In this article, I will delve into whether artificial intelligence (AI), particularly the emergence of tools like generative AI (GenAI), will replace classical software outsourcing as we know it.

AI vs software outsourcing: An opportunity or a threat?

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New Office to Provide Open-Source Expertise for Campus Community

A philanthropic award is set to transform open-source software at Georgia Tech and establish a new resource for students and faculty. A recent $623,790 grant from the Alfred P. Sl

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Connotate

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HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today! Tell us about yourself! I am a final year doctoral student at the Laboratory for Computational Social Systems based out of IIIT-Delhi, India. By employing computational tools, mainly applied Natural Language Processing (NLP) and Network Science, I investigate the contextual signals that can help improve the detection of hate speech on the web. I have been fortunate to receive financial aid via the Google Phd Fellowship and the Prime Minister Doctoral Fellowship (in partnership with Wipro AI) to support my studies. Now, towards the end of my Phd, I am expanding my horizon to investigate other forms of harmful content on the web. Lately, I have been exploring the themes of fact-checking, fake news, propaganda analysis, and news narrative understanding. The last of which is the topic of my upcoming postdoc work  at CopeNLU  at the University of Copenhagen. As part of my academic and professional journey, I have been fortunate to work full time at Red Hat, furnish my undergrads in CSE from Jamia Millia Islamia (JMI), be a visiting research student at TUM and volunteer with organizations such as AnitaB and Journal of Open Source Softwares (JOSS). Why did you choose this field in the first place? The idea of pursuing computer science or being a phd student was not something I had planned for. Early exposure to science and technology both at home and school, as well as my curiosity to understand how things work, led me to develop a love of science and engineering. However, once I started my undergraduate studies, I struggled to find exactly what I planned to apply my CSE knowledge towards. I have failed at more undertakings than I have succeeded in. I chose to continue my work and studies in computer science because of its interdisciplinary application, especially once I discovered that I enjoyed NLP as a subfield while doing my undergraduate thesis on topic modelling. My interest in applied NLP was further solidified when I got an opportunity to work in the area of computational social systems (CSS) as a part of my PhD. What was the biggest setback/failure that you faced, and how did you manage it? PhD in itself is full of setbacks and failures, with success sprinkled here and there. One incident I especially remember, as it lasted almost two years, was my work on developing a dataset and model to detect hate speech in codemixed Hinglish. It took us one year to curate and annotate the dataset from Twitter. Given the Indian context and language knowledge needed to detect hateful connotations correctly, we could not employ existing crowdsourcing platforms like  Amazon Mechanical Turk. We spend endless hours performing annotations ourselves to produce an annotation guideline and then seek Indian annotators to help perform large-scale annotations. This process was iterative, slow, and mentally taxing for my colleagues and me. However, our woes did not end there, as the standard practice of finetuning a language model (LM) for hate speech detection did not produce effective results. It took us multiple trials and errors to realise that instead of focusing on building a more "complex and fancy system", we needed to improve the input signals, and it took us another round of unsupervised auxiliary data curation and modelling to augment the standard finetuning pipeline to yield improvements when detecting hatefulness in Indic context. The two years that it took from start to finish of this work were full of uncertainty; coupled with the Covid lockdown, it made me feel as if my phd journey would come to an abrupt end. What got us out of the slump was revisiting the research around contextual information, which was not a common practice in NLP modelling for hate speech but was closer to how content moderators make decisions in the real world. What's your biggest achievement that you're really proud of? One project that I am really proud of is a work we recently published on Arxiv (it is still under review for publication). The project explores the use of GenAI tools like ChatGPT in understanding journalistic narratives of the 5W1H (What, Why, When, Where, Who and How) and uncovering neutrality or lack thereof in independent fact-checking. The research not only reiterates the need to look beyond the USA-centric left-right political bias but also contributes to the initial interdisciplinary of combining human-AI efforts to study large-scale corpus and quantify subtle reporting/narrative biases that go unnoticed. The project idea came to me when I was randomly scrolling through an independent news portal, which is known to be "right-aligned", and saw that they had a very active fact-checking project. It led to framing the question of how explicit or implicit political leaning of the independent fact-checking organizations might lead to deviation from neutrality in fact-checking. I was very happy when what I hypothesised from initial manual observations was successfully quantified by our experiments. The bigger question this leaves for the research community is how we quantify the impact of different versions of "truth" and the subtle biases that cause deviation from a neutral point of view. What tech are you most excited/passionate about right now and why? While working with a lot of Hindi and Hinglish datasets from Twitter for analysing hate speech, I kept coming across gaps in the performance of NLP models when working with non-English text and non-Western cultural references. In this regard, I am excited about exploring both modelling paradigms and evaluation benchmarks to improve the adaptability of current NLP systems to non-Western connotations. Of course, by developing more culturally tuned systems, we will be able to understand toxic and stereotyped references better. I think a safer deployment of NLP-based models and interfaces is only possible if, at the granular level, we actively work on improving their adaptability to specific cultures and linguistic patterns. Another area within CSS that I am keen on exploring is narrative building. I recently read "The Language of the Third Reich" by Victor Klemperer and was amazed by how many of the techniques and patterns of narrative building applied by today's media can be traced back to World War II and prior eras. Given that the human psyche remains more or less the same, no matter the language or medium of storytelling, I am keen on applying NLP to help establish long-range narrative understanding. What tech are you most worried about right now and why? It is not technology per se, but I am worried about AI being presented as either an impeccable panacea that will fix all humanity's problems or an existential threat that will wipe off the human race. Inflating the abilities of AI  beyond what is possible by current paradigms is leading to the investment of resources in the wrong direction. It takes the conversation away from current threats to deploying these systems without proper safety checks in place and ignores the biases it propagates. A  person of colour being wrongly convicted of a crime due to biased AI modelling is much more probable and very much a reality than AGI wiping off humanity. In your opinion, why do we see a huge gender gap in the tech industry, and how can we reduce it? Working in tech, whether in industry or academia, is a very consuming process. On the one hand,  technical skills and the accompanying soft skills are something anyone can acquire via varying training channels (formal or informal). On the other hand, the on-paper equality of the tech space is marred by differences in social expectations from one gender specifically. Unless our social systems become more accommodating of working women and provide adequate moral, financial and infrastructural support, women in tech or the workforce in general will suffer. Do you have any advice for aspiring girls who want to join the field? For girls who aspire to work in tech, I would say that they should not feel intimidated; the tech world is full of possibilities and opportunities. There is room for different projects and perspectives to thrive, so keep participating in as many projects as possible and develop your sense of what works and what does not. What are your hobbies and interests outside of tech? Outside of tech, I really enjoy visiting museums and historical sites. Whenever I find time on weekends, I participate in guided tours in my city. I also dabble in writing poetry sometimes; I like to refer to myself as a seasonal poet :P However, my favourite hobby has always been reading. From