Are AI Influencers the Future of Online Fame?

12

September

2025

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AI influencers are no longer novelty items and are now a real part of the creator economy. Lil’ Miquela, a created character that first appeared in 2016, has walked red carpets, appeared in campaigns for companies such as Prada and Calvin Klein, recorded songs, and gained millions of followers (Lil Miquela, 2025). Miquela’s never tried to hide the fact that she is artificial, but has been able to build trust with followers who interact with her as if she were real (Economic Times­, 2025).


Now let’s talk about Mia Zelu, a newer AI influencer who drew lots of attention when her photorealistic impressions of images at Wimbledon went viral (Mia Zelu, 2025). A fair amount of her fan base thought she was at Wimbledon, even though her profile did have a slight, tucked disclaimer that stated she was a “digital creator & influencer AI.” The potential of plausibility with some disclaimers highlights the opportunity and risk that exists with artificial figures; they may be highly convincing and yet also possibly mistaken at an non-aware interval to be a real person (Independent, 2025).


The EU AI Act does provide some insight on this. Article 50 requires that AI content deemed to have been (possibly manipulated by) AI should be clearly labelled (possibly with some kind of tagging or watermark), so audiences are aware when they are being presented with synthetic media, especially when advertising or posting sponsored posts (European Parliament, 2023). But it is already apparent how there would be grey areas, what does “clearly” mean in this context, and how regulators are going to enforce these provisions, considering there is no accountability with international social platforms?


As lawmakers are working through these varied issues, the cash is rolling in. Brands are increasingly more interested in working with AI Influencers due to their ability to not age, never create a scandal, and can be created for any marketing intention. They can also monetize behind the scenes and cut deals for the content they have created in the form of sponsorship deals, licensing deals, virtual performances, and even digital merchandise. Lil Miquela has pulled in millions in brand partnerships, showing us that companies are willing to dump money, sometimes hundreds of thousands into influence that simply does not exist in the physical world (Economic Times, 2025).

The rise of AI influencers makes us rethink both online authenticity and what it means to be a “creator” and monetize it. If digital characters are getting contracts and creating followings, what does that mean for human influencers competing against each other for people’s time and money? Should we view AI influencers as tools, as businesses, or as new types of celebrities?

References:

Economic Times. (2025, September 7). Who are Mia Zelu and Lil Miquela? The rich, famous and fake influencers. The Economic Times. https://economictimes.indiatimes.com/news/international/us/who-are-mia-zelu-and-lil-miquela-the-rich-famous-and-fake-set-of-influencers/articleshow/123743591.cms

European Parliament. (2023, October). Generative AI: Watermarking and transparency requirements (EPRS Briefing). European Parliamentary Research Service. https://www.europarl.europa.eu/RegData/etudes/BRIE/2023/757583/EPRS_BRI(2023)757583_EN.pdf

Independent. (2025, July 9). Wimbledon AI influencer Mia Zelu confuses fans on Instagram. The Independent. https://www.independent.co.uk/life-style/wimbledon-ai-influencer-mia-zelu-instagram-b2787956.html

Lil Miquela [@lilmiquela]. (n.d.). Lil Miquela (official profile) [Instagram profile]. Instagram. Retrieved September 12, 2025, from https://www.instagram.com/lilmiquela/

Mia Zelu [@miazelu]. (n.d.). Mia Zelu (official profile) [Instagram profile]. Instagram. Retrieved September 12, 2025, from https://www.instagram.com/miazelu/?hl=en

Official Journal of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council on Artificial Intelligence (AI Act). https://artificialintelligenceact.eu/article/50/

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How is AI Becoming a Game-Changer in Pharma?

11

September

2025

5/5 (1)

The pharmaceutical industry is known for its slow and expensive drug development cycle, often taking 10–15 years and billions of dollars to bring a single drug to market (Hamilton, 2024). AI is rewriting this equation. By simulating molecular interactions, predicting promising compounds, and automating lab work, AI can cut discovery timelines by up to 50% (Baur & Fath, 2024). Instead of screening millions of molecules in the lab, algorithms instantly narrow the field, allowing researchers to focus only on the most viable candidates. This is not just efficiency, it’s a revolution in how R&D decisions are made (Malesu, 2025; Baur & Fath, 2024).

