Credix is rethinking and rebuilding how debt capital markets should work

Credix

At HackerNoon, we believe transparency with the reader is the key to building trust between writer and reader. To give readers as much transparency as possible, we wanted to create quick and easy ways to tell the reader information they might want to know about a story before reading it. This could be something as simple as telling the reader if the story is a news piece or an opinion piece. On the other hand, there are more complicated things we might want to communicate to the reader, such as whether or not the writer has a vested interest in the companies they’re writing about, or if they used AI to write the piece. To accomplish these goals, we created Emoji Credibility Indicators! Now open-sourced on Figma Community & GitHub. Learn more about Emoji Credibility Indicators here. Designing Emoji Credibility Indicators Now, let’s dive into the process of designing Emoji Credibility Indicators. The initial design brief by David and our Editorial Team included 22 Emoji Credibility Indicators (related read, Every Emoji Credibility Indicator on HackerNoon Explained) divided into two categories: context/disclaimers and content types. In addition to their description, each credibility indicator was assigned an emoji for design inspiration. From here on in, it was on us (Me & Kien) to run wild with our imagination and bring these emoji indicators to life. Following HackerNoon’s design language, we chose the Pixel Art Style. After exploring several options for grid sizes, we opted for a 24px grid for pixelation since it retained the desired level of detail. As for the software of choice, I was a bit biased going for Adobe Illustrator since we get plenty of options when it comes to exporting to different file types + my familiarity with the grid tool (Adobe Photoshop, Aseprite, and Corel Draw are some good alternatives). Our pixel art design process consists of 5 steps, starting from basic shapes, drawing details, creating depth, adding strokes, and then merging shapes & exporting to different file types. Let’s take a deeper dive into these steps taking the Comedy/Satire emoji credibility indicator for example. Starting with Basic Shapes Whenever we do Pixel-Art/8-bit Designs, we start with a basic shape that resonates with the idea we have in mind. Being an ellipse, in the case of the Comedy/Satire emoji indicator (which was inspired by the ‘😂’ emoji). Drawing Details Then we head on to adding more details to the shape we just drew (like facial features in this case). Creating Depth: Highlights & Shadows To make it more realistic and less flat, we add highlights and shadows to the face. In this case, the light source was assumed to be up front and center which makes the shadows move to the edges and the highlights to the center. Pro Tip: Always try to use three shades of a color. One for the base, the lighter one for highlights, and the darker one for shadows. Adding Stroke To make it more defined, we added a stroke to wrap things up. Merging Shapes and Exporting To make them more usable, we merged the shapes and exported them as both PNGs and SVGs. Then, we repeated this whole process 21 more times and ended up with the Emoji Credibility Indicators we have on HackerNoon today. The Final Product: An Open Sourced Pixelated Emoji Pack In conclusion, the Emoji Credibility Indicators are yet another step towards enhancing online communication between our readers and writers. These pixelated emojis not only serve as credibility indicators adding context to content but have impacted users’ trust & engagement with content. They serve as an example of how thoughtful design can convey complex concepts in a simple yet intuitive manner. Join us on this quest of emojifying context, and try out The Emoji Credibility Indicators by HackerNoon. Now available on Figma Community and GitHub Stay Creative, Stay Iconic. At HackerNoon, we believe transparency with the reader is the key to building trust between writer and reader. To give readers as much transparency as possible, we wanted to create quick and easy ways to tell the reader information they might want to know about a story before reading it. This could be something as simple as telling the reader if the story is a news piece or an opinion piece. On the other hand, there are more complicated things we might want to communicate to the reader, such as whether or not the writer has a vested interest in the companies they’re writing about, or if they used AI to write the piece. To accomplish these goals, we created Emoji Credibility Indicators! Now open-sourced on Figma Community & GitHub . Figma Community GitHub Learn more about Emoji Credibility Indicators here. Learn more about Emoji Credibility Indicators here . here here Designing Emoji Credibility Indicators Now, let’s dive into the process of designing Emoji Credibility Indicators. The initial design brief by David and our Editorial Team included 22 Emoji Credibility Indicators (related read, Every Emoji Credibility Indicator on HackerNoon Explained ) divided into two categories: context/disclaimers and content types. In addition to their description, each credibility indicator was assigned an emoji for design inspiration. David Every Emoji Credibility Indicator on HackerNoon Explained From here on in, it was on us (Me & Kien ) to run wild with our imagination and bring these emoji indicators to life. Following HackerNoon’s design language, we chose the Pixel Art Style. After exploring several options for grid sizes, we opted for a 24px grid for pixelation since it retained the desired level of detail. As for the software of choice, I was a bit biased going for Adobe Illustrator since we get plenty of options when it comes to exporting to different file types + my familiarity with the grid tool ( Adobe Photoshop , Aseprite , and Corel Draw are some good alternatives). Kien Adobe Photoshop Aseprite Our pixel art design process consists of 5 steps, starting from basic shapes, drawing details, creating depth, adding strokes, and then merging shapes & exporting to different file types. Let’s take a deeper dive into these steps taking the Comedy/Satire emoji credibility indicator for example. Starting with Basic Shapes Whenever we do Pixel-Art/8-bit Designs, we start with a basic shape that resonates with the idea we have in mind. Being an ellipse, in the case of the Comedy/Satire emoji indicator (which was inspired by the ‘😂’ emoji). Drawing Details Then we head on to adding more details to the shape we just drew (like facial features in this case). Creating Depth: Highlights & Shadows To make it more realistic and less flat, we add highlights and shadows to the face. In this case, the light source was assumed to be up front and center which makes the shadows move to the edges and the highlights to the center. Pro Tip: Always try to use three shades of a color. One for the base, the lighter one for highlights, and the darker one for shadows. Pro Tip: Always try to use three shades of a color. One for the base, the lighter one for highlights, and the darker one for shadows. Adding Stroke To make it more defined, we added a stroke to wrap things up. Merging Shapes and Exporting To make them more usable, we merged the shapes and exported them as both PNGs and SVGs. Then, we repeated this whole process 21 more times and ended up with the Emoji Credibility Indicators we have on HackerNoon today. Emoji Credibility Indicators The Final Product: An Open Sourced Pixelated Emoji Pack An Open Sourced Pixelated Emoji Pack In conclusion, the Emoji Credibility Indicators are yet another step towards enhancing online communication between our readers and writers. These pixelated emojis not only serve as credibility indicators adding context to content but have impacted users’ trust & engagement with content. They serve as an example of how thoughtful design can convey complex concepts in a simple yet intuitive manner. Emoji Credibility Indicators Join us on this quest of emojifying context, and try out The Emoji Credibility Indicators by HackerNoon. Now available on Figma Community and GitHub Now available on Figma Community and GitHub Figma Community GitHub Stay Creative, Stay Iconic. Stay Creative, Stay Iconic.