books to blogs to research papers, the act of reading is very relaxing to me. HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today ! this interview series with women in tech Share your story today Tell us about yourself! Tell us about yourself! I am a final year doctoral student at the Laboratory for Computational Social Systems based out of IIIT-Delhi, India. By employing computational tools, mainly applied Natural Language Processing (NLP) and Network Science, I investigate the contextual signals that can help improve the detection of hate speech on the web . I have been fortunate to receive financial aid via the Google Phd Fellowship and the Prime Minister Doctoral Fellowship (in partnership with Wipro AI) to support my studies. Now, towards the end of my Phd, I am expanding my horizon to investigate other forms of harmful content on the web. Lately, I have been exploring the themes of fact-checking, fake news, propaganda analysis, and news narrative understanding. The last of which is the topic of my upcoming postdoc work  at CopeNLU  at the University of Copenhagen. hate speech on the web As part of my academic and professional journey, I have been fortunate to work full time at Red Hat, furnish my undergrads in CSE from Jamia Millia Islamia (JMI), be a visiting research student at TUM and volunteer with organizations such as AnitaB and Journal of Open Source Softwares (JOSS). Why did you choose this field in the first place? Why did you choose this field in the first place? The idea of pursuing computer science or being a phd student was not something I had planned for. Early exposure to science and technology both at home and school, as well as my curiosity to understand how things work, led me to develop a love of science and engineering. However, once I started my undergraduate studies, I struggled to find exactly what I planned to apply my CSE knowledge towards. I have failed at more undertakings than I have succeeded in. I chose to continue my work and studies in computer science because of its interdisciplinary application, especially once I discovered that I enjoyed NLP as a subfield while doing my undergraduate thesis on topic modelling. My interest in applied NLP was further solidified when I got an opportunity to work in the area of computational social systems (CSS) as a part of my PhD. What was the biggest setback/failure that you faced, and how did you manage it? What was the biggest setback/failure that you faced, and how did you manage it? PhD in itself is full of setbacks and failures, with success sprinkled here and there. One incident I especially remember, as it lasted almost two years, was my work on developing a dataset and model to detect hate speech in codemixed Hinglish. It took us one year to curate and annotate the dataset from Twitter. Given the Indian context and language knowledge needed to detect hateful connotations correctly, we could not employ existing crowdsourcing platforms like  Amazon Mechanical Turk. We spend endless hours performing annotations ourselves to produce an annotation guideline and then seek Indian annotators to help perform large-scale annotations. This process was iterative, slow, and mentally taxing for my colleagues and me. However, our woes did not end there, as the standard practice of finetuning a language model (LM) for hate speech detection did not produce effective results. It took us multiple trials and errors to realise that instead of focusing on building a more "complex and fancy system", we needed to improve the input signals, and it took us another round of unsupervised auxiliary data curation and modelling to augment the standard finetuning pipeline to yield improvements when detecting hatefulness in Indic context. The two years that it took from start to finish of this work were full of uncertainty; coupled with the Covid lockdown, it made me feel as if my phd journey would come to an abrupt end. What got us out of the slump was revisiting the research around contextual information, which was not a common practice in NLP modelling for hate speech but was closer to how content moderators make decisions in the real world. What's your biggest achievement that you're really proud of? What's your biggest achievement that you're really proud of? One project that I am really proud of is a work we recently published on Arxiv (it is still under review for publication). The project explores the use of GenAI tools like ChatGPT in understanding journalistic narratives of the 5W1H (What, Why, When, Where, Who and How) and uncovering neutrality or lack thereof in independent fact-checking. The research not only reiterates the need to look beyond the USA-centric left-right political bias but also contributes to the initial interdisciplinary of combining human-AI efforts to study large-scale corpus and quantify subtle reporting/narrative biases that go unnoticed. The project idea came to me when I was randomly scrolling through an independent news portal, which is known to be "right-aligned", and saw that they had a very active fact-checking project. It led to framing the question of how explicit or implicit political leaning of the independent fact-checking organizations might lead to deviation from neutrality in fact-checking. I was very happy when what I hypothesised from initial manual observations was successfully quantified by our experiments. The bigger question this leaves for the research community is how we quantify the impact of different versions of "truth" and the subtle biases that cause deviation from a neutral point of view. What tech are you most excited/passionate about right now and why? While working with a lot of Hindi and Hinglish datasets from Twitter for analysing hate speech, I kept coming across gaps in the performance of NLP models when working with non-English text and non-Western cultural references. In this regard, I am excited about exploring both modelling paradigms and evaluation benchmarks to improve the adaptability of current NLP systems to non-Western connotations. Of course, by developing more culturally tuned systems, we will be able to understand toxic and stereotyped references better. I think a safer deployment of NLP-based models and interfaces is only possible if, at the granular level, we actively work on improving their adaptability to specific cultures and linguistic patterns. Another area within CSS that I am keen on exploring is narrative building. I recently read "The Language of the Third Reich" by Victor Klemperer and was amazed by how many of the techniques and patterns of narrative building applied by today's media can be traced back to World War II and prior eras. Given that the human psyche remains more or less the same, no matter the language or medium of storytelling, I am keen on applying NLP to help establish long-range narrative understanding. What tech are you most worried about right now and why? What tech are you most worried about right now and why? It is not technology per se, but I am worried about AI being presented as either an impeccable panacea that will fix all humanity's problems or an existential threat that will wipe off the human race. Inflating the abilities of AI  beyond what is possible by current paradigms is leading to the investment of resources in the wrong direction. It takes the conversation away from current threats to deploying these systems without proper safety checks in place and ignores the biases it propagates. A  person of colour being wrongly convicted of a crime due to biased AI modelling is much more probable and very much a reality than AGI wiping off humanity. In your opinion, why do we see a huge gender gap in the tech industry, and how can we reduce it? In your opinion, why do we see a huge gender gap in the tech industry, and how can we reduce it? Working in tech, whether in industry or academia, is a very consuming process. On the one hand,  technical skills and the accompanying soft skills are something anyone can acquire via varying training channels (formal or informal). On the other hand, the on-paper equality of the tech space is marred by differences in social expectations from one gender specifically. Unless our social systems become more accommodating of working women and provide adequate moral, financial and infrastructural support, women in tech or the workforce in general will suffer. Do you have any advice for aspiring girls who want to join the field? For girls who aspire to work in tech, I would say that they should not feel intimidated; the tech world is full of possibilities and opportunities. There is room for different projects and perspectives to thrive, so keep participating in as many projects as possible and develop your sense of what works and what does not. What are your hobbies and interests outside of tech? What are your hobbies and interests outside of tech? Outside of tech, I really enjoy visiting museums and historical sites. Whenever I find time on weekends, I participate in guided tours in my city. I also dabble in writing poetry sometimes; I like to refer to myself as a seasonal poet :P However, my favourite hobby has always been reading. From books to blogs to research papers, the act of reading is very relaxing to me.