But speed alone isn’t the only breakthrough. AI empowers scientists to make smarter decisions by analysing vast datasets, genomic sequences, clinical trial data, and chemical libraries, to detect patterns invisible to the human eye (Hamilton, 2024; Baur & Fath, 2024). This drastically reduces the number of failed trials, improves the accuracy of predictions, and enables the design of more targeted therapies (Suri et al., 2024). In other words, AI doesn’t just help scientists work faster, it helps them work smarter.

The financial impact is equally transformative. By streamlining trials, automating lab tasks, and cutting down on costly failures, AI reduces operational costs while accelerating time to market (Baur & Fath, 2024; Walch, 2025). For pharma companies, this means higher ROI and a stronger competitive edge in an industry where every day counts. For patients, it translates to faster access to life-saving drugs.

Yet, alongside the promise, ethical challenges remain significant. Many AI models operate with limited transparency, making it difficult for scientists and regulators to fully understand how decisions are reached. Bias in training data can reinforce health disparities, and the risk of mishandling sensitive patient data is ever-present. Regulators are racing to keep up, but the pace of innovation often outstrips policy (Suri et al., 2024; Malesu, 2025). This tension between innovation and accountability may ultimately determine how much trust society places in AI-driven healthcare.

How much trust would you place in AI to guide critical decisions in healthcare, and what safeguards would make you feel confident in its use?

References:

Baur, M., & Fath, S. (2024, October 8). Why AI is a game changer for the pharmaceutical industry. Roland Berger. https://www.rolandberger.com/en/Insights/Publications/Why-AI-is-a-game-changer-for-the-pharmaceutical-industry.html

Hamilton, C. (2024, December 17). Reinventing pharma: How AI is revolutionizing drug discovery. BioLife Health Center. https://www.biolifehealthcenter.com/post/reinventing-pharma-how-ai-is-revolutionizing-drug-discovery

Malesu, V. K. (2025, June 11). Why drug discovery needs robots and artificial intelligence. News-Medical.net. https://www.news-medical.net/health/Why-Drug-Discovery-Needs-Robots-and-Artificial-Intelligence.aspx

Suri, G.S., Kaur, G. & Shinde, D. Beyond boundaries: exploring the transformative power of AI in pharmaceuticals. Discov Artif Intell 4, 82 (2024). https://doi.org/10.1007/s44163-024-00192-7                                       

Walch, K. (2025, March 2). How AI is transforming the pharmaceutical industry. Forbes. https://www.forbes.com/sites/kathleenwalch/2025/03/02/how-ai-is-transforming-the-pharmaceutical-industry/

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The SaaS Paradox: From Yesterday´s Disruptors to Today´s Victims

11

September

2025

5/5 (1)

In the early 2000s, Salesforce revolutionized the software industry with the launch of its cloud-based CRM solution. Businesses started to swift from expensive on-premise installations of software programs to cloud-based systems with subscription models, later catalogued as Software as a Service (SaaS) companies. However, the market is in a completely different situation: Now we see that yesterday’s disruptors are being categorized as today´s incumbents. 

This market sentiment is demonstrated by the stock price of leading companies such as HubSpot, which is -28,32% YTD (Yahoo!Finance, 2025), Salesforce, which is -26,32% YTD (Yahoo!Finance, 2025), and Adobe, which is -21,35% YTD (Yahoo!Finance, 2025). This general stock downfall is partially driven by the statements of executives at, coincidentally, other technological companies. Charles Lamanna, Microsoft’s corporate vice president leading business applications and platforms, believes that SaaS companies will be replaced by AI business agents, which understand and adapt better to businesses’ needs without predefined systems and menus (Taft, 2025). Klarna’s CEO recently announced they got rid of over 1,200 software tools and built their own AI-powered system instead, making use of LLMs to reduce the data fragmentation (Klarna, 2025).

To respond to the current disruption generated by AI, SaaS companies are trying to fight back. In 2023, Salesforce announced the release of Einstein GPT, a generative AI for its CRM platform (Salesforce, 2023). In 2024, SalesForce unveiled AgentForce, a suite of autonomous AI agents (Salesforce, 2024). In the case of Adobe, they tried to acquire Figma in 2023, even though the deal was abandoned due to fears of anti-trust regulations (Peters, 2023). Earlier this year, they launched Adobe Marketing Agent and Adobe Express Agent to dive into the AI agent sector (Adobe, 2025). 

From your point of view, do you think that Software as a Service (SaaS) companies are going to be displaced by AI agents? Or is it just that SaaS companies have experienced excessive investor speculation, similar to the dot-com bubble? Are we reaching the consolidation phase?

