainstorming but tend to use recursive questioning when deepening an argument. Or perhaps you have a favorite way of setting up contrarian prompts that reliably generates fresh insights. These preferences form the foundation of your signature style.

Your goal is to build on what works and make it uniquely yours. Once you have a handful of go-to strategies, start combining them in different ways, creating a flow that feels natural. Think of it as building your own playbook of techniques, a set of principles that you can rely on, but that also leaves room for growth and experimentation.

The beauty of this process is that there’s no right or wrong way to do it. It’s about finding what resonates with you and what consistently leads to the kinds of conversations and outcomes you want. As you refine your approach, you’ll not only become more effective at working with AI—you’ll also find that the process becomes more enjoyable and rewarding.

Closing Thoughts on Practical Applications

Creating your own philosophy isn’t about memorizing a list of rules. It’s about developing a mindset that allows you to interact with AI in a way that aligns with your goals, your style, and your way of thinking. By starting with a clear goal, embracing experimentation, and documenting what you learn, you’ll build an approach that’s uniquely yours—one that grows and evolves over time.

Ultimately, the goal isn’t just to master prompt engineering techniques. It’s to create a way of working with AI that feels natural, effective, and even a little bit exciting. Because when you’re truly engaged with the process, that’s when the real magic happens.

Call to Action—Taking the First Steps Toward Collaboration

Now that we’ve explored the principles, techniques, and mindsets that redefine AI interactions, I want to challenge you to take the first step. It’s easy to read through these ideas and think, “That sounds interesting, but how does it apply to me?” The truth is, you won’t fully grasp the power of this collaborative approach until you experience it firsthand. The real value emerges not just from understanding these concepts, but from trying them out in your own interactions.

So, here’s what I propose: pick a project, a topic, or even a simple problem you’ve been wrestling with, and experiment with one of the techniques we’ve discussed. Maybe you start by reframing your prompts as scenarios, inviting the AI to explore possibilities with you. Or perhaps you try a role-based prompt, asking the AI to approach the topic from a unique perspective. It doesn’t have to be perfect or even particularly structured—just approach the conversation with an open mind and a willingness to be surprised.

Step One: Choose Your Technique

Look back at the techniques we covered—scenario framing, recursive questioning, role-based prompting, contrarian prompting, or evolutionary prompting—and choose one that stands out. If you’re not sure where to start, pick the one that feels most unfamiliar or even a little uncomfortable. Growth often comes from pushing ourselves into new territory.

Step Two: Set a Goal, But Stay Flexible

Define a loose goal for your interaction, whether it’s generating new ideas, refining an argument, or simply exploring a topic from a new angle. But remember: stay flexible. If the AI takes the conversation in an unexpected direction, lean into it and see where it leads. Treat the exercise as a chance to discover, not just to validate what you already know.

Step Three: Reflect and Adapt

After the session, take a few moments to reflect. What worked well? What surprised you? Were there moments when the AI seemed to bring something new to the table? Document your thoughts, even if it’s just a few notes, and think about how you might adapt the technique next time.

Start Small, But Start Today

You don’t have to overhaul your entire approach all at once. Just pick one small interaction and experiment with treating the AI like a partner rather than a tool. You might be surprised at what emerges from even a brief session when you allow the conversation to breathe and evolve.

And here’s something you might not expect: the more you practice this collaborative mindset with AI, the more it will shape how you approach collaboration with others. When you train yourself to engage openly and flexibly with AI, you’re also training yourself to be more collaborative and adaptable in general—even when working with human colleagues. This process isn’t just about transforming how you interact with AI—it’s about transforming how you approach teamwork and problem-solving as a whole.

So, I encourage you: start small, stay curious, and see where this new approach can take you.

Conclusion—The Future of AI as a Creative Partner

Throughout this post, we’ve explored a shift in how we approach AI—from a rigid, command-driven tool to a dynamic, collaborative partner. This isn’t just a minor adjustment; it’s a fundamental change in how we think about technology and what it means to interact with it. The impact of this shift goes beyond improving prompt results or generating better outputs. It’s about redefining the very nature of our relationship with intelligent systems.

I believe that if we embrace this mindset, we can unlock the true potential of AI. It’s not about replacing human effort or automating every task—it’s about creating a partnership where AI complements our abilities, expands our creative boundaries, and helps us achieve more than we could alone. This collaborative dynamic is what I see as the future of human-AI interaction.

Looking Ahead: A New Era of Human-Machine Synergy

As AI continues to evolve, so will the ways we interact with it. The techniques and frameworks we’ve discussed here are just the beginning. With every new model, every iteration, and every breakthrough, the possibilities will continue to expand. And it’s up to us to decide how we engage with these new capabilities—whether we fall back into seeing AI as a tool to control or lean forward into a future where it’s a co-creator, a partner in exploration.

I envision a future where AI is seamlessly integrated into our workflows, not as a passive assistant, but as an active participant in our thinking and creating. Whether it’s brainstorming ideas, solving complex problems, or even challenging our assumptions, AI has the potential to become a powerful force that pushes us to think deeper and more creatively.

From Techniques to Mindset: Internalizing the Change

But for that vision to become reality, it’s not just about mastering techniques—it’s about internalizing a mindset. One where every interaction, every prompt, and every response is treated as an opportunity to engage more deeply, to explore new perspectives, and to build something that neither human nor machine could create alone. This mindset won’t just transform how we use AI—it will transform how we approach our own work, our thinking, and even our interactions with others.

By seeing AI as a partner, we step into a new era of augmented intelligence—one where the goal isn’t efficiency or automation, but collaboration and synergy. It’s about working with AI to enhance our capabilities and discover what’s possible when we combine human intuition and creativity with machine logic and speed.

The Real Goal: A Collaborative Future

In the end, this isn’t just about prompt engineering. It’s about reimagining what it means to create, to learn, and to collaborate. It’s about building a future where AI is more than just a tool—it’s a creative partner that amplifies our strengths and fills in our gaps, enabling us to reach new heights together.

The path forward won’t always be clear, and there will be moments when the conversation feels awkward or the results seem off. But that’s part of the process. Every successful partnership goes through its growing pains, and this is no different. The key is to keep engaging, keep experimenting, and keep evolving the relationship.

So, whether you’re new to prompt engineering or a seasoned expert, I encourage you to embrace this shift. Treat your AI interactions not as rigid queries to optimize, but as conversations to explore. Approach the AI not as a tool to control, but as a collaborator to partner with.

Because when we do that—when we treat AI as a creative partner rather than a mechanical assistant—that’s when we’ll truly start to see the future of human-AI collaboration take shape.

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