Will Randy Arozarena and the Rays earn a win over their AL East rivals when they face Juan Soto and the Yankees in the opening game of a three-game set Friday night?

Tampa Bay Rays vs. New York Yankees Prediction, Preview, and Odds - 4-19-2024

Hot-hitting Amed Rosario went 2-for-4 with a triple, Ryan Pepiot fired six strong innings and the Tampa Bay Rays managed a split of their four-game series against the Los Angeles Angels, beating them 2-1 Thursday afternoon in St. Petersburg, Fla.

Amed Rosario, Ryan Pepiot help Rays split 4-game set with Angels

Complete team stats and game leaders for the Los Angeles Angels vs. Tampa Bay Rays MLB game from April 18, 2024 on ESPN.

Angels vs. Rays (Apr 18, 2024) Game Stats - ESPN

Tampa Bay Rays closer Pete Fairbanks gave an unusual assessment of his pitching performance after he allowed two runs in a loss to the Los Angeles Angels.

Rays' Pete Fairbanks gives blunt assessment of pitching performance after loss | Fox News

picks, predictions, best bets, and betting preview for the Los Angeles Angels and Tampa Bay Rays on Thursday afternoon

Los Angeles Angels vs Tampa Bay Rays Prediction, Odds and Picks | Pickswise

Can the Rays slug past the Angels on Thursday afternoon?

Los Angeles Angels vs. Tampa Bay Rays Prediction, Preview, and Odds - 4-18-2024

I know he was really unhappy about it, but I love what Pete Fairbanks had to say.

Rays Closer Pete Fairbanks With a Wonderful and Brutally Honest Line About His Performance - Bleacher Nation

Notes | The Angels’ Nolan Schanuel breaks from first to second, which later leads to a run scored, before Zack Littell and the infielders are fully aware.

A lesson for the Rays: Keep an eye on the man at first base

Tampa Bay Rays vs. LA Angels 4/18/24: MLB free preview, analysis, prediction, odds and pick against the spread.

LA Angels vs Tampa Bay Rays Prediction MLB Picks 4/18/24 - PickDawgz

2.2K votes, 114 comments. 2.6M subscribers in the baseball community. The BIGGEST and BEST subreddit for America&#x27;s pastime: baseball. The focus is…

r/baseball on Reddit: Nolan Schanuel sneaks his way to 2nd base against the Rays

Raiys

raiys

Boom! In a flash, MFEV Coin’s public sale’s status went from live to SOLD OUT, leaving a wave of FOMO in its wake. This wasn’t just any sale; it was one of the most anticipated events in the crypto world this year, and the hype did not disappoint. Coins flew off the shelves, and if you blinked, you missed it. Now, the question on everyone’s mind is — what’s next for MFEV and those who missed out? Why MFEV Coin Is the Hottest Ticket in Web3 Right Now This wasn’t just luck; MFEV Coin has been building serious buzz. Here’s why everyone’s talking about it: Revolutionary Proof-of-Distribution Model: Unlike other chains, MFEV rewards validators, delegators, and smart contract creators, making it one of the most inclusive platforms out there.
Insane Interoperability: MFEV is bridging blockchains like a pro, allowing seamless asset transfers and easy migration for projects from EVM-based platforms.
Built for Real People: With low fees, high speed, and a killer UX, MFEV is out to make blockchain accessible for everyone, not just the crypto pros. It’s no wonder retail and institutional investors jumped in headfirst. MFEV’s unique approach and game-changing tech created a buying frenzy that few saw coming — and many are kicking themselves for not getting in on it. The Numbers Don’t Lie: Overall $47 Million Raised MFEV has collectively raised a massive $47.25 million in the Seed, Private, Pre-sale, and Public Round ICO, with each phase fully selling out. MFEV has sealed its spot as one of the most successful projects of the year. From Gen Z crypto enthusiasts to savvy investors, everyone wanted a piece — and only the fastest got in. Missed Out? Here’s What You Need to Know If you missed the public sale, you’re not alone — the speed at which coin sold out has left many eager fans and would-be investors wondering about their next move. The good news? MFEV’s journey is just beginning. Keep an eye out for upcoming listings, potential exchange drops, and staking opportunities. FOMO Kicking In? Here’s What’s Next for MFEV Coin For those lucky enough to grab an MFEV coin, the future is looking bright. MFEV’s roadmap is packed with releases, from a forex trading platform and crypto exchange to a metaverse and NFT marketplace. With this level of excitement, it’s clear MFEV isn’t just another blockchain project — it’s positioning itself as a serious player in the next generation of Web3. The Takeaway: MFEV Coin Isn’t Just Hype — It’s Here to Stay With this sell-out public sale, MFEV has shown it’s the real deal. This isn’t just another coin; it’s a movement. As MFEV gears up for its next steps, all eyes are on what this powerhouse blockchain will do next. And for those who missed out? You’ll want to stay close, because this story is only getting started. MFEV will be listed soon on crypto exchanges. Web3 fans, Gen Z investors, and everyone who loves a good FOMO moment — MFEV Coin is proving that it’s more than hype. Keep watching because the best is yet to come! This press release contains forward-looking statements, including statements regarding company’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition. The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements.
This article is published under HackerNoon’s Business Blogging program. Boom! In a flash, MFEV Coin’s public sale’s status went from live to SOLD OUT, leaving a wave of FOMO in its wake. This wasn’t just any sale; it was one of the most anticipated events in the crypto world this year, and the hype did not disappoint. Coins flew off the shelves, and if you blinked, you missed it. Now, the question on everyone’s mind is — what’s next for MFEV and those who missed out? Why MFEV Coin Is the Hottest Ticket in Web3 Right Now This wasn’t just luck; MFEV Coin has been building serious buzz. Here’s why everyone’s talking about it: MFEV Revolutionary Proof-of-Distribution Model: Unlike other chains, MFEV rewards validators, delegators, and smart contract creators, making it one of the most inclusive platforms out there. Insane Interoperability: MFEV is bridging blockchains like a pro, allowing seamless asset transfers and easy migration for projects from EVM-based platforms. Built for Real People: With low fees, high speed, and a killer UX, MFEV is out to make blockchain accessible for everyone, not just the crypto pros. Revolutionary Proof-of-Distribution Model: Unlike other chains, MFEV rewards validators, delegators, and smart contract creators, making it one of the most inclusive platforms out there. Insane Interoperability: MFEV is bridging blockchains like a pro, allowing seamless asset transfers and easy migration for projects from EVM-based platforms. Built for Real People: With low fees, high speed, and a killer UX, MFEV is out to make blockchain accessible for everyone, not just the crypto pros. It’s no wonder retail and institutional investors jumped in headfirst. MFEV’s unique approach and game-changing tech created a buying frenzy that few saw coming — and many are kicking themselves for not getting in on it. The Numbers Don’t Lie: Overall $47 Million Raised MFEV has collectively raised a massive $47.25 million in the Seed, Private, Pre-sale, and Public Round ICO, with each phase fully selling out. MFEV has sealed its spot as one of the most successful projects of the year. From Gen Z crypto enthusiasts to savvy investors, everyone wanted a piece — and only the fastest got in. Missed Out? Here’s What You Need to Know If you missed the public sale, you’re not alone — the speed at which coin sold out has left many eager fans and would-be investors wondering about their next move. The good news? MFEV’s journey is just beginning. Keep an eye out for upcoming listings, potential exchange drops, and staking opportunities. FOMO Kicking In? Here’s What’s Next for MFEV Coin For those lucky enough to grab an MFEV coin, the future is looking bright. MFEV’s roadmap is packed with releases, from a forex trading platform and crypto exchange to a metaverse and NFT marketplace. With this level of excitement, it’s clear MFEV isn’t just another blockchain project — it’s positioning itself as a serious player in the next generation of Web3. The Takeaway: MFEV Coin Isn’t Just Hype — It’s Here to Stay With this sell-out public sale, MFEV has shown it’s the real deal. This isn’t just another coin; it’s a movement. As MFEV gears up for its next steps, all eyes are on what this powerhouse blockchain will do next. And for those who missed out? You’ll want to stay close, because this story is only getting started. MFEV will be listed soon on crypto exchanges. MFEV MFEV Web3 fans, Gen Z investors, and everyone who loves a good FOMO moment — MFEV Coin is proving that it’s more than hype. Keep watching because the best is yet to come! This press release contains forward-looking statements, including statements regarding company’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition. The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements. This article is published under HackerNoon’s Business Blogging program. This press release contains forward-looking statements, including statements regarding company’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition. The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements. This press release contains forward-looking statements, including statements regarding company’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition. The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements. This article is published under HackerNoon’s Business Blogging program. This article is published under HackerNoon’s Business Blogging program. Business Blogging

