DENTRA

dentra

Topographical Notes

As This is the place where the traditional art market finds itself disrupted, augmented, digitised and in some cases utterly transformed. topics of discussion go, the often disparate fields of art and technology rarely share the same breath, but at the cutting edge of the blockchain, the two have found a host of exciting new ways to come together. While and art might appear to be as opposite as the yin and yang of Taoist philosophy, they have found to travel upon the same path. Together they are taking in a diverse series of locations and landscapes on a truly global scale. I invite you to join me on this journey as we visit six (and a half) projects disrupting how we think about, consume and interact with art. Together we’ll see everything from artists to art markets, tokens and tokenization, riddles and games — and we’ll explore beyond even the three dimensions of our own universe. blockchain technology the way Oh, just one thing before we set off: please remember to keep your hands inside the vehicle at all times. ArtPro We begin this tour with , an art provenance platform focused on creating a peer-to-peer marketplace. Whereas most art projects still work according to an outmoded system that includes auction houses, galleries and other intermediaries, ArtPro is a mobile app and online market where artists and buyers can directly connect, cutting out the middleman and lowering costs. ArtPro Using a number of novel technologies, ArtPro looks like a genuine disruptor, aiming to lower the barriers to entry with an affordable art investment fund and by educating buyers. In a , CEO Oliver Hams explained the goal of the project: press release “Art should be for the many, not the few. With ArtPro we’re bringing art to the masses, allowing more people than ever before to invest in and own a piece of fine art.” Another eye-catching aspect of the ArtPro project is their plan to use an Augmented Reality (AR) smartphone app to allow prospective buyers to view any piece of art that is for sale on the ArtPro platform in a real world space of their choosing. And talking of AR, that leads us nicely to our next destination. Trevor Jones When it comes to the confluence of AR, art and blockchain, one name which instantly stands out is . The Scottish-Canadian artist has recently announced a cryptocurrency inspired exhibition, ‘DISRUPTION: The Art of Blockchain’, which will feature 14 large paintings ‘fuelled’ with augmented reality. If you look now you can see a video which shows the technology in action. Trevor Jones quotes Jones as stating that: Bitcoin.com “The last 7 years I’ve been focused on creating innovative art that pushes formal boundaries and adds to the creative knowledge set, that challenges and perhaps even threatens the status quo, and that encourages people to question what a painting can actually be. Bitcoin and other cryptocurrencies are doing similar things with fiat and the financial sector. It made sense to me to try to tie this all together and explore the notion of a crypto themed exhibition.” For those based in Scotland, the upcoming exhibition will be from 22nd-26th October at Dundas Street Gallery in Edinburgh. Don’t worry, we’ll have you home long before then. CryptoArte Telling the story of the Ethereum blockchain in a series of 9,895 paintings, each canvas holds 576 individual blocks of shapes, colors, decorations. The project is the brainchild of technology entrepreneur Sebastián Brocher — an architect by trade rather than artist. However, the resulting images have a kind of hypnotic beauty to them, especially when viewed in sequence. With each block miner having its own unique symbol, it is possible to chart how frequently miners have discovered each new block. The result is an artistic and visually beautiful archive of the history of the Ethereum network. CryptoArte One of the neat tricks of CryptoArte is that it allows you to find paintings created from the Ethereum blockchain on a particular date of your choosing. If you’d like to have a painting which represents your birthday or an anniversary, it is very easily found. The day you first received ETH? No problem. Just take a look at some of the works which the CryptoArte project has produced so far. Neon District Our next stop is , the latest project from bitcoin artist and serial frustration merchant YT, otherwise known by her twitter handle @coin_artist. YT’s most famous foray into the world of blockchain art was a blockchain painting-come-puzzle with a prize of 5 BTC for finding the solution. Launched in 2015, it took three years until a solution was found by which time the prize fund was worth $50,000. Neon District TORCHED H34R7S, Now YT is back with another project blending art, blockchain, puzzles and RPG gaming in the form of . Neon District The AI Puzzle in Neon District Although Neon District has yet to fully launch, it is already causing much anticipation within the crypto community which is no less than we’d expect from this tricky crypto-puzzler. Maecenas The project is grounded in a common theme among cryptocurrency projects, allowing investors to tokenize and buy shares in assets. In this particular case the assets are famous paintings. The result is a stock market of sorts within the art world. Maecenas Maecenas recently made news with their first auction selling a 31.5% stake in Andy Warhol’s . The tokenized auction successfully raised $1.7 million USD by breaking the ownership of painting into tamper-proof certificates of ownership recorded on the blockchain. In a press release, Marcelo Garcia Casil, CEO of Maecenas, said: 14 Small Electric Chairs “Tokenization of assets is the most prominent and exciting use case of blockchain technology, and we’re proud to be pioneers in this space. This Warhol painting is the first of many more to come and we are looking forward to seeing and leading the financial revolution for the art market.” Decentraland Our final stop is , a virtual reality world of possibilities built on the Ethereum blockchain. In this virtual reality platform, users are able to buy plots of virtual land and create almost anything they desire. Purchased using MANA tokens, the virtual land is then completely in their control. Although Dentraland is yet to go fully live, it will be accessible and explorable via mobile, web browser, or VR headset. Decentraland The Map of Decentraland Fascinatingly, Decentraland will allow for different zones such as Festival Land, the University and the Museum. In the virtual reality Museum, visitors will be able to view artworks, attend VR art events and buy and sell real artwork which can be delivered to your (non-virtual) home address. According to the , the vision for the Museum is as follows: “We want to build the main Museum Experience in Decentraland. Showcasing art we curate, inviting curators, and renting a Gallery or Experience Modules to anyone interested in showcasing their creations”. Decentraland wiki In Decentraland it will be fully possible to interact with art in a completely simulated world hosted on the blockchain. Which brings us to… And a Half …just a little further. It is believed that the eventual heat death of our Universe will occur in 10¹⁰⁰⁰ years — give or take a few — and that everything that ever existed will simply cease to exist. Another theory states that the Universe is itself just another simulation, possibly much like Decentraland. From the inside there is simply no way to tell. If that is the case, perhaps there will be no heat death after all; the hash of the Universe will simply be solved and another Universe will be added to the chain. It’s possible that our reality is simply a cosmic art project in a celestial simulation, hosted on the canvas of an ever-growing blockchain. So before our tour ends you might want to read that last sentence again, if you’re really reading it at all. You can follow Robert D. Knight on Twitter @redcryptoknight

