Branding \u0026 Design Consultancy Agency

CCS Branding

From the perspective of a founder/engineer turned investor* Art by Jonathan Vermersch * Many of our friends ask us about how we identify solid projects. We decided to share a brief due diligence about some of the ones that we check out (or invest). We hope that this will help create a healthier investment ecosystem. One of the main challenges that and the other cryptocurrencies are facing is . bitcoin user adoption Most merchants don’t believe in crypto (yet), or are slow to adopt because of friction. To accept , merchants need to change their POS, create wallets, etc. cryptocurrency At the same time, as the team explained us, the overall cost to a is a sum of fees, fraud and overhead to manage which accounts up to 15%. merchant on CCs The team at created a protocol and payment app that enables us to use crypto currencies for our daily transactions in an easy way. Flexa Flexa is not a blockchain project. It’s an infustracture play that merges existing payment networks with blockchains (cryptocurrencies). Paying for my coffee with bitcoin earlier this morning How it works First of all, there is no need for the merchant to install any new hardware or software. The merchant only needs to allow a new payment provider (Flexa) onto their payment channels. This technology works directly with their existing POS. Now, let’s say that you want to buy a coffee from Starbucks and want to pay with BTC. Open the payment Flexa app and select your merchant, let’s say Starbucks. . A unique QR payment code is generated through the Flexa Protocol (FNP), which authorizes a certain amount of your total payment asset (let’s assume you hold BTC in this wallet). Step 1 . When you scan the QR code to the POS, the FNP asks the App to lock FNP collateral equal to the payment amount, in the form of FlexaCoin. This happens in milliseconds and ensures zero risk for the FNP. There is no risk because the developer has to stake the amount in the form of FlexaCoin. Step 2a . In parallel, the app subtracts the same amount of BTC from the user’s wallet. Remember, BTC can take up to 10 minutes to arrive. However, the payment needs to be done in milliseconds. Step 2b . When the collateral is locked, the FPN authorizes a direct payment from FPN’s bank account to the merchant’s bank account. The merchant gets paid instantly (with zero credit risks and zero fees). Step 3 . Once the crypto is cleared from the wallet into the FNP’s wallet, the collateral is returned to the developer’s account. Step 4 Both the consumer and the merchant don’t see any of the above steps; they don’t even interact with FlexaCoin. We’ve seen on other projects that tokens are designed in a way that adds complexity to the users. In this case, the users pay with their cryptocurrency, and the merchants get dollars (or any other fiat currency). The above design gives zero credit risk for both merchant and FNP. The way it achieves this, is by using the FlexaCoin as collateral for every merchant payment and direct banking payment. The result: no friction for merchants equals better user adoption. Also, the above design gives 0 credit risk for both merchant and FNP and the way it achieves it is by using the FlexaCoin as collateral for every payment and direct banking payments. Challenges One of the challenges that the Flexa Network is probably going to face is that the price of the FlexaCoin (that is used as collateral) and bitcoin need to have very low volatility at the time that the transaction takes place. Another challenge is that the ERC-20 choice as an underlying protocol will cause slow transaction speed on high volume transaction rates. Also, the fast growing market of crypto owners prefers to stake or speculate their crypto, mainly due to price volatility. Why give away something that will appreciate in value, in just a few days? Please note all opinions in this post are my own. Nothing in this post is intended to serve as investment advice.

Pay for coffee with bitcoin: Flexa case

One of the most powerful applications of the Internet of Things (IoT) is wearable technology, carrying the potential to positively transform the way many sectors conduct business. Smartwatches, VR headsets, and other wearable devices are already gaining significant popularity with both consumers and companies. In fact, technology analysis firm CCS Insight predicts that an impressive 96 million wearables will be sold in 2017, at a value of $10.8 billion, which will increase to 185 million devices by 2021, bringing in $16.9 billion in revenue. Apparently, interest in wearable devices is exploding rapidly, with no signs of slowing down in the coming years. While many people are focused on consumer devices such as the Apple Watch, Fitbit, and VR headsets for gaming, like the Oculus Rift, a growing number of businesses now recognize the potential for this technology to be effectively used to enhance the customer experience and increase revenue. An excellent example of this concept being put into practice comes from travel company Carnival. It created the “Ocean Medallion,” which is a small token that can be worn on a wristband or chain. The intention is for this device to replace smartphones and credit cards once a consumer boards one of the company’s cruise ships, as the token can be used in a multitude of ways, including checking in and paying for goods and services aboard the boat. IoT is a fundamental component of this technology, with the token alerting staff when a customer approaches the restaurant where they have pre-ordered their meal, allowing the company to correctly anticipate their arrival to provide a personalized greeting and immediate seating. Carnival also uses the device to alert the housekeeping team when guests leave their cabin, allowing staff to do their job without disturbing customers whatsoever. And how about real estate? Some firms have embraced wearables to provide consumers with the ability to view properties from the comfort of their own home using a pair of VR goggles, truly making it feel like they are walking through the rooms without having to actually visit the property. This virtual viewing saves a massive amount of time for both interested buyers and real estate agents. The aforementioned business applications of wearable technology are only two of many current examples. The Internet of Things is already prevalent in countless aspects of our daily lives, and wearables are the perfect companion to help immerse this technology deeper into the fabric of our world, connecting devices to objects seamlessly to improve the customer experience while increasing revenue in a broad range of industries. Written by Igor Ilunin, head of IoT at DataArt .