MFEV Coin's Public Sale: $47.25 Million Raised

Veřejný prodej MFEV Coin: 47,25 milionu dolarů

Venta pública de la moneda MFEV: se recaudaron 47,25 millones de dólares

MFEV մետաղադրամի հանրային վաճառք՝ $47,25 միլիոն հավաքագրված

MFEVコインの公開販売：4,725万ドルを調達

MFEV монетасының ашық сатылымы: 47,25 миллион доллар жиналды

Viešas MFEV monetų išpardavimas: surinkta 47,25 mln. USD

Shitja publike e monedhës MFEV: 47,25 milion dollarë të mbledhura

Јавна продаја МФЕВ новчића: Прикупљено 47,25 милиона долара

Mauzo ya Umma ya Sarafu ya MFEV: Dola Milioni 47.25 Zimeongezwa

MFEV நாணயத்தின் பொது விற்பனை: $47.25 மில்லியன் திரட்டப்பட்டது

การขายต่อสาธารณะของเหรียญ MFEV ระดมทุนได้ 47.25 ล้านดอลลาร์

MFEV Coin کی عوامی فروخت: $47.25 ملین اکٹھا ہوئے۔

WILMINGTON, Delaware, November 7th, 2024/Chainwire/--Rekt Brands Inc. (Rekt), the parent company behind the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property, is excited to announce the completion of a $1.5 million seed funding round. Funded exclusively by angel investors and the community—without institutional venture capital—this achievement highlights the support Rekt received from its loyal and growing network. This funding announcement follows the debut of Rekt Drinks, Rekt’s first consumer product: a lime-flavored sparkling water with zero caffeine and zero alcohol, featuring the Rekt branding. Rekt Drinks made its debut last week with 222,456 units available across 32 countries. The entire stock sold out in under 48 hours, with demand in the U.S. alone resulting in a complete sell-out within just 4 hours and 20 minutes. This launch represents one of the largest real-world product sales by a Web3 brand, reinforcing Rekt’s potential as a cultural force in both digital and traditional markets. Rekt was founded from the success of the Rektguy NFT collection, created by artist Ovie Faruq (OSF) in May 2022. Since then, the Rektguy project has expanded into Rekt Brands, which oversees the Rekt IP and pioneered a unique industry model by offering equity in the parent company directly to its NFT holders, utilizing a Reg CF exemption under SEC guidelines in the U.S. “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” With this new funding, Rekt is poised to expand its product lineup, strengthen its brand awareness in and outside of Web3, while further bridging digital culture with physical consumer products. The Rekt community remains at the heart of the brand’s mission, with upcoming projects that will continue to empower, engage, and reward its dedicated supporters. About Rekt Rekt Brands Inc. is the parent company that owns the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property. Established from the success of the Rektguy NFT collection by artist Ovie Faruq (OSF), the brand has expanded into consumer goods and groundbreaking community equity models. Rekt aims to redefine brand ownership and engagement through its Web3 foundation, innovative products, and viral global community. Contact CEO Ovie Faruq Rekt Brands Inc. ovie@rektdrinks.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here WILMINGTON, Delaware, November 7th, 2024/Chainwire/--Rekt Brands Inc. (Rekt), the parent company behind the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property, is excited to announce the completion of a $1.5 million seed funding round. Funded exclusively by angel investors and the community—without institutional venture capital—this achievement highlights the support Rekt received from its loyal and growing network. This funding announcement follows the debut of Rekt Drinks , Rekt’s first consumer product: a lime-flavored sparkling water with zero caffeine and zero alcohol, featuring the Rekt branding. Rekt Drinks made its debut last week with 222,456 units available across 32 countries. Rekt Drinks Rekt Drinks The entire stock sold out in under 48 hours, with demand in the U.S. alone resulting in a complete sell-out within just 4 hours and 20 minutes. This launch represents one of the largest real-world product sales by a Web3 brand, reinforcing Rekt’s potential as a cultural force in both digital and traditional markets. Rekt was founded from the success of the Rektguy NFT collection, created by artist Ovie Faruq (OSF) in May 2022. Since then, the Rektguy project has expanded into Rekt Brands, which oversees the Rekt IP and pioneered a unique industry model by offering equity in the parent company directly to its NFT holders, utilizing a Reg CF exemption under SEC guidelines in the U.S. Rektguy Rektguy Ovie Faruq (OSF) Ovie Faruq (OSF) “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” With this new funding, Rekt is poised to expand its product lineup, strengthen its brand awareness in and outside of Web3, while further bridging digital culture with physical consumer products. The Rekt community remains at the heart of the brand’s mission, with upcoming projects that will continue to empower, engage, and reward its dedicated supporters. About Rekt Rekt Brands Inc. is the parent company that owns the Rektguy NFT project, Rekt Drinks , and the Rekt brand intellectual property. Established from the success of the Rektguy NFT collection by artist Ovie Faruq (OSF) , the brand has expanded into consumer goods and groundbreaking community equity models. Rekt aims to redefine brand ownership and engagement through its Web3 foundation, innovative products, and viral global community. Rekt Brands Inc. Rekt Brands Inc. Rektguy Rektguy Rekt Drinks Rekt Drinks Rektguy Rektguy Ovie Faruq (OSF) Ovie Faruq (OSF) Contact CEO Ovie Faruq Rekt Brands Inc. ovie@rektdrinks.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Rekt Raises $1.5M Seed Round Backed By Angels and Community, Following Sell-Out Of Rekt Drinks

Rekt samel $1,5 miljoen saadronde in, gerugsteun deur engele en gemeenskap, na die uitverkoop van Rekt-drankies

Rekt набира 1,5 милиона долара начален кръг, подкрепен от ангели и общност, след разпродаването на Rekt напитки

Η Rekt συγκεντρώνει $1,5 εκατομμύρια σπόρους με την υποστήριξη των αγγέλων και της κοινότητας, μετά το ξεπούλημα των ποτών Rekt

Rekt recauda 1,5 millones de dólares en una ronda de capital semilla respaldada por inversores ángeles y la comunidad, tras la venta de Rekt Drinks

רקט מגייס 1.5 מיליון דולר סבב זרעים בגיבוי מלאכים וקהילה, בעקבות מכירת משקאות רקט

Rekt prikupio 1,5 milijuna dolara početni krug uz podršku anđela i zajednice, nakon rasprodaje Rekt pića

Rekt Mengumpulkan Dana Awal Sebesar $1,5 Juta yang Didukung oleh Angel dan Komunitas, Setelah Penjualan Habis dari Rekt Drinks

Rekt は Rekt ドリンクの完売を受けて、エンジェル投資家とコミュニティの支援を受けて 150 万ドルのシードラウンドを調達

Рэкт ундаануудаа зарсны дараа Тэнгэр элч нар болон олон нийтийн дэмжлэгтэйгээр 1.5 сая долларын үрийг цуглуулж байна.

Rekt haalt $ 1,5 miljoen op in een startkapitaalronde, gesteund door investeerders en de gemeenschap, na de uitverkoop van Rekt Drinks

Rekt ระดมทุนรอบ Seed Round ได้ 1.5 ล้านเหรียญสหรัฐ โดยได้รับการสนับสนุนจาก Angels และชุมชน หลังจากการขายเครื่องดื่ม Rekt หมดเกลี้ยง

Rekt, Rekt içgileriniň satylmagyndan soň, perişdeler we jemgyýet tarapyndan goldanýan 1.5 million dollarlyk tohum tegelek ýygnaýar

