Artificial Technology – Rōnin Consulting https://www.ronin.consulting Expert Engineers Delivering Superior Software Thu, 23 Apr 2026 17:53:16 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://www.ronin.consulting/wp-content/uploads/2022/01/cropped-Logo-Red-100x100-1-32x32.png Artificial Technology – Rōnin Consulting https://www.ronin.consulting 32 32 The AI Coding Agent Reality: Your Developers Got 10x Faster, Your SDLC Didn’t https://www.ronin.consulting/artificial-technology/ai-coding-agent/ Mon, 09 Feb 2026 18:01:28 +0000 https://www.ronin.consulting/?p=2187

The AI Coding Agent Reality: Your Developers Got 10x Faster, Your SDLC Didn’t

By the end of 2026, one developer should be able to complete an entire scrum team’s worth of story points per sprint.

That’s not hype; it’s what we’re predicting will happen right now with AI coding agents.

At Rōnin, we’ve spent months stress-testing Claude Code to find its edges. One of our founders, Byron McClain, recently ported a legacy game engine with 900,000+ lines of code from Windows to macOS in four days using autonomous coding loops. Not four weeks. Four days.

And here is what is becoming clear to us, and what nobody is talking about yet: that coding isn’t the bottleneck anymore.

The productivity multiplier nobody’s talking about

Traditional scrum teams aim for 40-50 story points per two-week sprint, distributed across multiple developers. That number was sustainable because coding was the constraint.

But now with the introduction of agents, Byron estimates that “by the end of this year, there is no excuse for a developer not to be able to do a whole scrum team’s worth of points per sprint.”

That’s a potential for an 8-10x increase in productivity in one year.

So, if your SDLC is still built around the assumption that coding is slow, expensive, and needs rationing of developer time, you’re about to hit a wall.

Where the new bottlenecks are forming

If coding speed increases but everything else in your SDLC stays the same, something must give. The constraint itself doesn’t disappear; it will just move to areas you haven’t optimized yet.

As coding speed increases, we’re predicting that three major bottlenecks will emerge across projects, and if you’re already using AI coding agents (or planning to), here are the three problems you’ll hit first:

The QA crunch

When a single developer can produce what used to require a full team, your QA team becomes the bottleneck. They weren’t staffed or structured to validate 5x the code output. You’ll have developers finishing sprints in the first week, then sitting idle while QA scrambles to catch up.

The spec vacuum

Product managers and BAs are now the constraint. If it takes you two weeks to write and refine user stories for a sprint, but your developer can execute them in three days, you’ve got a serious pacing problem. Developers will be starved for well-defined work.

The context gathering crisis

AI agents are only as good as the information you give them. Vague requirements produce vague code. This forces a return to more upfront specification work, something that feels like the waterfall approach we spent 20 years moving away from. Except now, that upfront work pays off immediately instead of six months later.

By the end of this year, there is no excuse for a developer not to be able to do a whole scrum team’s worth of points per sprint.” – Byron McClain

 

Why waterfall thinking suddenly makes sense again

The waterfall methodology didn’t fail because planning was bad; it failed because coding took so long that requirements went stale. When you waited 6 months to see results, the world had already changed. Your business learned something new. Your carefully crafted specs became obsolete before the first deployment.

But if AI can code your comprehensive specs in a day? Suddenly, front-loaded planning isn’t a liability anymore; it’s the optimal strategy.

Byron puts it this way: “Back in the day, waterfall was a big deal. You would spend a lot of time creating the product requirements document, and it took a long time. That’s why agile happened: you could start iterating tiny little chunks so people could see it and make changes along the way. But now, coding can happen almost instantaneously. What happens to the cycle? Well, now the risk profile completely changes.”

AI agents are context-consuming machines

Feed them a small set of requirements (agile-style), and you will incur context loss and constant manual work to maintain architectural coherence.

But if you feed your AI agent comprehensive upfront specs (waterfall-style), it will consistently execute from start to finish.

“You’re gonna have to spend more time up front,” says Byron. “You need to get really, really good context of what you want to build and then decompose that into tasks. When you do that, the coding aspect will be super short, and we’re going to reach a point where it’s instantaneous.”