Wearable Technology Driving Innovation in Business

Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References ABSTRACT Large language model (LLM) prompting is a promising new approach for users to create and customize their own chatbots. However, current methods for steering a chatbot’s outputs, such as prompt engineering and fine-tuning, do not support users in converting their natural feedback on the model’s outputs to changes in the prompt or model. In this work, we explore how to enable users to interactively refine model outputs through their feedback, by helping them convert their feedback into a set of principles (i.e. a constitution) that dictate the model’s behavior. From a formative study, we (1) found that users needed support converting their feedback into principles for the chatbot and (2) classified the different principle types desired by users. Inspired by these findings, we developed ConstitutionMaker, an interactive tool for converting user feedback into principles, to steer LLM-based chatbots. With ConstitutionMaker, users can provide either positive or negative feedback in natural language, select auto-generated feedback, or rewrite the chatbot’s response; each mode of feedback automatically generates a principle that is inserted into the chatbot’s prompt. In a user study with 14 participants, we compare ConstitutionMaker to an ablated version, where users write their own principles. With ConstitutionMaker, participants felt that their principles could better guide the chatbot, that they could more easily convert their feedback into principles, and that they could write principles more efficiently, with less mental demand. ConstitutionMaker helped users identify ways to improve the chatbot, formulate their intuitive responses to the model into feedback, and convert this feedback into specific and clear principles. Together, these findings inform future tools that support the interactive critiquing of LLM outputs. CCS CONCEPTS → • Human-centered computing → Empirical studies in HCI; Interactive systems and tools; • Computing methodologies Machine learning. KEYWORDS Large Language Models, Conversational AI, Interactive Critique 1 INTRODUCTION Large language models (LLMs) can be applied to a wide range of problems, ranging from creative writing assistance [8, 26, 36, 44] to code synthesis [13, 14, 20]. Users currently customize these models to specific tasks through strategies such as prompt engineering [4], parameter-efficient tuning [19], and fine-tuning [10]. In addition to these common methods for customizing LLMs, recent work has shown that users would also like to directly steer these models with natural language feedback (Figure 2A). More specifically, some users want to be able to critique the model’s outputs to specify how they should be different [5]. We call this customization strategy interactive critique. When interacting with a chatbot like ChatGPT[1] [28] or Bard[2], interactive critique will often alter the chatbot’s subsequent responses to conform to the critique. However, these changes are not persistent: users must repeat these instructions during each new interaction with the model. Users must also be aware that they can actually alter the model’s behavior in this way, and must formulate their critique in a way that is likely to lead to changes in the model’s future responses. Given the potential value of this mode of customizing there is an opportunity to provide first-class support for empowering users to customize LLMs via natural language critique. In the context of model customization, Constitutional AI [1] offers a specific customization strategy involving natural language principles. A principle can be thought of as a rule that the language model should follow, such as, “Do not create harmful, sexist, or racist content”. Given a set of principles, a Constitutional AI system will 1) rewrite model responses that violate principles and 2) fine-tune the model with the rewritten responses. Returning to the notion of interactive critique, one can imagine deriving new or refined Constitutional AI principles from users’ critiques. These derived principles could then be used to alter an LLM’s prompt (Figure 2B) or to generate new training data, as in the original Constitutional AI work. While this recent work has shown principles can be an explainable and effective strategy to customize an LLM, little is known about the human processes of writing these principles from our feedback. From a formative study, we discovered that there are many cognitive challenges involved in converting critiques into principles. To address these challenges, we present ConstitutionMaker, an interactive critique system that transforms users’ model critiques into principles that refine the model’s behavior. ConstitutionMaker generates three candidate responses at each conversational turn. In addition to these three candidate responses, ConstitutionMaker provides three principle-elicitation features: 1) kudos, where users can provide positive feedback for a response, 2) critique, where users can provide negative feedback for a response, and 3) rewrite, where users can rewrite a given response. From this feedback, ConstitutionMaker infers a principle, which is incorporated in the chatbot’s prompt. To evaluate how well ConstitutionMaker helps users write principles, we conducted a within-subjects user study with 14 industry professionals familiar with prompting. Participants used ConstitutionMaker and an ablated version that lacked the multiple candidate responses and the principle-elicitation features. In both cases, their goal was to write principles to customize two chatbots. From the study, we found that the two different versions yielded very different workflows. With the ablated version, participants only wrote principles when the bot deviated quite a bit from their expectations, resulting in significantly fewer principles being written, in total. In contrast, in the ConstitutionMaker condition, participants engaged in a workflow where they scanned the multiple candidate responses and gave kudos to their favorite response, leading to more principles overall. These different workflows also yielded condition-specific challenges in writing principles. With the ablated version, users would often under-specify principles; whereas, with ConstitutionMaker, users sometimes overspecified their principles, though this occurred less often. Finally, both conditions would sometimes lead to an issue where two or more of the principles were in conflict with one another. Overall, with ConstitutionMaker, participants felt that their principles could better guide the chatbot, that they could more easily convert their feedback into principles, and that they could write principles more efficiently, with less mental demand. ConstitutionMaker also supported their thought processes as they wrote principles by helping participants 1) recognize ways responses could be better through the multiple candidate responses, 2) convert their intuition on why they liked or disliked a response into verbal feedback, and 3) phrase this feedback as a specific principle. Collectively, this work makes the following contributions: • A classification of the kinds of principles participants want to write to steer chatbot behavior. • The design of ConstitutionMaker, an interactive tool for converting user feedback into principles to steer chatbot behavior. ConstitutionMaker introduces three novel principleelicitation features: kudos, critique, and rewrite, which each generate a principle that is inserted into the chatbot’s prompt. • Findings from a 14-participant user study, where participants felt that ConstitutionMaker enabled them to 1) write principles that better guide the chatbot, 2) convert their feedback into principles more easily, and 3) write principles more efficiently, with less mental demand. • We describe how ConstitutionMaker supported participants’ thought processes, including helping them identify ways to improve responses, convert their intuition into natural language feedback, and phrase their feedback as specific principles. We also describe how the different workflows enabled by the two systems led to different challenges in writing principles and the limits of principles. Together, these findings inform future tools for interactively refining LLM outputs via interactive critique. [1] https://chat.openai.com/ [2] https://bard.google.com This paper is under CC 4.0 license. available on arxiv