Emojifying Context: Designing HackerNoon's Emoji Credibility Indicators

ইমোজিফাইং কনটেক্সট: হ্যাকারনুনের ইমোজি বিশ্বাসযোগ্যতা সূচক ডিজাইন করা

Emojifying-Kontext: Entwicklung der Emoji-Glaubwürdigkeitsindikatoren von HackerNoon

Contexto emojificante: diseño de indicadores de credibilidad emoji de HackerNoon

Contexte émoticône : conception des indicateurs de crédibilité Emoji de HackerNoon

इमोजी संदर्भ: हैकरनून के इमोजी विश्वसनीयता संकेतकों का डिज़ाइन

絵文字によるコンテキストの表現: HackerNoon の絵文字信頼性指標の設計

이모티콘 컨텍스트: HackerNoon의 이모티콘 신뢰성 지표 설계

Contexto Emojifying: Projetando Indicadores de Credibilidade Emoji do HackerNoon

Контекст эмодзи: разработка индикаторов достоверности эмодзи от HackerNoon

Emojileştirme Bağlamı: HackerNoon'un Emoji Güvenilirlik Göstergelerini Tasarlama

Bối cảnh biểu tượng cảm xúc: Thiết kế các chỉ số đáng tin cậy về biểu tượng cảm xúc của HackerNoon

表情符号化语境：设计 HackerNoon 的表情符号可信度指标

The Noonification: HackerNoons Emoji Credibility Indicators are Live on GitHub and Figma! (4/16/2024)

Emoji Credibility Indicators by HackerNoon are now live on GitHub and Figma!!! Our open-source pixelated emoji pack was designed to communicate context about a story's content on HackerNoon and you can be amongst the first to try them! But before we tell you what you’ll get from this package, here’s a little refresher ⬇️ What are Emoji Credibility Indicators on HackerNoon Stories? Emoji Credibility Indicators provide essential context to the content within every HackerNoon story. Below the author’s profile on the story page, the “Credibility” section appears with a corresponding custom emoji and tooltip to indicate relevant information about the story to the reader. In the example above, you can see that David’s story contains two emoji credibility indicators: HODL, which indicates mentions of one or more cryptos that the author holds, and Associated Companies, which is used when the writer has or had any sort of business relationship with companies mentioned in the story. Learn more about Emoji Credibility Indicators here. Types of Emoji Credibility Indicators We have around 20 emoji credibility indicators for Context and Disclaimers, and Content Types, each with an attributed meaning and instructions on how to use them. So if you have a technological project, and want amazing pixelated emojis to represent/communicate credibility, this repo is for you! Read about all our emoji credibility indicators, what they mean, and how to use them here. Why Adding Context to Content Matters to the Future of Digital Text When lost in a good read, it may feel like there are only words, but the reality is, no text exists in a vacuum. All text has a setting. Too often on social media and the current internet, what surrounds the text is vanity metrics designed to keep you in the newsfeed, designed to re-affirm that what you’re doing is liked/upvoted/not-a-waste-of-time, instead of being designed to provide you with structured indications that define the context of the story. Read our help page fo more context about our emoji credibility indicators. What will you get from this open-source package? Pixelated Emojis, designed on a 24px grid.
Well crafted SVGs
Fully customizable
Ready-to-use for development via GitHub Repo and Figma! Sounds good? Try them out today and tell us about your experience here. Designing HackerNoon's Emoji Credibility Indicators: Get the developer’s perspective here. Emoji Credibility Indicators by HackerNoon are now live on GitHub and Figma!!! Emoji Credibility Indicators by HackerNoon are now live on GitHub and Figma !!! GitHub Figma Our open-source pixelated emoji pack was designed to communicate context about a story's content on HackerNoon and you can be amongst the first to try them! But before we tell you what you’ll get from this package, here’s a little refresher ⬇️ What are Emoji Credibility Indicators on HackerNoon Stories? Emoji Credibility Indicators provide essential context to the content within every HackerNoon story. Below the author’s profile on the story page, the “Credibility” section appears with a corresponding custom emoji and tooltip to indicate relevant information about the story to the reader. In the example above, you can see that David’s story contains two emoji credibility indicators: HODL, which indicates mentions of one or more cryptos that the author holds, and Associated Companies, which is used when the writer has or had any sort of business relationship with companies mentioned in the story. Learn more about Emoji Credibility Indicators here. Learn more about Emoji Credibility Indicators here . here Types of Emoji Credibility Indicators We have around 20 emoji credibility indicators for Context and Disclaimers, and Content Types, each with an attributed meaning and instructions on how to use them. So if you have a technological project, and want amazing pixelated emojis to represent/communicate credibility, this repo is for you! Read about all our emoji credibility indicators, what they mean, and how to use them here. Read about all our emoji credibility indicators, what they mean, and how to use them here . here Why Adding Context to Content Matters to the Future of Digital Text When lost in a good read, it may feel like there are only words, but the reality is, no text exists in a vacuum. All text has a setting. Too often on social media and the current internet, what surrounds the text is vanity metrics designed to keep you in the newsfeed, designed to re-affirm that what you’re doing is liked/upvoted/not-a-waste-of-time, instead of being designed to provide you with structured indications that define the context of the story. Read our help page fo more context about our emoji credibility indicators. Read our help page fo more context about our emoji credibility indicators. Read our help page fo more context about our emoji credibility indicators. What will you get from this open-source package? What will you get from this open-source package? Pixelated Emojis, designed on a 24px grid. Well crafted SVGs Fully customizable Ready-to-use for development via GitHub Repo and Figma! Pixelated Emojis, designed on a 24px grid. Well crafted SVGs Fully customizable Ready-to-use for development via GitHub Repo and Figma ! GitHub Repo Figma Sounds good? Try them out today and tell us about your experience here. Try them out today and tell us about your experience here . here Designing HackerNoon's Emoji Credibility Indicators: Get the developer’s perspective here. Designing HackerNoon's Emoji Credibility Indicators : Get the developer’s perspective here . Designing HackerNoon's Emoji Credibility Indicators here

HackerNoon's Emoji Credibility Indicators are Live on GitHub and Figma!