Ethereum co-founder Vitalik Buterin has found himself in the spotlight again, this time, over his charitable engagement with meme coin projects. Buterin recently acknowledged two meme coins, EBULL and MOODENG, for their charitable contributions, stating that any digital tokens sent to him would be donated to charity. This statement has sparked conversations within the crypto community, particularly regarding the potential risks for investors. Vitalik's Acknowledgment of Meme Coins In a tweet shared this week, Vitalik Buterin expressed appreciation for meme coin projects that are donating portions of their supply directly to charity. He specifically mentioned the EBULL and MOODENG tokens, acknowledging their efforts in supporting charitable causes, and noted that digital tokens sent to him would be donated. The 10 billion MOODENG tokens, for example, were directed towards anti-airborne disease technology. While Buterin's gesture appeared genuine, it did not go unnoticed by the crypto community, where questions arose regarding the impact of such public endorsements. The acknowledgment of meme coin projects that may not have undergone thorough scrutiny has raised concerns among investors and market analysts. Concerns From the Community One notable figure in the crypto space, known as Crypto Rug Muncher, who has built a reputation for exposing potential cryptocurrency scams, voiced concerns  about Buterin's actions. Crypto Rug Muncher specifically raised alarms since September this year, about the EBULL token, claiming it could be a potential scam, and is warning now that public endorsements from influential figures could inadvertently legitimize projects that may not have transparent operations and could even be fraudulent. According to Crypto Rug Muncher, the acknowledgment of EBULL meme coins contributed to a price surge in this digital token, attracting new crypto investors. However, he warned that such recognition could allow insiders to capitalize on the elevated prices by selling off their holdings, potentially leaving unsuspecting buyers with losses. Crypto Rug Muncher emphasized that charitable donations are commendable and should not come at the cost of investor safety. He argued that projects might misuse the charity angle to gain credibility and attract more investors, all while masking potentially deceptive practices. His specific concerns about the EBULL token highlight the risks of boosting projects without thorough due diligence. Ethical Questions About Promotion and Due Diligence The situation has also prompted broader discussions on the ethical responsibility of public figures and crypto platforms when promoting crypto projects, even indirectly. The crypto industry is no stranger to scams, and cryptocurrency investors often rely on the endorsements of trusted figures. This makes it critical for those with influence to exercise caution and conduct due diligence before publicly acknowledging any projects. Many in the community believe that even charitable gestures can have unintended consequences, especially if the promoted projects turn out to be fraudulent. The key question that arises is: Should influential figures be more cautious when accepting tokens for charity from projects that may not have undergone proper vetting? Conclusion Vitalik Buterin's recent actions have brought to light the delicate balance between supporting charitable causes and maintaining investor trust. While Buterin's intentions may have been noble, the reaction from the community underscores the importance of thorough due diligence, even when engaging in charity. As the crypto market continues to evolve, transparency and caution are essential to prevent any unintended harm to investors. Ethereum co-founder Vitalik Buterin has found himself in the spotlight again, this time, over his charitable engagement with meme coin projects. Buterin recently acknowledged two meme coins, EBULL and MOODENG, for their charitable contributions, stating that any digital tokens sent to him would be donated to charity. This statement has sparked conversations within the crypto community, particularly regarding the potential risks for investors. Vitalik's Acknowledgment of Meme Coins In a tweet shared this week, Vitalik Buterin expressed appreciation for meme coin projects that are donating portions of their supply directly to charity. He specifically mentioned the EBULL and MOODENG tokens, acknowledging their efforts in supporting charitable causes, and noted that digital tokens sent to him would be donated. The 10 billion MOODENG tokens, for example, were directed towards anti-airborne disease technology. tweet While Buterin's gesture appeared genuine, it did not go unnoticed by the crypto community, where questions arose regarding the impact of such public endorsements. The acknowledgment of meme coin projects that may not have undergone thorough scrutiny has raised concerns among investors and market analysts. Concerns From the Community One notable figure in the crypto space, known as Crypto Rug Muncher, who has built a reputation for exposing potential cryptocurrency scams, voiced concerns about Buterin's actions. Crypto Rug Muncher specifically raised alarms since September this year, about the EBULL token, claiming it could be a potential scam, and is warning now that public endorsements from influential figures could inadvertently legitimize projects that may not have transparent operations and could even be fraudulent. voiced concerns According to Crypto Rug Muncher, the acknowledgment of EBULL meme coins contributed to a price surge in this digital token, attracting new crypto investors. However, he warned that such recognition could allow insiders to capitalize on the elevated prices by selling off their holdings, potentially leaving unsuspecting buyers with losses. Crypto Rug Muncher emphasized that charitable donations are commendable and should not come at the cost of investor safety. He argued that projects might misuse the charity angle to gain credibility and attract more investors, all while masking potentially deceptive practices. His specific concerns about the EBULL token highlight the risks of boosting projects without thorough due diligence. Ethical Questions About Promotion and Due Diligence The situation has also prompted broader discussions on the ethical responsibility of public figures and crypto platforms when promoting crypto projects, even indirectly. The crypto industry is no stranger to scams, and cryptocurrency investors often rely on the endorsements of trusted figures. This makes it critical for those with influence to exercise caution and conduct due diligence before publicly acknowledging any projects. broader discussions Many in the community believe that even charitable gestures can have unintended consequences, especially if the promoted projects turn out to be fraudulent. The key question that arises is: Should influential figures be more cautious when accepting tokens for charity from projects that may not have undergone proper vetting? Conclusion Vitalik Buterin's recent actions have brought to light the delicate balance between supporting charitable causes and maintaining investor trust. While Buterin's intentions may have been noble, the reaction from the community underscores the importance of thorough due diligence, even when engaging in charity. As the crypto market continues to evolve, transparency and caution are essential to prevent any unintended harm to investors.

Vitalik Buterin's Charity Meme Coins Donations Raise Questions in Crypto Community

Leveraging Turbo 8: Best Additions to Implement in Rails 8 Projects

The Rails World 2024 conference ended last week, and a lot of new components of the Rails ecosystem were released during it including Kamal 2. I decided to make some preparations for the upcoming changes. In March 2024, the Basecamp team released Thruster, a small, Go-based proxy server for Rails. While the original idea of Thruster was to provide the ability to build production-ready Rails applications from a single container for the ONCE project, it will fit perfectly into the Kamal ecosystem. Isn’t It a Redundancy? Wait? Another proxy server? Why do we need it? Allow me to explain. It doesn’t matter whether we use Traefik with Kamal v1 or Kamal-proxy with Kamal v2; inside the container, we still run Puma (or any other Ruby application server) that serves requests to the application. Besides this, Puma is also responsible for serving application assets – images, stylesheets, JavaScript files, etc. – to the clients. Before Kamal came into the scene, we usually had Nginx in our infrastructure schema. Nginx is a classic web server that can serve static files efficiently. Unfortunately, it is not adapted to container-based setups as you need to manage its configuration via a config file. However, without Nginx, Puma is not the best choice to serve static files. This is where Thruster comes into play. It wraps the Puma instance and efficiently serves static files with X-Sendfile acceleration, while Puma is responsible for the dynamic content. Thruster also provides GZip compression which was available in Traefik via middleware but won’t exist in kamal-proxy. GZip compression plays a crucial role in optimizing web performance by significantly reducing the size of files transmitted between a server and a client. By compressing assets like HTML, CSS, and JavaScript, GZIP decreases bandwidth usage and accelerates the loading times of web pages, providing users with a faster, more seamless browsing experience. This not only enhances user satisfaction but also improves search engine rankings, as faster websites tend to rank higher in search results. Additionally, reducing the size of data transfers lightens the load on servers, allowing them to handle more requests efficiently and lowering hosting costs. A reasonable question would be “Why I cannot do a GZIP compression in Rails?” Technically, you can with Rack::Deflater. But since Thruster is already responsible for serving public assets, it can also efficiently handle compression. Besides compression, Thruster also provides basic HTTP caching of public assets and HTTP/2 support. With Thruster, your Rails application can be treated as a black box, which can serve any kind of request efficiently. How to Use Thruster is straightforward to use if you’re already using Puma. First, you need to add Thruster to your Gemfile: # Gemfile

gem 'thruster' Run bundle install, and you are ready to go. Since Thruster is written in Go, the platform-specific binary will be downloaded and installed with the gem. I always create binstubs to have all the necessary commands in one place: $ bundle binstubs thruster By default, Thruster will listen on port 80, while Puma will stay on port 3000. Knowing this, we should update our application Dockerfile accordingly. I assume here that your application is already dockerized. I replaced the port that is getting exposed from 3000 to 80 and added ./bin/thrust to the default command. # Dockerfile

# Entrypoint prepares the database.
ENTRYPOINT ["/rails/bin/docker-entrypoint"]

# Start server via Thruster by default, this can be overwritten at runtime
EXPOSE 80
CMD ["./bin/thrust", "./bin/rails", "server"] The last change is to update ./bin/docker-entrypoint script. I always try to keep it as close to the original Rails template as possible. Below is the final version of the script. #!/bin/bash -e

# If running the rails server then create or migrate existing database
if [ "${@: -2:1}" == "./bin/rails" ] && [ "${@: -1:1}" == "server" ]; then
  ./bin/rails db:prepare
fi

exec "${@}" That is all! Thanks to the Traefik container-based configuration, it will read the exposed port from the container and route the traffic accordingly after you deploy the changes. After deploy is finished, you can log in to the container and check if Thruster is running: rails@docker:~$ ps aux
USER         PID %CPU %MEM    VSZ   RSS TTY      STAT START   TIME COMMAND
rails          1  0.0  0.3 1232848 13124 ?       Ssl  09:26   0:06 /usr/local/bundle/ruby/3.3.0/gems/thruster-0.1.8-x86_64-linux/exe/x86_64-linux/thrust ./bin/rails server Summary Since there are no visible cons of using Thruster in your Rails application, I highly recommend using it. It can improve the performance of your application and reduce the load on the server. It is also a good idea to use it as a preparation for the upcoming Kamal 2 release. The Rails World 2024 conference ended last week, and a lot of new components of the Rails ecosystem were released during it including Kamal 2. I decided to make some preparations for the upcoming changes. In March 2024, the Basecamp team released Thruster , a small, Go-based proxy server for Rails. While the original idea of Thruster was to provide the ability to build production-ready Rails applications from a single container for the ONCE project, it will fit perfectly into the Kamal ecosystem. Thruster Isn’t It a Redundancy? Wait? Another proxy server? Why do we need it? Allow me to explain. It doesn’t matter whether we use Traefik with Kamal v1 or Kamal-proxy with Kamal v2; inside the container, we still run Puma (or any other Ruby application server) that serves requests to the application. Besides this, Puma is also responsible for serving application assets – images, stylesheets, JavaScript files, etc. – to the clients. Before Kamal came into the scene, we usually had Nginx in our infrastructure schema. Nginx is a classic web server that can serve static files efficiently. Unfortunately, it is not adapted to container-based setups as you need to manage its configuration via a config file. However, without Nginx, Puma is not the best choice to serve static files. This is where Thruster comes into play. It wraps the Puma instance and efficiently serves static files with X-Sendfile acceleration, while Puma is responsible for the dynamic content. Thruster also provides GZip compression which was available in Traefik via middleware but won’t exist in kamal-proxy. GZip compression plays a crucial role in optimizing web performance by significantly reducing the size of files transmitted between a server and a client. By compressing assets like HTML, CSS, and JavaScript, GZIP decreases bandwidth usage and accelerates the loading times of web pages, providing users with a faster, more seamless browsing experience. via middleware This not only enhances user satisfaction but also improves search engine rankings, as faster websites tend to rank higher in search results. Additionally, reducing the size of data transfers lightens the load on servers, allowing them to handle more requests efficiently and lowering hosting costs. A reasonable question would be “Why I cannot do a GZIP compression in Rails?” Technically, you can with Rack::Deflater . But since Thruster is already responsible for serving public assets, it can also efficiently handle compression. Rack::Deflater Besides compression, Thruster also provides basic HTTP caching of public assets and HTTP/2 support. With Thruster, your Rails application can be treated as a black box, which can serve any kind of request efficiently. With Thruster, your Rails application can be treated as a black box, which can serve any kind of request efficiently. How to Use Thruster is straightforward to use if you’re already using Puma. First, you need to add Thruster to your Gemfile: # Gemfile