References

Adobe. (2025, March 18). Adobe and Microsoft Empower Marketers with AI Agents in Microsoft 365 Copilot. Adobe Newsroom. Retrieved September 11, 2025, from https://news.adobe.com/news/2025/03/adobe-and-microsoft-empower-marketers-with-ai-agents-in-microsoft-365-copilot

Klarna. (2025, June 12). Klarna opens direct line to CEO Sebastian Siemiatkowski – powered by AI. Klarna. Retrieved September 11, 2025, from https://www.klarna.com/international/press/klarna-opens-direct-line-to-ceo-sebastian-siemiatkowski-powered-by-ai/

Peters, J. (2023, December 20). Adobe explains why it abandoned the Figma deal. The Verge. Retrieved September 11, 2025, from https://www.theverge.com/2023/12/20/24008189/adobe-figma-deal-eu-explained-decoder

Salesforce. (2023, March 7). Salesforce Announces Einstein GPT, the World’s First Generative AI for CRM. Salesforce. Retrieved September 11, 2025, from https://www.salesforce.com/news/press-releases/2023/03/07/einstein-generative-ai/

Salesforce. (2024, September 12). Salesforce Unveils Agentforce–What AI Was Meant to Be. Salesforce. Retrieved September 11, 2025, from https://www.salesforce.com/news/press-releases/2024/09/12/agentforce-announcement/

Taft, D. K. (2025, August 16). Microsoft: AI ‘Business Agents’ Will Kill SaaS by 2030. The New Stack. Retrieved September 11, 2025, from https://thenewstack.io/microsoft-ai-business-agents-will-kill-saas-by-2030/

Yahoo!Finance. (2025, August 12). HubSpot, Inc. (HUBS) Stock Price, News, Quote & History. Yahoo Finance. Retrieved September 11, 2025, from https://finance.yahoo.com/quote/HUBS/

Yahoo!Finance. (2025, September 11). Adobe Inc. (ADBE) Stock Price, News, Quote & History. Yahoo Finance. Retrieved September 11, 2025, from https://finance.yahoo.com/quote/ADBE/

Yahoo!Finance. (2025, September 11). Salesforce, Inc. (CRM) Stock Price, News, Quote & History. Yahoo Finance. Retrieved September 11, 2025, from https://finance.yahoo.com/quote/CRM/

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The destructive effects of generative AI

11

September

2025

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Recently, a tremendous shift in the use of technology has taken place. When ChatGPT was first introduced, people treated it as an interesting novelty that you could use to create entertaining content. However, as generative AI became increasingly advanced, people started using it for a broader spectrum of uses. (Liang et al., 2025) In this moment in time, an overwhelming number of people use generative AI daily. (Beshay & Beshay, 2025) While generative AI is technologically very exciting, I think we should all proceed with great caution and be more aware when we use it.

The first reason for this is the massive burden that generative AI imposes on the environment. The enormous number of resources it takes to maintain the status quo is immense. (Zhuk, 2023) In an era where we are already being challenged with environmental issues on many fronts, minimizing the impact of generative AI on the environment should prove to be significant, and compared to other environmental issues, an easy win. (Berthelot, Caron, Jay, & Lefèvre, 2024)

Not only are we suffering from the effects of generative AI on a global scale, but also in our personal lives. I think there would be great benefits in limiting our usage of generative AI. By using generative AI as a personal companion, we can lose touch with reality. (Fang et al., 2025) Generative AI tends to react in a way that validates whatever we say. (Sharma, Liao, & Xiao, 2024) So, if we are faced with different opinions in real life, a feeling of detachment can arise. (Idem.) In addition, there are more implications on a personal level, such as a negative impact on attention (Zhai et al., 2024).

While generative AI is a tool that can be very effective in a work environment, I think we should refrain from using it excessively. It is still a very novel technique, so long-term effects have not been studied yet. However, it is a fact that it impacts the environment negatively. I think it is also safe to say that not relying on generative AI too much will positively impact our brain health.