HackerNoon এর ইমোজি বিশ্বাসযোগ্যতা সূচকগুলি GitHub এবং Figma-এ লাইভ!

Die Emoji-Glaubwürdigkeitsindikatoren von HackerNoon sind live auf GitHub und Figma!

¡Los indicadores de credibilidad Emoji de HackerNoon están disponibles en GitHub y Figma!

Les indicateurs de crédibilité Emoji de HackerNoon sont en direct sur GitHub et Figma !

हैकरनून के इमोजी विश्वसनीयता संकेतक गिटहब और फिग्मा पर लाइव हैं!

HackerNoon の絵文字信頼性インジケーターが GitHub と Figma で公開されました。

HackerNoon의 이모티콘 신뢰성 지표가 GitHub 및 Figma에 게시되었습니다!

Os indicadores de credibilidade de Emoji do HackerNoon estão disponíveis no GitHub e no Figma!

Индикаторы достоверности Emoji от HackerNoon доступны на GitHub и Figma!

HackerNoon'un Emoji Güvenilirlik Göstergeleri GitHub ve Figma'da Yayında!

Các chỉ số đáng tin cậy về biểu tượng cảm xúc của HackerNoon hiện có trên GitHub và Figma!

HackerNoon 的 Emoji 可信度指标已在 GitHub 和 Figma 上线！

A Game-Changer for On-chain Borrowers The launch of Credit Vaults aims to address the growing demand for stable and accessible credit among new on-chain entities, like fintech and payment companies. These borrowers are given the ability to design their own terms, which includes the ability to choose their own interest rates and payback schedules, which is one of the distinguishing characteristics of Credit Vaults. In addition, this model guarantees greater annual percentage yields (APYs) for lenders due to its full utilisation, which makes it a profitable choice for all parties involved in the relationship. Morgan Krupetsky of Ava Labs shared, “Avalanche is the ideal platform to support the launch of Clearpool’s Credit Vaults. The collaboration with Clearpool showcases a mutual commitment to developing innovative digital financial products and a shared focus on bringing tokenized assets and greater utility into the Avalanche ecosystem.” Transforming Fintech Lending: Introducing Short-term, Liquid Options Banxa teamed up with Clearpool to create the first Credit Vault. This will let Banxa borrow up to 5 million USDT. This agreement is very important because it lets lenders use a unique seven-day payback choice and get extra AVAX token benefits. The easy availability and flow of money is one of the main issues in the field of autonomous finance. This tool solves the issue and sets a new standard for adaptability and effectiveness. Leading the Charge Banxa, a key player in the crypto infrastructure space, has embraced the flexibility and opportunities provided by Clearpool's Credit Vaults. "Clearpool's Credit Vaults offer us the flexibility to meet our liquidity requirements," said Holger Arians, CEO of Banxa, highlighting the importance of customizable terms and the potential to attract a broader lender base. Cauris, specializing in private credit for fintech companies, plays a crucial role in managing the Credit Vault, emphasizing security and integrity in the system's design. Credit Vaults, which were added by Clearpool to Avalanche, are a major step forward in on-chain banking. Clearpool is not only meeting the needs of the market right now by making credit options more open, easy to get to, and effective, but they are also actively shaping the future of DeFi loans. This new way of doing things is likely to bring even more institutional and fintech players to the platform, making Clearpool even more of a star in the decentralised finance credit market. The announcement that Clearpool has recently made on its most recent initiative, which is the expansion into the Avalanche platform, as well as the release of a cutting-edge product known as Credit Vaults, is a big step forward for the company. Through this strategic choice, Clearpool is able to expand its horizons inside the DeFi market, marking the beginning of a new chapter for the company. Clearpool at the Forefront The launch of the Credit Vaults is a significant milestone, establishing Clearpool as a leader in the rapidly evolving DeFi space. The platform's ability to attract over 20 institutions for borrowing, including giants like Jane Street, underscores the growing interest and confidence in Clearpool’s offerings. Jakob Kronbichler, CEO & Co-founder of Clearpool, stated, “Launching Credit Vaults on Avalanche marks a major milestone for Clearpool and the RWA sector as we pioneer migrating credit on-chain.” Don’t forget to like and share the story! Image credits: Shubham's Web3 Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Clearpool Revolutionizes DeFi Lending with Credit Vaults on Avalanche

ক্লিয়ারপুল তুষারপাতের উপর ক্রেডিট ভল্টের সাথে ডিফাই ঋণের বিপ্লব ঘটায়

Clearpool revolutioniert die DeFi-Kreditvergabe mit Credit Vaults auf Avalanche

Clearpool revoluciona los préstamos DeFi con bóvedas de crédito en Avalanche

Clearpool révolutionne les prêts DeFi avec des coffres-forts de crédit sur Avalanche

क्लियरपूल ने एवलांच पर क्रेडिट वॉल्ट के साथ DeFi लेंडिंग में क्रांति ला दी

Clearpool が Avalanche の Credit Vault で DeFi 融資に革命を起こす

Clearpool은 Avalanche의 신용 금고를 통해 DeFi 대출에 혁명을 일으켰습니다.

Clearpool revoluciona empréstimos DeFi com cofres de crédito no Avalanche

Clearpool совершает революцию в кредитовании DeFi с помощью кредитных хранилищ на Avalanche

Clearpool, Avalanche'ta Kredi Kasaları ile DeFi Kredilendirmesinde Devrim Yaratıyor