Following this thought process, you could spend three days on discovery, have AI code within a single day, and still iterate faster than traditional agile sprints ever allowed. The rapid feedback loop isn’t lost; it’s just moved.

You’re not going back to 18-month waterfall death marches. You’re front-loading your process with planning and intensive criteria, then executing and iterating at speeds agile never allowed.

The proof is in the execution

Byron worked under the same criteria he used when transferring a legacy video game from Microsoft to Mac. He and Claude spent significant time upfront understanding the entire 900,000-line codebase, generating a detailed 14-phase plan with specific tasks. Only then did any code get written, and he did it all within 4 days.

Some will argue that this is premature, that AI agents aren’t reliable enough yet, or that we’re relying too heavily on a single exceptional example. Which is fair. But even if Byron’s 4-day port becomes a 2-week port for most teams, that’s still a 4- to 5x productivity increase, and that directional shift remains true even if the magnitude varies.

So here’s the reality: for AI agents, agile’s piecemeal iteration becomes a handicap. You’re optimizing for a constraint (slow coding) that no longer exists, while ignoring the new constraint AI introduces (agents that need comprehensive context to excel).

This doesn’t mean agile principles are dead; human-in-the-loop and feedback remain very important. What it means is that the cadence and approach need to shift.

The role collapse is coming

As if the resurgence of waterfall didn’t already throw us, here’s where it gets uncomfortable for many organizations and employees: a role change is on the horizon.

“The titles and job roles are gonna collapse,” Byron predicts. “Instead of having a BA, a developer, and a QA engineer, you’re gonna just have a solution engineer.”

This doesn’t mean specialization will disappear; complex domains will still require deep expertise. But the walls between specific tech roles are coming down. A BA who can’t understand the development or technical context will struggle, just as a developer who can only write code will find their skills commoditized.

This isn’t a layoff narrative. It’s a skills evolution.

The best BAs will become solution architects. The best developers will become technical strategists. The best QA engineers will become validation specialists who design automated testing frameworks rather than manually click through interfaces. The work isn’t disappearing, it’s elevating.

Byron predicts that the future belongs to “solutionists,” or people who can:

  • Sit with the business and extract precise requirements.
  • Break down complex problems into clear, executable tasks.
  • Frame problems in ways that AI agents can understand and execute.
  • Put on the QA hat to validate outputs.
  • Revise based on business feedback.

The coding part? That’s becoming the easy part. It’s everything else that needs to catch up.

The companies that will win using AI coding agents

The organizations that thrive won’t be the ones that get the best AI tools first (everyone will have access to similar tools). They’ll be the ones who reimagine their entire SDLC around the new constraint of human understanding, not machine execution.

That means flipping your resources with more time in discovery and less in development. It means tighter spec discipline but looser code reviews, because the code quality problem largely solves itself when you give AI agents explicit directions.

It means building cross-functional “solution engineers” instead of maintaining siloed specialists who hand off work at each stage. Your QA approaches need to scale with code output, not linearly with headcount.

The shift isn’t about implementing new tools. It’s about restructuring everything that happens before and after the code gets written.

We’re not all ready, but it’s happening anyway

Our customers are already saying it: “We don’t want to get rid of people. We want to do more.”

That’s the transition path. Companies won’t or don’t need to downsize their engineering teams. They will be able to significantly increase their output and tackle ambitious projects they previously couldn’t justify.

But this will only work if they fix the SDLC first.

Agile and sprint planning were designed for a process where coding was the bottleneck… and right now that world has just imploded.

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Unveiling the Unmatched Brilliance of Human Intelligence Amidst Advancing AI https://www.ronin.consulting/artificial-technology/advancing-ai/ Wed, 10 Jan 2024 23:01:15 +0000 https://www.ronin.consulting/?p=1326 In the rapidly evolving landscape of advancing AI, where algorithms and language models dominate conversations, the enduring supremacy of the human brain remains an undisputed marvel. While we are inundated with the potential use cases of AI systems, we must not forget the profound capabilities that set human intelligence apart from even the most sophisticated AI models.

Creativity: A Symphony of Imagination

At the heart of human creativity lies an irreplaceable spark. While AI models can simulate and generate outputs, our organic and intricate ability to weave together unrelated concepts, birth novel ideas, and express emotions through art, music, and literature stands as a testament to the unique ingenuity of the human psyche. Humans have not dominated just because they are apex predators. Humans have an innate ability to express advanced cognition and the ability to adapt to new and changing environments.