References:

Beshay, & Beshay. (2025, April 3). 1. Artificial intelligence in daily life: Views and experiences. Pew Research Center. https://www.pewresearch.org/internet/2025/04/03/artificial-intelligence-in-daily-life-views-and-experiences/

Berthelot, A., Caron, E., Jay, M., & Lefèvre, L. (2024). Estimating the environmental impact of Generative-AI services using an LCA-based methodology. Procedia CIRP, 122, 707–712. https://doi.org/10.1016/j.procir.2024.01.098

Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W. T., Pataranutaporn, P., Maes, P., Phang, J., Lampe, M., Ahmad, L., & Agarwal, S. (2025, March 21). How AI and human behaviors shape psychosocial effects of chatbot use: a longitudinal randomized controlled study. arXiv.org. https://arxiv.org/abs/2503.17473

Liang, W., Zhang, Y., Codreanu, M., Wang, J., Cao, H., & Zou, J. (2025, February 13). The widespread adoption of large language Model-Assisted writing across society. arXiv.org. https://arxiv.org/abs/2502.09747

Sharma, N., Liao, Q. V., & Xiao, Z. (2024). Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking. Roceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), 1–17. https://doi.org/10.1145/3613904.3642459

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1). https://doi.org/10.1186/s40561-024-00316-7

Zhuk, A. (2023). Artificial intelligence impact on the environment: Hidden ecological costs and Ethical-Legal Issues. Journal of Digital Technologies and Law, 1(4), 932–954. https://doi.org/10.21202/jdtl.2023.40

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NutriNet – a personal assistant for your grocery shopping

18

October

2024

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Have you ever taken hours browsing around supermarkets searching for the most nutritious food options? Did it take you too much time figuring out which recipes to cook best, with the groceries you bought? Struggles when dealing with food are numerous, starting from choosing products with appropriate nutrients simply not knowing enough recipes.

• Grocery shopping & meal planning simplification:

NutriNet simplifies grocery shopping and meal planning, by analyzing which products and recipes fit the users’ desired grocery item wishes, nutrition values and store preferences best. NutriNet aims to address the challenges being implicit to personalized nutrition and helps consumers make healthier food choices by simplifying the grocery shopping and meal planning process. This shall be done by eliminating the need to perfectly understand all nutrition values or to search for numerous recipes. NutriNet completes all these tasks for you in real-time and provides clear and accessible recommendations for you.

• Real-time personal assitant: NutriNet acts as a multifunctional application providing value to consumers by solving various food related problems in real-time. Appearing as a chatbot, it is aimed at taking general grocery shopping lists or meal wishes as input query, combined with preferences for food characteristics (e.g., nutrients, allergies) and grocery stores. It then provides brand specific and personalized grocery shopping lists, as well as meal recommendations, if so desired. Moreover, grocery items can also be added into the initial grocery shopping list query, by scanning them with the integrated AR tool. The product will then be detected visually and thus will be integrated into the shopping list input query.

• Long-term customer engagement: NutriNet distinguishes from competition by providing personalized and customized advice. This is possible, as NutriNet consists of a database, having incorporated stock and product information of the major supermarkets in the Netherlands. In contrast, classic applications, which try to meet similar needs (e.g., meal recommendation) rather focus on counting nutrients for the purpose of short-term weight loss, instead of personalizing grocery shopping lists and meal recommendation to enable a healthier lifestyle for users. Those applications are usable on short term but are proven to have low adherence over the time (Chen et al., 2015).

• Personalized recommendations: NutriNet leverages generative AI to offer accurately personalized recommendations. Users can simply enter their preferences, while prompting a grocery list or a meal, such as gluten-free or high in protein, and the generative AI powered application will provide accurately personalized results.
However, personalization and raising awareness for healthy foods are not the only purposes of NutriNet. It also addresses sustainability issues that supermarkets are facing. By gathering consumer purchase and search data in the application, consulting services can be offered to supermarkets, enabling them to plan ordering and stockholding processes more efficient. Hence, supermarkets should be able to reduce food waste due to overstocking on long-term.

Contributors

574051 – Duong Dao
728070 – David Wurzer
738898 – David Do
562387 – Roxi Ni

References

Chen, J., Berkman, W., Bardouh, M., Ng, C. Y. K., & Allman-Farinelli, M. (2019). The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition, 57, 208-216.

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Unlocking Culinary Potential: How tasteTAILOR elevates your Cooking Experience with Generative AI

18

October

2024

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In today’s fast-paced world, health-conscious individuals and families often struggle to find the time and inspiration to cook nutritious meals. Therefore, we are presenting tasteTAILOR: a revolutionary digital culinary assistant that harnesses the power of Generative AI (GenAI) to streamline your cooking experience. It is designed to create recipes tailored to users’ diet choices, available ingredients, and kitchen appliances – offering a unique and personalized way of cooking. To streamline the cooking experience even further, tasteTAILOR seamlessly integrates its ingredient list with partner supermarkets. TasteTAILOR even offers a social community where users can share their experiences in the kitchen, get inspiration from other users, and connect with individuals with similar culinary interests.