gem 'thruster' # Gemfile

gem 'thruster' Run bundle install, and you are ready to go. Since Thruster is written in Go, the platform-specific binary will be downloaded and installed with the gem. bundle install, I always create binstubs to have all the necessary commands in one place: $ bundle binstubs thruster $ bundle binstubs thruster By default, Thruster will listen on port 80, while Puma will stay on port 3000. Knowing this, we should update our application Dockerfile accordingly. I assume here that your application is already dockerized. I replaced the port that is getting exposed from 3000 to 80 and added ./bin/thrust to the default command. Dockerfile ./bin/thrust # Dockerfile

# Entrypoint prepares the database.
ENTRYPOINT ["/rails/bin/docker-entrypoint"]

# Start server via Thruster by default, this can be overwritten at runtime
EXPOSE 80
CMD ["./bin/thrust", "./bin/rails", "server"] # Dockerfile

# Entrypoint prepares the database.
ENTRYPOINT ["/rails/bin/docker-entrypoint"]

# Start server via Thruster by default, this can be overwritten at runtime
EXPOSE 80
CMD ["./bin/thrust", "./bin/rails", "server"] The last change is to update ./bin/docker-entrypoint script. I always try to keep it as close to the original Rails template as possible. ./bin/docker-entrypoint original Rails template Below is the final version of the script. #!/bin/bash -e

# If running the rails server then create or migrate existing database
if [ "${@: -2:1}" == "./bin/rails" ] && [ "${@: -1:1}" == "server" ]; then
  ./bin/rails db:prepare
fi

exec "${@}" #!/bin/bash -e

# If running the rails server then create or migrate existing database
if [ "${@: -2:1}" == "./bin/rails" ] && [ "${@: -1:1}" == "server" ]; then
  ./bin/rails db:prepare
fi

exec "${@}" That is all! Thanks to the Traefik container-based configuration, it will read the exposed port from the container and route the traffic accordingly after you deploy the changes. After deploy is finished, you can log in to the container and check if Thruster is running: rails@docker:~$ ps aux
USER         PID %CPU %MEM    VSZ   RSS TTY      STAT START   TIME COMMAND
rails          1  0.0  0.3 1232848 13124 ?       Ssl  09:26   0:06 /usr/local/bundle/ruby/3.3.0/gems/thruster-0.1.8-x86_64-linux/exe/x86_64-linux/thrust ./bin/rails server rails@docker:~$ ps aux
USER         PID %CPU %MEM    VSZ   RSS TTY      STAT START   TIME COMMAND
rails          1  0.0  0.3 1232848 13124 ?       Ssl  09:26   0:06 /usr/local/bundle/ruby/3.3.0/gems/thruster-0.1.8-x86_64-linux/exe/x86_64-linux/thrust ./bin/rails server Summary Since there are no visible cons of using Thruster in your Rails application, I highly recommend using it. It can improve the performance of your application and reduce the load on the server. It is also a good idea to use it as a preparation for the upcoming Kamal 2 release.