Clearpool cách mạng hóa hoạt động cho vay DeFi bằng kho tín dụng trên Avalanche

Clearpool 利用 Avalanche 上的信用库彻底改变了 DeFi 借贷

At HackerNoon, we believe transparency with the reader is the key to building trust between writer and reader. To give readers as much transparency as possible, we wanted to create quick and easy ways to tell the reader information they might want to know about a story before reading it. This could be something as simple as telling the reader if the story is a news piece or an opinion piece. On the other hand, there are more complicated things we might want to communicate to the reader, such as whether or not the writer has a vested interest in the companies they’re writing about, or if they used AI to write the piece. To accomplish these goals, we’ve ! More details can also be :-) launched Emoji Credibility Indicators found in our help section What Are Emoji Credibility Indicators? They are emojis that will appear underneath the author profile along the sidebar of each story: You’ll notice a new section under the writer profile titled “Credibility”. There you will see some emojis. If you highlight the emoji, it’ll tell you what it means: With these indicators we hope that writers can give readers full transparency and that readers will feel they have all the information they need in a convenient, easy-to-find, place. Below, you can read about all our current emoji credibility indicators, what they mean, and how to use them. We will update this post as new indicators are added. Note: Indicator Index: Context and Disclaimers Original Reporting Code License DYOR Vested Interest On the Ground Referral Associated Companies HODL AI-Assisted Content Types News Guide Reviews Thought Leadership/Opinion Piece Product Launch Comedy Fiction Press Release Interview Video Podcasts Context and Disclaimers Original Reporting Use this indicator when the content in your story hasn’t been published anywhere else before. Original reporting means this is not only new information to the HackerNoon community, but new to the internet as a whole. What is not original reporting? Writing about or repurposing a press release Guides, walkthroughs, opinion pieces, reviews Thought leadership What is original reporting? Breaking news Reporting from an event/conference topics/news not covered anywhere else Exposés, pieces with original research (not paraphrasing other people’s research/stories) Code License HackerNoon edits all stories for quality, moderation, and fact checking. However, we do not check every piece of code included within our articles. We cannot guarantee the quality or safety of the code written by our contributors please exercise caution when using any of the code you see on our platform. DYOR: Do Your Own Research This disclaimer is for investment, crypto, and financial articles. We don’t have the resources to vet our contributor’s investment portfolios or career history, so we can’t guarantee their advice will make you rich 🤣 At the same time please use this indicator for any content around health, or mental health, or safety. We publish lots of content about how to approach work life balance, life hacking, and more. But none of that should be taken as professional health advice. Always seek a certified professional before making important decisions regarding your health or safety. Vested Interest We use this indicator when the writer has a vested interest in a product, stock, crypto, company, or anything mentioned in the story. This can be monetary vested interest, business, or otherwise. Vested interest doesn’t have to be just money. Being part of a product or company’s community or having any sort of business relationship is also vested interest. Basically if you have any reason not to be 100% objective/unbiased, we need to let the reader know. On the Ground We use this indicator when the writer was physically present in relevant location(s) to this story. It also should only be used when location is an important factor of the story. This can apply to local or regional news, in-person events, and more. On the other hand, it shouldn’t be used for things like technical guides, reviews, etc. Referral Referral and affiliate links are allowed on HackerNoon as long as you follow our guidelines. Please review all our referral link and backlink rules here: https://help.hackernoon.com/backlinks-guideline Please be sure to tell the reader using this emoji indicator and and in the intro to the story what sites you have affiliate partnerships with. Associated Companies This emoji indicator is used when the writer has any sort of business relationship with companies mentioned in the story, use this indicator to tell the reader exactly which companies those are. or had If your company is not listed within the indicator’s company list, please skip it and use the vested interest indicator mentioned above instead. HODL Capitalism is strong in this one. Well, it’s strong in most societies and if you stand to benefit from the profits of a crypto or stock, you need to use this indicator for full transparency. Of course, this is only relevant when the story you’re writing mentions one or more cryptos that you hold. If you’re writing about the latest iPhone specs, we don’t really need to know that you own ETH 🤣. AI-Assisted At HackerNoon we believe transparency with the reader is key to gaining trust. HackerNoon contributors to use this indicator when the story contains AI-generated text, images, videos, or otherwise. NEED Even if the author has just used AI (like ) either for research, editing, or to generate outlines, you must still use this indicator and tell the reader in your intro how you used AI in the story. ChatGPT clearly Content Types News This indicator is for stories that contain factual information about a recent event. It can be a full report or a short news byte, but should mostly be objective reporting of events. In traditional HackerNoon style, of course you’re free (and encouraged) to comment on the events or give your opinions as well. Guide This indicator is for stories that will teach you how to do something new or how to do something better. It includes walkthroughs, coding guides, life hacks, career tips, and more. Reviews Use this indicator when the story praises and/or roasts a product, company, service, book, game, or anything else people like to review on the Internet. We publish reviews on hardware, software, services, games, books, and more. Thought Leadership / Opinion Piece We use this indicator when the story is an opinion piece based on the author’s POV and likely contains subjective thoughts. The difference between guides and thought leadership is typically that guides contain universally true information. To screenshot on Windows, hold For example: Windows Key → Shift → S On the other hand an opinion piece can hold thoughts that are well-educated and logical, but can’t just be published as a universal truth. Android is better than iOS for multiple reasons. People who enjoy owning complete control over their device should opt for an Android phone. For example: It’s important to note that we do and often publish stories that do not necessarily reflect the views of HackerNoon as a whole. Product Launch This indicator is for stories that provide insights into new product. We do allow product launch stories on HackerNoon, provided they are mostly about the tech itself and not promotional/shilly marketing or sales copy. Comedy / Satire Just for laughs! Don’t cancel us. This indicator is for pieces that were written for humor or satire and may include nonfactual statements, stories, or anecdotes. Please don’t take it seriously and just enjoy it. Fiction Sometimes life is stranger than fiction…but not these stories. These stories are literally . fiction See what we did there? Press Release Please use this indicator when the story is a PR written by or for the company mentioned within it. This indicator will signal to the reader that the writer has a vested interest in the company and products mentioned within. Interview Computer Monitors: This story includes an interview in which the author is either the interviewer or guest/interviewee. Between Two Ferns Video The best videos on the Internet archived and shared on HackerNoon. In case you didn’t know, we allow users to share YouTube videos within articles in HackerNoon, provided that there is enough text on the page to still be an article. Here are some examples: https://hackernoon.com/tagged/youtubers Podcasts The best podcasts on the Internet archived and shared on HackerNoon. In case you didn’t know this either, we allow users to share podcasts on HackerNoon too, provided there is enough text on the page to constitute an article. We currently only accept podcasts via YouTube embeds, but we’ll support more formats moving forward. Here are some examples: https://hackernoon.com/tagged/podcast More details can also be :-) found in our help section