Emotional Intelligence: Beyond Binary Processing

Understanding emotions, navigating intricate social dynamics, and demonstrating empathy are intrinsic to human intelligence. This type of processing has played a significant role in human survival and evolution, contributing to the functioning and growth of human societies.

While proficient in recognizing patterns, AI models fail to comprehend the nuanced complexities of human emotions.

Genuine understanding of humor, sarcasm, and adept navigation of social intricacies are hallmarks of the human experience. With so much of human communication skills relying on non-verbal tells, AI has yet to catch up. Currently, AI lacks the basic human experience and emotional comprehension essential for understanding body language cues.

Adaptability: A Dynamic Learning Symphony

The human brain is a dynamic learning machine. It constantly evolves and adapts to new information and changing circumstances. While AI models excel in specific tasks, they lack the holistic adaptability inherent in the human mind. Researchers have studied the variances between a computer’s processing and the speed and efficiency of the human brain, and they are definitely not on the same playing field.

Computer tasks are performed as a step in a series, while the human brain uses both serial steps and parallel processing. This parallel processing allows the brain to reference and learn from diverse experiences, draw connections across disciplines, and apply knowledge in various contexts remains unparalleled. With AI, this process must be created, inputted, and followed in a specific series of events to achieve. 

Common Sense: The Silent Wisdom

Common sense involves practical judgment based on experience and an innate understanding of the world. While we might know someone who “lacks common sense,” humans are born with the skills to develop and grow this attribute through learning, observation, and adapting to the nuances of life.

For example, it’s common sense that you would not put your hand in a fire, lest you get burned. However, a child would only know this through experience or if somebody told them. AI models can not experience the growth of common sense as humans do. AI models frequently struggle in practical situations because they lack the inherent common sense pre-wired into human beings. 

Processing Speed: The Human Advantage

One of the remarkable facets of human intelligence is its efficiency in processing information from all five senses. For example, the retina transmits visual information to the brain at about 10 million bits per second. The auditory system processes sound at a speed of up to 20,000 bits per second. These rapid processing speeds contribute to our real-time perception and interaction with the world, showcasing the unmatched capabilities of the human sensory system. 

In comparison, AI often fails to replicate the rapid and simultaneous information processing inherent in human vision, hearing, touch, taste, and smell. The efficiency of human sensory systems remains a distinctive advantage, enabling us to navigate and comprehend our surroundings with unparalleled speed and precision.

AI Hardware Realities: Dispelling the Singularity Myth

While AI has made substantial progress, achieving a level comparable to the human brain requires incredible power and hardware advancements. The human brain operates on an energy budget of about 20 watts, which is incredibly energy efficient. Compare that to a typical desktop computer, which draws around 175 watts, and a consumer-level machine-learning setup, which draws about 900 watts, and you can already see the incredible power disparity.

The concept of the AI “Singularity,” often envisioned as a hypothetical point where AI surpasses human intelligence, faces significant challenges due to these hardware realities. Achieving human-level cognitive abilities in AI would demand unprecedented computational power and energy efficiency on par with the human brain. Currently, the road to Singularity involves addressing substantial hardware gaps and redefining the energy efficiency of AI systems.

Conclusion: Advancing AI Should Not Overshadow The Human Mind 

As we witness the wonders of artificial intelligence, we must not lose sight of a critical point: AI is a tool crafted by the genius of the human mind. In a world shaped by technology, let us celebrate the irreplaceable brilliance of the human mind. Our brains, with their unparalleled creativity, emotional intelligence, adaptability, common sense, and power, stand as a testament to the extraordinary nature of human intelligence. 

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8 Steps to Pick AI For Your Business https://www.ronin.consulting/business/ai-for-your-business/ Thu, 19 Oct 2023 16:29:56 +0000 https://www.ronin.consulting/?p=1279

Integrating Artificial Intelligence (AI) technology in business practices has become a hot topic. Today, it’s nearly impossible to perform a Google search without coming across a statistic about AI and its profound impact on our work. These statistics, like how 83% of companies surveyed consider using AI in their strategy a high priority and how over 81% of employees believe that AI improves their overall performance at work, are not numbers we can ignore.