Meet Sarah Masud, Google Phd Fellow and Final Year Doctoral Student at IIIT-Delhi

A Deep Dive Into Streaming Advertising: Types, Advantages, Challenges, and Best Practices

Navigating the startup world as the founding marketer can be challenging, yet quite a rewarding journey. Being the first marketing hire often comes with the pressure of building a brand from the ground up, defining the company’s voice, and driving growth with limited resources. However, it also offers the unique opportunity to shape the company’s future and leave a lasting impact. I would like to share with you the nine key lessons I’ve learned as a founding marketer, and by sharing them, I hope to provide valuable guidance to those embarking on a similar path. Lesson 1: Be Resourceful In the early stages of a startup, money is often scarce, and being resourceful becomes fundamental. Maximizing limited resources is what will help you leverage what you have to its fullest potential and help you grow. Being resourceful will also spur you to start thinking outside the box - finding new ways to achieve the end goal without the support and resources that bigger companies have. This will ultimately improve your creative skills which will be fundamental as the company grows. By creatively leveraging limited resources, startups can overcome obstacles, drive growth, and establish a solid foundation for future success. Lesson 2: Wear Different Hats As the founding marketer, you will find yourself often having to wear different hats. You will have to adapt and learn new skills that might fall outside your remit - balancing marketing with other startup tasks often requires a flexible mindset. My role in the years extended beyond traditional marketing activities. I found myself involved in product development, customer support, and even sales & investor pitches. This cross-functional involvement not only provided a broader understanding of the business but was also fundamental in enriching our marketing strategies with insights from different perspectives. Embracing various roles not only enhances personal growth but will also give a 360 view and understanding of the company, which will propel the startup towards its goals with innovation. Lesson 3: Understand the Market A deep understanding of the market is foundational for any startup's success. It can be defined in 3 ways: Researching the fintech landscape.
Identifying your target audience.
Analyzing competitors. Making sure to research your specific sector will provide valuable insights into trends, emerging technologies, and much more which can influence your business strategy. Therefore, comprehensive market research is essential. Understanding the broader ecosystem helps in identifying opportunities. However, understanding the market doesn’t only have a broad connotation. It also includes identifying your target audience. This goes beyond basic demographics; it requires a nuanced understanding of your customer's pain points, preferences, and behaviors. Identifying your ICP and building specific user personas will be crucial in crafting the right messaging. Lastly, analyzing competitors is equally important. This involves not just identifying who they are but also understanding their strengths, weaknesses, and market positioning. This will ultimately guide your strategy in the way your company differentiates from the others, highlighting unique value propositions that set you apart. Lesson 4: Build a Strong Brand A compelling brand not only differentiates your startup from competitors but also fosters trust and loyalty among customers. The importance of branding cannot be overstated. A strong brand reassures customers about the security and reliability of your services. Moreover, a well-established brand can attract investors, partners, and top talent, all of which are vital for growth and innovation. Key elements of a compelling brand include a clear value proposition, consistent messaging, and a distinct visual identity. Your value proposition should communicate what sets you apart and why customers should choose you. Consistent messaging across all channels builds recognition and reinforces your brand values. Additionally, a distinct visual identity, encompassing your logo, color scheme, and design elements, ensures that your brand is memorable and easily recognizable. Building a strong brand in fintech involves more than just marketing; it's about creating a trustworthy, consistent, and memorable presence in the market. Lesson 5: Leverage Data and Analytics Leveraging data and analytics is indispensable for crafting effective strategies. Data provides actionable insights into customer behavior, campaign performance, and market trends, enabling more informed and precise decision-making. The role of data in marketing strategies is multifaceted. It helps identify and understand your target audience, measure the effectiveness of marketing campaigns, and optimize resource allocation. By analyzing customer interactions, preferences, and feedback, you can tailor the strategy to better meet user needs and enhance engagement. Additionally, data allows for tracking key performance indicators (KPIs) such as conversion rates, customer acquisition costs, and lifetime value, ensuring that marketing efforts are aligned with business objectives. Tools and techniques for data analysis are essential in harnessing the power of data. A/B testing, cohort analysis, and predictive modeling further refine marketing strategies by revealing what works best and predicting future trends - all while helping you put more effort and budget into what works. Lesson 6: Content Is King Creating valuable content is a powerful way to engage and educate your audience. High-quality content not only attracts potential customers but also builds trust and establishes your startup as an authority in the industry. Think educational articles and how-to guides, video content, including tutorials and explainer videos, as well as case studies and success stories that can demonstrate the real-world impact of your products and services, building credibility and trust. Content truly is king in marketing. By creating valuable, educational, and engaging content tailored to your audience’s needs, you can effectively attract, engage, and convert customers, driving long-term success for your startup. Lesson 7: Hire Go-Getters The importance of