Every Emoji Credibility Indicator on HackerNoon Explained

হ্যাকারনুন-এ প্রতিটি ইমোজি বিশ্বাসযোগ্যতা সূচক ব্যাখ্যা করা হয়েছে

Jeder Emoji-Glaubwürdigkeitsindikator auf HackerNoon erklärt

Explicación de cada indicador de credibilidad de Emoji en HackerNoon

Chaque indicateur de crédibilité Emoji sur HackerNoon expliqué

HackerNoon पर प्रत्येक इमोजी विश्वसनीयता संकेतक की व्याख्या की गई

HackerNoon のすべての絵文字の信頼性指標の説明

HackerNoon의 모든 이모티콘 신뢰성 지표에 대한 설명

Explicação de cada indicador de credibilidade de emoji no HackerNoon

Объяснение каждого индикатора достоверности эмодзи на HackerNoon

HackerNoon'daki Her Emoji Güvenilirlik Göstergesi Açıklandı

Giải thích về mọi chỉ báo độ tin cậy của biểu tượng cảm xúc trên HackerNoon

HackerNoon 上的每个表情符号可信度指标均已解释

In the ever-evolving world of artificial intelligence, verifiable machine intelligence is quietly revolutionizing machine learning. This paradigm shift is not just a technological advancement; it represents a fundamental change in how we interact with and perceive the capabilities of machine learning models. At its core, verifiable machine intelligence is about establishing a new standard of reliability and trust in machine learning models. Traditionally, these models have been somewhat opaque, offering little insight into their inner workings or decision-making processes. Verifiable confronts this challenge head-on, introducing methods that allow for greater transparency and understanding of how models arrive at their conclusions. machine intelligence Simply put, it's redefining how we deal with and trust machine learning models. This innovative approach tackles issues like transparency, accuracy, and protecting intellectual property, ushering in a new era of trustworthiness and accountability. The potential benefits of verifiable machine intelligence go far beyond smart contracts. Most of the really important algorithms affecting people’s lives today, such as credit scores, insurance payouts, or even Twitter newsfeeds, are created by teams of data scientists behind closed doors. There’s usually very little recourse if there’s an error, which does occur with alarming frequency, such as the which garbled millions of credit scores and distorted everything from mortgage decisions to car loans to apartment rentals. , 2022 Equifax data glitch Bringing verifiable machine intelligence to such crucial applications using a crypto-economic concept like an Inference Economy could allow institutions to make accurate, accountable, and much more reliable decisions without revealing their underlying models to competitors. Perhaps the real killer application is holding artificial intelligence accountable. Semi-autonomous agents, such as the “roll your own” ones beginning to be offered by OpenAI, are capable of in-taking feeds of inferences and interacting with one another, but they’re just as susceptible to hallucination and error as ChatGPT is with text. Verifiable intelligence would allow models to interact with one another while verifying that any data passed between them was sourced correctly. At the heart of this quiet revolution is a library of technologies called zero-knowledge machine learning (zkML) and a handful of pioneering companies building applications with it. One of these is Spectral, who announced their Machine Intelligence Network on December 5th, creating an “Inference Economy” of model-makers, validators, and developers who solicit machine learning (ML) models or simply consume feeds of inferences in their smart contracts the way an oracle brings price feeds into a decentralized exchange. “Spectral’s network is a continuous competition,” says Spectral CEO Sishir Varghese. “Zero-knowledge proofs are the key to the system. They allow users to verify that inferences are coming from top-performing models—Modelers don’t need to reveal their proprietary methodology and can still generate revenue. This is especially useful for smart contracts which require validated information to be used in their executions.” The author acknowledges a vested interest in the organization(s) highlighted in this story. However, the views expressed within are delivered impartially and without bias. Disclosure: Understanding Verifiable Machine Intelligence Verifiable machine intelligence is built on three core principles: transparency, interpretability, and traceability. These principles significantly shift how we view and expect machine learning models to operate. Essentially, verifiable machine intelligence aims to make the decision-making process of these models clear and understandable when generating specific predictions or decisions. The commitment to transparency ensures that the inner workings of these models are not unnecessarily complex but accessible and easy to interpret. Transparency, a fundamental aspect of verifiable machine intelligence, is vital for building trust and allowing scrutiny of machine learning processes. This principle involves making the decision-making mechanisms of models understandable to stakeholders, enabling them to grasp the factors that contribute to the predictions. Verifiable models break the historical trend of black-box algorithms, providing clarity and insight into how decisions are made. Interpretability, another crucial principle, takes transparency further by emphasizing the need for human-understandable explanations. Verifiable machine intelligence strives to create models with decisions that are not only clear but also intelligible to individuals without an advanced understanding of machine learning. This approach ensures that the reasoning behind a model's predictions is articulated in a way that aligns with human cognitive capacities. Traceability completes the trio of principles by allowing the tracking of the decision-making process back to its origins. Verifiable models record the steps and features contributing to each prediction, enabling retrospective analysis and ensuring accountability. This traceability not only strengthens the reliability of the model but also helps identify and rectify potential biases or errors. How Verifiable Machine Intelligence works in practice — creating a Web3 Credit Score To return to the credit bureau glitch, if there were a community of data scientists and model-makers competing to create more accurate credit scoring models, any errors would be much more likely to have been spotted, and anyone competing for a share of revenue generated by those scores would have a very compelling reason to update the model with a more accurate version. Spectral got their start creating the MACRO Score, an on-chain credit score that looked at the transactions and contents of an Ethereum wallet to generate a score measuring that wallet’s likelihood of liquidation on an on-chain lending protocol. The Score was a success, with a capital efficiency simulation proving that their scores could help lower collateral requirements and interest rates for well-qualified borrowers, boosting profitability. They took the dataset they used to create the MACRO Score’s underlying ML model and are now using it as fodder for their first stream of inferences. They offered the data science community a significant bounty – $100,000 (with an additional 50K top-up) – and began a data science challenge, in addition to an 85 percent share of all revenue generated from the model after the bounties had been exhausted. The way it works is that there’s a leaderboard of modelers who meet benchmarks, and the top ten who meet benchmarks share revenue while their models are online, i.e., generating inferences. Because it’s a continuous challenge, payments are streamed to model makers. Anyone can submit a better version and knock out one of the challengers, getting a share of the proceeds. Unlike competitive machine learning platforms, because of the verifiable machine intelligence used, competitors won’t have to reveal their models to one another, preserving their IP, and dissuading sniping and over-optimization. After the initial bounties are paid out, anyone can add credit scores to their smart contract or application simply by incorporating the feed and paying for their use. Spectral is gradually offering new challenges, aiming to eventually have a library of ML inferences available. They will also offer a flexible format that makes integration into smart contracts or contributing a model simple despite the complex cryptography involved. Challenges and Future Developments As we delve into the realm of verifiable machine intelligence, some challenges could impact its widespread adoption. One key challenge involves finding the right balance between transparency and complexity. While the goal is to boost understanding and trust in machine learning models, overly complex models might become hard to interpret, potentially undermining the core idea of verifiability. Striking this balance becomes crucial to ensure that the interpretability promised by verifiable models doesn't unintentionally compromise their accuracy or usefulness. Another challenge relates to a potential trade-off between verifiability and the performance of machine learning models. Verifiable models might, in some cases, introduce a computational overhead due to extra processes for transparency and traceability. Balancing high-performance standards with achieving verifiability is an ongoing challenge that researchers and practitioners must address. Looking to the future, ongoing research offers hope for overcoming these challenges and enhancing verifiable models. One avenue involves refining techniques for model distillation, where complex verifiable models can be transformed into simpler, more interpretable versions without sacrificing accuracy. This could tackle the challenge of interpretability by providing simplified representations of intricate models. Advancements in and interpretable machine learning are crucial for the future of verifiable machine intelligence. As research progresses, models will likely become inherently more interpretable without losing complexity, mitigating the trade-off between verifiability and performance. explainable AI (XAI) Techniques offering detailed insights into the decision-making processes of machine learning models are essential to ensure transparency is not just a checkbox but a meaningful aspect of verifiable models. Additionally, the integration of privacy-preserving technologies is a significant future development. While verifiable machine intelligence stresses transparency, it must also coexist with the need to protect sensitive information. Research into techniques like federated learning and homomorphic encryption can contribute to developing verifiable models that respect privacy, ensuring transparency and security can go hand in hand. In conclusion, the challenges faced by verifiable machine intelligence are not insurmountable. Ongoing research indicates a promising trajectory for future developments. Balancing transparency and complexity, addressing performance concerns, and advancing interpretability are critical areas for research and innovation. As the field matures, the evolution of verifiable models will likely bring solutions that not only overcome existing challenges but also set new standards for accountable, reliable, and interpretable machine intelligence.