Innovative businesses and employees are embracing the AI movement and learning to use this new technology within their operations. However, there is more than one type of AI for your business.

Choosing the right AI technology can be overwhelming, from off-the-shelf options to customized AI offerings. How do you know common problem your AI solution will help you solve? To help streamline the process, we have pulled together some techniques your business can follow to pick the best AI solution for your unique workflow.

These eight steps ensure that whatever AI platform you pick aligns with your business goals and requirements:

  • Understand what AI can do for your business.
  • Evaluate AI solutions.
  • Assess data requirements.
  • Consider fine-tuning vs. prompt engineering.
  • Acknowledge the user experience.
  • Align with future growth.
  • Consider costs and returns.
  • Implement a pilot program.

 

Why Use AI for Your Business?

Choosing an AI solution because a peer you know is using it or you have seen advertisements online for them is not the best way to pick your platform. Before considering the best AI technologies, start with your business.

Conduct an in-depth analysis of your business workflow and identify pain points and areas that could benefit from AI, such as:

  • Workflow efficiency
  • Decision-making
  • Customer engagement
  • Data analysis
  • Marketing or advertising
  • Inventory planning

While this is not an exhaustive list of everything you could find, identifying areas where your workflow might have blocks will help you create a more precise roadmap for the specific AI capabilities your business requires.

 

Evaluate AI Solutions

Once you have outlined how your business can benefit from AI, your next step is to evaluate the various AI solutions available in the market. Don’t fear the AI solutions, but consider each of them seriously and explore all the options, determining your potential solution’s features, functionalities, and scalability.

Will the AI solution you want to invest in seamlessly integrate with your existing infrastructure and not substantially disrupt the workflow? Is the potential AI software easy to use, implement, and train? In this step, consider your future AI software solution’s potential for customization and adaptability, and whether private or public AI better suits your data security needs.” Always think about how your AI technology can solve your problem right now and how it can cater to the evolving demands of your business.

 

Assess Data Requirements

Understanding the data requirements of the AI technology is crucial to ensure the efficient and effective functioning of the system. Don’t be fooled by AI systems offering all the bells and whistles and a low price tag. Some off-the-shelf solutions follow a pay-as-you-go model that can get pricy. For example, ChatGPT offers a cost per token to use their product. If you want to use this platform for prompt engineering or run extensive data sets through it, you might use more tokens than anticipated and quickly eat up your budget.

The takeaway here is to take your data usage seriously. Evaluate the volume, variety, and quality of data your business generates and accumulates. Consider your AI technology’s capability to handle and process this data, ensuring it can derive actionable insights and facilitate informed decision-making.

 

Consider Fine Tuning vs. Prompt Engineering

The learning capabilities of the AI technology you pick will play a pivotal role in its long-term viability and relevance. Not all AI platforms are the same, and you will need to determine if your business will benefit from an AI platform built through prompt engineering or fine-tuning.

Prompt Engineering: This is when the AI shapes its answers based on the responses provided by the user or engineer. The user will input a prompt, and the AI will use the prompt to take action and yield results.

Fine-Tuning (FT): This process involves adjusting a pre-trained model on specific tasks or datasets. An FT system can optimize an existing model’s performance and shape its behavior by applying new data sets to train its weights.

Which one is better AI for your business? It depends on your business goals and AI use case. Both prompt engineering and fine-tuning have their own pros and cons, but the technology platform you pick depends on the nature of your business requirements and your intended application of AI.

 

Acknowledge the User Experience

While the technical aspects of AI are crucial, the user experience is equally vital for successfully adopting and integrating AI technology. Prioritize AI solutions that offer:

  • Intuitive interfaces
  • Seamless interactions
  • User-friendly functionalities

Choose an AI platform technology that can be easily integrated into your employees’ workflows and does not need extensive training. A positive user experience fosters a culture of acceptance and encourages widespread adoption of AI across various departments within your organization.

The same can be said if your AI solution is customer-facing. These AI solutions, such as the ever-popular chatbots and virtual assistants, should be designed to provide a smooth and intuitive experience for your customers, not another pain point to work through.