hiring motivated and ambitious team members cannot be overstated. Go-getters bring energy, innovation, and a relentless drive to succeed, which are essential traits for overcoming the challenges faced by startups. You want to start by identifying individuals who demonstrate a proactive attitude, resilience, and a passion for their work. Look for candidates who have shown initiative in previous roles, have a track record of problem-solving, and exhibit a strong sense of ownership. The impact of a driven team on startup success is profound. Motivated and ambitious team members are more likely to go above and beyond, contributing creative solutions and pushing the company forward. Their energy and determination can be contagious, inspiring others to elevate their performance. Lesson 8: Customer Feedback is Crucial Gathering and utilizing customer feedback is essential for staying relevant and effective. This feedback provides invaluable insights that can help refine marketing strategies and improve overall service offerings. Using feedback to refine marketing strategies involves analyzing the data to identify patterns and areas for improvement. Feedback can also reveal preferences for specific channels or content types, guiding more targeted and effective marketing efforts. Most importantly, customer feedback is not just a box-ticking exercise; it's a crucial component of an effective marketing strategy. By systematically gathering and analyzing feedback, and making informed adjustments based on it, startups can enhance their marketing efforts, meet customer needs more effectively, and drive sustainable growth. Lesson 9: Always Be Learning Continuous learning and development are essential for maintaining a competitive edge. The importance of always being in learning mode cannot be overstated, as it enables marketers and entrepreneurs to stay abreast of new trends, technologies, and best practices, thereby driving innovation and growth. Personal growth has a significant impact on the startup. As a founding marketer, your development directly influences your ability to lead, strategize, and innovate. By continuously enhancing your skills and knowledge, you can make more informed decisions, mentor your team more effectively, and drive the startup's vision forward. Conclusion In conclusion, the journey of a founding marketer in a startup is marked by unique challenges and opportunities. The nine lessons shared here underscore the importance of being resourceful, adaptable, and data-driven, while also emphasizing the significance of understanding the market, building a strong brand, and creating valuable content. Hiring motivated team members and continuously gathering customer feedback are also crucial for navigating the startup landscape. Above all, maintaining a mindset of continuous learning ensures that you stay ahead in an ever-evolving environment. Navigating the startup world as the founding marketer can be challenging, yet quite a rewarding journey. Being the first marketing hire often comes with the pressure of building a brand from the ground up, defining the company’s voice, and driving growth with limited resources. However, it also offers the unique opportunity to shape the company’s future and leave a lasting impact. Navigating the startup world as the founding marketer can be challenging, yet quite a rewarding journey. Being the first marketing hire often comes with the pressure of building a brand from the ground up, defining the company’s voice, and driving growth with limited resources. However, it also offers the unique opportunity to shape the company’s future and leave a lasting impact. I would like to share with you the nine key lessons I’ve learned as a founding marketer, and by sharing them, I hope to provide valuable guidance to those embarking on a similar path. I would like to share with you the nine key lessons I’ve learned as a founding marketer, and by sharing them, I hope to provide valuable guidance to those embarking on a similar path. Lesson 1: Be Resourceful Lesson 1: Be Resourceful In the early stages of a startup, money is often scarce, and being resourceful becomes fundamental. Maximizing limited resources is what will help you leverage what you have to its fullest potential and help you grow. Being resourceful will also spur you to start thinking outside the box - finding new ways to achieve the end goal without the support and resources that bigger companies have. This will ultimately improve your creative skills which will be fundamental as the company grows. By creatively leveraging limited resources, startups can overcome obstacles, drive growth, and establish a solid foundation for future success. Lesson 2: Wear Different Hats Lesson 2: Wear Different Hats As the founding marketer, you will find yourself often having to wear different hats. You will have to adapt and learn new skills that might fall outside your remit - balancing marketing with other startup tasks often requires a flexible mindset. My role in the years extended beyond traditional marketing activities. I found myself involved in product development, customer support, and even sales & investor pitches. This cross-functional involvement not only provided a broader understanding of the business but was also fundamental in enriching our marketing strategies with insights from different perspectives. Embracing various roles not only enhances personal growth but will also give a 360 view and understanding of the company, which will propel the startup towards its goals with innovation. Lesson 3: Understand the Market Lesson 3: Understand the Market A deep understanding of the market is foundational for any startup's success. It can be defined in 3 ways: Researching the fintech landscape. Identifying your target audience. Analyzing competitors. Researching the fintech landscape. Identifying your target audience. Analyzing competitors. Making sure to research your specific sector will provide valuable insights into trends, emerging technologies, and much more which can influence your business strategy. Therefore, comprehensive market research is essential. Understanding the broader ecosystem helps in identifying opportunities. However, understanding the market doesn’t only have a broad connotation. It also includes identifying your target audience. This goes beyond basic demographics; it requires a nuanced