Rails Thruster 101: A Quick Guide

The crypto world is no stranger to fast-paced innovations and staggering fundraising events. But even by these standards, raising $30 million in just 30 minutes is an extraordinary feat. This is exactly what happened with Dexter’s GM.ai project, an event that captured the attention of investors and developers alike. However, along with its meteoric rise came whispers of controversy, questions about legitimacy, and ethical concerns. This article delves into the history and details Em behind GM.ai and the controversies that have surfaced since. Editor’s note: The claims in this story belong to the author. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR A Fast Start: $30M in 30 Minutes The story starts with a supposedly anonymous group of developers, operating under the alias "@dexter_cap" on twitter. In a matter of minutes, they raised $30 million for their decentralized AI project, GM.ai, which promised to revolutionize the Solana trading experience by combining AI with DeFi (decentralized finance) for automated, permissionless interactions. The GM.ai project created a frenzy, attracting crypto investors eager to be part of the next big innovation. The idea of AI-driven market strategies in the world of decentralized finance was tantalizing, offering a fresh way to automate and potentially dominate the market. Early backers believed they were getting in on a technology that could offer unparalleled returns. However, some have raised eyebrows at how the project was able to raise such a large sum in such a short time, especially considering the team’s anonymity. This lack of transparency has fueled speculation about the legitimacy and long-term potential of the project, but the money speaks for itself, and GM.ai has made a massive entry into the crypto space. Early Success with LootBot and Whales Market The project's hyped fundraise can be traced back to LootBot and Whales Market, two sub projects that helped establish credibility for the Dexter-led initiative. LootBot, an automated airdrop-farming bot, has achieved significant success in the blockchain space. With integrations across 8 blockchains and a total of 6 airdrops received, it has generated over $2 million in airdropped value (excluding $WHALES). LootBot boasts more than 112,000 lifetime active wallets, with a total looting volume exceeding $71 million and gas fees amounting to 495 ETH. The bot has garnered media attention, including mentions in Binance Research. Besides, Whales Market has established itself as the ultimate OTC decentralized exchange (DEX) for trading airdrop allocations, points, and tokens. The platform has processed a total volume of $182 million, attracting over 33,000 users and facilitating the creation of 100,538 orders. With platform fees exceeding $2 million, Whales Market has provided significant value to its community (according to Dune Analysis). The $WHALES token surged from an initial price of $0.0075 to a peak of $4, delivering a remarkable over x500 return for investors. Notably, 60% of the platform’s revenue, amounting to over $1.2 million, has been distributed to $WHALES token holders. These two projects helped bolster the early reputation of Dexter’s team and subsequently lead to the famous presale - 150,000 SOL raised in under 30 minutes. Needless to say, investors saw the previous capabilities and work ethics of the team, and chose to place their trust in them for the third time. The Months Following: Uncertainty Looms Shortly after the whirlwind presale conclusion, Dexter finally stepped forward to unveil key details about the highly-anticipated project. GM.ai, the name behind the cryptic presale, was officially introduced to the public. Along with the name, Dexter revealed critical information about the project’s vision, outlining how the fusion of AI and blockchain would create a game-changing ecosystem on Solana. However, there was one glaring omission that immediately caused concern: the launch date. Despite the hype surrounding GM.ai and the substantial funds raised, Dexter did not provide a concrete timeline for when the platform would go live. While the roadmap showed a detailed path forward, the absence of a specific launch date began to sow seeds of doubt among early presale investors. Many had expected that a project that raised $30 million in mere minutes would already be far along in development. The lack of a clear timeline triggered uncertainty. Whispers of concern began to circulate in various crypto communities, with some speculating whether the project might be more ambitious in theory than in practice. Could it be that GM.ai was still in its conceptual stage, with little more than a vision and a few proofs of concept? Over the next few months, Dexter and his anonymous team remained committed to delivering on their ambitious vision. In 5 months, they showed promising attempts to build an ecosystem of AI-powered products, introducing a suite of decentralized tools aimed at different facets of the crypto space. Key projects like AnotherUs.ai, Intent.trade, and gm.fun emerged, each with its own unique focus. As the months rolled on, investors continued to wait for GM.ai's official launch date, but the ecosystem’s expansion provided a glimpse of what might be coming. At the same time, the Dexter twitter account started tweeting less, leaving investors confused. The Disaster Launch After months of anticipation, GM.ai's long-awaited launch finally occurred on August 14th, but it quickly turned into a nightmare for presale investors. Without going into too much detail, the launch was far from the groundbreaking success that had been envisioned. At the heart of the disappointment was the decision to add only $2 million in liquidity—a stark contrast to the $30 million raised in the presale. This low liquidity meant that trading the token was difficult, and price volatility was extreme right from the start. Early investors who hoped for quick returns were hit hard as the token price began to plummet almost immediately after trading opened. Adding fuel to the fire was the 6% tax imposed on every transaction, which further discouraged trading activity and frustrated investors. The tax, intended to thicken the liquidity pool, was seen as an additional barrier in an already struggling launch. From the moment trading began, the token entered down-only mode, with prices continuing to slide day after day. Confidence among early backers eroded as it became clear that the project had misstepped in its handling of the launch, and investors began to sell off their tokens en masse, exacerbating the downward spiral. The Aftermath As the fallout from GM.ai's disastrous launch grew, so did the concerns surrounding the future of the project. Dexter, the anonymous leader behind the project, went silent once again. Investors were left in the dark, unsure whether Dexter was hard at work behind the scenes trying to salvage the project or considering cutting his losses and running with the remaining funds. The absence of communication only fueled fears of an impending rug pull, as rumors began to swirl across online crypto forums. Despite Dexter’s disappearance, GM.ai’s official Twitter account continued to operate. It became clear that while the core project had faltered, the team—or whoever was still running the account—hadn’t given up entirely. In an attempt to shift focus and regain some positive momentum, the Twitter account began promoting GM.fun, one of the ecosystem’s experimental meme-launching platforms. Until now, the platform has gained minimal traction. Conclusion The GM.ai presale was hyped for good reasons. The allure of the next big AI x Crypto project, combined with the early success of the teams behind LootBot and Whales Market, fueled investor optimism. However, the project's potential was quickly undermined by a series of critical mistakes. Mismanagement plagued the project from the beginning, starting with the decision to add only $2 million in liquidity, which led to extreme volatility and a plummeting token price. The introduction of a 6% transaction tax further discouraged trading, making the ecosystem less attractive to investors. Miscommunication also played a major role in the project’s downfall. Dexter’s repeated silence, lack of transparency, and failure to set clear timelines eroded trust among early backers. When the launch failed to deliver, the absence of leadership left investors feeling abandoned, and attempts to redirect attention to gm.fun were seen as distractions rather than solutions. In the end, GM.ai’s hype and ambition couldn’t overcome the fundamental issues of poor execution and inadequate communication, leaving it as a cautionary tale in the volatile world of crypto projects. This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the  program here. The crypto world is no stranger to fast-paced innovations and staggering fundraising events. But even by these standards, raising $30 million in just 30 minutes is an extraordinary feat. This is exactly what happened with Dexter’s GM.ai project, an event that captured the attention of investors and developers alike. However, along with its meteoric rise came whispers of controversy, questions about legitimacy, and ethical concerns. This article delves into the history and details Em behind GM.ai and the controversies that have surfaced since. Editor’s note: The claims in this story belong to the author. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Editor’s note: The claims in this story belong to the author. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR Editor’s note: The claims in this story belong to the author. The author is not affiliated with HackerNoon staff and wrote this story on their own. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not condone/condemn any of the claims contained herein. #DYOR A Fast Start: $30M in 30 Minutes The story starts with a supposedly anonymous group of developers, operating under the alias "@dexter_cap" on twitter. In a matter of minutes, they raised $30 million for their decentralized AI project, GM.ai, which promised to revolutionize the Solana trading experience by combining AI with DeFi (decentralized finance) for automated, permissionless interactions. The GM.ai project created a frenzy, attracting crypto investors eager to be part of the next big innovation. The idea of AI-driven market strategies in the world of decentralized finance was tantalizing, offering a fresh way to automate and potentially dominate the market. Early backers believed they were getting in on a technology that could offer unparalleled returns. However, some have raised eyebrows at how the project was able to raise such a large sum in such a short time, especially considering the team’s anonymity. This lack of transparency has fueled speculation about the legitimacy and long-term potential of the project, but the money speaks for itself, and GM.ai has made a massive entry into the crypto space. Early Success with LootBot and Whales Market The project's hyped fundraise can be traced back to LootBot and Whales Market, two sub projects that helped establish credibility for the Dexter-led initiative. LootBot , an automated airdrop-farming bot, has achieved significant success in the blockchain space. With integrations across 8 blockchains and a total of 6 airdrops received, it has generated over $2 million in airdropped value (excluding $WHALES). LootBot boasts more than 112,000 lifetime active wallets, with a total looting volume exceeding $71 million and gas fees amounting to 495 ETH. The bot has garnered media attention, including mentions in Binance Research . LootBot LootBot Binance Research Binance Research Besides, Whales Market has established itself as the ultimate OTC decentralized exchange (DEX) for trading airdrop allocations, points, and tokens. The platform has processed a total volume of $182 million, attracting over 33,000 users and facilitating the creation of 100,538 orders. With platform fees exceeding $2 million, Whales Market has provided significant value to its community (according to Dune Analysis ). The $WHALES token surged from an initial price of $0.0075 to a peak of $4, delivering a remarkable over x500 return for investors. Notably, 60% of the platform’s revenue, amounting to over $1.2 million, has been distributed to $WHALES token holders. Whales Market Whales Market Dune Analysis Dune Analysis These two projects helped bolster the early reputation of Dexter’s team and subsequently lead to the famous presale - 150,000 SOL raised in under 30 minutes. Needless to say, investors saw the previous capabilities and work ethics of the team, and chose to place their trust in them for the third time. The Months Following: Uncertainty Looms Shortly after the whirlwind presale conclusion, Dexter finally stepped forward to unveil key details about the highly-anticipated project. GM.ai, the name behind the cryptic presale, was officially introduced to the public. Along with the name, Dexter revealed critical information about the project’s vision, outlining how the fusion of AI and blockchain would create a game-changing ecosystem on Solana. However, there was one glaring omission that immediately caused concern: the launch date. Despite the hype surrounding GM.ai and the substantial funds raised, Dexter did not provide a concrete timeline for when the platform would go live. While the roadmap showed a detailed path forward, the absence of a specific launch date began to sow seeds of doubt among early presale investors. Many had expected that a project that raised $30 million in mere minutes would already be far along in development. The lack of a clear timeline triggered uncertainty. Whispers of concern began to circulate in various crypto communities, with some speculating whether the project might be more ambitious in theory than in practice. Could it be that GM.ai was still in its conceptual stage, with little more than a vision and a few proofs of concept? Over the next few months, Dexter and his anonymous team remained committed to delivering on their ambitious vision. In 5 months, they showed promising attempts to build an ecosystem of AI-powered products, introducing a suite of decentralized tools aimed at different facets of the crypto space. Key projects like AnotherUs.ai, Intent.trade, and gm.fun emerged, each with its own unique focus. As the months rolled on, investors continued to wait for GM.ai's official launch date, but the ecosystem’s expansion provided a glimpse of what might be coming. At the same time, the Dexter twitter account started tweeting less, leaving investors confused. The Disaster Launch After months of anticipation, GM.ai's long-awaited launch finally occurred on August 14th, but it quickly turned into a nightmare for presale investors. Without going into too much detail, the launch was far from the groundbreaking success that had been envisioned. At the heart of the disappointment was the decision to add only $2 million in liquidity—a stark contrast to the $30 million raised in the presale. This low liquidity meant that trading the token was difficult, and price volatility was extreme right from the start. Early investors who hoped for quick returns were hit hard as the token price began to plummet almost immediately after trading opened. Adding fuel to the fire was the 6% tax imposed on every transaction, which further discouraged trading activity and frustrated investors. The tax, intended to thicken the liquidity pool, was seen as an additional barrier in an already struggling launch. From the moment trading began, the token entered down-only mode, with prices continuing to slide day after day. Confidence among early backers eroded as it became clear that the project had misstepped in its handling of the launch, and investors began to sell off their tokens en masse, exacerbating the downward spiral. The Aftermath As the fallout from GM.ai's disastrous launch grew, so did the concerns surrounding the future of the project. Dexter, the anonymous leader behind the project, went silent once again. Investors were left in the dark, unsure whether Dexter was hard at work behind the scenes trying to salvage the project or considering cutting his losses and running with the remaining funds. The absence of communication only fueled fears of an impending rug pull, as rumors began to swirl across online crypto forums. Despite Dexter’s disappearance, GM.ai’s official Twitter account continued to operate. It became clear that while the core project had faltered, the team—or whoever was still running the account—hadn’t given up entirely. In an attempt to shift focus and regain some positive momentum, the Twitter account began promoting GM.fun, one of the ecosystem’s experimental meme-launching platforms. Until now, the platform has gained minimal traction. Conclusion The GM.ai presale was hyped for good reasons. The allure of the next big AI x Crypto project, combined with the early success of the teams behind LootBot and Whales Market, fueled investor optimism. However, the project's potential was quickly undermined by a series of critical mistakes. Mismanagement plagued the project from the beginning, starting with the decision to add only $2 million in liquidity, which led to extreme volatility and a plummeting token price. The introduction of a 6% transaction tax further discouraged trading, making the ecosystem less attractive to investors. Miscommunication also played a major role in the project’s downfall. Dexter’s repeated silence, lack of transparency, and failure to set clear timelines eroded trust among early backers. When the launch failed to deliver, the absence of leadership left investors feeling abandoned, and attempts to redirect attention to gm.fun were seen as distractions rather than solutions. In the end, GM.ai’s hype and ambition couldn’t overcome the fundamental issues of poor execution and inadequate communication, leaving it as a cautionary tale in the volatile world of crypto projects. This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the  program here. This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the  program here. This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the  program here. This story was distributed as a release by ZEX MEDIA under HackerNoon’s Brand As An Author Program. Learn more about the  program here . here