Objectives and Value Proposition

TasteTAILOR’s most important objective is to grant customers the highest level of user satisfaction possible by making meal planning easy, seamless shopping through APIs, and engaging with the community through food recommendations powered by GenAI. Fundamentally, tasteTAILOR is committed to reducing food waste and fostering sustainable and healthy cooking with recipes to fit every taste.
We at tasteTAILOR present a multi-dimensional value proposition. We propose new dishes tailored to specific tastes but it will also generate shopping lists in supermarket applications in a hassle-free manner. Our customers can benefit from the step-by-step visual and audio instructions so that cooking can become enjoyable for everybody. The community feature provides an exciting way to connect with other like-minded culinary enthusiasts. 

Target Customer Segments

TasteTAILOR’s services are a perfect fit for several customer segments, for instance, busy professionals who have limited time to cook  but desire healthy recipes, students who are on an extremely tight budget and advanced hobby cooks who want to experiment with different types of cuisine and connect with other home cooks. The application also applies to people with special diet needs, making it the most suitable solution for those looking for a personalized cooking experience.

Key Activities and Resources

Key activities of tasteTAILOR include the design of customized recipes, using the power of GenAI technology. The personalization power comes from the analysis of user behavior, tracking one’s activity in the community, and through capturing user’s (daily) preferences.  Logically, the key resource of tasteTAILOR is the GenAI system that is woven throughout the entire business model, from generating recipes to chatbots intended for user interaction, and analytics that are inducted continuously for better personalization.

Challenges and Solutions

While the integration of GenAI offers significant benefits to the tasteTAILOR platform, it also comes with potential pitfalls, such as data privacy issues or technical glitches. The platform has taken stringent actions to consider these risks, implementing robust security measures and algorithm enhancement. The education of users through tutorials and community building is another crucial component in the adoption of AI-powered cooking solutions.

The Future of Meal Planning

Among the new generation of meal-planning applications and technological substitutions in a broader perspective, tasteTAILOR stands at the very top of elevating one’s cooking experience. TasteTAILOR offers personalized solutions that are not just tailored to the individual but are also ecologically-conscious. Taking everything into account, tasteTAILOR is not a meal planner; instead, it is a community-driven solution to empower its users to cook healthy recipes based on their own preferences. TasteTAILOR powered by GenAI is making cooking joyful.

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Innovating Learning with Canv-AI: A GenAI Solution for Canvas LMS

17

October

2024

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In today’s educational landscape, generative AI (GenAI) is reshaping how students and instructors interact with learning platforms. A promising example is Canv-AI, an AI-powered tool designed to integrate into the widely used Canvas Learning Management System (LMS). This tool aims to transform both student learning and faculty workload by leveraging advanced AI features to provide personalized, real-time support.

The integration of Canv-AI focuses on two primary groups: students and professors. For students, the key feature is a chatbot that can answer course-specific questions, provide personalized feedback, and generate practice quizzes or mock exams. These features are designed to enhance active learning, where students actively engage with course material, improving their understanding and retention. Instead of navigating dense course content alone, students have instant access to interactive support tailored to their learning needs.

Professors benefit from Canv-AI through a dashboard that tracks student performance and identifies areas where students struggle the most. This insight allows instructors to adjust their teaching strategies in real-time, offering targeted support without waiting for students to seek help. Additionally, the chatbot can help reduce the faculty workload by answering common questions about lecture notes or deadlines, allowing professors to focus more on core teaching tasks.

From a business perspective, Canv-AI aligns with Canvas’s existing subscription-based revenue model. It is offered as an add-on package, giving universities access to AI-driven tools for improving educational outcomes. The pricing strategy is competitive, with a projected $2,000 annual fee for universities already using Canvas. The integration also brings the potential for a significant return on investment, with an estimated 29.7% ROI after the first year. By attracting 15% of Canvas’s current university customers, Canv-AI is expected to generate over $700,000 in profit during its first year.

The technological backbone of Canv-AI relies on large language models (LLMs) and retrieval-augmented generation (RAG). These technologies allow the system to understand and respond to complex queries based on course materials, ensuring students receive relevant and accurate information. The system is designed to be scalable, using Amazon Web Services (AWS) to handle real-time AI interactions efficiently.