understanding of your customer's pain points, preferences, and behaviors. Identifying your ICP and building specific user personas will be crucial in crafting the right messaging. Lastly, analyzing competitors is equally important. This involves not just identifying who they are but also understanding their strengths, weaknesses, and market positioning. This will ultimately guide your strategy in the way your company differentiates from the others, highlighting unique value propositions that set you apart. Lesson 4: Build a Strong Brand Lesson 4: Build a Strong Brand A compelling brand not only differentiates your startup from competitors but also fosters trust and loyalty among customers. The importance of branding cannot be overstated. A strong brand reassures customers about the security and reliability of your services. Moreover, a well-established brand can attract investors, partners, and top talent, all of which are vital for growth and innovation. Key elements of a compelling brand include a clear value proposition, consistent messaging, and a distinct visual identity. Your value proposition should communicate what sets you apart and why customers should choose you. Consistent messaging across all channels builds recognition and reinforces your brand values. Additionally, a distinct visual identity, encompassing your logo, color scheme, and design elements, ensures that your brand is memorable and easily recognizable. Building a strong brand in fintech involves more than just marketing; it's about creating a trustworthy, consistent, and memorable presence in the market. Lesson 5: Leverage Data and Analytics Leveraging data and analytics is indispensable for crafting effective strategies. Data provides actionable insights into customer behavior, campaign performance, and market trends, enabling more informed and precise decision-making. The role of data in marketing strategies is multifaceted. It helps identify and understand your target audience, measure the effectiveness of marketing campaigns, and optimize resource allocation. By analyzing customer interactions, preferences, and feedback, you can tailor the strategy to better meet user needs and enhance engagement . Additionally, data allows for tracking key performance indicators (KPIs) such as conversion rates, customer acquisition costs, and lifetime value, ensuring that marketing efforts are aligned with business objectives. enhance engagement Tools and techniques for data analysis are essential in harnessing the power of data. A/B testing, cohort analysis, and predictive modeling further refine marketing strategies by revealing what works best and predicting future trends - all while helping you put more effort and budget into what works. Lesson 6: Content Is King Creating valuable content is a powerful way to engage and educate your audience. High-quality content not only attracts potential customers but also builds trust and establishes your startup as an authority in the industry. Think educational articles and how-to guides, video content, including tutorials and explainer videos, as well as case studies and success stories that can demonstrate the real-world impact of your products and services, building credibility and trust. Content truly is king in marketing. By creating valuable, educational, and engaging content tailored to your audience’s needs, you can effectively attract, engage, and convert customers, driving long-term success for your startup. Lesson 7: Hire Go-Getters The importance of hiring motivated and ambitious team members cannot be overstated. Go-getters bring energy, innovation, and a relentless drive to succeed, which are essential traits for overcoming the challenges faced by startups. You want to start by identifying individuals who demonstrate a proactive attitude, resilience, and a passion for their work. Look for candidates who have shown initiative in previous roles, have a track record of problem-solving, and exhibit a strong sense of ownership. The impact of a driven team on startup success is profound. Motivated and ambitious team members are more likely to go above and beyond, contributing creative solutions and pushing the company forward. Their energy and determination can be contagious, inspiring others to elevate their performance. Lesson 8: Customer Feedback is Crucial Gathering and utilizing customer feedback is essential for staying relevant and effective. This feedback provides invaluable insights that can help refine marketing strategies and improve overall service offerings. Using feedback to refine marketing strategies involves analyzing the data to identify patterns and areas for improvement. Feedback can also reveal preferences for specific channels or content types, guiding more targeted and effective marketing efforts. Most importantly, customer feedback is not just a box-ticking exercise; it's a crucial component of an effective marketing strategy. By systematically gathering and analyzing feedback, and making informed adjustments based on it, startups can enhance their marketing efforts, meet customer needs more effectively, and drive sustainable growth. Lesson 9: Always Be Learning Continuous learning and development are essential for maintaining a competitive edge. The importance of always being in learning mode cannot be overstated, as it enables marketers and entrepreneurs to stay abreast of new trends, technologies, and best practices, thereby driving innovation and growth. Personal growth has a significant impact on the startup. As a founding marketer, your development directly influences your ability to lead, strategize, and innovate. By continuously enhancing your skills and knowledge, you can make more informed decisions, mentor your team more effectively, and drive the startup's vision forward. Conclusion In conclusion, the journey of a founding marketer in a startup is marked by unique challenges and opportunities. The nine lessons shared here underscore the importance of being resourceful, adaptable, and data-driven, while also emphasizing the significance of understanding the market, building a strong brand, and creating valuable content. Hiring motivated team members and continuously gathering customer feedback are also crucial for navigating the startup landscape. Above all, maintaining a mindset of continuous learning ensures that you stay ahead in an ever-evolving environment.