Interactively Critiquing Large Language Models by Converting Feedback into Principles 

মতামতকে নীতিতে রূপান্তর করে ইন্টারেক্টিভভাবে বড় ভাষার মডেলের সমালোচনা করা

Interaktive Kritik großer Sprachmodelle durch Umwandlung von Feedback in Prinzipien

Criticar interactivamente grandes modelos de lenguaje convirtiendo la retroalimentación en principios

Critique interactive de grands modèles de langage en convertissant les commentaires en principes

फीडबैक को सिद्धांतों में परिवर्तित करके बड़े भाषा मॉडलों की अंतःक्रियात्मक रूप से आलोचना करना

フィードバックを原則に変換することで大規模な言語モデルを対話的に批判する

피드백을 원칙으로 변환하여 대규모 언어 모델을 대화형으로 비판

Crítica interativa de grandes modelos de linguagem, convertendo feedback em princípios

Интерактивная критика больших языковых моделей путем преобразования обратной связи в принципы

Geri Bildirimi İlkelere Dönüştürerek Büyük Dil Modellerini Etkileşimli Olarak Eleştirmek

Phê bình một cách tương tác các mô hình ngôn ngữ lớn bằng cách chuyển đổi phản hồi thành nguyên tắc

通过将反馈转化为原则来交互式批评大型语言模型

Authors: (1) Yuwen Lu, he contributed equally to this work, University of Notre Dame, USA; (2) Chao Zhang, he contributed equally to this work and work done as a visiting researcher at the University of Notre Dame; (3) Yuewen Yang, Work done as a visiting researcher at the University of Notre Dame; (4) Yaxin Yao, Virginia Tech, USA; (5) Toby Jia-Jun Li, University of Notre Dame, USA. Table of Links Introduction Background and Related Work Co-Design Workshops Technology Probe Study Results Scaling Up: A Research Agenda Limitations and Future Work Conclusions and References Appendix The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer’s perspective on education and advocacy for ethical designs; and (2) the policymaker’s perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users’ needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users’ awareness of and action on dark patterns being empowered in a realistic in-situ setting. CCS Concepts: • Human-centered computing → HCI theory, concepts and models; Empirical studies in interaction design. Additional Key Words and Phrases: dark patterns, user experience, design ethics, end-user empowerment, web augmentation ACM Reference Format: Yuwen Lu, Chao Zhang, Yuewen Yang, Yaxing Yao, and Toby Jia-Jun Li. 2023. From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX. Proc. ACM Hum.-Comput. Interact. 6, CSCW, Article 1 (November 2023), 41 pages. https://doi.org/XXXXXXX.XXXXXXX 1 INTRODUCTION Dark patterns [39] are user interface design choices that lead to certain decisions users might not otherwise make, often for the purpose of benefiting an online service [38, 40, 92]. Such dark patterns often result in user behaviors that are against the best interests of users e.g., pressured selling, video binge-watching, giving up personal data, and installing applications that they do not need [19, 26, 81]. Dark patterns vary in their complexity and impact. Ranging from subtle manipulative nudges [46] like non-consensual additions to shopping carts [82] and prioritized options [101], to overt deception like alarming virus alerts, these patterns have led to adverse user impacts, raising public awareness and prompting regulation [32, 127]. Most current efforts in addressing dark patterns take (1) the designer’s perspective on education and advocacy for ethical designs [38, 42, 89]; and (2) the policymaker’s perspective on new regulations [7, 65, 80]. For example, a CHI 2021 workshop titled “What can CHI do about dark patterns? [74]” was held to discuss how designers can address dark patterns and what changes designers can advocate for through interactions with stakeholders. Another instance is an act named “Deceptive Experiences To Online Users Reduction (DETOUR)” introduced to prohibit large online platforms from using dark patterns [32, 127]. However, these efforts often fell short of fully utilizing the autonomy of end users in self-protection [2, 13]. End users have strong incentives and the desire to protect themselves from online threats, but often lack the capacity and associated support [53, 75, 112, 136]. Moreover, dark patterns are generative and shapeshifting, thus will continuously evolve, making it difficult to fully define and regulate through policies. Therefore, to complement previous efforts, we take on an end-user-empowerment orientation in this paper to explore the design of interventions for dark patterns considering the autonomy of end users. Guided by the Protection-Motivation Theory (PMT) [106], we coined two types of intervention for our end-user-empowerment approach, targeting users’ awareness and action. First, we enhance awareness by increasing transparency about the presence and impacts of dark patterns. Second, we enable users to take action against dark patterns, as previous studies have shown that awareness alone is not sufficient [11]. We employ a web augmentation approach, allowing users to select between pre-defined UI enhancements to dark patterns according to their preferences. We also propose a Design-Behavior-Outcome framework, to map out the design space for UI enhancements in user action. This framework situates individual intervention techniques (e.g., hiding, disabling, friction, etc.) from previous work [11, 13, 17, 34, 57, 75, 89, 126] at different interaction phases between users and dark patterns. The resulting UI enhancements can change interface designs and user flows, or evoke users to reflect on the consequences caused by dark patterns. To explore the design of our end-user-empowerment intervention for UX dark patterns, we conducted a two-phase co-design study. The first phase was five exploratory co-design workshops with 12 participants. We investigated user needs, challenges, and preferences in handling UX dark patterns. Through the workshops, we found that users have the desire to actively learn about dark patterns’ impact, and their perceptions and coping mechanisms of dark patterns are individualized and dynamically changing. They also expect to be able to counteract dark patterns by changing interfaces, adjusting user flows, and reflecting on behavioral outcomes. Informed by the results of the first phase workshops, we further curated and deployed a technology probe study with 15 new participants for two weeks. The probe study aims to contextualize users in their everyday experience, investigate their feedback towards our approach throughout 2 weeks, and elicit more design implications for future end-user-empowerment interventions. We materialized our awareness and action interventions as a probe named Dark Pita[1] in the form of a browser extension against a representative sample of dark patterns in popular online services. The results showed that with our end-user empowerment approach, users gained transferable knowledge about dark patterns, felt empowered with autonomy over UIs, and chose UI enhancements to act against undesired dark patterns based on their dynamic, contextualized goals on different platforms. Although the current version of Dark Pita is limited to handling a small sample of dark patterns with hand-crafted design enhancements, it exemplifies a new bottom-up end-user-empowerment approach. The study findings confirmed the effectiveness and presented useful design implications. The paper also outlines a research roadmap towards scaling up our approach with development in user behavior modeling, interface semantic understanding, and citizen science platforms. We end this paper with a discussion on how the end-user-empowerment approach connects to the ongoing efforts in policy-making and advocacy for design ethics. In summary, this paper makes the following contributions. (1) A novel end-user-empowerment intervention approach for counteracting dark patterns in UX by enabling the end users of interfaces to recognize, understand, and take action upon dark patterns, including raising the transparency of dark patterns’ presence and impacts; and modifying dark patterns by switching between UI enhancements according to their own personal preferences and goals. (2) Findings and design implications from a two-phase co-design study consisting of 5 co-design workshops (N=12) to explore users’ underlying needs, preferences, and challenges in handling dark patterns; and a 2-week technology probe study (N=15) to investigate users’ feedback and reactions to their awareness of and action on dark patterns being empowered in an everyday setting. (3) An agenda for the research community to scale up this approach and deploy it in conjunction with ongoing efforts in crowd-sourced collective intelligence, citizen science, machine learning, policy making, and advocacy for design ethics. [1] Dark Pita is an acronym for Dark Pattern Intervention for Transparency, and Accountability. This paper is under CC 4.0 license. available on arxiv