 

Align with Future Growth

Investing in AI technology is not just about addressing current business needs but also about preparing for future growth and scalability. Choose AI solutions that you can adapt to your business’s evolving dynamics. Look for technologies that offer:

  • Scalability in processing power
  • Data handling capabilities
  • Integration with emerging technologies

A future-oriented AI technology will enable your business to stay ahead of competitors already using this technology and capitalize on new opportunities.

 

Consider Costs and Returns

While the potential benefits of executing AI technology within your business are great, don’t overlook the costs associated with its implementation and maintenance. As previously mentioned, pay-per-use platforms can be cost-prohibitive for some, so conduct a comprehensive cost-benefit analysis to determine the investment required to integrate AI technology into your business operations.

This analysis should cover the long-term return on investment, including increased efficiency, improved productivity, and enhanced decision-making capabilities. It should also cover the cost of choosing an off-the-shelf solution vs. a custom AI implementation. While an off-the-shelf AI solution might be cheaper initially, a custom solution might be a wiser long-term option concerning upgrades, scalability, security, and control over your AI datasets.

Strive to strike a balance between the initial costs and the potential returns, ensuring that the AI technology you choose offers a sustainable and profitable solution for your business in the long run.

 

Implement a Pilot Program

Before fully integrating AI technology into your business operations, consider implementing a pilot program to assess its functionality and performance in a controlled environment. Test the AI system’s capabilities on a small scale, monitoring its impact on your specific organizational processes or departments.

Gather feedback from the users and stakeholders involved in the pilot program to evaluate the effectiveness and suitability of the AI technology. Use any insights from the pilot program to make informed decisions regarding a broader implementation and rollout of the AI solution across your business.

This step is extremely important, as humans must always be in the loop. Your AI platform should never be left fully automated without human minds checking in and making sure everything is working as it should. When you implement a pilot program, you have the ability to see how the AI software runs within your organization and have the power to guide and shape it how you want it to be.

 

Seek Expert Guidance with Rōnin Consulting

Navigating the complex landscape of AI technology can be challenging, especially for businesses without prior experience. If choosing an AI solution is overwhelming, consider seeking guidance from AI experts and consultants. At Rōnin Consulting, our software developers and AI technologists can provide valuable insights and recommendations based on your business requirements.

When you work with Rōnin, you will collaborate with professionals with the experience to build and implement AI for your business. Rōnins expertise and guidance will help your business significantly streamline the process of selecting and integrating the right AI technology for your business.

 

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Should We Fear AI? Navigating the 5 Truths & Myths of AI Technology https://www.ronin.consulting/artificial-technology/fear-ai-technology/ Tue, 12 Sep 2023 16:13:12 +0000 https://www.ronin.consulting/?p=1247 Artificial Intelligence (AI) has become a buzzword that often elicits mixed emotions in people. Some of us are fascinated by the potential of AI to revolutionize industries and make our lives more convenient, and others fear that AI might one day outsmart us or replace our jobs. 

As someone who has waffled between all the AI emotions, I can see why there are so many conflicting opinions. While some of my feelings might be completely groundless (no, an AI robot will not abduct us while we are sleeping), others are entirely justifiable (yes, some people will lose jobs to AI).

In this article, I will delve into some of the myths and realities surrounding AI technology and shed some light on whether these fears are justified or if we should embrace AI with cautious optimism.

Myth 1: AI Will Definitely Take Over the World

One of the most pervasive AI myths is that AI will surpass human intelligence and take control of the world. While I will never turn off “The Terminator,” and I’m a sucker for a good dystopian movie – the AI reality is far less dramatic.

Reality: Current AI models lack consciousness and intent. AI models are designed to perform extremely specific tasks, and their abilities are constrained by the data and algorithms they’re built upon. They don’t possess self-awareness, emotions, or the capacity to plot world domination. AI’s power lies in its ability to process vast amounts of data and make predictions based on patterns, but it still does not possess the cognitive flexibility and creativity humans do.

Also, humans develop and maintain AI technologies, and strict ethical guidelines and regulations are in place to ensure their responsible use. As long as these safeguards remain in effect, the idea of AI taking over the world remains firmly in science fiction. 

Myth 2: AI Technology Will Steal Our Jobs

Another common fear surrounding AI is that its use will lead to widespread job loss as machines become more proficient at tasks traditionally performed by humans.