Six and a Half Projects Bridging the Gap Between Art and Blockchain Technology

A man compromises with insomnia and rolls out of bed at 4:30 AM. The sun is hours away from making its daily debut, but it matters not for this man. There’s no need to shave. He hasn’t in four days. He immediately lights a cigarette—a hand-rolled cigarette of unknown (to you) origin. He flicks on the radio. Immediately turns it off. This moment deserves silence. Stares into the mirror. Naked. Buck naked.  Looks into himself. Deep into himself. Puts the cigarette out on the back of his hand and flicks it into the toilet. Finally, the words rattling around in his brain slither their way past his lips in exasperated murmur-- “Our training data f*cking sucks.” And it’s expensive too! Look, everyone and their grandmother knows AI is huge.  Maybe your grandmother probably talks to Snapchat AI more than she talks to you. Either way, while AI certainly provides an entertainment factor, more than anything it can be downright . And businesses are adopting AI initiatives at unprecedented pace. I know the world doesn’t need another blog about the growth of AI, but I’ll mix it up in a second. useful First, get this: In 1923, only 0% of businesses considered artificial intelligence to be of high priority to their organization. Wow. By 2020, 54% of surveyed IT professionals were highly prioritizing AI. (nice), a 15% increase in just two years. By the end of 2022, that number had climbed to 69% But, close to half (47%) of AI/ML users have begun their initiatives in the past two years and 78% of the surveyed had moved past the ideation stage into execution. What does this mean? Statistically speaking, there’s a lot of businesses out there running AI programs and initiatives who are total newbies to the field and likely have no idea what they are doing. Which percentage of the 47% are that old dog chemist meme? Well, I can’t answer that for you. What I can tell you is that the biggest reported challenge in companies AI/ML journeys is a shortage of skilled talent (67%), followed by algorithm and model failure (61%). When it comes to adopting AI, the barrier reported most is the cost of implementation. And what takes up the biggest chunk of AI budgets? Sourcing and implementing training data, checking in at 13% of budgets. A lot of data is just flat out . It’s unreliable, difficult to manage, and it’s entirely possible the AI is trained on , meaning the data used to train the model is sourced from another AI model that was already trained on sketchy data. Shout out to for the intro to this terminology. bad laundered data Olga Mack So data is bad, it’s expensive, it could be the equivalent to a t-shirt with typos purchased from a thrift store (shout out to my friend’s “Garciapara” Red Sox shirt), and a gigantic swath of businesses implementing AI are new and lack the resources and talent to make things work, let alone keep it sustainable. Nomar To this end, a whopping , while 66% predict their need for training data to only increase compared to 0% of them predicting it to decrease. This is a 0% increase from my make-believe 1923 survey. 87% of executives are willing to pay more for higher quality training data More numbers you say? More numbers you’ll receive. In 2022, . By 2026, the number is expected to reach $300 billion. 13% of $300 billion is…$39 billion. Now I know this isn’t exactly how statistics work, so don’t grill me. But in short: global spend for training data for AI is a multi-billion dollar industry. Factor in that 66% of these execs expect the need for training data to increase and 87% are willing to spend more for higher quality data then…well, you get the point. global spend on artificial intelligence was around $118 billion More factors On top of this, the ability to source reliable data is way more difficult in 2023 than it was in the past. Privacy initiatives like GDPR and CCPA aim to protect consumer data. Major tech players like Google and Apple are making third-party data collection increasingly more difficult. Ongoing legal battles have AI training data at the forefront, with a popular sentiment being that scraping web data to train AI and claiming it “fair use” is in jeopardy of becoming a thing of the past. An apt comparison may be the Napster fallout of the early 2000s. While it was clearly evident then that Napster was powered by the illegal sharing of copyrighted material and intellectual property, a similar trajectory is something businesses using AI are forced to consider. The sand may filter down through the hourglass, and Metallica’s is likely to play for those who haven’t put in the effort to futureproof their AI initiatives. “To Whom the Bell Tolls” A new Spotify So, what is the solution? Well, it’s complicated. But out of the ashes of Napster, Kazaa and Limewire came Spotify, who operated on the premise of building something “better than piracy.” This involved hashing out deals with record labels and agencies to properly license the content streamed on Spotify platforms. Is the same thing possible for AI? We think so. 85% of consumers will exchange data for coupons or discounts. This paves the way for a data acquisition model that incentivizes users to participate, generating valuable zero-party data which can be used for a multitude of things, including training AI. We built something to license zero-party data, and even built a feature in partnership with Snowflake to allow for businesses to re-list licensed zero-party data. Based on the desire for higher-quality training data, this could prove to be a gigantic opportunity for an additional revenue stream that can also built customer loyalty. But enough brand-y stuff. You can learn more . here In summary… A lot of training data f*cking sucks. I haven’t dug up the correlation between training data sucking and sales for Gillette razors, but I would imagine there’s something there. On top of it sucking, it’s expensive. More and more companies are dedicating time and resources to implement AI, but many of them are new to the game and lack the proper team, infrastructure, and quality data to optimize their initiatives. Legal battles have thrown a wrench into the “old ways” of AI training data sourcing and collection, and privacy initiatives have made it increasingly difficult for businesses to collect the data needed to fuel their business. Looking to companies like Spotify for inspiration, it’s known to be possible to overcome the legal aspect. Given consumer sentiment on data sharing coupled with a desire for more personalization and customization in their brand experiences, we’ve recognized a giant market for licensing zero-party data for re-sale (among many other use cases). Hey, what’s 13% of $300 billion again? Written by Shane Faria, co-founder @TIKI