$30M Raised in 30 Minutes and the Controversies to Follow: The Story Behind Dexter’s GM.ai Project

30 millones de dólares recaudados en 30 minutos y las controversias que siguieron: la historia detrás del proyecto GM.ai de Dexter

30分で3000万ドルを調達、その後の論争：デクスターのGM.aiプロジェクトの裏話

ລະດົມທຶນໄດ້ 30 ລ້ານໂດລາສະຫະລັດພາຍໃນ 30 ນາທີ ແລະຂໍ້ຂັດແຍ່ງທີ່ຈະຕິດຕາມ: ເລື່ອງທີ່ຢູ່ເບື້ອງຫຼັງໂຄງການ GM.ai ຂອງ Dexter

$30M E Kgobokeditšwe ka Metsotso ye 30 le Dingangišano tšeo di swanetšego go Latelwa: Pale ya ka morago ga Dexter’s GM.ai Project

$30M په 30 دقیقو کې راټول شوي او د تعقیب لپاره شخړې: د ډیکسټر د GM.ai پروژې شاته کیسه

$30M Yakasimudzwa muMaminitsi makumi matatu uye Makakatanwa ekutevera: Iyo Nyaya Kuseri kweDexter's GM.ai Project

30 milion dollarë të mbledhura në 30 minuta dhe polemikat që duhen ndjekur: Historia pas projektit GM.ai të Dexter

30 милиона долара прикупљено за 30 минута и контроверзе које следе: прича иза Дектеровог ГМ.аи пројекта

$30M Nalikom sa loob ng 30 Minuto at ang mga Kontrobersyang Susundan: Ang Kuwento sa Likod ng GM.ai Project ni Dexter

30 Dakikada 30 Milyon Dolar Toplandı ve Ardından Gelen Tartışmalar: Dexter'ın GM.ai Projesinin Arkasındaki Hikaye

30 منٹ میں $30M اکٹھا کیا گیا اور پیروی کرنے کے لیے تنازعات: Dexter's GM.ai پروجیکٹ کے پیچھے کی کہانی

I-$ 30M ephakanyiswe kwiMizuzu ye-30 kunye neeNgxaki zokulandela: Ibali elingemva kweProjekthi ye-GM.ai ye-Dexter

How OpenAI’s new ‘Strawberry’ o1 model sets a new benchmark for AI in law by shifting from imitation to reasoning. OpenAI's recently announced ‘Strawberry’ o1 model is a significant leap forward that will change how we think about machine reasoning. While previous models like GPT-4o often relied on generating answers in a single pass, sometimes stumbling over logical consistency, the o1 model introduces a new paradigm: it's designed to "reason" through problems step by step, mirroring the methodical approach of human experts. This shift from imitation to genuine reasoning is particularly exciting for industries that demand nuanced analysis and meticulous attention to detail – and the legal sector, already ripe for AI innovation, is one of those. An AI that can understand the complexities of legal reasoning, that can break down arguments and that can construct logical pathways with precision is huge news for the legal industry. I think this latest update means that the landscape of AI-assisted legal work is about to transform quickly;y, and those who adapt fast will win the game. Here are eight ways in which I think the o1 model could transform legal practices beyond the capabilities of previous OpenAI models: Supercharged legal research and analysis: The o1 model's ability to parse complex legal texts and reason through case law step-by-step could really transform legal research. That’s an AI that can not only find relevant precedents but also construct nuanced arguments, potentially finding insights that even experienced lawyers might miss.


Actually intelligent contract management: Contract drafting and review could become a lot more efficient and accurate. The o1 model's logical reasoning can dissect convoluted clauses, identify potential risks and even assist in drafting watertight contracts - which could dramatically reduce the likelihood of future disputes arising from contractual ambiguities.


Superhuman e-discovery: The o1 model's advanced reasoning capabilities could transform e-discovery processes. Its ability to understand context and content more deeply could hugely improve the accuracy of document review, potentially saving ever more hours of human labour.


Predictive analysis: By reasoning through past case data and legal precedents, the o1 model could provide more accurate predictions about case outcomes and settlement value and the potential impact of certain legal strategies.


Ethical AI: Perhaps one of the aspects that intrigue me the most is the o1 model's potential to reason through ethical considerations. The model is better at avoiding unpleasant or potentially harmful output by reasoning about the consequences of its actions. And that could lead to AI-generated recommendations that are not just legally sound but also align with professional ethics standards, a very important development that so far has been a blocker for many lawyers.


Interactive legal assistants: Like in customer service, the o1 model could also power chatbots or virtual assistants that provide clients with preliminary legal information, triaging inquiries before they reach an attorney.


A new way to educate lawyers: The o1 model, properly trained and customised, could serve as a great learning tool for law students and professionals, offering interactive ways to understand complex legal concepts and practise legal reasoning.


Transparent AI decision-making: The model's ability to provide step-by-step reasoning offers transparency in AI-assisted legal work that was until now impossible. This "think aloud" process could increase trust in AI outputs and make it easier to identify and correct any errors, which would impact AI adoption’s speed across the sector massively. While the potential of the o1 model is undeniably exciting, we need, as an industry, to approach its implementation with a clear mind, understanding the challenges it also presents. Here are a few: A speed-accuracy trade-off: The o1 model's slower processing speed compared to GPT-4o could be a problem in legal contexts that are particularly fast paced. But, and I lean towards that belief, the increased accuracy it offers may just enough justify this trade-off in many scenarios.


Integration complexity: Incorporating the o1 model into existing legal workflows won’t work like plug-and-play solution. It will require a big investment in technical infrastructure and wide-ranging training programs.