Reality: While it’s true that AI can automate specific tasks, the relationship between AI and employment is more complex than AI busting in through walls like the Kool-Aid man to steal all our jobs. AI is more nuanced than that. It shows up at the party ready to help with its ability to augment our human capabilities and create new job opportunities–it’s not showing up to steal our good time.

Using AI correctly can help us with many repetitive, dull, data-driven tasks. It is another tool we can use to free up our work hours so we can focus on strategic or creative work. For example, AI can help analyze medical images and data in healthcare, allowing doctors to spend more time with patients and make more accurate diagnoses. When used within the finance industry, AI can analyze vast amounts of financial data in real time, helping investors make informed decisions and manage portfolios more effectively. The usage of AI is limitless – it all depends on how you want to build and develop AI for your business.

Myth 3: AI Technology is Infallible

Some people believe that AI is infallible and that its predictions and decisions are always correct and unbiased.

Reality: AI is not infallible and only as good as the data it’s trained on and its algorithms. AI systems can inherit biases in the data, which could lead to incorrect outcomes and misleading answers. For instance, if a facial recognition system is trained primarily on data from one demographic group, it may perform poorly on individuals from underrepresented groups. Also, AI might inadvertently perpetuate existing biases and discrimination within historical data, raising a gambit of ethical concerns about the truthfulness of its output.

Unfortunately, bias in AI is a serious concern and an ongoing challenge. Researchers and organizations are actively developing methods to reduce bias and improve the fairness and transparency of AI systems. When using AI platforms within your business, it’s crucial to approach AI critically and recognize that it can make mistakes, especially when used in complex or nuanced decision-making scenarios.

Myth 4: AI Understands Like Humans

Many people assume that AI systems, particularly natural language processing models like chatbots and virtual assistants, understand language and context just like humans do.

Reality: AI processes language differently from humans. AI models are based on statistical patterns and do not possess true understanding or consciousness. They rely on vast datasets and mathematical algorithms to predict the most likely responses based on their input.

While AI has made impressive strides in natural language understanding, it still struggles with nuances, sarcasm, and context

With human language estimated to be at least 73-90% nonverbal cues, it should be no surprise that an AI model might also struggle to unravel the nuances of humor, sarcasm, and cultural and situational context.

Myth 5: AI Technology Can Replace Human Creativity

AI technology 1

Some fear AI will render human creativity obsolete by generating all our art, music, and literature.

Reality: AI can assist and enhance human creativity but will never replace it entirely. AI can generate content based on patterns in the existing data, but it cannot innovate or express genuine human emotions and experiences.

While AI-generated content or design prompts can be initially helpful, it soon becomes clear that AI-generated creativity lacks the depth and originality we human creators bring to our work. For example, the image depicted above was created using the AI image-generating program Midjourney. Midjourney can generate images based on patterns and styles it has learned, but it lacks the deeper understanding or emotional connection a human artist can infuse into their work. Also, AI art creation platforms like Midjourney are still under scrutiny, so the question of whether AI-generated art can be truly considered “creative” remains a subject of debate.

Creativity is a uniquely human trait, encompassing the ability to think beyond established boundaries. While AI may excel in pattern recognition and generating content based on existing data, it still lacks the consciousness, intuition, and subjective experiences that fuel human creativity.

Embrace AI with Caution and Understanding

In the grand debate surrounding AI, you must navigate the myths and realities with a balanced perspective. AI is a powerful tool that can potentially improve various aspects of our lives, but it has limitations and ethical challenges.

There is no need to fear AI. Instead, we should approach it cautiously, understanding its capabilities and limitations. 

Ultimately, the future of AI depends on how we choose to use it and regulate it. If we embrace AI with transparency and are responsible for innovation, AI can become an asset that enhances our lives without replacing our humanity.

At Rōnin Consulting, we choose not to fear AI but to embrace it with open arms. As a software consulting company, we recognize AI’s immense potential for our clients and their industries. We understand that AI is a tool that can bring about transformative results when wielded with care and expertise–and that is exactly what we plan to do. So, watch out for our custom AI tools as we continue to explore this new technology to create innovative solutions that address real-world challenges for all our clients.

Want to learn more about AI technology? Contact the team at Rōnin today!

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