9 Lessons I Learned as the Founding Marketer of a Tech Startup

At a certain point, it becomes unclear how many notifications are demanding to be clicked on your phone. As you wake, your groggy brain attempts to recall what you tweeted as you dozed off last night. What did you say to earn this ephemeral moment of attention and glory? Last year, I experienced such a moment. I work for Manifold, a prediction markets platform, which hosts questions such as “Will TikTok be forced to sell?” or “When will GPT-5 be released?”. Users will then bet on these questions to earn play money and work their way up the leaderboards. This prices the market and creates accurate probabilities about the future. One night, while scrolling Manifold for content to share on our socials, I stumbled onto a market predicting the discovery of room-temperature superconductors. It gave a 17% chance that a recently published study claiming to create such a superconductor would be replicated. Before sharing it, I searched for other news articles and related tweets to engage with. To my surprise, I found… nothing. We had beaten the news to the story. Over the next week, our market was widely shared and received millions of views. But something interesting was happening: many users were signing up and returning each morning to check the odds, yet most had little interest in trading. On X, Paul Graham wrote, “A couple times a day I check the prediction market on LK-99. It has doubled since its low on July 29. Still only 24%, but it's moving up, not down.” This revealed two crucial insights. First, we had focused too much on proving the accuracy of prediction markets when people were drawn to them for the fast, aggregated data they provided. Second, we realised that most users were less interested in trading and more engaged as viewers, simply wanting to observe market trends. This required a fundamental shift in our product design and marketing strategies. From Academic Experiments to Consumer Product Prediction markets have traditionally been confined to academic experiments and niche applications, often brushed aside as speculative or impractical. However, our journey with Manifold has revealed untapped potential in this space if you can nail the product-market fit. In the last few years, several startups have taken different approaches to making prediction markets more accessible, valuable, and mainstream. Polymarket is the leading crypto platform and offers real-money markets on topics that other platforms, especially those in the U.S., can’t touch—like political elections. This strategy has attracted recent investment of $70 million from big names like Peter Thiel and Vitalik Buterin, with Nate Silver even coming on board as an advisor. With the election cycle heating up, their trading volume increased to over $100 million per month. Even Trump showed interest and regularly shared their odds on his socials, perhaps because Polymarket’s odds favoured him more than the polls. On the other hand, Kalshi is playing the long game by focusing on regulatory compliance and appealing to institutions. Unlike Polymarket, Kalshi allows US customers and has gone through the rigorous process of securing approval from the Commodity Futures Trading Commission (CFTC) to operate legal betting markets on specific economic indicators and events. This approach aligns with their goal of becoming the go-to platform for traditional financial institutions. But Kalshi isn’t stopping there—they’re actively lobbying and even suing the CFTC to expand their offerings to include more popular markets, like elections. Their recent partnership with SIG as a market maker to provide liquidity is a big step toward making Kalshi a trusted, compliant platform for large-scale traders and investors. Meanwhile, Manifold is taking a different route by focusing on accessibility and user engagement. By opting for a play-money model, Manifold sidesteps the regulatory headaches that come with real-money platforms. We are the only site where users can create their own markets allowing us to scale the range of questions being asked compared to other platforms. This user-generated content model ensures that Manifold is always the fastest to respond to breaking news and the questions that come with it. While our markets currently operate on play money, we’re planning to introduce a sweepstakes model that will let users win real money on certain markets. Because of the gamification and reputation at stake, users are still incentivised to trade accurately allowing our markets to be just as good as our competitors. As these three platforms—Polymarket, Kalshi, and Manifold—demonstrate, the path to product-market fit in prediction markets is not a one-size-fits-all journey. That said, all 3 platforms have had to overcome some shared challenges that come with prediction markets. Identifying the challenges with your product’s marketability Every new product faces the challenge of overcoming initial skepticism, especially when it's something unfamiliar. For example, most people have no connotation with the terms “prediction market” or “forecasting platform”. This presents a rare opportunity to create their initial impression but comes with the risk of them comparing it to a gambling platform with a negative connotation. This makes prediction markets arduous to share. Manifold users who tried to share links without framing them properly were barraged with downvotes on Reddit. But this isn’t the fault of the sharers, but rather the product itself and what it communicates about itself when viewed at first glance. There’s an intuition people have that an idea or product can only grow as much as people are willing to share it. I would argue that this isn’t the bottleneck. You have to look one step further. A product’s growth is limited by how easy it is to share. You could have a cult-like following obsessed with an idea and bombarding anyone who dares to listen. Yet, if it is too complex and cannot be presented in a relatable way, it’s not going to grow. And even if you can convey an accurate picture of what a prediction market is, it still may not be obvious to someone what value it presents to them. Luckily, prediction markets are now in a position where enough credible people and news outlets understand them and are willing to leverage their reputation to share them. Presenting them as a way to “follow the odds on something you care about” rather than “play this game/make money betting about something you care about” has helped a lot too. Narrowing your focus on Product-market-fit As we developed our product, we found ourselves constantly pulled in different directions, often sacrificing key use cases. Initially, we over-optimised for traders, believing that product-market fit (PMF) depended on teaching as many people as possible how to trade and making it fun for them. However, this limited approach overlooked a significant segment of users who were more interested in the information prediction markets provided than in trading itself. The superconductor rumours and the ongoing election cycle offered strong examples of this. Many users cared about the probabilities but had little interest in trading. Also, they didn’t need the markets to be perfectly accurate; they just wanted a quick gauge of how events, like Biden potentially dropping out, could impact the race. The speed and convenience of prediction markets filled this gap far better than slow polls could. By making the shift to focus on highlighting our speed and readability, it becomes harder for skeptical potential users to dismiss us. Previously, they could choose to believe we didn’t have enough proof of accuracy and thus weren’t worth anyone’s time. Or that the only reason to interact with prediction markets is if you were a gambler, which made sense as our marketing and copy were solely focused on pushing people to make trades. However, it’s much harder to deny that prediction markets create the fastest, aggregated data that is convenient to read. Manifold’s data still suggests that prediction markets are incredibly accurate and we still care about our traders. But, by focusing on the value we provide in other areas, we aren’t so reliant on winning the accuracy argument or trying to convert everyone to enjoy trading. What’s next for prediction markets? We are entering exciting times for prediction markets. This election cycle will be their first test with significant volume and mainstream attention to see how they perform compared to more traditional forecasting methods. Even more exciting will be seeing how many news outlets and people begin including prediction markets as a data point when forming their own opinions. We are also continuously discovering more advantages prediction markets have that align with what users care about. For example, news sites heavily optimise for reporting breaking news as soon as possible. When Biden dropped out Manifold sent an email 20 minutes before news outlets such as the NYT did to alert our users. Finally, questions remain about prediction markets’ longevity in the US, especially as the CFTC continues its legal battle with Kalshi. How this will play out could have significant implications for the future of the industry. At a certain point, it becomes unclear how many notifications are demanding to be clicked on your phone. As you wake, your groggy brain attempts to recall what you tweeted as you dozed off last night. What did you say to earn this ephemeral moment of attention and glory? Last year, I experienced such a moment. I work for Manifold , a prediction markets platform, which hosts questions such as “Will TikTok be forced to sell?” or “When will GPT-5 be released?”