Exploring End-User Empowerment Interventions for Dark Patterns in UX

GHG-removal is a $10trln annual market opportunity that is yet to be tapped by new investors, innovators and entrepreneurs. It would be fair to admit that there have been some positive signals in the GHG-emissions problem resolution recently. European climate law increased the EU-goal to cut emission by 55% by 2030 (compared to 2005) instead of former 40%. US Inflation Reduction Act aims to raise almost $800bln towards things like energy security and climate change programs over the next ten years. There’s an increasing global adoption of the Carbon Boarder Adjustment Mechanism that would touch almost all the countries that want to trade with the US, EU, Canada, or Japan. The list goes on… But UN says that despite all the changes, humanity is the set targets, even those that governments set themselves (like Nationally Determined Contributions). still missing Even if we were on track to meet the targets set, it would still to prevent the environmental catastrophe. be not enough Unfortunately, it seems, the situation is even worse. The point is that all the targets focus on preventing the new emissions, that are being produced each year, that, after showing a by 6,4% (or 2,5bln tons) in 2020 (the largest-ever historical decline as the pandemic halted global economic activity) – stroke a new record this year (reaching (GT)). slight decrease 58 gigatons But what about the GHG-emissions, that have already accumulated in the atmosphere? That are not dissipating on their own due to the exceeded natural abilities of Earth of doing it itself? According to (2022.11.20) - the atmospheric CO2 concentration increased 2.5 parts per million (ppm) in 2021 and is projected to increase by around 2.5ppm in 2022, resulting in global atmospheric concentrations of 417.2 ppm on average for the year. This represents an increase in atmospheric CO2 of around 51%, relative to pre-industrial levels. World Economic Forum But what exactly are the preindustrial levels? (2022.06.23) states that before the Industrial Revolution started in the mid-1700s, atmospheric carbon dioxide was 280 ppm or less. That means that currently there's an 137.2 ppm excess. Climate.gov So, how does this figure compare to the emissions we produce every year? To calculate this we'll need to use the conversion formula from the - each part per million of CO2 in the atmosphere represents approximately 2.13 gigatonnes of carbon, or 7.82 gigatonnes of CO2. Carbon Dioxide Information Analyses Center That means that the overall excessive amount of the CO2 accumulated to this moment is 137.2 * 7.82 = 1 072,904 gigatons of CO2 (so, about 20 times more, that we've produced only this year). What's more, according to – In 2016 CO2 was accountable only for 74,4% of total GHG-emissions worldwide (the others being: methane – 17.3%; nitrous oxide – 6.2%; and other emissions (F-gases – HFCs, CFCs, SF6) – 2.1%). Using this proportion we could add 34,4% on top of the CO2-emissions to approximately calculate the overall amount of accumulated GHG-emissions – 1 442 gigatons of CO2 equivalent (CO2e). Our World in Data To understand even further how big the problem is, let's use the most common tool to do it - money. The price of emissions There are different ways to calculate the carbon price, but most usually the price, used in the emissions trading systems is used. The price of emissions allowances (EUA) traded on the European Union's Emissions Trading System (ETS) was 85.22 euros per metric ton of carbon dioxide on . The highest carbon price during 2022 was on August 19, at 98.01 euros per metric ton. A study published by (2022.09.01) found that the USA's value of one tonne of carbon dioxide should be $185 – almost four times the current cost of $51. December 1, 2022 Nature So, if we use these figures to calculate how much we need to pay to remove all that excessive GHG-emissions out of the air we'll get $73,55 - $266,78 trillion dollars needed to do this (again, so that you can imagine how big these figures are, compare it to the global GDP – in 2022 it is predicted to be . $103.86trln So, it seems to be impossible to remove all those GHG-emissions in an instant, but we still understand that we have to do it till 2030, or at least 2050 (to prevent the catastrophe). If we choose the latter, then the average annual price (market volume) would be from $2,63 to $9,53 trillion dollars. What about the future? (2018.08.27) predicts that by 2050 CO2 levels will reach 550ppm in a business as usual scenario and (2022.06.23) estimates that by 2100 we could reach even 800ppm levels. Carbonbrief.org Climate.gov If current economic growth, demography, and emissions intensity trends continue, the level of annual emissions will continue to rise, reaching . 62 GT by 2030 Why bother? Global GDP could drop by 18% over the next 30 years in the case of non-action on climate change, . 200 of the largest global companies that they will collectively incur $1T in climate change-related costs if they do not take measures to address it at present. according to Swiss Re estimate But that's only if we don't do anything, and we are doing a lot of things, at least something, right? Like renewable energy, forcing the main pollutants to abate their emissions, at least partially, with taxes and limited carbon allowances, increasing the regulatory scrutiny with reporting and etc?.. Well, yes, of course. But those things have their limits. For instance, the share of U.S. power generation from renewables is going to increase only from . Plus, there are “hard-to-abate” industries that account for more than of global emissions. 