Beyond Credit Scores: Exploring the Potential of Verifiable Models in Diverse Industries

What erodes credibility at work and what builds it? Most employees don’t think about credibility consciously which prevents them from taking the right steps to build it. They think that if they keep their heads down and keep doing good work, someone will take notice and good opportunities will land their way. But workplaces aren’t designed to lift people with good intentions and good skills. Working hard doesn’t necessarily translate into recognition and promotions. This mismatch between what employees expect and what they get is often the biggest root cause of disappointment, dissatisfaction, and frustration at work. But it doesn’t have to be this way. By incorporating simple habits and practices into their daily work, employees can do more impactful work that not only generates value for the organization but also gets them the attention they need. Building credibility requires earning the trust of people who work with you—your colleagues, manager, stakeholders, and others. It requires more than just competence and knowledge. Being an expert in your domain or enthusiasm and motivation about your job can only take you so far when you lack credibility with your team and others. With credibility: Your ideas are heard and valued. Others approach you for guidance and advice. You’re entrusted with higher-level responsibilities and better opportunities. You get a sense of progress, purpose, and belonging mitigating and keeping you happy, satisfied, and fulfilled. burnout Here are a few practical strategies that I have used throughout my career to become a more credible team member and employee: 1. Do things without being asked Completing assigned tasks, delivering good quality code, and meeting deadlines and expectations establishes you as a sincere employee—someone who will do the job they’ve been asked to do. But waiting for tasks to be assigned doesn’t make you credible. To establish credibility, do work without being asked. Identify your stakeholders and customers' unmet needs. Which areas will deliver the biggest impact? What important things have been pushed aside that need time and attention? How can you contribute? For example: Resolve a bug that’s a pain point for customers. Create dashboards for the team to see all tech metrics in one place without wasting time looking it up in multiple systems and places. Automate reports for stakeholders that makes it easy for them to get data without raising a tech request each time. Credibility comes with doing work that no one is paying attention to, not by simply meeting expectations. Excellence comes from an internal standard that asks, “How can I deliver beyond what’s expected? ― Brendon Burchard 2. Never say it’s not your Job What do you do when your manager asks you to work on things that don't directly fit your job profile or appear boring? For example: Picking up another employee's slack. Working on a task that you’ve done many times before. Coordinating an event at work. Do you turn them down saying “it’s not my job” or do you roll up your sleeves and get the job done? Showing resistance towards certain kinds of work and preference towards others makes you inflexible. Others can’t judge what you’ll accept and what you’ll deny which makes it hard for them to consider you when a new opportunity opens up. Credibility isn’t built by working on fun stuff or doing work that always matches your interests. It’s built by gauging the need of the hour and making yourself available to contribute where your presence will matter. 3. Have a strong sense of agency How do you respond to obstacles and challenges? Do you wait for your manager to unblock you or do you find creative ways to solve the problem and move forward? When you blame, complain and sob and pass the responsibility to people above you, you may feel that you’ve done the job by informing the right people, but absolving yourself from responsibility does not build credibility. Others find you credible when you approach problems with high agency. When you push through in the face of adversity and find a way to get what you want without waiting for the conditions to be perfect or otherwise blaming the circumstances, others come to rely on you for problems with unknowns and uncertainties. Challenge and adversity are meant to help you know who you are. Storms hit your weakness, but unlock your true strength. ― Roy T. Bennett 4. Proactively identify problems and create a plan to tackle them upfront Most employees react to problems as they surface. They don’t take the time to think about challenges and obstacles that might get in the way. Unexpected events throw their balance off a bit. Not having sufficient time to think through the problem leads to poor choices and bad decisions. Throwing bad news at the manager at the last minute also breaks trust. People who build credibility at work aren’t reactive. They proactively anticipate what might get in the way. Thinking through the risks and problems upfront mentally prepares them to deal with the challenges as they arise—even if it’s something unexpected they aren’t surprised. To build credibility, use the premortem technique—place yourself into the future and look back. Visualize the project, task or whatever you intend to do has gone horribly wrong. Work backward to identify what mistakes might cause it to fail. Create a plan to solve these issues—address the risks and obstacles thereby increasing your chances of success. The only guarantee, ever, is that things will go wrong. The only thing we can use to mitigate this is anticipation. Because the only variable we control completely is ourselves. — Ryan Holiday 5. Be a problem solver When something catches your attention, do you simply state the problem or do you also suggest a few ways in which it can be solved? Rejecting ideas, pointing out flaws, and stating what wouldn’t work is easy. It’s what most employees do. They limit their contribution to finding problems and consider the job done. Credible employees don’t work this way. They’re experimenters, strategists, and ‘solutionists’ who look for different ways to solve a problem. Instead of simply stating the problem, they also do their homework and come prepared with solutions. Approaching problems with a solution mindset helps them draw valuable insights. They’re able to think creatively and find unique ways to address concerns. They build credibility by solving hard problems. 