However, the integration of GenAI into educational systems does come with challenges. One concern is data security, especially the protection of student information. To address this, Canv-AI proposes the use of Role-Based Access Control (RBAC), ensuring that sensitive data is only accessible to authorized users. Another challenge is AI accuracy. To avoid misinformation, Canv-AI offers options for professors to review and customize the chatbot’s responses, ensuring alignment with course content.

In conclusion, Canv-AI offers a transformative solution for Canvas LMS by enhancing the learning experience for students and reducing the workload for professors. By integrating GenAI, Canvas can stay competitive in the educational technology market, delivering personalized, data-driven learning solutions. With the right safeguards in place, Canv-AI represents a promising step forward for digital education.

Authors: Team 50

John Albin Bergström (563470jb)

Oryna Malchenko (592143om)

Yasin Elkattan (593972yk)

Daniel Fejes (605931fd)

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Learning how to code? Let Generative AI help you!

12

October

2024

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When I started on my Python learning journey with Datacamp, I was excited, but I also faced challenges that tested my patience. As someone from a non-technical background, the structured logic of coding felt overwhelming. Even Python’s supposedly beginner-friendly syntax often appeared complex, especially when I encountered errors that I couldn’t quickly resolve.

Early on, one of the most frustrating issues I faced was debugging. It often led to roadblocks in simple mistakes like indentation errors or variable mismanagement. Despite the comprehensive learning modules on Datacamp, I often found it difficult to understand why my code wasn’t functioning as expected. Traditional resources usually provided solutions that didn’t quite align with my specific problem.

Moreover, applying theoretical concepts like loops, functions, and list comprehensions in practice was a significant challenge. While I could follow along with the lessons, I often found myself lost when it came time to solve problems independently. It became clear that I needed more personalized explanations to bridge the gap between theory and application.

That’s when I began using Datacamp’s integrated AI assistant, which proved to be a lifesaver. The AI provided on-demand explanations of the coding assignments, breaking down what each line of code was doing and helping me understand the purpose behind every function and operation. For example, when working on loops, the AI would offer examples and explain them in simpler terms, helping me grasp how to apply these concepts to real-world problems. It even helped me understand more complex concepts like recursion by providing step-by-step explanations and visualizations.

The AI didn’t just solve problems for me—it taught me how to approach coding challenges. Offering multiple ways to write a function or fix an error encouraged me to think critically about my coding style and improved my overall understanding.

I know this may sound like a promotion, but I genuinely recommend Datacamp to anyone interested in learning to code. It provides the most interactive learning experience, and the integrated AI makes the journey much smoother and more enjoyable.

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Using Generative AI as My Marathon Training Coach

11

October

2024

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I want to run a marathon next summer, so I turned to ChatGPT to help me out. Not with the running part, but mostly everything else. With generative AI, I can have a personal marathon coach that understands my needs, optimizes my training plan, and continually adapts as my fitness level evolves.

Setting Up My AI Coach

To create my AI coach, I used the MyGPT function offered by OpenAI. It allows you to customize a ChatGPT model to your liking by providing sets of instructions and information. I instructed it to act as my professional marathon coach who understands my goals, fitness level, and the specific challenges of marathon training. I explained my current abilities, body metrics, and general athletic history. I also provided my time goal, the marathon course info, and the date of the event. Moreover, I uploaded important data:

  • Running metrics for current, regular runs (distance, time, heart rate zone info, cadence, pace, split pace, effort level, etc.)
  • Apple Watch training data
  • Calendar data

This data ensures that the model can give more specific advice and training plans. For example, it uses my previous data to set pace targets considering terrain and what heart rate zone I should be in for a particular session.

Phased Training Plan

The AI-generated plan broke my preparation into phases, each with a specific focus. The initial phase was about building endurance with consistent mileage and easy runs. Later phases introduced speed work and tempo runs to improve stamina and pace. The AI explains the purpose behind each phase, which keeps me motivated and committed.

The phased approach means I’m not overwhelmed. Instead of seeing it as one long journey, I focus on each phase, trusting that the AI has mapped out the best way to build my fitness gradually. Each phase has unique challenges and goals, which helps me stay focused and structured. Here is an example of what it created:


Phase 2: Strength & Speed Building (Weeks 11–20)

Goal: Increase mileage, speed, and introduce hill work. Continue to improve cadence and manage heart rate.

Weekly Structure:

  • 5 days running:
    • 1 long run (gradually increasing distance)
    • 1 interval session (e.g., 4 x 1 km at 5:00/km)
    • 1 tempo run at marathon pace (5:40–5:50/km)
    • 2 easy runs (HR < 150 bpm)
  • 1–2 days cross-training or strength training focused on core and lower body strength.