21% in 2021 to 44% in 2050 30% So, that leaves approximately half of the newly produced emissions by 2050 to be removed, meaning that the emissions removal market is not going to drop at least by 2050. How much market is already taken? Almost none. , that, probably, we capture less than 0,1% of GHG-emissions we produce. That leaves a great potential for creative minds, great engineers to focus their attention on tackling these challenges - and that means, innovations, investments and business! Experts say What are the main GHG-removal technologies? names the following GHG-removal methods: World Resources Institute natural strategies like tree restoration and agricultural soil management; high-tech strategies like direct air capture and enhanced mineralization; hybrid strategies like enhanced root crops, bioenergy with carbon capture and storage, and ocean-based carbon removal expands it like this: American University planting massive new forests (afforestation/reforestation) using no-till agriculture and other practices to increase the amount of carbon stored in soils (soil carbon sequestration) creating charcoal and burying it or plowing it into fields (biochar) capturing and sequestering carbon from biofuels and bioenergy plants (bioenergy with CCS or BECCS) spreading crushed rocks over land to absorb carbon dioxide from the air or exposing them to carbon dioxide-rich fluids (enhanced mineralization) building machines that would suck carbon dioxide directly out of the atmosphere and bury it (direct air capture)oceans-based methods, including: spreading alkaline materials, such as lime, over the ocean (ocean alkalinization) fertilizing selected areas of the ocean by spreading nutrients, such as iron, over the surface (ocean fertilization), fertilizing selected areas of the ocean by pumping nutrient-rich waters from the depths to the surface (artificial upwelling), accelerating the transport of carbon to the ocean depths by pumping surface waters downward (artificial downwelling) And where do corporations invest? First, it’s carbon utilization. When carbon is not only removed, but also utilized to some useful purpose – like fuels construction or other made from emissions (i.e. LanzaTech (nearly 2bln valuation) and Solidia (nearly 1bln valuation), Pure Earth (Nasdaq acquired a majority stake. Besides utilization does the direct capture and storage, sells the CO2 removal certificates), Twelve ($200mln of investment)). And then we have a lot of corporations investing in carbon removal in this or that way. In April 2022 Stripe launched a . It’s been funded as well by Alphabet, Shopify, Meta, McKinsey, and tens of thousands of businesses using Stripe Climate. The company has committed to buy an initial $925M of permanent carbon removal by 2030. It has now contracted to buy non "conventional" carbon removal from 14 different start-ups. It’s crucial to notice that it applies an intentionally non-efficient approach to buying carbon removal. On average, Stripe paid a “couple hundred dollars a ton” to remove carbon dioxide, but its purchases ranged from $75 to $2,052 a ton. That’s because they want to help startups to invent new ways with the high scaling potential in the future! Frontier Climate At COP26 there’s was a launched. Alphabet, Microsoft, and Salesforce pledged $500 million to new climate tech that’s supposed to pull carbon dioxide out of the atmosphere to keep it from heating up the planet (like the Frontier – through advance purchase commitments by 2030). It aims to tackle hard-to-abate emissions, to decarbonize the heavy industry and long-distance transport sectors responsible for 30% of global emissions. More than 50 companies have joined, including: Amazon, Apple, Airbus, BCG, Bank of America, Delta, Deloitte, Ford, FedEx, Nokia, SAP, Scania, DHL, EY, PWC, Volvo, Western Digital, Bain & Company, Boeing, Schneider Electric and Maersk. First Movers Coalition In addition, Microsoft launched a (that invests in carbon reduction and removal tech). So far, they’ve invested in (planting trees); - sustainable agriculture); Moreover, Microsoft has purchased of metric tons of carbon offsets from 15 organizations in FY 2021, and is going to increase the volume by 5mln gradually by 2030. $1B Climate Innovation Fund NCX ReGrow 1.3 millions Amazon has its own $2Bln . Has made 20 investments so far, including (planting trees) and - controlled environment agriculture. Additionally, Amazon has a $100M Right Now Climate Fund (that, among others, invests in restoring forests). Climate Pledge Fund Pachama Hippo Harvest Apple has a $200M fund (with Goldman Sachs and Conservation International) to invest in planting forests and other carbon removal initiatives. Climate tech funding in 2022 more than a quarter of every venture dollar invested in 2022 with US$15-20 billion range per quarter. In 2021, start-ups targeting sectors that are responsible for 85% of emissions attracted just 39% of investment. In 2022, start-ups in those sectors attracted 52% of climate tech investment. represented But, of course, that’s only the beginning. Investments would have to rise to tens or even hundreds of billions, we’ll see many gigacorns (startups that have a $1bln valuation but at the same time remove 1 gigaton of GHG), and, who knows, maybe even a $1trln company – not an unfair price for saving the world, right? :)