Ethical considerations: we’re not really any closer to solving some of the complex questions that come when we talk about AI and law: client confidentiality and liability for AI-generated errors will continue to be at the centre of the debate. The o1 model’s multi-step reasoning capabilities promise to crack the code on legal complexities that have long eluded machine learning, potentially announcing a new way to work with hyper-efficient, highly accurate legal services. I think o1 offers an incredible opportunity for lawyers, but those who decide to explore it will need to strike the right balance of innovation and ethics when it comes to implementation. How OpenAI’s new ‘Strawberry’ o1 model sets a new benchmark for AI in law by shifting from imitation to reasoning. How OpenAI’s new ‘Strawberry’ o1 model sets a new benchmark for AI in law by shifting from imitation to reasoning. How OpenAI’s new ‘Strawberry’ o1 model sets a new benchmark for AI in law by shifting from imitation to reasoning. OpenAI's recently announced ‘Strawberry’ o1 model is a significant leap forward that will change how we think about machine reasoning. While previous models like GPT-4o often relied on generating answers in a single pass, sometimes stumbling over logical consistency, the o1 model introduces a new paradigm: it's designed to "reason" through problems step by step, mirroring the methodical approach of human experts. This shift from imitation to genuine reasoning is particularly exciting for industries that demand nuanced analysis and meticulous attention to detail – and the legal sector, already ripe for AI innovation, is one of those. An AI that can understand the complexities of legal reasoning, that can break down arguments and that can construct logical pathways with precision is huge news for the legal industry. I think this latest update means that the landscape of AI-assisted legal work is about to transform quickly;y, and those who adapt fast will win the game. Here are eight ways in which I think the o1 model could transform legal practices beyond the capabilities of previous OpenAI models: Supercharged legal research and analysis: The o1 model's ability to parse complex legal texts and reason through case law step-by-step could really transform legal research. That’s an AI that can not only find relevant precedents but also construct nuanced arguments, potentially finding insights that even experienced lawyers might miss. Actually intelligent contract management: Contract drafting and review could become a lot more efficient and accurate. The o1 model's logical reasoning can dissect convoluted clauses, identify potential risks and even assist in drafting watertight contracts - which could dramatically reduce the likelihood of future disputes arising from contractual ambiguities. Superhuman e-discovery: The o1 model's advanced reasoning capabilities could transform e-discovery processes. Its ability to understand context and content more deeply could hugely improve the accuracy of document review, potentially saving ever more hours of human labour. Predictive analysis: By reasoning through past case data and legal precedents, the o1 model could provide more accurate predictions about case outcomes and settlement value and the potential impact of certain legal strategies. Ethical AI: Perhaps one of the aspects that intrigue me the most is the o1 model's potential to reason through ethical considerations. The model is better at avoiding unpleasant or potentially harmful output by reasoning about the consequences of its actions. And that could lead to AI-generated recommendations that are not just legally sound but also align with professional ethics standards, a very important development that so far has been a blocker for many lawyers. Interactive legal assistants: Like in customer service, the o1 model could also power chatbots or virtual assistants that provide clients with preliminary legal information, triaging inquiries before they reach an attorney. A new way to educate lawyers: The o1 model, properly trained and customised, could serve as a great learning tool for law students and professionals, offering interactive ways to understand complex legal concepts and practise legal reasoning. Transparent AI decision-making: The model's ability to provide step-by-step reasoning offers transparency in AI-assisted legal work that was until now impossible. This "think aloud" process could increase trust in AI outputs and make it easier to identify and correct any errors, which would impact AI adoption’s speed across the sector massively. Supercharged legal research and analysis: The o1 model's ability to parse complex legal texts and reason through case law step-by-step could really transform legal research. That’s an AI that can not only find relevant precedents but also construct nuanced arguments, potentially finding insights that even experienced lawyers might miss. Supercharged legal research and analysis: The o1 model's ability to parse complex legal texts and reason through case law step-by-step could really transform legal research. That’s an AI that can not only find relevant precedents but also construct nuanced arguments, potentially finding insights that even experienced lawyers might miss. Supercharged legal research and analysis: Actually intelligent contract management: Contract drafting and review could become a lot more efficient and accurate. The o1 model's logical reasoning can dissect convoluted clauses, identify potential risks and even assist in drafting watertight contracts - which could dramatically reduce the likelihood of future disputes arising from contractual ambiguities. Actually intelligent contract management: Contract drafting and review could become a lot more efficient and accurate. The o1 model's logical reasoning can dissect convoluted clauses, identify potential risks and even assist in drafting watertight contracts - which could dramatically reduce the likelihood of future disputes arising from contractual ambiguities. Actually intelligent contract management: Superhuman e-discovery: The o1 model's advanced reasoning capabilities could transform e-discovery processes. Its ability to understand context and content more deeply could hugely improve the accuracy of document review, potentially saving ever more hours of human labour. Superhuman e-discovery: The o1 model's advanced reasoning capabilities could transform e-discovery processes. Its ability to understand context and content more deeply could hugely improve the accuracy of document review, potentially saving ever more hours of human labour. Superhuman e-discovery: Predictive analysis: By reasoning through past case data and legal precedents, the o1 model could provide more accurate predictions about case outcomes and settlement value and the potential impact of certain legal strategies. Predictive analysis: By reasoning through past case data and legal precedents, the o1 model could provide more accurate predictions about case outcomes and settlement value and the potential impact of certain legal strategies. Predictive analysis: Ethical AI: Perhaps one of the aspects that intrigue me the most is the o1 model's potential to reason through ethical considerations. The model is better at avoiding unpleasant or potentially harmful output by reasoning about the consequences of its actions. And that could lead to AI-generated recommendations that are not just legally sound but also align with professional ethics standards, a very important development that so far has been a blocker for many lawyers. Ethical AI: Perhaps one of the aspects that intrigue me the most is the o1 model's potential to reason through ethical considerations. The model is better at avoiding unpleasant or potentially harmful output by reasoning about the consequences of its actions. And that could lead to AI-generated recommendations that are not just legally sound but also align with professional ethics standards, a very important development that so far has been a blocker for many lawyers. Ethical AI: Interactive legal assistants: Like in customer service, the o1 model could also power chatbots or virtual assistants that provide clients with preliminary legal information, triaging inquiries before they reach an attorney. Interactive legal assistants: Like in customer service, the o1 model could also power chatbots or virtual assistants that provide clients with preliminary legal information, triaging inquiries before they reach an attorney. Interactive legal assistants: A new way to educate lawyers: The o1 model, properly trained and customised, could serve as a great learning tool for law students and professionals, offering interactive ways to understand complex legal concepts and practise legal reasoning. A new way to educate lawyers: The o1 model, properly trained and customised, could serve as a great learning tool for law students and professionals, offering interactive ways to understand complex legal concepts and practise legal reasoning. A new way to educate lawyers: Transparent AI decision-making: The model's ability to provide step-by-step reasoning offers transparency in AI-assisted legal work that was until now impossible. This "think aloud" process could increase trust in AI outputs and make it easier to identify and correct any errors, which would impact AI adoption’s speed across the sector massively. Transparent AI decision-making: The model's ability to provide step-by-step reasoning offers transparency in AI-assisted legal work that was until now impossible. This "think aloud" process could increase trust in AI outputs and make it easier to identify and correct any errors, which would impact AI adoption’s speed across the sector massively. Transparent AI decision-making: While the potential of the o1 model is undeniably exciting, we need, as an industry, to approach its implementation with a clear mind, understanding the challenges it also presents. Here are a few: A speed-accuracy trade-off: The o1 model's slower processing speed compared to GPT-4o could be a problem in legal contexts that are particularly fast paced. But, and I lean towards that belief, the increased accuracy it offers may just enough justify this trade-off in many scenarios. Integration complexity: Incorporating the o1 model into existing legal workflows won’t work like plug-and-play solution. It will require a big investment in technical infrastructure and wide-ranging training programs. Ethical considerations: we’re not really any closer to solving some of the complex questions that come when we talk about AI and law: client confidentiality and liability for AI-generated errors will continue to be at the centre of the debate. A speed-accuracy trade-off: The o1 model's slower processing speed compared to GPT-4o could be a problem in legal contexts that are particularly fast paced. But, and I lean towards that belief, the increased accuracy it offers may just enough justify this trade-off in many scenarios. A speed-accuracy trade-off: The o1 model's slower processing speed compared to GPT-4o could be a problem in legal contexts that are particularly fast paced. But, and I lean towards that belief, the increased accuracy it offers may just enough justify this trade-off in many scenarios. A speed-accuracy trade-off: Integration complexity: Incorporating the o1 model into existing legal workflows won’t work like plug-and-play solution. It will require a big investment in technical infrastructure and wide-ranging training programs. Integration complexity: Incorporating the o1 model into existing legal workflows won’t work like plug-and-play solution. It will require a big investment in technical infrastructure and wide-ranging training programs. Integration complexity: Ethical considerations: we’re not really any closer to solving some of the complex questions that come when we talk about AI and law: client confidentiality and liability for AI-generated errors will continue to be at the centre of the debate. Ethical considerations: we’re not really any closer to solving some of the complex questions that come when we talk about AI and law: client confidentiality and liability for AI-generated errors will continue to be at the centre of the debate. Ethical considerations: The o1 model’s multi-step reasoning capabilities promise to crack the code on legal complexities that have long eluded machine learning, potentially announcing a new way to work with hyper-efficient, highly accurate legal services. I think o1 offers an incredible opportunity for lawyers, but those who decide to explore it will need to strike the right balance of innovation and ethics when it comes to implementation.

Did OpenAI's o1 Strawberry model just raise the bar for AI in law? 