6. Take responsibility When things don’t go as expected, where do you focus—other people, external circumstances, and conditions that prevented you from getting the desired outcomes or do you hold yourself accountable for it? When you don’t feel in control of your outcomes, your mind switches to a problem mode. You obsess about the problem and fail to take action when it’s needed the most. Employees who build credibility operate from an internal locus of control. They think about what they’re capable of doing instead of wasting time worrying about things outside their control. Stepping up and taking charge kicks off the solution region of their brain. They’re able to evaluate the situation from an objective point of view, consider different possibilities, and think creatively on the steps to take. Once people stop making excuses, stop blaming others, and take ownership of everything in their lives, they are compelled to take action to solve their problems. They are better leaders, better followers, more dependable and actively contributing team members, and more skilled in aggressively driving toward mission accomplishment. But they’re also humble—able to keep their egos from damaging relationships and adversely impacting the mission and the team. — Jocko Willink 7. Be a team player What is your intention when working with others? Do you think about your self-interests or do you think about adding value to others? When you think only about yourself, you make decisions that benefit you while ignoring how they may harm others. You act based on what you like and dislike without considering how your actions add value to the organization or its people. Credible employees don’t act solo. They’re team players. They work based on their team's needs or where their contribution will matter the most. Not trying to personally gain from every situation helps them keep a balanced perspective—they do work that matters to the organization while also contributing to their learning and growth. 8. Share knowledge If you’re an expert at something or knowledgeable in a specific field, you’ll have the capability to find solutions that others didn’t know existed. But having domain knowledge or expertise is not enough. What you do with that knowledge matters as much. Do you refuse to share knowledge with others or do you find opportunities to lift others up with it? You build credibility when your knowledge has an impact beyond projects and tasks. Helping others acquire your expertise even in small ways builds trust. They come to rely on you for guidance and advice. Lifting others up also has another advantage—they’re able to do your job which creates an opportunity for you to take on more challenging and complex tasks. 9. Be willing to change and adapt to the future needs Things change at a very fast pace in our work environments. When you stick to things you’ve always done before or choose a safe path, you may get the job done, but staying within your comfort zone also limits your growth. Ignoring future needs or not adapting to them makes your skills obsolete. You keep getting lower-level responsibilities that keep you busy but do not lead to growth. Employees who build credibility don’t lose sight of the future. They analyze what the future demands and constantly invest time in keeping up with it. Adopting a learning attitude helps them build new skills which keeps them prepared to tackle whatever comes their way. Efficiency remains important, but the ability to adapt to complexity and continual change has become an imperative. — Stanley McChrystal 10. Show up on time When you’re invited for a discussion or a meeting, are you most likely to be late? Do you even miss the clock when it’s you who invited others? You may think that a few minutes here and there don’t matter, but they do. Coming late makes others feel that you’re taking them for granted. Every additional second of waiting adds to their frustration and anxiety. People who build credibility are very conscious of time. They not only utilize their time well but also care for others' time as well. They don’t waste time in pointless debates, unnecessary meetings or by making others wait. Respecting other people’s time is what builds trust and respect. Summary Credibility isn’t built by fulfilling your job role and description. It requires mastering simple habits and incorporating them into your daily work and life. Doing a good job of the assigned tasks makes you sincere, not credible. To build credibility, do important work without being asked. Saying yes to only certain kinds of work and no to others makes you inflexible. You can’t be inflexible and credible at the same time. Pushing responsibility up the chain of command prevents you from taking control. Relying on others to fix the situation does not build credibility. Instead of waiting for things to go wrong and then reacting to it, credible employees proactively identify risks and make plans to address it. Credible employees simply don’t state problems. They also think through multiple ways to solve it. While most employees waste time worrying about things outside their control, credible employees look internally to their own behaviors and actions. Credible employees prioritize their team over their personal interests. They seek a balance between interests, opportunities and doing work that matters. Instead of withholding knowledge, credible employees share it with others. Helping others do well lifts them up as well. Sticking to old methods feels safe, but it also limits your growth. Build credibility by expanding your comfort zone and taking a step outside it. Turning up late to meetings kills credibility. How can others respect you when you don’t respect their time? This story was previously published . here Follow me on or here for more stories. LinkedIn