If the Training Data Sucks, So Does the AI Itself

যদি প্রশিক্ষণের ডেটা খারাপ হয়, তবে এআই নিজেই করে

Wenn die Trainingsdaten scheiße sind, ist es auch die KI selbst

Si los datos de entrenamiento apesta, la IA también apesta

Si les données d'entraînement sont nulles, l'IA craint aussi

यदि प्रशिक्षण डेटा बेकार है, तो एआई भी बेकार है

トレーニング データがダメなら AI もダメ

훈련 데이터가 형편없다면 AI 자체도 형편없다

Se os dados de treinamento são ruins, a IA também é ruim

Eğitim Verileri Berbatsa Yapay Zekanın Kendisi de Berbattır

Nếu dữ liệu đào tạo tệ thì AI cũng tệ

如果训练数据很糟糕，那么 AI 也很糟糕

Data collection for machine learning projects can be a real pain. It's time-consuming and tedious, and did we mention expensive? It's unfair that some machine learning projects never even begin because the cost of data collection can be so prohibitive. Let's examine why data acquisition is so expensive, even though it shouldn't be. The cost of labor, infrastructure, quality control, pre-processing, data cleaning, and ethical considerations are just a few of the cost segments that are associated with data collection costs. Now, it is definitely not a good idea to skip any of these segments, but the catch is that you can cut costs by making each data collection step as efficient as possible. We must ensure that our strategy includes more than just cost-cutting; we also need to ensure that the data we are gathering is of high quality! Let's start by examining how prioritizing quality can help with cost-effective dataset collection. 1. Prioritizing Quality Over Quantity Any machine-learning model development process starts with gathering a training dataset. The process of gathering training data is not a one-time occurrence; rather, it can be repeated repeatedly throughout the entire period of developing a ground-breaking AI solution. While testing our model, if the efficiency of the model is not up to par in any scenario, then in order to train our model for that scenario, we need to collect new and more specific data in that case. In order to lower the cost of data collection, our strategy should be to reduce this repetitive collection of new datasets. Now, the maxim "the more, the better" cannot apply to the collection of training datasets without paying attention to the . dataset's quality Also, it is obvious that the size of the dataset has a direct impact on the total cost of training data collection. It can be expensive and time-consuming to gather a lot of training data, especially if the data needs to be labeled or annotated. However, collecting high-quality data, even if it's a smaller dataset, can actually help reduce overall costs in training data collection. First off, by gathering high-quality data, we can avoid gathering redundant or irrelevant data that might not improve the performance of the machine learning model. As a result, it is less expensive to gather, store, and manage massive amounts of data. Secondly, high-quality data can help reduce the time and cost associated with data cleaning and preprocessing. Cleaning and preparing the data for use in the machine learning model is easier when it is reliable and consistent. Thirdly, a quality dataset can improve the performance of machine learning models, which in turn lessens the requirement for additional training data. As a result, there will be no need to collect extra data to make up for the model's shortcomings, which can help lower the overall cost of data collection. In an ideal case, we must be clear about what we are expecting in terms of quality with any data collection process, and then finding the optimum balance between quality and quantity will significantly reduce the overall cost. 2. Leverage Human-in-the-Loop People are what make data collection possible. Depending on the use case, complexity, and volume, we have to onboard people from various places to gather the data. This is where most of the money goes when collecting data. Recruiting qualified and knowledgeable crowds in accordance with the task at hand is the first step when dealing with the crowd in order to acquire a high-quality dataset. If you want German conversational speech data, then you must focus on onboarding native German people who already have experience working on similar projects. Simply because they have experience, they can easily comprehend your requirements and can help you more when it comes to gathering high-quality datasets. Aside from that, all dataset requirements are distinctive in some way, and some dataset requirements can be particularly complicated. In these situations, it is strongly advised to spend some time developing appropriate guidelines and training materials in order to save money and time. It can be beneficial to have instructions and training materials in the native language. If the guideline is clear from the start, then training people on it can be easy and can boost confidence in data providers. This also reduces the continuous back and forth in case of confusion over guidelines, which eventually saves more time and money. Setting clear expectations can improve contributors' job satisfaction and lower their likelihood of giving it up. That reduces the cost and time associated with finding and onboarding new people. An ideal guideline must have clear acceptance and rejection criteria for participants, which gives them a clear understanding of what to do and what not! This remarkably aids in lowering rejection and rework, which ultimately saves time and money. 3. Adopt Transfer Learning A pre-trained model is reused for a new task with less training data using the machine learning technique known as transfer learning. Transfer learning can lower the cost of gathering training datasets by lowering the quantity of new data that needs to be gathered and labeled. To train a model from scratch in conventional machine learning models, a significant amount of labeled data is needed. But with transfer learning, programmers can begin with a model that has already been trained and has picked up general features from a sizable dataset. Developers can quickly and effectively train a model that excels at the new task by fine-tuning the previously trained model on a smaller, task-specific dataset. Let's say a business is creating a machine-learning model to find objects in pictures. They can use a pre-trained model like or , which has already learned general features from a large dataset of images, rather than collecting and labeling a large dataset of images from scratch. ResNet VGG The pre-trained model can then be fine-tuned using a smaller dataset of images relevant to their use case, such as pictures of industrial or medical equipment. The business can significantly reduce the quantity of fresh data that must be gathered and labeled while still creating a top-notch machine-learning model by utilizing transfer learning. Leveraging existing datasets is another way that transfer learning can assist in lowering the cost of training data collection. For instance, a developer can use the dataset from an earlier project as a starting point for a new machine learning project they are working on that is in a related field. In conclusion, transfer learning is an effective method for cutting the expense of obtaining training data in machine learning. Developers can drastically reduce the amount of fresh data that must be gathered and labeled while still producing high-quality machine-learning models that excel at novel tasks by utilizing pre-trained models and existing datasets. Making the decision to implement transfer learning can be difficult and crucial because there are numerous restrictions, such as Fine-tuning might not be beneficial if a pre-trained model has already been created for the task that is not your primary concern. could occur if the model was developed using a sparse or unrelated dataset that is relevant to your task. Overfitting Fine-tuning can be expensive computationally if the pre-trained model is very large and requires a lot of computational resources. 4. Explore Readymade Dataset When working with large datasets, starting from scratch on a new dataset can be a daunting task. In this situation, a pre-made, or off-the-shelf (OTS) dataset might be a wise choice. Finding an open-source training dataset that meets your needs can help you save time and money. Even though finding a perfectly structured dataset that meets your requirements in open source is extremely rare, there is no guarantee that it will be diverse and representative enough to support the development of reliable AI solutions. Another option to acquire off-the-shelf datasets is through commercial licensing from organizations like FutureBeeAI. FutureBeeAI has a pool of more than 2,000 training datasets, including , image, video, and text datasets. speech There is a good chance that we have already created the dataset you need. This pre-made dataset not only reduces collection time but also frees you from the hassle of managing crowds and aids in the scaling of your AI solution. Choosing an OTS dataset can make it very simple to adhere to compliance because the company has already taken all necessary ethical precautions. Finding the right partner and purchasing the appropriate off-the-shelf dataset can be a very economical solution. 5. Automate With Tools From our discussion up to this point, it is clear that the only opportunity to lower the cost of data collection is to find the most effective means of carrying out each of these minor yet important tasks. In this situation, using cutting-edge tools can be extremely helpful. The cost of data preparation is another element on which we should concentrate. For the datasets to be ready for deployment after collection, proper metadata and ground truths are required. Now, manually generating this metadata can be a time-consuming and highly error-prone task. You can automate the creation of metadata and speed up the collection of structured datasets by using data collection tools. In addition, collecting data without the proper tools only results in longer collection times, higher costs, and frustrated data collectors. Using data collection tools can greatly speed up the procedure and cut down on the total amount of time. This facilitates the participant's entire data collection task and can lower the overall budget! 6. Data Augmentation The process of "data augmentation" involves applying different transformations to existing data in order to produce new training data. By enabling developers to produce more data from a smaller dataset, this technique can aid in lowering the overall cost of data collection for machine learning. Consider the case where you have gathered . You can use data augmentation to expand your training dataset's overall size by: speech data for your ASR model Adding different types of noises, like white noise, pink noise, babble noise, etc. Noise Injection: Different room environments can be simulated by adding room acoustics to the speech signal. Environment Simulations: Changing the pitch of the speech signal by increasing or decreasing the frequency of the signal. Pitch Shifting: Changing the speed of the speech signal by increasing or decreasing the speed of the audio signal Speed Perturbation: Such transformations allow us to expand the dataset's size and add more data for a machine learning model's training. Here, there are also cost savings because we can transfer the original labeling. In addition to saving money and time, it lessens the need for additional data and enhances the model's performance with the available dataset. Data augmentation is a potent tool but also a complicated one. If not done properly, there are a lot of consequences. A dataset with many similar data points could result from its aggressive adoption, which could overfit models trained on the dataset. In a nutshell, it is a task that relies on expertise and should be approached with caution. 7. Ethical & Legal Consideration In the field of machine learning, the legal considerations surrounding training datasets are of critical importance. Developing and deploying machine learning models based on improperly sourced, biased, or discriminatory training datasets can have serious legal, ethical, and reputational consequences. Several data privacy laws, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), govern the gathering and use of personal data. These regulations provide precise instructions for gathering, handling, and storing personal data. There may be penalties and legal repercussions if these rules are disregarded. It is essential to abide by intellectual property laws when working with proprietary and copyrighted data; failing to do so could result in legal action. Such legal disputes between have recently come to light. generative AI companies and artists Furthermore, it is crucial to compile a dataset that is , fair to all, and representative of the population. Legal action and reputational damage may result if the model is prejudicial or discriminatory toward any particular group. unbiased Before collecting any personal data, it is advisable to review all the compliance requirements you must adhere to. In an ideal collection, make sure the data contributor is aware of the type of data he or she is sharing and what potential uses there are for it. Data providers must be aware of worst-case consequences as well. To prevent any further issues, make sure your data collection procedure is consensual and includes obtaining written consent from each data provider. Remember, loss avoided is money saved! Originally published at - futurebeeai.com

7 Strategies to Reduce Training Data Acquisition Cost

7 estrategias para reducir el costo de adquisición de datos de capacitación

7 stratégies pour réduire le coût d'acquisition des données de formation

प्रशिक्षण डेटा अधिग्रहण लागत को कम करने के लिए 7 रणनीतियाँ

トレーニング データの取得コストを削減する 7 つの戦略

7 estratégias para reduzir o custo de aquisição de dados de treinamento

7 chiến lược để giảm chi phí thu thập dữ liệu đào tạo

降低训练数据获取成本的 7 种策略

Hi Hackers, Wellbeing Think Tank has been nominated in HackerNoon's annual Startups of The Year awards in Portland, Oregon, USA! Please vote for us here:
Portland, OR Startups
Think Tanks
Events
E-Learning Read more about us below to understand why we deserve your vote. TL;DR - we help organizations create environments that prioritize worker health and well-being and we’d appreciate your vote! Meet Wellbeing Think Tank Wellbeing Think Tank (WTT) is on a mission to educate and empower employers to foster both individual & organizational wellbeing, creating environments where everyone can thrive. We envision a world where every employer supports the whole-person needs of their employees, fostering healthier, more resilient organizations & communities. How Wellbeing Think Tank achieved product-market fit We addressed unmet needs in workplace wellbeing by identifying gaps like the lack of accessible, evidence-based education tailored to diverse workplaces. Key factors driving our fit include: Community Engagement: Our growth has been organic, driven entirely by word of mouth and consistently high attendance.


Feedback Loops: Surveys, event analytics, and direct conversations consistently show high satisfaction, with attendees citing meaningful improvements to workplace practices.


Community Growth: An expanding network of leaders and practitioners from varied industries reflects trust in our resources and programming. By staying inclusive, practical, and forward-thinking, we’ve bridged the gap between research and real-world application, solidifying WTT as a go-to resource for workplace wellbeing. The Evolution of Wellbeing Think Tank Wellbeing Think Tank started as a quarterly educational event series led by our founder (I’m also the one writing this - hi!) in 2019. During the pandemic, we rebranded and expanded, focusing on accessible, evidence-based workplace wellbeing education. We’ve adapted how we organize our events and education, but have remained true to our mission, vision and core values, including: “Evidence-Based but Experimental”: We balance proven strategies with innovative approaches.


Experts over Influencers: Our events prioritize educators and practitioners over influencers, offering actionable, research-backed insights.


Engagement-Driven Formats: Feedback shaped our decision to use interactive virtual meetings for most events, while webinars are reserved for larger-scale gatherings. Addressing emerging topics, such as supporting neurodiverse workers, discussing menopause, the use of ketamine for mental health and tackling loneliness at work, keep us ahead of the curve. These pivots reflect our commitment to addressing pressing challenges while staying aligned with community needs. Milestones and achievements Wellbeing Think Tank (WTT) is proud to celebrate milestones that highlight our growth and impact. Expanding Reach: From 1,466 attendees in 2021-2023 to 2,005 attendees in 2024—a 1,000% increase in just four years!
Thought Leadership: Invited to prestigious conferences to share insights on creating thriving workplaces.
Community Impact:

100% of attendees rated our presenters as engaging and knowledgeable.
93% gained actionable insights for personal or workplace wellbeing.
100% felt more connected after attending WTT Connected events.