. Users will then bet on these questions to earn play money and work their way up the leaderboards. This prices the market and creates accurate probabilities about the future. Manifold One night, while scrolling Manifold for content to share on our socials, I stumbled onto a market predicting the discovery of room-temperature superconductors. It gave a 17% chance that a recently published study claiming to create such a superconductor would be replicated. Before sharing it, I searched for other news articles and related tweets to engage with. To my surprise, I found… nothing. We had beaten the news to the story. Over the next week, our market was widely shared and received millions of views. But something interesting was happening: many users were signing up and returning each morning to check the odds, yet most had little interest in trading. On X, Paul Graham wrote , “A couple times a day I check the prediction market on LK-99. It has doubled since its low on July 29. Still only 24%, but it's moving up, not down.” On X, Paul Graham wrote This revealed two crucial insights. First, we had focused too much on proving the accuracy of prediction markets when people were drawn to them for the fast, aggregated data they provided. Second, we realised that most users were less interested in trading and more engaged as viewers, simply wanting to observe market trends. This required a fundamental shift in our product design and marketing strategies. From Academic Experiments to Consumer Product Prediction markets have traditionally been confined to academic experiments and niche applications, often brushed aside as speculative or impractical. However, our journey with Manifold has revealed untapped potential in this space if you can nail the product-market fit. In the last few years, several startups have taken different approaches to making prediction markets more accessible, valuable, and mainstream. Polymarket is the leading crypto platform and offers real-money markets on topics that other platforms, especially those in the U.S., can’t touch—like political elections. This strategy has attracted recent investment of $70 million from big names like Peter Thiel and Vitalik Buterin, with Nate Silver even coming on board as an advisor . With the election cycle heating up, their trading volume increased to over $100 million per month . Even Trump showed interest and regularly shared their odds on his socials, perhaps because Polymarket’s odds favoured him more than the polls. Polymarket investment of $70 million Nate Silver even coming on board as an advisor $100 million per month On the other hand, Kalshi is playing the long game by focusing on regulatory compliance and appealing to institutions. Unlike Polymarket, Kalshi allows US customers and has gone through the rigorous process of securing approval from the Commodity Futures Trading Commission (CFTC) to operate legal betting markets on specific economic indicators and events. This approach aligns with their goal of becoming the go-to platform for traditional financial institutions. But Kalshi isn’t stopping there—they’re actively lobbying and even suing the CFTC to expand their offerings to include more popular markets, like elections. Their recent partnership with SIG as a market maker to provide liquidity is a big step toward making Kalshi a trusted, compliant platform for large-scale traders and investors. Kalshi suing the CFTC partnership with SIG Meanwhile, Manifold is taking a different route by focusing on accessibility and user engagement. By opting for a play-money model, Manifold sidesteps the regulatory headaches that come with real-money platforms. We are the only site where users can create their own markets allowing us to scale the range of questions being asked compared to other platforms. This user-generated content model ensures that Manifold is always the fastest to respond to breaking news and the questions that come with it. While our markets currently operate on play money, we’re planning to introduce a sweepstakes model that will let users win real money on certain markets. Because of the gamification and reputation at stake, users are still incentivised to trade accurately allowing our markets to be just as good as our competitors. sweepstakes model As these three platforms—Polymarket, Kalshi, and Manifold—demonstrate, the path to product-market fit in prediction markets is not a one-size-fits-all journey. That said, all 3 platforms have had to overcome some shared challenges that come with prediction markets. Identifying the challenges with your product’s marketability Every new product faces the challenge of overcoming initial skepticism, especially when it's something unfamiliar. For example, most people have no connotation with the terms “prediction market” or “forecasting platform”. This presents a rare opportunity to create their initial impression but comes with the risk of them comparing it to a gambling platform with a negative connotation. This makes prediction markets arduous to share. Manifold users who tried to share links without framing them properly were barraged with downvotes on Reddit. But this isn’t the fault of the sharers, but rather the product itself and what it communicates about itself when viewed at first glance. There’s an intuition people have that an idea or product can only grow as much as people are willing to share it. I would argue that this isn’t the bottleneck. You have to look one step further. A product’s growth is limited by how easy it is to share. You could have a cult-like following obsessed with an idea and bombarding anyone who dares to listen. Yet, if it is too complex and cannot be presented in a relatable way, it’s not going to grow. And even if you can convey an accurate picture of what a prediction market is, it still may not be obvious to someone what value it presents to them. Luckily, prediction markets are now in a position where enough credible people and news outlets understand them and are willing to leverage their reputation to share them. Presenting them as a way to “follow the odds on something you care about” rather than “play this game/make money betting about something you care about” has helped a lot too. Narrowing your focus on Product-market-fit As we developed our product, we found ourselves constantly pulled in different directions, often sacrificing key use cases. Initially, we over-optimised for traders, believing that product-market fit (PMF) depended on teaching as many people as possible how to trade and making it fun for them. However, this limited approach overlooked a significant segment of users who were more interested in the information prediction markets provided than in trading itself. The superconductor rumours and the ongoing election cycle offered strong examples of this. Many users cared about the probabilities but had little interest in trading. Also, they didn’t need the markets to be perfectly accurate; they just wanted a quick gauge of how events, like Biden potentially dropping out, could impact the race. The speed and convenience of prediction markets filled this gap far better than slow polls could. By making the shift to focus on highlighting our speed and readability, it becomes harder for skeptical potential users to dismiss us. Previously, they could choose to believe we didn’t have enough proof of accuracy and thus weren’t worth anyone’s time. Or that the only reason to interact with prediction markets is if you were a gambler, which made sense as our marketing and copy were solely focused on pushing people to make trades. However, it’s much harder to deny that prediction markets create the fastest, aggregated data that is convenient to read. Manifold’s data still suggests that prediction markets are incredibly accurate and we still care about our traders. But, by focusing on the value we provide in other areas, we aren’t so reliant on winning the accuracy argument or trying to convert everyone to enjoy trading. Manifold’s data What’s next for prediction markets? We are entering exciting times for prediction markets. This election cycle will be their first test with significant volume and mainstream attention to see how they perform compared to more traditional forecasting methods. Even more exciting will be seeing how many news outlets and people begin including prediction markets as a data point when forming their own opinions. We are also continuously discovering more advantages prediction markets have that align with what users care about. For example, news sites heavily optimise for reporting breaking news as soon as possible. When Biden dropped out Manifold sent an email 20 minutes before news outlets such as the NYT did to alert our users. Manifold sent an email 20 minutes before news outlets Finally, questions remain about prediction markets’ longevity in the US, especially as the CFTC continues its legal battle with Kalshi. How this will play out could have significant implications for the future of the industry.