Example Week 15:

  • Monday: Rest or cross-training
  • Tuesday: 8 km easy run (HR < 150 bpm)
  • Wednesday: Interval run: 5 x 1 km at 4:55–5:05/km with 2-min recovery
  • Thursday: Cross-training (strength-focused)
  • Friday: 8 km tempo run at marathon pace (5:40/km)
  • Saturday: 18 km long run (pace 6:10–6:30/km)
  • Sunday: Rest or mobility work

Long Run Progression:

  • Week 11: 18 km at 6:10/km
  • Week 14: 22 km at 6:00–6:20/km (HR under 160 bpm)
  • Week 17: 26 km at 6:10/km with last 3 km at marathon pace (5:40/km)
  • Week 20: 30 km long run (gradual build, HR < 160 bpm)

End of Phase Goal:

  • Cadence: Improve to around 165–170 SPM.
  • Long Runs: 30 km long run with a fast finish.
  • Tempo Pace: Consistent pacing at 5:40/km for up to 12 km.

How AI Adjusts Based on Real-Time Data

The AI adjusts based on real-time data from my Apple Watch, including cadence, heart rate, pace, and distance. If my heart rate is higher than usual during long runs, the AI might recommend more rest or adjust upcoming workouts. On strong days, it suggests more intensity or extra mileage. These adjustments keep my plan in sync with my body’s needs. The AI doesn’t just follow a set schedule—it adapts continuously to prevent burnout or injuries and to push me when I’m ready.

Personalized Insights & Accountability

The AI can provide insights into my performance, spotting patterns I might not notice. I can ask it to analyze my recent running data and give me advice if it is necessary to adjust the training plan. For example, when my pace stagnated, the AI suggested changes—more recovery days and different speed workouts. This helped me break through the plateau, improving my pace within weeks. These insights are invaluable and something I wouldn’t easily notice on my own.

Looking Ahead: My Marathon Journey Continues

With about eight months until the marathon, I’m excited to continue building on the progress I’ve made. Having a personalized plan gives me confidence that I will be well-prepared when race day arrives. Generative AI has transformed my training goals. It’s personalized, adaptive, and insightful. It helps me understand my capabilities, push limits, and gain confidence in my training approach.

Have you ever considered using AI to help with your fitness goals? I’d love to hear your thoughts or experiences—feel free to share in the comments below!

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My Personal Chef

10

October

2024

5/5 (2)

Since its launch, I’ve mainly used ChatGPT to help me summarize academic articles and brainstorm ideas. Yesterday, I decided to let the LLM help me decide what to eat that night (OpenAI, 2024).

I rearranged the ingredients in my fridge so the main items were visible, moving condiments toward the back. I took a picture and uploaded it to ChatGPT with the prompt:

“What can I make for dinner tonight with these ingredients? I’m willing to go to the store for some additional ingredients needed.”

That’s it—just as if I asked a friend on the phone for a suggestion on what to make for dinner tonight, I asked the OpenAI model.

What it returned blew me away: (please note that formatted the output for visual appearances in this blog post).

“Here’s a simple recipe using the ingredients you have, plus some optional additions you could pick up from the store.

Butterscotch Pumpkin and Vegetable Curry

Ingredients from your pantry/fridge:

  • Butterscotch pumpkin
  • Carrots
  • Zucchini
  • Red bell peppers
  • Onions
  • Potatoes (optional, for a heartier curry)
  • Curry powder or paste (red or yellow)

Additions from the store (optional):

  • Coconut milk (for a creamier curry)
  • Fresh cilantro (for garnish)
  • Protein like chickpeas, tofu, or chicken

Instructions:

  1. Prepare the Veggies: Peel and cube the butterscotch pumpkin, and chop the zucchini, bell peppers, carrots, and potatoes.
  2. Sauté the Aromatics: In a large pot, sauté the onions in oil until they’re translucent.
  3. Add Spices: If you have curry powder or paste, add it to the onions and cook for a minute to release the flavours. If you’re using the peanut sauce as a curry base, add it now along with a dash of water.
  4. Add Veggies: Add the butterscotch pumpkin, carrots, and potatoes (if using). Let them cook for a few minutes.
  5. Simmer: Add coconut milk (if using) or water/stock, and simmer until the pumpkin and other veggies are tender (about 20 minutes).
  6. Add Other Veggies: Toss in zucchini and bell peppers, letting them cook for the last 5-7 minutes.
  7. Season: Taste and adjust seasoning with salt, pepper, or a touch of sugar if needed.
  8. Serve: Serve your curry over rice (you have poke rice) and garnish with fresh cilantro if you like.