Marketing Success: A 300% increase in social media following and an impressive 40% email open rate this year. Lessons learned As we’ve grown, these lessons have shaped WTT: Center User Feedback, but Stay Focused: We listen closely to our community but focus on doing a few things exceptionally well - aka the “In-N-Out Burger” approach.
Never Compromise on Values: Staying small initially allowed us to build a strong, engaged foundation without sacrificing quality.
Celebrate Progress: Recognizing wins, big or small, keeps us grounded and motivated amidst the chaos of startup life.
Community Over Competition: Collaborating with like-minded organizations amplifies impact. We’ve shifted energy away from detractors and toward partnerships aligned with our mission. What Startups of The Year means to us Being nominated is an honor! It’s a chance to connect with HackerNoon’s innovative community and advocate for the importance of wellbeing in all industries. We look forward to growing alongside this incredible community! Conclusion Vote for Wellbeing Think Tank today and join us at one of our free events to learn how to build thriving workplaces. Together, we can create a culture of wellbeing for all. Hi Hackers, Wellbeing Think Tank has been nominated in HackerNoon's annual Startups of The Year awards in Portland, Oregon, USA! Please vote for us here: Portland, OR Startups Think Tanks Events E-Learning Please vote for us here: Please vote for us here: Portland, OR Startups Portland, OR Startups Think Tanks Think Tanks Events Events E-Learning E-Learning Read more about us below to understand why we deserve your vote. TL;DR - we help organizations create environments that prioritize worker health and well-being and we’d appreciate your vote! TL;DR - we help organizations create environments that prioritize worker health and well-being and we’d appreciate your vote! Meet Wellbeing Think Tank Wellbeing Think Tank (WTT) is on a mission to educate and empower employers to foster both individual & organizational wellbeing, creating environments where everyone can thrive. We envision a world where every employer supports the whole-person needs of their employees, fostering healthier, more resilient organizations & communities. How Wellbeing Think Tank achieved product-market fit We addressed unmet needs in workplace wellbeing by identifying gaps like the lack of accessible, evidence-based education tailored to diverse workplaces. Key factors driving our fit include: Community Engagement: Our growth has been organic, driven entirely by word of mouth and consistently high attendance. Feedback Loops: Surveys, event analytics, and direct conversations consistently show high satisfaction, with attendees citing meaningful improvements to workplace practices. Community Growth: An expanding network of leaders and practitioners from varied industries reflects trust in our resources and programming. Community Engagement: Our growth has been organic, driven entirely by word of mouth and consistently high attendance. Community Engagement: Our growth has been organic, driven entirely by word of mouth and consistently high attendance. Community Engagement: Feedback Loops: Surveys, event analytics, and direct conversations consistently show high satisfaction, with attendees citing meaningful improvements to workplace practices. Feedback Loops: Surveys, event analytics, and direct conversations consistently show high satisfaction, with attendees citing meaningful improvements to workplace practices. Feedback Loops: Community Growth: An expanding network of leaders and practitioners from varied industries reflects trust in our resources and programming. Community Growth: An expanding network of leaders and practitioners from varied industries reflects trust in our resources and programming. Community Growth: By staying inclusive, practical, and forward-thinking, we’ve bridged the gap between research and real-world application, solidifying WTT as a go-to resource for workplace wellbeing. The Evolution of Wellbeing Think Tank Wellbeing Think Tank started as a quarterly educational event series led by our founder (I’m also the one writing this - hi!) in 2019. During the pandemic, we rebranded and expanded, focusing on accessible, evidence-based workplace wellbeing education. We’ve adapted how we organize our events and education, but have remained true to our mission, vision and core values, including: “Evidence-Based but Experimental”: We balance proven strategies with innovative approaches. Experts over Influencers: Our events prioritize educators and practitioners over influencers, offering actionable, research-backed insights. Engagement-Driven Formats: Feedback shaped our decision to use interactive virtual meetings for most events, while webinars are reserved for larger-scale gatherings. “Evidence-Based but Experimental”: We balance proven strategies with innovative approaches. “Evidence-Based but Experimental” : We balance proven strategies with innovative approaches. “Evidence-Based but Experimental” Experts over Influencers: Our events prioritize educators and practitioners over influencers, offering actionable, research-backed insights. Experts over Influencers: Our events prioritize educators and practitioners over influencers, offering actionable, research-backed insights. Experts over Influencers: Engagement-Driven Formats: Feedback shaped our decision to use interactive virtual meetings for most events, while webinars are reserved for larger-scale gatherings. Engagement-Driven Formats: Feedback shaped our decision to use interactive virtual meetings for most events, while webinars are reserved for larger-scale gatherings. Engagement-Driven Formats: Addressing emerging topics, such as supporting neurodiverse workers, discussing menopause, the use of ketamine for mental health and tackling loneliness at work, keep us ahead of the curve. These pivots reflect our commitment to addressing pressing challenges while staying aligned with community needs. Milestones and achievements Wellbeing Think Tank (WTT) is proud to celebrate milestones that highlight our growth and impact. Expanding Reach: From 1,466 attendees in 2021-2023 to 2,005 attendees in 2024—a 1,000% increase in just four years! Thought Leadership: Invited to prestigious conferences to share insights on creating thriving workplaces. Community Impact:

100% of attendees rated our presenters as engaging and knowledgeable.
93% gained actionable insights for personal or workplace wellbeing.
100% felt more connected after attending WTT Connected events. Marketing Success: A 300% increase in social media following and an impressive 40% email open rate this year. Expanding Reach: From 1,466 attendees in 2021-2023 to 2,005 attendees in 2024—a 1,000% increase in just four years! Expanding Reach: Thought Leadership: Invited to prestigious conferences to share insights on creating thriving workplaces. Thought Leadership: Community Impact: 100% of attendees rated our presenters as engaging and knowledgeable.
93% gained actionable insights for personal or workplace wellbeing.
100% felt more connected after attending WTT Connected events. Community Impact: 100% of attendees rated our presenters as engaging and knowledgeable. 93% gained actionable insights for personal or workplace wellbeing. 100% felt more connected after attending WTT Connected events. 100% of attendees rated our presenters as engaging and knowledgeable. 93% gained actionable insights for personal or workplace wellbeing. 100% felt more connected after attending WTT Connected events. Marketing Success: A 300% increase in social media following and an impressive 40% email open rate this year. Marketing Success: Lessons learned As we’ve grown, these lessons have shaped WTT: Center User Feedback, but Stay Focused: We listen closely to our community but focus on doing a few things exceptionally well - aka the “In-N-Out Burger” approach. Never Compromise on Values: Staying small initially allowed us to build a strong, engaged foundation without sacrificing quality. Celebrate Progress: Recognizing wins, big or small, keeps us grounded and motivated amidst the chaos of startup life. Community Over Competition: Collaborating with like-minded organizations amplifies impact. We’ve shifted energy away from detractors and toward partnerships aligned with our mission. Center User Feedback, but Stay Focused: We listen closely to our community but focus on doing a few things exceptionally well - aka the “In-N-Out Burger” approach. Center User Feedback, but Stay Focused: Never Compromise on Values: Staying small initially allowed us to build a strong, engaged foundation without sacrificing quality. Never Compromise on Values: Celebrate Progress: Recognizing wins, big or small, keeps us grounded and motivated amidst the chaos of startup life. Celebrate Progress: Community Over Competition: Collaborating with like-minded organizations amplifies impact. We’ve shifted energy away from detractors and toward partnerships aligned with our mission. Community Over Competition: What Startups of The Year means to us Being nominated is an honor! It’s a chance to connect with HackerNoon’s innovative community and advocate for the importance of wellbeing in all industries. We look forward to growing alongside this incredible community! Conclusion Vote for Wellbeing Think Tank today and join us at one of our free events to learn how to build thriving workplaces. Together, we can create a culture of wellbeing for all. Vote for Wellbeing Think Tank today and join us at one of our free events to learn how to build thriving workplaces. Together, we can create a culture of wellbeing for all. Vote for Wellbeing Think Tank today Wellbeing Think Tank

Creating Workplaces that Thrive: Interview with SOTY 2024 Nominee, Wellbeing Think Tank