10 Effective Tips for Building Credibility at Work 

Marrying and impact is nothing new. From dynamic and value chain traceability to the exploration of mechanisms like quadratic funding for public goods, the fusion of technology and impact continues to break new ground. web3 NFTs Now, there’s another innovation in the spotlight: . Born out of research by Protocol Labs started in the middle of last year, the new protocol answers the difficult question: How can we better align incentives to work on impactful work that might not have a business model or might be viewed as high-risk but potentially high upside? hypercerts Hypercerts propose a solution: Displaying impact on chain. As fractionalized, semi-fungible tokens, they enable the capture, definition, and representation of impact recorded on an immutable ledger. Such an improvement in transparency and evaluation structure paves the way for a better understanding of positive impact, fueling more funding and recognition. And, with a dash of retrospective funding magic, hypercerts’ ultimate goal is to empower humans to rather than mere intentions—by actually providing resources after the fact. fund tangible impact So, how exactly do hypercerts work, and how can they transform areas like carbon credits? Hypercert: The new public good primitive Leveraging , hypercerts’ creators defined a single hypercerts standard. Users can thus populate the tokens with unique information and create various classes of hypercerts—all managed within a single smart contract. This way, the hypercerts protocol represents a single, open, shared, and decentralized database for different impact funding mechanisms. ERC-1155 The philosophy is straightforward: Today’s economic systems don’t reflect what we value as humans, such as environmental and social well-being. Yet, we do have the power to design systems that guide our actions and reward the outcomes we desire. Hypercerts present an opportunity to establish structures that favor positive impact, support the funding of important causes, and implement transparent, trustworthy evaluation methods, effectively bridging the traditional divide between private and public sectors. NFTs have shown us that powerful and dynamic marketplaces can exist in the web3 world when the right tools are employed. By combining human values with large-scale structures, we can make a significant difference in our societies. Hypercerts facilitate coordination, laying the groundwork for various decision and funding mechanisms, including quadratic voting/funding, auctions, voting, bounties, grants, and more. And, while they don’t solve the coordination issues on their own, they do provide a solid foundation for these diverse strategies. The ability to “enable” makes hypercerts a great . In the context of web3, Francesco Renzi from Superfluid as a fundamental functionality upon which everything else is built. For example, in the automotive world, the wheel is a primitive that enables the construction of cars. So, introducing more primitives allows for the development of entirely new systems. public good primitive describes a primitive Therefore, hypercerts empower people to identify impactful work, leading to the creation of new building blocks for assessing community values, determining the most influential work, and, ultimately, enabling the development of novel utilities on top of these foundations. And for funders, hypercerts provide a convenient way to “put their wallet where their values are.” How do hypercerts work? Hypercerts serve as an interoperable data layer, creating a standardized method to capture key information about work intended to have a positive impact. Each token represents a claim detailing the scope of work performed by specific contributors within a given time frame and the scope of impact achieved during a specified period. Gitcoin has already retrospectively granted hypercert fractions to anyone who participated with at least $1 DAI in its Alpha Grants Round (in January) and Beta Grants Round (in April). All the metadata was stored on IPFS; the hypercerts protocol itself lives on Optimism (though you can also experiment with it on Ethereum’s Goerli Testnet). Each hypercert contains information about: The scope of work and its corresponding impact Time frames for both the work and its impact Contributors involved in the project Rights obtained through hypercert ownership Let’s look at how hypercert funding can work step by step Each effort involves various stakeholders: beneficiaries, contributors, prospective funders, retrospective funders, and evaluators. To receive funds, contributors develop an impact activity, create hypercerts defining the impact they aim to achieve and obtain funding from prospective funders. Upon minting the hypercerts, fractions are awarded to the prospective funders in—this is the initial, highest-risk stage. As the project progresses and creates impact, funders enter the scene to finance evaluators, who then assess the impact on beneficiaries. Evaluators create and publicize impact reports, allowing retrospective funders to compare the project’s goals with their results. This process can lead to the disbursement of award funds based on any further retrospective funding. At that point, the interesting thing is that hypercerts represent , enabling multiple evaluations to point to the same hypercert, increasing the variety of methodologies used, and allowing evaluations to differ over time. This, naturally, facilitates learning about the most effective impact assessment methodologies over time. completed work With an evaluation layer embedded into the semi-fungible “NFTs”, contributors must clearly define their goals and commitments beforehand, making evaluators’ jobs much easier. This separates the interests of projects and evaluators, as independent impact evaluators can remain neutral and are incentivized to develop a reputation for credibility. For hypercerts, ideal funders are then those who care about impact and seek to fund projects with credible impact claims and high-quality evaluations from reputable evaluators. For example, they could establish an impact evaluation pool, allocating a percentage of funding to reward impact evaluations for all projects under their consideration. If they choose to support an effort with retrospective funding, they can buy more hypercerts fractions from contributors and even the prospective funders who retain a fraction of their hypercerts. This mechanism has a crucial role: It allows contributors to work now, anticipating future cash flow for their positive impact, effectively "borrowing" from future funding. This approach incentivizes them to undertake public goods projects with uncertain yet potentially high impact and promotes a more efficient market by evaluating impactful outcomes post-hoc. We spoke to Carl Cervone, Holke Brammer, and Raymond Cheng, some of the key minds behind the hypercert protocol, about what happens at the very last step—once the contributors receive retrospective funding. Raymond shares that it’s the projects themselves that must decide how to use their funding best, often applying it to support ongoing operations and development. The way projects utilize funding will likely contribute to their overall reputation, as funding events on impact claims are expected to be a regular occurrence. According to Carl, retrospective funding supports various project needs, including compensating contributors, funding the next phase of work, or prospectively repaying backers who financed the work. The long-term goal is to encourage more people to do good work without waiting for grant proposals. Let’s talk carbon credits Carbon credits provide financial incentives for reducing greenhouse gas emissions by assigning a monetary value to the cost of polluting air. Since their trade began in 1997, they have become a key instrument for fighting climate change. But even today, carbon offsets continue to face significant challenges, such as a lack of standardization and methodology, low trust in credit quality, and fragmented marketplaces. But hypercerts could change that. Hypercerts offer a new perspective on carbon credits, democratizing the process and adding more rigor to evaluations. By addressing many of the Monitoring, Reporting, and Verification (MRV) challenges, they facilitate a more effective, trustworthy system for verifying and registering environmental impact. As Carl Cervone explains, hypercerts capture the work that was done, but there is still often still a need to evaluate the impact of the work. For example, we could create a hypercert for planting 10 trees. If you buy the hypercert, then you would own any impact that comes from our work. But someone would still need to measure how much carbon those trees removed. From water preservation to biodiversity, hypercerts force participants to fit their proposed impact into a framework, making it easier to incentivize or feel comfortable providing the funding. This allows contributors to better manage both capital and operating costs. For projects like tree planting, while the initial cost may not be high, the cost of maintaining the project can be significant. Hypercerts become a means to secure upfront funding to cover these costs and continue funding the sequestration process through the sale of additional fractions as the project starts generating impact. Yet, in order for that to happen successfully, there should be more transparency around how credits are created and where they are sourced from. Hypercerts can’t help us access data points to create this provenance and make sure that the data is verifiable. Imagine a small community starting a reforestation venture. They don’t have much capital, but with hypercerts, they can still secure initial funding for the project. As the trees grow and sequester carbon, the community sells more hypercert fractions, funding ongoing sustenance and further tree planting. This way, the community is incentivized to maintain the project for the long term, while backers can trust their contributions are making a real, measurable impact. It’s the retrospective funding piece that makes it really interesting. It encourages anyone to start working on a project—such as agroforestry—much like academic research. Let’s say a small-scale farmer or landowner wants to restore a degraded piece of land, knowing it has the potential to sequester huge amounts of carbon. They might think twice if they can't get money upfront or know for sure they'll earn carbon credits later. Without guaranteed upfront funding or the assurance of carbon credits, they may be reluctant to embark on this journey. With hypercerts, the landowner can begin the restoration work with the understanding that they will be able to generate and sell carbon credits based on the actual amount of carbon their project sequesters. As the project progresses and the land begins to recover, storing more carbon, the landowner can sell more fractions. If a contributor believes in their project’s potential, hypercerts can provide ongoing funding for maintenance and further restoration. Funders, on the other hand, can easily access the impact data of the project they fund, linking their funding directly to quantifiable carbon sequestration. This brings a more intuitive way to understand the information that’s being put in front of them. Simply put, hypercerts confront your impact goals with the results—so you can see all the information for yourself. So, where does this leave us? We believe that hypercerts do more than just legitimize carbon credits funding—they unlock projects previously deemed unviable due to a lack of initial funding. They represent a step toward a more inclusive, diverse impact dynamic, and we’re eager to see the ripples they create—in carbon and beyond. - By & Nicholas Edmonds Tereza Bizkova

What Are Hypercerts & How Can They Transform Impact Certification? (+ Carbon Credits Example)

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