GHG-Removal Could Be the Biggest Market of the 21st Century

La eliminación de GEI podría ser el mercado más grande del siglo XXI

L'élimination des GES pourrait être le plus grand marché du 21e siècle

जीएचजी-रिमूवल 21वीं सदी का सबसे बड़ा बाजार हो सकता है

GHG 除去は 21 世紀最大の市場になる可能性がある

A remoção de GEE pode ser o maior mercado do século XXI

Loại bỏ GHG có thể là thị trường lớn nhất của thế kỷ 21

温室气体排放可能成为 21 世纪最大的市场

With almost 34% of the DEX market choosing to trade via Uniswap or PancakeSwap, we are taking a look at some of the alternatives out there, and why looking outside the big two might prove valuable unlocked the gates to decentralized finance (DeFi) by offering its user-centric take on an automated market maker (AMM) in 2018; powered by the Ethereum ecosystem. Uniswap Since then, it climbed its way to the top of the DEX (Decentralized Exchange) tree in 2020. All was not well however, as high gas fees on the Ethereum network began pushing the average user to more affordable alternatives. Step forward . Pancakeswap Leveraging the BSC (Binance Smart Chain) to reduce fees commonly seen on the Ethereum-based exchanges like Uniswap, and tapping into the craze for cute interfaces, it rapidly climbed in popularity and competed with UniSwap for liquidity domination. While these two platforms have been battling it out for dominance, a new breed of decentralized exchanges and aggregators have been quietly building in popularity. By offering greater pricing visibility, increased liquidity and enhanced yield generating potential, these new platforms are beginning to offer users a significant amount of choice when it comes to DEX and DeFi opportunities. What are the benefits of looking outside the top two? -  DEX aggregators are able to source pricing data from a number of different DEXs to find the best possible price for a specific token or trading pair Price - Higher APY attracts more users, thus higher liquidity and lower APY over time. Looking to yield farm outside of the big two provides greater opportunities to make high APY for longer Yield - Some of the new DEXs are offering hard-capped token burning guarantees; meaning their native tokens will continue to rise in value due to their finite volume. Tokenomics - Finding price deltas between DEXs and making profit on trading these differences Arbitrage Taking a look at the competition - is a popular UniSwap fork, and therefore built on the Ethereum network. It offers an automated market-making (AMM) model that allows users to trade, and earn yields in the form of trading fees and SUSHI tokens. SushiSwap’s native token is SUSHI, and this acts as it’s governance token. SushiSwap SushiSwap Many UniSwap users began moving liquidity across to SushiSwap from launch. This was mainly because Uniswap had already completed certain key token distribution benefits that SushiSwap was able to continue supplying, making it a more lucrative choice. This is often seen as a benefit to new exchanges - i.e. they can offer greater rewards to users as they fight for market share. - provides DEX aggregation, as opposed to providing strictly DEX services itself. It collects and measures liquidity across numerous liquidity pools, trading platforms, and exchanges to find the best trade possible. BY being able to access the entire DeFi marketplace, 1Inch is able to reduce fees and slippage for users, whilst providing visibility of market positions. 1inch 1inch Aggregation platforms like this provide excellent opportunities for arbitrage; effectively finding pricing differences between multiple exchanges and finding profit margins by trading these differences. - The Binance Smart Chain (BSC) has offered a fresh perspective on DEX trading by significantly reducing fees, while increasing translation speeds. leverage the capabilities of this new chain in a unique way: not only is it handsomely rewarding its users and community through utility on its native token CSS, it also offers NFT (non-fungible token) marketplaces. CoinSwap Space CoinSwap The CoinSwap team has also set out to dominate on tokenomics. The CCS token will have a hard-cap, meaning users earning and trading in CSS will continue to see scarcity and value increase as time goes on - as there is only a limited supply. Coupled with intentionally lower fees than other BSC competitors, we could see this platform make some significant growth over the coming months ParaSwap is another aggregator which pulls together a number of popular DEXs (Decentralized Exchanges) and DeFi platform pricing data. Once again, one of the key benefits of this type of platform is the ability to leverage pricing differences across multiple networks to make a margin. ParaSwap As ParaSwap acts as a middle layer between DeFi liquidity protocols and users, it is capable of delivering some of the fastest trades available right now across the Ethereum ecosystem. Matcha is an aggregated trading platform powered by . By aggregating liquidity across numerous decentralized platforms (currently, users can trade across 26 separate exchanges like 0x, Curve, and Uniswap) Matcha allows users to easily route orders and find optimal pricing. Matcha 0x Matcha is a great entry point into the DEX and DeFi ecosystems, as it provides users with a simple to understand and intuitive guide to each trade they make. This is really useful to gain clarity on things like detailed order review, estimated slippage, and transaction time estimations. Conclusion however, there are a number of platforms now available to those willing to seek them out, which could offer a lot more bang for your buck. UniSwap and PancakeSwap continue to make headlines Whether that’s venturing deeper in the BSC ecosystem to find reduced gas fees, better yields and faster transaction times, or exploring the world of aggregators to find valuable price deltas and huge marketplace scale, there are many more opportunities now then there were even three months ago.