Discussions about Generative AI safety are more urgent than ever. As AI systems become more embedded in our daily lives, from personalized recommendations to complex decision-making processes, ensuring their safety and ethical alignment with human values is paramount. However, the complexity of AI systems requires a nuanced understanding of the multiple layers that contribute to their reliability and societal impact. To unravel this complexity, it helps to think of AI safety as an onion, with each layer addressing a critical facet of the AI lifecycle. Training Data (Quality and Fairness): At the core of any AI system lies the data on which it is trained. The adage "garbage in, garbage out" could not be more applicable here. AI models rely on vast amounts of data, and the quality of that data directly affects their performance. Poor-quality data introduces biases and inaccuracies, which can result in skewed outputs or even harmful outcomes. Ensuring the quality, diversity, and fairness of training data is a foundational step towards building systems that are not only accurate but also fair and representative of diverse groups. Algorithm (Tuning and Transparency): Moving outward, we reach the algorithm itself. Algorithms define how the AI interprets data, learns from it, and generates predictions. The choices made during algorithm tuning and development can have far-reaching consequences, and transparency in these processes is essential for accountability. By understanding how an AI model makes decisions, we can prevent the "black box" phenomenon, where even developers don’t fully understand how certain outputs are produced. Transparency enables stakeholders to scrutinize, audit, and improve AI systems continually, aligning them with intended ethical standards. Inference (Accuracy): As AI systems process data and make predictions, the accuracy of these inferences becomes the next critical layer. Inaccurate inferences can lead to costly errors, especially in high-stakes fields such as healthcare, finance, and law enforcement. Rigorous testing and validation of AI models are essential to ensure that they perform as expected in real-world scenarios. This layer underscores the importance of accuracy and reliability, setting a high standard for performance before deployment. Publication (Accountability): Accountability comes into play when AI models are deployed into society. Publication and documentation of AI systems' goals, methods, and expected outcomes create an essential checkpoint for their use. This layer of the onion ensures that the developers and operators of AI systems can be held accountable for their actions and decisions. Clear documentation, including the limitations and potential risks of the AI, helps stakeholders understand and anticipate its impact, fostering a culture of responsibility in AI deployment. Societal Impact (Human in the Loop): At the outermost layer lies the societal impact, where AI systems intersect with human lives. This is where "human in the loop" becomes essential. No AI system should operate in complete isolation from human oversight, especially when it comes to decisions affecting individuals and communities. Ensuring that humans have the ability to monitor, intervene, and correct AI processes can prevent unintended consequences, biases, or harmful actions. This final layer emphasizes the need for a balance between AI autonomy and human oversight, promoting AI systems that enhance human well-being rather than detract from it. Peeling back these layers reveals the complexity of AI safety, which cannot be addressed by focusing on any single aspect. Just like an onion, each layer builds upon the previous ones, contributing to a holistic approach to safety that spans from data quality to societal impact. As we continue to integrate AI into more areas of life, it's crucial to adopt this layered perspective. AI safety is not just a technical issue; it is a societal one that requires ongoing vigilance, transparency, and a commitment to ethical standards at every level. The future of AI depends on our ability to address each layer thoughtfully, creating systems that are as beneficial as they are innovative. By treating AI safety as an onion, we peel away the superficial and dig deep into the core of responsible AI development, creating a future where technology serves humanity rather than the other way around. For those interested, I wrote a more detailed discussion on this layered approach here: http://dx.doi.org/10.13140/RG.2.2.15152.57604 Happy for your comments and feedback. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here. Discussions about Generative AI safety are more urgent than ever. As AI systems become more embedded in our daily lives, from personalized recommendations to complex decision-making processes, ensuring their safety and ethical alignment with human values is paramount. However, the complexity of AI systems requires a nuanced understanding of the multiple layers that contribute to their reliability and societal impact. To unravel this complexity, it helps to think of AI safety as an onion, with each layer addressing a critical facet of the AI lifecycle. Training Data (Quality and Fairness): At the core of any AI system lies the data on which it is trained. The adage "garbage in, garbage out" could not be more applicable here. AI models rely on vast amounts of data, and the quality of that data directly affects their performance. Poor-quality data introduces biases and inaccuracies, which can result in skewed outputs or even harmful outcomes. Ensuring the quality, diversity, and fairness of training data is a foundational step towards building systems that are not only accurate but also fair and representative of diverse groups. Algorithm (Tuning and Transparency): Moving outward, we reach the algorithm itself. Algorithms define how the AI interprets data, learns from it, and generates predictions. The choices made during algorithm tuning and development can have far-reaching consequences, and transparency in these processes is essential for accountability. By understanding how an AI model makes decisions, we can prevent the "black box" phenomenon, where even developers don’t fully understand how certain outputs are produced. Transparency enables stakeholders to scrutinize, audit, and improve AI systems continually, aligning them with intended ethical standards. Inference (Accuracy): As AI systems process data and make predictions, the accuracy of these inferences becomes the next critical layer. Inaccurate inferences can lead to costly errors, especially in high-stakes fields such as healthcare, finance, and law enforcement. Rigorous testing and validation of AI models are essential to ensure that they perform as expected in real-world scenarios. This layer underscores the importance of accuracy and reliability, setting a high standard for performance before deployment. Publication (Accountability): Accountability comes into play when AI models are deployed into society. Publication and documentation of AI systems' goals, methods, and expected outcomes create an essential checkpoint for their use. This layer of the onion ensures that the developers and operators of AI systems can be held accountable for their actions and decisions. Clear documentation, including the limitations and potential risks of the AI, helps stakeholders understand and anticipate its impact, fostering a culture of responsibility in AI deployment. Societal Impact (Human in the Loop): At the outermost layer lies the societal impact, where AI systems intersect with human lives. This is where "human in the loop" becomes essential. No AI system should operate in complete isolation from human oversight, especially when it comes to decisions affecting individuals and communities. Ensuring that humans have the ability to monitor, intervene, and correct AI processes can prevent unintended consequences, biases, or harmful actions. This final layer emphasizes the need for a balance between AI autonomy and human oversight, promoting AI systems that enhance human well-being rather than detract from it. Peeling back these layers reveals the complexity of AI safety, which cannot be addressed by focusing on any single aspect. Just like an onion, each layer builds upon the previous ones, contributing to a holistic approach to safety that spans from data quality to societal impact. As we continue to integrate AI into more areas of life, it's crucial to adopt this layered perspective. AI safety is not just a technical issue; it is a societal one that requires ongoing vigilance, transparency, and a commitment to ethical standards at every level. The future of AI depends on our ability to address each layer thoughtfully, creating systems that are as beneficial as they are innovative. By treating AI safety as an onion, we peel away the superficial and dig deep into the core of responsible AI development, creating a future where technology serves humanity rather than the other way around. For those interested, I wrote a more detailed discussion on this layered approach here: http://dx.doi.org/10.13140/RG.2.2.15152.57604 http://dx.doi.org/10.13140/RG.2.2.15152.57604 Happy for your comments and feedback. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here . here