    This will result in a rich, creamy, and slightly sweet curry with the pumpkin flavor shining through!Would you like to go ahead with this, or need adjustments?”

Amazing, right?! I was instantly inspired to cook this delicious-sounding meal and ran to the store for some chicken thighs and cilantro. Lucky for me, I already had the coconut milk, which the AI didn’t recognize in the picture.

For this blog post, I asked GPT to create an image inspired by this recipe:

While writing this blog post, I wondered if ChatGPT could give me an accurate estimate of the nutritional value of the dish, so I asked:

Caloric breakdown (per serving):

  • B. Pumpkin (100g)
    • Calories: 45
    • Carbohydrates: 11g
    • Protein: 1g
    • Fat: 0g
  • Carrots (50g)
    • Calories: 20
    • Carbohydrates: 4.5g
    • Protein: 0.5g
    • Fat: 0g
  • Zucchini (50g)
    • Calories: 8
    • Carbohydrates: 1.5g
    • Protein: 0.5g
    • Fat: 0g
  • Total:
  • Calories: 605 kcal
  • Carbohydrates: 86g
  • Protein: 28.8g
  • Fat: 15.5g
  • Bell Pepper (50g)
    • Calories: 15
    • Carbohydrates: 3g
    • Protein: 0.5g
    • Fat: 0g
  • Onion (30g)
    • Calories: 12
    • Carbohydrates: 3g
    • Protein: 0.3g
    • Fat: 0g
  • Coconut Milk (50ml)
    • Calories: 90
    • Carbohydrates: 2g
    • Protein: 1g
    • Fat: 9g
  • Curry Paste (15g)
    • Calories: 25
    • Carbohydrates: 5g
    • Protein: 0.5g
    • Fat: 0g
  • Rice (200g cooked)
    • Calories: 260
    • Carbohydrates: 56g
    • Protein: 5g
    • Fat: 0.5g
  • Chicken Thighs (100g)
    • Calories: 130
    • Carbohydrates: 0g
    • Protein: 19g
    • Fat: 6g

I am stunned! I checked the values online, and the caloric values only differed slightly (Grutto, 2024a; Voedingscentrum, 2024b). Not only does it give dinner suggestions and inspire me to make dinner with the ingredients I already have at home, but I can also keep a rough estimate of the macros I’ve consumed.
Imagine being on a diet and going out to eat with a friend. You want to keep track of your caloric intake but don’t want to bother bringing a scale to weigh your food with every new dish. Now you just take a picture, upload it to your Instagram story, and later upload it to ChatGPT to make a nutritional value estimate for you.

While this experience was undeniably impressive for me, it’s important to consider some limitations. For instance, the AI didn’t recognize the coconut milk in my picture, which was a key ingredient in the recipe it suggested. This highlights that image recognition technology isn’t foolproof, and you cannot rely on it 100%.
Also, while the nutritional estimates were close to official sources, they weren’t exact. For those with strict dietary requirements or allergies, relying solely on AI for nutritional information might not be a wise idea.
Lastly, uploading photos of your fridge or meals means sharing personal data with an AI service and thus can be a privacy concern. It’s important to be mindful of what you’re sharing and know how that data might be used or stored.

My experiment with using ChatGPT as a personal AI-chef was both enlightening and exciting. The ease of requesting a tailored dinner suggestion and a nutritional breakdown based on the contents of my fridge, shows me the potential of AI in everyday life. While there are limitations to consider, the benefits show a glimpse of the exciting future to come.

In the end, I find it amazing how the technology I first only use as a study and search tool, can also inspire me in other parts of everyday life. With my personal AI-chef, dinner dilemmas are a thing of the past.

Bibliography

Grutto, 2024, Bio Kipdijfilets bereiding en informatie Grutto! Available at: https://www.grutto.com/nl/vleesstuk/bio-kipdijfilet.

OpenAI, 2024, ChatGPT Available at: https://chatgpt.com.

Voedingscentrum, 2024, Hoeveel calorieën zitten erin? – Caloriechecker | Voedingscentrum Available at: https://www.voedingscentrum.nl/nl/service/vraag-en-antwoord/gezonde-voeding-en-voedingsstoffen/hoeveel-calorieen-zitten-erin-.aspx.

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