Which DEXs Stand The Best Chance of Besting The Uniswap and Pancakeswap Dominance?

Analysis of transaction fees has long been overlooked in the area, with the first papers in the academic literature being published from 2014 (Houy) onwards. blockchain It has still not been a research priority considering that only a few papers have been dedicated to this field. But transaction fees should not be neglected for at least two reasons. The first one relates to their importance in maintaining security incentives in a Proof-of-Work context. The second reason pertains to the very nature of the economic relationship between users, miners, and the protocol itself, as we will see below. As of today, the block reward is sufficient to keep miners’ incentives aligned: they earn enough money to maintain the network secure. However, the way transaction fees are designed is critical when the block reward tends toward zero, or, as we will see, when the network has a large throughput. We provide in this article a brief literature review, and we also describe our view of a possible design for an efficient transaction market. In Bitcoin, transaction fees are set by users. Any user can theoretically set the fee at zero, knowing that her transaction will probably be delayed if others choose a positive value for their fees. Indeed, being rational agents, miners will always prioritize the transactions with the highest fees. Transactions that are verified but not yet processed in the coming block (i.e. confirmed) are put together in a ‘waiting room’ called the mempool. Since each block has a limited size, users thus compete for space (Huberman et al., 2017; Dimitri, 2019) in each block, so that the fees reach an equilibrium price for each time-interval. Transactions that are not processed are left in the mempool, waiting for the next blocks to be validated by miners. However, Bitcoin-like blockchains do not come without flaws when viewed from a transaction fee perspective. Firstly, there is no priority given to the transaction (Aune et al., 2017) like in any normal queuing system based on standard “first-come, first-served” service delivery. Priority is advertised by users with by proposing a higher than average transaction fee. Miners usually just pick the transactions that advertise the highest fee. Secondly, fees may be too low to cover the losses incurred by mining pools, particularly when the block reward tends to zero (Carlsten et al., 2016; Huberman et al., 2017). Following the same logic, fees might become too high for a day-to-day user of the network to compensate fully for his or her loss of the block reward (Easley et al., 2019). Therefore, the small block size imposes a high risk of congestion in the system, when transactions are flowing into the mempool more rapidly than they are validated by miners. Waiting time leads to congestion in the network, thus to higher fees. On the one hand, congestion of the increases the incentives for miners to allocate their computing power to particular users on that blockchain, and therefore undermine consistent security. On the other hand, it creates delays in transaction processing which may deter some users from using the network at all. In the long run, Bitcoin might need high transaction fees in order to function both properly and securely (Lavi et al., 2017), an outcome which may tend to defeat the purpose of a global payment system. In this context, studying the incentives behind PoW networks’ transaction fees might be important for optimizing future designs. Letting users choose their own transaction fees might not be an optimal solution. Is there an equilibrium price for which these fees are sufficient to ensure the security of the network, regardless of how much miners are at that time earning from minting new coins? In current PoW blockchains, the answer is clearly no. Block size is limiting the number of transactions that can be processed for each time-interval. In sum, users shouldn’t be allowed to privately set their own fee, in order to avoid a typical free-rider problem (Auer, 2019). Fees are set up individually, while transactions are bulked together into the next block. This ensures the same level of security for all transactions in the block, regardless of the level of the fees taken individually. Put it simply, user A has an incentive to “free-ride”, i.e. posting lower fees until his or her transaction is included within the block, hence piggy-backing on other users who advertise higher transaction fees. The direct consequence is a decrease in the system’s security, if everyone behaves like user A, by reducing transaction fees to the point that it is no longer economically viable for miners to confirm transactions. We can solve most of these issues by I) having a much higher throughput than any other PoW blockchain-based networks, and II) adopting a different approach to transaction fees, in order to reap the full benefits of network effects. We will next consider the second point. Even if having a high throughput decentralized transaction network solves the scalability trilemma, it brings about other problems. In most papers arguing for a change in PoW consensus protocols, auction theory is used to optimize the transaction fees (Lavi et al., 2017). Another approach is to link fees with the amount of storage space secured by miners (Chepurnoy et al., 2019). Although these solutions could work in environments where users compete for block space, it is unlikely that they could work in ours. Indeed, our network’s ability to process a lot of transactions would push transaction fees towards zero, if fees were to be selected through any type of auctions. If left to market forces, users, knowing that their transaction will be processed, would collectively choose an optimal fee that tends toward zero, as any game theoretic analysis would suggest. Therefore, auction-style transaction fees need to be ruled out. Another point to consider is the influence of cross-side network effects. The protocol can be viewed as a platform where supply and demand for global transactions meet. Miners (i.e. supply) want high transaction fees, while users want low ones. This conflict of interests must be reflected not only in the price, but also within its structure (Rochet & Tirole, 2006)[1]. This is a well-known problem in so-called two-sided markets. As we already mentioned, in Bitcoin’s present design, users are more or less free to determine their own fees . Bitcoin Competing for block space and the risk of free-riding network Possible solutions and the road ahead In the current system, interests are not aligned, since users will tend to free-ride, thus negatively affecting the overall security of the protocol.  