Peeling the Onion on AI Safety

Transforming Legacy with Domain-Driven Design, V: Insights

For those who might not be deeply familiar with blockchain, can you explain why it's so crucial in today’s digital age? One of the core parts of the digital age is giving power back to the people; you see it in the way ordinary users can have their voices heard and businesses can be seen without the approval of gatekeepers. Blockchain is the highest form of this because it is fully democratized. Imagine a global computing system that belongs to everyone and no one at the same time. That is blockchain in a nutshell. Blockchain is important in the digital age because it puts people in control while making computer systems better and safer with things like Decentralized Autonomous Organizations (DAOs) and crypto wallets. What are some of the most innovative projects or products your company is working on right now? How do they address current market needs? We’ve been lucky to work behind the scenes of some truly amazing projects so far. We worked with top crypto exchange OKX to increase user growth through a PR campaign and banner ads. The result was over 492,000 user engagement and increased brand awareness. These days, we’re working with clients to better leverage influencer and PR marketing. Both are huge markets delivering results for many other industries and we believe that the blockchain space has even more to gain from it. What are the biggest challenges your company faces in the crypto/blockchain space, and how are you planning to overcome them? First, there’s the matter of regulation. Laws about how you can market blockchain products are always changing so we have to keep track of them to stay on the right side of the law. We also have to convince clients of the benefits of more ‘traditional’ advertising methods. Most of them understand digital ads and influencer sponsorships but getting them to do radio or billboards is sometimes a feat in itself. Then, of course, you have the holdout consumers who don’t see the value of Web3 just yet. How do you envision Web3 transforming the internet as we know it? What are the key benefits and potential drawbacks of this transition? Web3 is going to create an internet landscape where data and power are split across millions of nodes and not concentrated in a single palace. The benefit of this is that hacks, especially of widely used systems, will be close to impossible. It also means that users will have more control over their own data and content and can enjoy more privacy. The Web2 space has, with all of its perks, been dominated by big corporations like Google with centralized systems and near-monopolies on the Internet. Web3 is going to change that. Are there any traditions you would want the world of web3 to adopt from the web2 world? Web2 notably ushered in a new era of user-generated content and changed the way we share information thanks to the platforms it enabled. Web3 would do well to integrate this as it becomes a reality. Are there any challenges to transition from web2 to web3? There is an obvious reputational issue as some consumers and even businesses are reluctant to take the leap. Part of this is because they don’t understand how Web3 works and what it can do for them. As the space expands, better regulations will be needed to avoid it becoming a Wild West. Most of these issues arise because they are relatively new and should be addressed over time. Can you explain some of the technical advancements that are making Web3 possible? All the technologies that allow for decentralization are fast making Web3 a reality. You have blockchain which is the basis of things like cryptocurrency and NFTs. You also have smart contracts that allow transactions to be completed automatically and permissionlessly. From all these, we can begin to build a truly decentralized future. How can developers get involved in this space? Developers can get involved by, well, getting involved. Developing new tools and programs that leverage Web3 technologies and make them easier to use. We need all hands on deck to make Web3 a reality and many of our clients are proud to have worked with a wealth of developers. How do you think influencers and social media personalities impact the cryptocurrency market? They certainly influence the demand for tokens. We see this when influencers launch their own tokens or promote existing ones. They have the power to drive demand for every product you can imagine and crypto is no different. Do you see this as a positive or negative trend? It has the potential to be both. If influencers are using their platforms to encourage their fans to invest responsibly in worthwhile projects then it can be positive. If not, then it’s negative. TikTokers, Instagrammers, X (formerly Twitter) accounts -- how are they shaping the discourse? They’re amplifying every kind of crypto opinion there is. From giving verdicts about assets to discussing laws and even scandals, they help to cultivate an engaged community that has a vested interest in the success of the industry. What do you think is happening? Are these platforms credible sources for information? I think the industry is expanding and with this expansion comes a demand for content. These platforms can be credible sources depending on who you’re listening to. Misinformation is an issue in every corner of the internet so users need to be cautious. What do you believe are the strongest arguments in favor of cryptocurrencies and blockchain technology? These technologies give power to the people; historically underserved people can get access to financial services through DeFi, prosecuted populations can access funds through crypto, information can be stored forever on the blockchain, and even elections can be made fairer using this tech. There is something for everyone and if harnessed properly, blockchain can help humanity become its best self. How do you respond to critics? We respond by showing the value we deliver to clients. You can argue about tech preferences but you can’t argue with years of an impressive track record. Blockchain and cryptocurrencies do have their detractors. What’s an argument or rationale you use to convince naysayers or those on the fence about blockchain and crypto? Our argument would simply be to take a look at all the amazing things happening in the space. The crypto industry is complex but that is to be expected of any new sector. Even outside of crypto, look at all the ways blockchain is being implemented and it’s pretty obvious that it has a lot to offer. What advice would you give to someone who is just starting to explore the world of cryptocurrencies and blockchain technology? Our advice would be to do your research and engage with the community. Succeeding with blockchain and crypto is all about knowledge and research plus community is how you get it. Also, try to have fun with it. Any sources to check out? Any media sites to visit? Any X (Twitter) accounts to follow? Share the deets! Hackernoon, definitely (wink) but also BeInCrypto, Cointelegraph, Coindesk, and the like. These sites have been giving top-notch blockchain reporting for years so we highly recommend them. Twitter/X founder Jack Dorsey, Ethereum founder Vitalik Buterin, POMP, and Gemini-Cofounder Tyler Winklevoss are some insightful personalities we love on Twitter/X. How do you see events like HackerNoon’s Startups of The Year shaping public understanding and engagement of the crypto and blockchain industry? It’s great. We see it the way you might see awards like the MTV VMAs; it lets the world know that certain acts (or businesses, in this case) have a solid community behind them. By giving businesses in the space a platform for recognition, skeptics can see that they are as legitimate as any other start-up. It’s also great for morale; a niche industry like blockchain can sometimes feel isolated but initiatives like this bring the community together and show us how far we have come. Why we decided to participate in HackerNoon's Startups of The Year awards We’re a marketing agency so we’re all about collaborating and connecting others. The HackerNoon's Startups of The Year awards give us a chance to connect with other businesses in the space as well as the community that supports our work. As the awards unravel, we’re learning more about other firms that operate in the same sector and this creates the avenue for possible collaboration. Similarly, other potential partners and clients can find us and this is a win for everyone. We’re proud to be part of the blockchain community and this is a great opportunity to connect even more with it. Final Thoughts Proelo.io has worked behind the scenes of some of the biggest marketing campaigns in the blockchain industry for years now. We’re immensely proud of what we do and would be honored to receive HackerNoon's Startups of The Year award. We find ourselves in great company with a wealth of respected companies and hope to keep serving our industry for years to come. Vote for us today! For those who might not be deeply familiar with blockchain, can you explain why it's so crucial in today’s digital age? One of the core parts of the digital age is giving power back to the people; you see it in the way ordinary users can have their voices heard and businesses can be seen without the approval of gatekeepers. Blockchain is the highest form of this because it is fully democratized. Imagine a global computing system that belongs to everyone and no one at the same time. That is blockchain in a nutshell. Blockchain is important in the digital age because it puts people in control while making computer systems better and safer with things like Decentralized Autonomous Organizations (DAOs) and crypto wallets. What are some of the most innovative projects or products your company is working on right now? How do they address current market needs? We’ve been lucky to work behind the scenes of some truly amazing projects so far. We worked with top crypto exchange OKX to increase user growth through a PR campaign and banner ads. The result was over 492,000 user engagement and increased brand awareness. These days, we’re working with clients to better leverage influencer and PR marketing. Both are huge markets delivering results for many other industries and we believe that the blockchain space has even more to gain from it. What are the biggest challenges your company faces in the crypto/blockchain space, and how are you planning to overcome them? First, there’s the matter of regulation. Laws about how you can market blockchain products are always changing so we have to keep track of them to stay on the right side of the law. We also have to convince clients of the benefits of more ‘traditional’ advertising methods. Most of them understand digital ads and influencer sponsorships but getting them to do radio or billboards is sometimes a feat in itself. Then, of course, you have the holdout consumers who don’t see the value of Web3 just yet. How do you envision Web3 transforming the internet as we know it? What are the key benefits and potential drawbacks of this transition? Web3 is going to create an internet landscape where data and power are split across millions of nodes and not concentrated in a single palace. The benefit of this is that hacks, especially of widely used systems, will be close to impossible. It also means that users will have more control over their own data and content and can enjoy more privacy. The Web2 space has, with all of its perks, been dominated by big corporations like Google with centralized systems and near-monopolies on the Internet. Web3 is going to change that. Are there any traditions you would want the world of web3 to adopt from the web2 world? Web2 notably ushered in a new era of user-generated content and changed the way we share information thanks to the platforms it enabled. Web3 would do well to integrate this as it becomes a reality. Are there any challenges to transition from web2 to web3? There is an obvious reputational issue as some consumers and even businesses are reluctant to take the leap. Part of this is because they don’t understand how Web3 works and what it can do for them. As the space expands, better regulations will be needed to avoid it becoming a Wild West. Most of these issues arise because they are relatively new and should be addressed over time. Can you explain some of the technical advancements that are making Web3 possible? All the technologies that allow for decentralization are fast making Web3 a reality. You have blockchain which is the basis of things like cryptocurrency and NFTs. You also have smart contracts that allow transactions to be completed automatically and permissionlessly. From all these, we can begin to build a truly decentralized future. How can developers get involved in this space? Developers can get involved by, well, getting involved. Developing new tools and programs that leverage Web3 technologies and make them easier to use. We need all hands on deck to make Web3 a reality and many of our clients are proud to have worked with a wealth of developers. How do you think influencers and social media personalities impact the cryptocurrency market? They certainly influence the demand for tokens. We see this when influencers launch their own tokens or promote existing ones. They have the power to drive demand for every product you can imagine and crypto is no different. Do you see this as a positive or negative trend? It has the potential to be both. If influencers are using their platforms to encourage their fans to invest responsibly in worthwhile projects then it can be positive. If not, then it’s negative. TikTokers, Instagrammers, X (formerly Twitter) accounts -- how are they shaping the discourse? They’re amplifying every kind of crypto opinion there is. From giving verdicts about assets to discussing laws and even scandals, they help to cultivate an engaged community that has a vested interest in the success of the industry. What do you think is happening? Are these platforms credible sources for information? I think the industry is expanding and with this expansion comes a demand for content. These platforms can be credible sources depending on who you’re listening to. Misinformation is an issue in every corner of the internet so users need to be cautious. What do you believe are the strongest arguments in favor of cryptocurrencies and blockchain technology? These technologies give power to the people; historically underserved people can get access to financial services through DeFi, prosecuted populations can access funds through crypto, information can be stored forever on the blockchain, and even elections can be made fairer using this tech. There is something for everyone and if harnessed properly, blockchain can help humanity become its best self. How do you respond to critics? We respond by showing the value we deliver to clients. You can argue about tech preferences but you can’t argue with years of an impressive track record. Blockchain and cryptocurrencies do have their detractors. What’s an argument or rationale you use to convince naysayers or those on the fence about blockchain and crypto? Our argument would simply be to take a look at all the amazing things happening in the space. The crypto industry is complex but that is to be expected of any new sector. Even outside of crypto, look at all the ways blockchain is being implemented and it’s pretty obvious that it has a lot to offer. What advice would you give to someone who is just starting to explore the world of cryptocurrencies and blockchain technology? Our advice would be to do your research and engage with the community. Succeeding with blockchain and crypto is all about knowledge and research plus community is how you get it. Also, try to have fun with it. Any sources to check out? Any media sites to visit? Any X (Twitter) accounts to follow? Share the deets! Hackernoon, definitely (wink) but also BeInCrypto, Cointelegraph, Coindesk, and the like. These sites have been giving top-notch blockchain reporting for years so we highly recommend them. Twitter/X founder Jack Dorsey, Ethereum founder Vitalik Buterin, POMP, and Gemini-Cofounder Tyler Winklevoss are some insightful personalities we love on Twitter/X. How do you see events like HackerNoon’s Startups of The Year shaping public understanding and engagement of the crypto and blockchain industry? It’s great. We see it the way you might see awards like the MTV VMAs; it lets the world know that certain acts (or businesses, in this case) have a solid community behind them. By giving businesses in the space a platform for recognition, skeptics can see that they are as legitimate as any other start-up. It’s also great for morale; a niche industry like blockchain can sometimes feel isolated but initiatives like this bring the community together and show us how far we have come. Why we decided to participate in HackerNoon's Startups of The Year awards We’re a marketing agency so we’re all about collaborating and connecting others. The HackerNoon's Startups of The Year awards give us a chance to connect with other businesses in the space as well as the community that supports our work. As the awards unravel, we’re learning more about other firms that operate in the same sector and this creates the avenue for possible collaboration. Similarly, other potential partners and clients can find us and this is a win for everyone. We’re proud to be part of the blockchain community and this is a great opportunity to connect even more with it. Final Thoughts Proelo.io has worked behind the scenes of some of the biggest marketing campaigns in the blockchain industry for years now. We’re immensely proud of what we do and would be honored to receive HackerNoon's Startups of The Year award. We find ourselves in great company with a wealth of respected companies and hope to keep serving our industry for years to come. Vote for us today! Vote for us today! Vote for us today! Vote for us today!