Could Prediction Markets Offer Better Odds at Who Wins the US Elections?

¿Podrían los mercados de predicción ofrecer mejores probabilidades sobre quién ganará las elecciones en Estados Unidos?

Os mercados de predición poderían ofrecer mellores probabilidades de quen gañe as eleccións estadounidenses?

予測市場は、米国の選挙で誰が勝利するかについてより良いオッズを提供できるか？

შეუძლია თუ არა პროგნოზირების ბაზრებს უკეთესი შანსები შესთავაზოს ვინ მოიგებს აშშ-ს არჩევნებს?

Урьдчилан таамаглах зах зээл нь АНУ-ын сонгуульд хэн ялах талаар илүү сайн магадлалыг санал болгож чадах уу?

Kunnen voorspellingsmarkten betere kansen bieden op wie de Amerikaanse verkiezingen wint?

¿Predicción qhatukuna aswan allin probabilidadkunata qonmanchu Pikunachus EE.UU.

Mafekitari Mashambadziro Anogona Kupa Zvirinani Odd kune Ndiani Anokunda Sarudzo dzeUS?

Suuqyada Saadaasha Miyay Bixiyaan Khatar Wanaagsan Cidda Ku Guuleysata Doorashooyinka Mareykanka?

ዕዳጋታት ትንበያ ኣብ ምርጫ ኣሜሪካ መን ይዕወት ዝሓሸ ዕድል ከቕርቡ ይኽእሉ ድዮም?

ABŞ-nyň saýlawlarynda kim ýeňiş gazansa, çaklama bazarlary has gowy teklip hödürläp bilermi?

预测市场能否为美国大选的获胜提供更好的预测机会？

The New Private Cloud From the Eyes of an Architect

একজন স্থপতির চোখ থেকে নতুন ব্যক্তিগত মেঘ

Die neue Private Cloud aus der Sicht eines Architekten

La nueva nube privada desde la perspectiva de un arquitecto

Le nouveau cloud privé vu par un architecte

एक आर्किटेक्ट की नज़र से नया प्राइवेट क्लाउड

建築家の視点から見た新しいプライベートクラウド

건축가의 눈으로 본 새로운 프라이빗 클라우드

A nova nuvem privada vista por um arquiteto

Новое частное облако глазами архитектора

Bir Mimarın Gözünden Yeni Özel Bulut

Đám mây riêng mới từ góc nhìn của một kiến trúc sư

从建筑师的角度看新私有云

Calling All Writers: Learn What The Decentralize-AI Writing Contest Is All About From The ICP Team

সমস্ত লেখকদের আহ্বান করা হচ্ছে: আইসিপি টিম থেকে বিকেন্দ্রীভূত-এআই লেখার প্রতিযোগিতাটি কী তা জানুন

Aufruf an alle Autoren: Erfahren Sie vom ICP-Team, worum es beim Decentralize-AI-Schreibwettbewerb geht

Convocatoria para todos los escritores: conozca de qué se trata el concurso de escritura Decentralize-AI del equipo de ICP

Appel à tous les écrivains : découvrez en quoi consiste le concours d'écriture Decentralize-AI grâce à l'équipe ICP

सभी लेखकों का आह्वान: आईसीपी टीम से जानें कि विकेन्द्रीकृत-एआई लेखन प्रतियोगिता क्या है

作家の皆さんへ: ICP チームから Decentralize-AI ライティング コンテストについて学びましょう

모든 작가에게: ICP 팀에서 Decentralize-AI Writing Contest가 무엇인지 알아보세요

Chamando todos os escritores: aprenda sobre o que é o concurso de redação Decentralize-AI com a equipe do ICP

Приглашаем всех писателей: узнайте, в чем суть конкурса писателей Decentralize-AI от команды ICP

Tüm Yazarlara Çağrı: ICP Ekibinden Decentralize-AI Yazma Yarışmasının Ne Hakkında Olduğunu Öğrenin

Kêu gọi tất cả các nhà văn: Tìm hiểu về cuộc thi viết Decentralize-AI từ nhóm ICP

召集所有作家：从 ICP 团队了解去中心化 AI 写作比赛的全部内容

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