In our view, the fee should follow a more market-based mechanism. The price function fluctuates according to the supply and demand of transactions, without the user being able to set up a lower fee, provided that he is ready to wait for his transaction to being processed. Behind the curtains, the setup is quite complex, but in practice, it turned out to be very user-friendly. In order to better understand the price structure of the on-chain transaction fees, we need to analyze the price structure of these types of networks. In our case, we follow a queuing game (Kendall, 1953; Bhat, 2015) in a two-sided market (Rochet & Tirole, 2006). We need a proper price structure, in order to incentivize both users and miners on the network. As it has been argued, congestion should be lower, as compared to congestion on existing PoW blockchains. However, even with a large throughput, it is likely that some congestion will occur, and that some delay costs will partially remain (Huberman et al., 2017). Also, taken independently, one shard can theoretically be overloaded at some point in its history. We propose a market-based mechanism, in order to reflect these situations. The dynamic pricing adjusts transaction fees in order to respond to supply and demand fluctuations. In the event of network congestion, surge pricing can solve some economic problems, by smoothing out the demand curve (Cohen et al., 2016). This function should ensure a market-based approach toward transaction fees, an approach in which surge pricing for reducing delay costs plays a central role. And exactly like the system used within Uber, the user can “tip” the mining pool, thereby demonstrating that he has higher delay costs, and that  he wants his transaction to be processed as soon as possible. The framework will optimize the waiting time for the its users, while guaranteeing miners a minimal fee, even when competition for block space is low. In other words, the floor price should at least cover the costs per transaction incurred by the miners. Such economics are used by ride-hailing apps such as Uber or Lift, and have proven to be quite efficient. Written by Lucas Leger . REFERENCES: Auer, R. (2019). Beyond the doomsday economics of “Proof-of-work” in cryptocurrencies, BIS Working Papers No. 765. Aune, R. T., Krellenstein, A., O’Hara, M., & Slama, O. (2017). Footprints on a Blockchain: Trading and Information Leakage in Distributed Ledgers, The Journal of Trading, 12(3), 5–13. Bhat, U. N. (2015). An Introduction to Queueing Theory: Modeling and Analysis in Applications, Springer Second Edition. Caillaud, B., and Jullien, B. (2003). Chicken & Egg: Competition among Intermediation Service Providers, RAND Journal of Economics, 34(2): 309–28. Carlsten, M., Kalodner, H., Weinberg, S. M., and Narayanan, A. (2016). On the Instability of Bitcoin Without the Block Reward, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS’16. Chepurnoy, A., Kharin, V., and Meshkov, D. (2019). A Systematic Approach To Cryptocurrency Fees, in A. Zohar, I. Eyal, V. Teague, J. Clark, A. Bracciali, F. Pintore, M. Sala (Eds.) Financial Cryptography and Data Security FC 2018 International Workshops, BITCOIN, VOTING, and WTSC, Nieuwpoort, Curaçao, March 2, 2018, Revised Selected Papers Berlin: Springer 19-30. Cohen, P., Hahn, R., Hall, J., Levitt, S., and Metcalfe, R. (2016). Using Big Data to Estimate Consumer Surplus: The Case of Uber, NBER Working Paper No. 22627. Dimitri, N. (2019). Transaction Fees, Block Size Limit, and Auctions in Bitcoin. Ledger, 4. Easley, D., O’Hara, M., Basu, S. (2019). From Mining to Markets: The Evolution of Bitcoin Transaction Fees. Journal of Financial Economics, Vol 134 (1), pp 91-109. Houy, N. (2014). The Economics of Bitcoin Transaction Fees. SSRN Working Paper. Huberman, G., Leshno, J. and Moellemi, C. (2017). Monopoly without a monopolist: an economic analysis of the Bitcoin payment system, Columbia Business School Research Papers no 17-92. Kendall, D.G. (1953), Stochastic processes occurring in the theory of queues and their analysis by the method of imbedded Markov chains, Annales of Mathematical Statistics, 24(3), 338-354. Lavi, R., Sattath, O., and Zohar, A. (2017). Redesigning Bitcoin's fee market, arXiv:1709.08881 Rochet, J-C., and Tirole, J. (2006). Two-Sided Markets: A Progress Report, RAND Journal of Economics, 37(3): 645–67. [1] Transaction fees can be viewed as a two-sided market, where the incentives of the miners are co-dependent on the users’ response on price structure. As put by Rochet & Tirole (2006): “We define a two-sided market as one in which the volume of transactions between end-users depends on the structure and not only on the overall level of the fees charged by the platform. A platform’s usage or variable charges impact the two sides’ willingness to trade once on the platform, and thereby their net surpluses from potential interactions; the platforms’ membership or fixed charges in turn condition the end-users’ presence on the platform.” We want to minimize transaction fees to attract most users and at the same time to maximize the speed at which transactions are processed by miners, which require high transaction fees. It is a typical two-sided (Caillaud & Jullien, 2003; Rochet & Tirole, 2006) pricing setup. If transaction fees are too high, users go on other networks, too low, miners would not take of the transactions right away. In a nutshell, we need to set the price to incentivize opposite behaviors, so as to internalize cross-side network effects.

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Since n.d.

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