Meet PROLEO.IO: Startups of The Year 2024 Nominee 

የ HackerNoon የአመቱ ጅምር ሽልማት፡ ምላሾች

Прэмія «Стартапы года» ад HackerNoon: адказы

Награда за стартиращи компании на годината на HackerNoon: Отговори

Premio Startups del año de HackerNoon: respuestas

HackerNoon-en Urteko Startups saria: erantzunak

Prim HackerNoon pou Startups pou Ane a: Repons

A HackerNoon Az év startupjai díja: Válaszok

HackerNoonの今年のスタートアップ賞：反応

HackerNoon's Startups of the Year Award: Valiny

د هیکرنون د کال د پیل جایزه: ځوابونه

Sagwadi ra HackerNoon ra Masungulo ya Lembe: Tinhlamulo

I-HackerNoon's Startups of the Year Award: Iimpendulo

Seven Things Angel Investors Want To See In A Startup Before Investing

A tough reality is settling in for small business owners across America, who already have a full plate: beyond staffing challenges, rising rents, and supply chain issues, now even your local deli needs to figure out how to appear on Perplexity and ChatGPT. Visibility on the increasing number of platforms - especially voice and artificial intelligence - is a necessity for business owners. I don’t use the word “necessity” lightly - it’s the truth. A survey of 550 American small businesses conducted by The Discoverability Company offers the proof. Respondents rated “how critical online discovery is” to their businesses an 8.4 out of 10, clearly linking their success to how they appear online. And yet, only 38% of these same respondents said they felt “sufficiently represented” on the platforms that matter so much. Woof. These responses underscore a broader reality: digital discovery is already a tough challenge for small businesses, and it’s only going to get tougher. Historically, small businesses have focused their efforts on Google search rankings, maps, and social media. But as AI platforms grow and traditional search engines lose ground, small businesses need to adapt to stay competitive. Optimizing an online footprint for AI visibility is quickly becoming one of the most critical strategies for businesses hoping to stand out in a fragmented user experience landscape. Here are a few key tips to improve your small business visibility in AI-based platforms and help future-proof your online footprint. The First Step: Optimize Your Google Business Listing for Voice and AI Search AI-driven platforms like to pull information from established online sources that offer information in a way that is clear and easy to understand. That means that your listings and reviews on platforms like Google My Business, Yelp, Bing Places, Trustpilot, and elsewhere have a renewed importance: the more complete and accurate your business listings, the more likely you are to appear in responses from AI tools. Claim and Optimize Your Google My Business Listing AI platforms, especially those offered by Google, often use information from Google My Business listings when providing results. Ensure your listing is accurate, detailed, and complete, including up-to-date hours, phone numbers, locations, and service information. Prioritize Consistency Across Directory Listings AI-driven algorithms look for consistent information to verify a business’s authenticity. Inconsistent listings across Google, Yelp, Bing Places, and other directories can hurt your chances of being recommended. Ensure your business name, address, phone number, and other details match exactly across every platform. Create AI-Friendly Content on Your Google Business Listing AI platforms look for specific, relevant answers to questions, so by tailoring your content accordingly, you can significantly improve your visibility. Remember that end-users ask AI platforms direct questions, so create content that answers these questions concisely. For instance, if you own a dental practice, you might look to ensure your Google Business listing Q&A section addresses questions like, “How often should I get my teeth cleaned?” or “What’s the best way to whiten teeth at home?” AI algorithms favor clear, straightforward answers, so make sure you provide them. Along those same lines, be mindful of “near me” and location-based phrases in your questions and answers. Many voice searches and AI-driven queries are location-based. Creating Q&A content, blog posts, service pages, or FAQ entries that reflect this intent—such as “quick oil change in (your city)” or “where to find quality computer repair near me”—can help capture local searches and boost visibility. Collect Reviews and Be Mindful of Your Online Reputation Reviews have always been valuable, but they’re even more important in the age of AI-driven search. Positive reviews increase trust, which are a factor in whether an AI platform recommends your business over others. Positive reviews on Google, Yelp, and other platforms increase your credibility and can boost your visibility in AI-driven searches, since the nature of review sites is such that it’s easy for an AI platform to understand. Engaging with customer reviews (both positive and negative) shows that your business is active and invested in customer satisfaction. AI algorithms value this engagement as a sign of trustworthiness and relevance, which can improve your position in search results. How to Update Your Website and Content So It’s Featured on AI Search While this is a little more technical, it’s critical to understand: to ensure your website is legible to the scrapers curating content for AI platforms, you need to ensure your page has properly-configured schema markup or structured data. Schema markup, also known as structured data, is a form of code you add to your website to provide search engines and AI-driven platforms with extra context about your business. Think of schema markup as a way of “tagging” specific details—like your location, services, hours, and even pricing—in a way that makes it easy for AI scrapers to understand and categorize your business. It takes the guess-work out and helps AI platforms quickly interpret what your business offers, thereby making it more likely you’ll show up in relevant results. If you use popular content management platforms like Wordpress, there are many guides to adding schema markup to your website by AIOSEO or you can engage the services of a consultant, though don’t be over-sold: it’s the kind of thing that should not be an expensive or time-intensive effort. But if you’re the DIY type, once you get “under the hood” and find where to implement this, there are two best-practices to keep in mind: Implement Local Business Schema: Adding local business schema allows AI platforms to recognize important details about your business, such as your industry, address, hours, and service offerings. This structured information improves your chances of appearing in local voice search results and “near me” queries, putting you front and center when potential customers are looking for nearby solutions.
Optimize for Common Queries: Schema markup can also help you target specific questions customers often ask. Think about the most common inquiries your business receives—questions like, “What are your sandwich rolls made from?” or “Do you offer same-day services?” Adding structured data for these queries makes your content more likely to appear in response when users ask an AI platform similar questions, increasing your visibility for search phrases that matter to your audience. Using schema markup strategically allows you to target AI-driven platforms with precision, ensuring they pull the right information about your business to answer user questions accurately. By leveraging this structured approach, you can improve your chances of reaching customers in a way that’s both relevant and reliable. Extra Credit: Social Proof and Off-Page Signals While optimizing your website with structured data and schema markup enhances your visibility on AI platforms, there’s another essential piece to digital discoverability: social proof and off-page signals. These elements—things like customer reviews, mentions on other websites—provide AI platforms with valuable context about the credibility and popularity of your business. Those off-page signals, like backlinks from news outlets that permit AI-scraping of their content, bolster your discoverability. When reputable sites link back to your website, it demonstrates credibility and authority, things AI scrapers consider. Future-Proofing Your Online Footprint The truth is this article, while accurate for how to drive AI visibility, also contains universal truths that will help bolster your small business visibility everywhere your users spend time online, including search and other platforms, too. That’s something we call a “holistic approach to digital discovery.” Done properly, these steps won’t just boost your current visibility—it lays the groundwork for long-term relevance and growth as AI and other technologies continue to evolve. A tough reality is settling in for small business owners across America, who already have a full plate: beyond staffing challenges, rising rents, and supply chain issues, now even your local deli needs to figure out how to appear on Perplexity and ChatGPT. now even your local deli needs to figure out how to appear on Perplexity and ChatGPT. Visibility on the increasing number of platforms - especially voice and artificial intelligence - is a necessity for business owners. I don’t use the word “necessity” lightly - it’s the truth. A survey of 550 American small businesses conducted by The Discoverability Company offers the proof. Respondents rated “how critical online discovery is” to their businesses an 8.4 out of 10 , clearly linking their success to how they appear online. survey of 550 American small businesses conducted by The Discoverability Company 8.4 out of 10 clearly And yet, only 38% of these same respondents said they felt “sufficiently represented” on the platforms that matter so much . only 38% of these same respondents said they felt “sufficiently represented” on the platforms that matter so much Woof . Woof These responses underscore a broader reality: digital discovery is already a tough challenge for small businesses, and it’s only going to get tougher. Historically, small businesses have focused their efforts on Google search rankings, maps, and social media. But as AI platforms grow and traditional search engines lose ground, small businesses need to adapt to stay competitive. already Optimizing an online footprint for AI visibility is quickly becoming one of the most critical strategies for businesses hoping to stand out in a fragmented user experience landscape. Optimizing an online footprint for AI visibility is quickly becoming one of the most critical strategies for businesses hoping to stand out in a fragmented user experience landscape. Here are a few key tips to improve your small business visibility in AI-based platforms and help future-proof your online footprint. The First Step: Optimize Your Google Business Listing for Voice and AI Search AI-driven platforms like to pull information from established online sources that offer information in a way that is clear and easy to understand. That means that your listings and reviews on platforms like Google My Business, Yelp, Bing Places, Trustpilot, and elsewhere have a renewed importance: the more complete and accurate your business listings, the more likely you are to appear in responses from AI tools. Claim and Optimize Your Google My Business Listing Claim and Optimize Your Google My Business Listing AI platforms, especially those offered by Google, often use information from Google My Business listings when providing results. Ensure your listing is accurate, detailed, and complete, including up-to-date hours, phone numbers, locations, and service information. Google My Business listings Prioritize Consistency Across Directory Listings Consistency Across Directory Listings AI-driven algorithms look for consistent information to verify a business’s authenticity. Inconsistent listings across Google, Yelp, Bing Places, and other directories can hurt your chances of being recommended. Ensure your business name, address, phone number, and other details match exactly across every platform. AI-driven algorithms look for consistent information Create AI-Friendly Content on Your Google Business Listing AI platforms look for specific, relevant answers to questions, so by tailoring your content accordingly, you can significantly improve your visibility. Remember that end-users ask AI platforms direct questions, so create content that answers these questions concisely. Remember that end-users ask AI platforms direct questions, so create content that answers these questions concisely. For instance, if you own a dental practice, you might look to ensure your Google Business listing Q&A section addresses questions like, “How often should I get my teeth cleaned?” or “What’s the best way to whiten teeth at home?” AI algorithms favor clear, straightforward answers, so make sure you provide them. Along those same lines, be mindful of “near me” and location-based phrases in your questions and answers. Many voice searches and AI-driven queries are location-based. Creating Q&A content, blog posts, service pages, or FAQ entries that reflect this intent—such as “quick oil change in (your city)” or “where to find quality computer repair near me”—can help capture local searches and boost visibility. Collect Reviews and Be Mindful of Your Online Reputation Reviews have always been valuable, but they’re even more important in the age of AI-driven search. Positive reviews increase trust, which are a factor in whether an AI platform recommends your business over others. Positive reviews on Google, Yelp, and other platforms increase your credibility and can boost your visibility in AI-driven searches, since the nature of review sites is such that it’s easy for an AI platform to understand. Engaging with customer reviews (both positive and negative) shows that your business is active and invested in customer satisfaction. AI algorithms value this engagement as a sign of trustworthiness and relevance, which can improve your position in search results. How to Update Your Website and Content So It’s Featured on AI Search While this is a little more technical, it’s critical to understand: to ensure your website is legible to the scrapers curating content for AI platforms, you need to ensure your page has properly-configured schema markup or structured data. Schema markup, also known as structured data, is a form of code you add to your website to provide search engines and AI-driven platforms with extra context about your business. Think of schema markup as a way of “tagging” specific details—like your location, services, hours, and even pricing—in a way that makes it easy for AI scrapers to understand and categorize your business. It takes the guess-work out and helps AI platforms quickly interpret what your business offers, thereby making it more likely you’ll show up in relevant results. If you use popular content management platforms like Wordpress, there are many guides to adding schema markup to your website by AIOSEO or you can engage the services of a consultant, though don’t be over-sold: it’s the kind of thing that should not be an expensive or time-intensive effort. guides to adding schema markup to your website by AIOSEO But if you’re the DIY type, once you get “under the hood” and find where to implement this, there are two best-practices to keep in mind: Implement Local Business Schema: Adding local business schema allows AI platforms to recognize important details about your business, such as your industry, address, hours, and service offerings. This structured information improves your chances of appearing in local voice search results and “near me” queries, putting you front and center when potential customers are looking for nearby solutions. Optimize for Common Queries: Schema markup can also help you target specific questions customers often ask. Think about the most common inquiries your business receives—questions like, “What are your sandwich rolls made from?” or “Do you offer same-day services?” Adding structured data for these queries makes your content more likely to appear in response when users ask an AI platform similar questions, increasing your visibility for search phrases that matter to your audience. Implement Local Business Schema : Adding local business schema allows AI platforms to recognize important details about your business, such as your industry, address, hours, and service offerings. This structured information improves your chances of appearing in local voice search results and “near me” queries, putting you front and center when potential customers are looking for nearby solutions. Implement Local Business Schema Optimize for Common Queries : Schema markup can also help you target specific questions customers often ask. Think about the most common inquiries your business receives—questions like, “What are your sandwich rolls made from?” or “Do you offer same-day services?” Adding structured data for these queries makes your content more likely to appear in response when users ask an AI platform similar questions, increasing your visibility for search phrases that matter to your audience. Optimize for Common Queries Using schema markup strategically allows you to target AI-driven platforms with precision, ensuring they pull the right information about your business to answer user questions accurately. By leveraging this structured approach, you can improve your chances of reaching customers in a way that’s both relevant and reliable. Extra Credit: Social Proof and Off-Page Signals While optimizing your website with structured data and schema markup enhances your visibility on AI platforms, there’s another essential piece to digital discoverability: social proof and off-page signals . These elements—things like customer reviews, mentions on other websites—provide AI platforms with valuable context about the credibility and popularity of your business. social proof and off-page signals Those off-page signals, like backlinks from news outlets that permit AI-scraping of their content, bolster your discoverability. When reputable sites link back to your website, it demonstrates credibility and authority, things AI scrapers consider. Future-Proofing Your Online Footprint The truth is this article, while accurate for how to drive AI visibility, also contains universal truths that will help bolster your small business visibility everywhere your users spend time online , including search and other platforms, too. That’s something we call a “holistic approach to digital discovery.” universal truths that will help bolster your small business visibility everywhere your users spend time online Done properly, these steps won’t just boost your current visibility—it lays the groundwork for long-term relevance and growth as AI and other technologies continue to evolve. current

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