Google’s TGIF meetings have long been a hallmark of open dialogue and transparency within the company. These monthly gatherings allowed employees to ask tough questions and hear straight answers from the top brass, including CEO Sundar Pichai. However, recent changes to these meetings are leaving many employees disappointed. Earlier this year, Google introduced a new AI tool called Ask, which streamlines the question-and-answer process at TGIF meetings. While the intention behind this move was to make things more efficient, many Googlers feel the AI is doing more than summarizing inquiries — it’s softening them. Employees are now questioning whether Ask was designed to avoid addressing the hard-hitting issues they want leadership to confront. Google’s AI Tool: A Shift in TGIF Meetings Google’s new AI tool has significantly changed how its TGIF meetings operate. Previously, employees could submit their queries through a platform called Dory, where others could upvote the most pressing or popular inquiries. Leadership would then respond to the most upvoted questions, even if they were on the more uncomfortable side. However, Google recently replaced Dory with Ask, an AI-powered system that now consolidates and summarizes employee topics for discussion. According to Google, it meant for this change to streamline the process, avoid repetitiveness and allow for leaders to address more inquiries. On paper, it seems like a win for efficiency. The company even claims that engagement has increased, with twice as many employees now participating in submitting and voting on questions. Not everyone is convinced. Some employees feel the Ask tool is doing more than making meetings run smoothly. The AI’s summaries soften the tone of more pointed questions, removing the bite from inquiries that were once direct and challenging. Critics argue that this allows leadership to sidestep issues, replacing the once-candid discussions with more controlled, sanitized responses. For many, the meetings have become less engaging. Some workers have even stopped attending, feeling that the opportunity to ask tough questions no longer exists. As a result, there is a growing apprehension that Google’s leadership is using Ask to manage the narrative. The Broader Implications of AI in Internal Communications The introduction of Google’s Ask tool raises an important question — could AI tools like this reshape internal communication in companies beyond Google? While Ask aims to improve efficiency, its ability to soften and filter doubts may be a potential risk for businesses everywhere. There’s no doubt that event technology is a huge time- and money-saver. Almost 90% of businesses using event tech save around 200 hours a year — a huge chunk of employees’ time that’s better spent on the more creative aspects of their role. However, there’s a real anxiety that they could use these tools to control employee voices. AI’s capacity to summarize and paraphrase queries can certainly help avoid redundancies. However, it also opens the door for leadership to sidestep challenging topics. By softening the language — these tools can make tough questions easier to answer — but that comes at the cost of transparency. In workplaces where employees rely on open forums to raise matters, AI could lead to more scripted dialogues. Workers may feel discouraged from asking difficult questions if they believe AI will water down their concerns. This could result in a breakdown of trust between team members and leadership, weakening the company culture. Moreover, organizations risk alienating their workforce as companies increasingly integrate AI to handle more aspects of communication. Workers may view these tools as a buffer to protect management rather than to foster more openness. With transparency crucial for employee engagement, the misuse of AI could create a culture of frustration and poor communication, costing establishmentsan average of $1.2 trillion every year. The Ethics of Rephrasing Employee Concerns Tools like Ask raise serious doubts about transparency and fairness in internal dialogue. The main concern with AI-driven systems is that they could keep team members from raising genuine concerns. By summarizing or rephrasing inquiries, these tools risk stripping away the urgency or tone of critical feedback. This could create an environment that shields leaders from the realities of employee sentiment, making it harder for real issues to surface. Research has supported these concerns. Studies have shown that some machine learning (ML) algorithms lack transparency and consistency — often known as “black box” algorithms — which prevent any detection of biases. This raises issues about the accountability and responsibility of companies that use such systems. In Google’s case, the Ask tool could prevent leadership from understanding the true depth of employee concerns, especially regarding sensitive questions. Moreover, a growing body of research indicates that organizations must be prepared to handle these ethical dilemmas. Studies found that 84% of HR executives need more direction to ensure that new AI tools are fair and unbiased. When leadership relies on AI to filter employee questions, is it truly engaging with its workforce? AI is an excellent tool for managing loads of communication. Yet, companies must avoid using it to distance themselves from the realities their employees face. How Businesses Can Use AI While Maintaining Transparent Internal Communication When using AI, businesses should prioritize transparency, ensuring that employees feel heard clearly without interference from algorithms that may skew their original intent. With that in mind, here are a few ways to maintain transparency in meetings when using AI tools like Ask. 1. Establish Channels for Feedback While AI revolutionizes how companies operate, employees should still have direct avenues to express concerns about how they are using these tools. Businesses should encourage workers to provide input on how AI systems impact communication. This strategy will help leaders realize the impact the technology is creating, whether enhancing transparency or eroding it. As such, they can make adjustments to address any lack of openness or fairness before AI tools compromise trust. 2. Implement Human Oversight Relying solely on AI to post employee queries can be risky, as 71% of surveyed workers cited they would lose trust in AI if it produced inaccurate outputs consistently. That is why human oversight is crucial. Business leaders should integrate a system where human supervisors review sensitive questions to ensure AI is not misrepresenting any important issues. They could also give employees the autonomy to escalate their queries if they feel the AI’s processes are inadequate. By enabling these options, executives can still take accountability in handling delicate matters. 3. Enable AI Tools to Provide Balanced Responses Organizational leaders should also ensure responses are complete and fair. Doing so prevents the perception that they are using these tools to dodge challenging topics. This tactic also guarantees more purposeful discussions as AI provides a balanced representation of the issues, resulting in more trust and open communication. Balancing Efficiency and Transparency As more businesses use AI tools to manage internal communications, it is vital to ensure these technologies do not come at the cost of transparency. AI may be able to streamline meetings, but companies must be careful not to let these systems dilute employees’ concerns. Instead, they should strike a balance to preserve workers’ trust and ensure the technology builds connection rather than division. Google’s TGIF meetings have long been a hallmark of open dialogue and transparency within the company. These monthly gatherings allowed employees to ask tough questions and hear straight answers from the top brass, including CEO Sundar Pichai. However, recent changes to these meetings are leaving many employees disappointed. Earlier this year, Google introduced a new AI tool called Ask, which streamlines the question-and-answer process at TGIF meetings. While the intention behind this move was to make things more efficient, many Googlers feel the AI is doing more than summarizing inquiries — it’s softening them. Employees are now questioning whether Ask was designed to avoid addressing the hard-hitting issues they want leadership to confront. Google’s AI Tool: A Shift in TGIF Meetings Google’s new AI tool has significantly changed how its TGIF meetings operate. Previously, employees could submit their queries through a platform called Dory, where others could upvote the most pressing or popular inquiries. Leadership would then respond to the most upvoted questions, even if they were on the more uncomfortable side. However, Google recently replaced Dory with Ask, an AI-powered system that now consolidates and summarizes employee topics for discussion. According to Google, it meant for this change to streamline the process, avoid repetitiveness and allow for leaders to address more inquiries. On paper, it seems like a win for efficiency. The company even claims that engagement has increased, with twice as many employees now participating in submitting and voting on questions. twice as many employees now participating twice as many employees now participating Not everyone is convinced. Some employees feel the Ask tool is doing more than making meetings run smoothly. The AI’s summaries soften the tone of more pointed questions, removing the bite from inquiries that were once direct and challenging. Critics argue that this allows leadership to sidestep issues, replacing the once-candid discussions with more controlled, sanitized responses. For many, the meetings have become less engaging. Some workers have even stopped attending, feeling that the opportunity to ask tough questions no longer exists. As a result, there is a growing apprehension that Google’s leadership is using Ask to manage the narrative. The Broader Implications of AI in Internal Communications The introduction of Google’s Ask tool raises an important question — could AI tools like this reshape internal communication in companies beyond Google? While Ask aims to improve efficiency, its ability to soften and filter doubts may be a potential risk for businesses everywhere. There’s no doubt that event technology is a huge time- and money-saver. Almost 90% of businesses using event tech save around 200 hours a year — a huge chunk of employees’ time that’s better spent on the more creative aspects of their role. However, there’s a real anxiety that they could use these tools to control employee voices. AI’s capacity to summarize and paraphrase queries can certainly help avoid redundancies. 90% of businesses using event tech 90% of businesses using event tech However, it also opens the door for leadership to sidestep challenging topics. By softening the language — these tools can make tough questions easier to answer — but that comes at the cost of transparency. In workplaces where employees rely on open forums to raise matters, AI could lead to more scripted dialogues. Workers may feel discouraged from asking difficult questions if they believe AI will water down their concerns. This could result in a breakdown of trust between team members and leadership, weakening the company culture. Moreover, organizations risk alienating their workforce as companies increasingly integrate AI to handle more aspects of communication. Workers may view these tools as a buffer to protect management rather than to foster more openness. With transparency crucial for employee engagement, the misuse of AI could create a culture of frustration and poor communication, costing establishments an average of $1.2 trillion every year. an average of $1.2 trillion an average of $1.2 trillion The Ethics of Rephrasing Employee Concerns The Ethics of Rephrasing Employee Concerns Tools like Ask raise serious doubts about transparency and fairness in internal dialogue. The main concern with AI-driven systems is that they could keep team members from raising genuine concerns. By summarizing or rephrasing inquiries, these tools risk stripping away the urgency or tone of critical feedback. This could create an environment that shields leaders from the realities of employee sentiment, making it harder for real issues to surface. Research has supported these concerns. Studies have shown that some machine learning (ML) algorithms lack transparency and consistency — often known as “black box” algorithms — which prevent any detection of biases. This raises issues about the accountability and responsibility of companies that use such systems. In Google’s case, the Ask tool could prevent leadership from understanding the true depth of employee concerns, especially regarding sensitive questions. from understanding the true depth from understanding the true depth Moreover, a growing body of research indicates that organizations must be prepared to handle these ethical dilemmas. Studies found that 84% of HR executives need more direction to ensure that new AI tools are fair and unbiased. 84% of HR executives 84% of HR executives When leadership relies on AI to filter employee questions, is it truly engaging with its workforce? AI is an excellent tool for managing loads of communication. Yet, companies must avoid using it to distance themselves from the realities their employees face. How Businesses Can Use AI While Maintaining Transparent Internal Communication How Businesses Can Use AI While Maintaining Transparent Internal Communication When using AI, businesses should prioritize transparency, ensuring that employees feel heard clearly without interference from algorithms that may skew their original intent. With that in mind, here are a few ways to maintain transparency in meetings when using AI tools like Ask. 1. Establish Channels for Feedback While AI revolutionizes how companies operate, employees should still have direct avenues to express concerns about how they are using these tools. Businesses should encourage workers to provide input on how AI systems impact communication. This strategy will help leaders realize the impact the technology is creating, whether enhancing transparency or eroding it. As such, they can make adjustments to address any lack of openness or fairness before AI tools compromise trust. 2. Implement Human Oversight 2. Implement Human Oversight Relying solely on AI to post employee queries can be risky, as 71% of surveyed workers cited they would lose trust in AI if it produced inaccurate outputs consistently. That is why human oversight is crucial. Business leaders should integrate a system where human supervisors review sensitive questions to ensure AI is not misrepresenting any important issues. 71% of surveyed workers 71% of surveyed workers They could also give employees the autonomy to escalate their queries if they feel the AI’s processes are inadequate. By enabling these options, executives can still take accountability in handling delicate matters. 3. Enable AI Tools to Provide Balanced Responses 3. Enable AI Tools to Provide Balanced Responses Organizational leaders should also ensure responses are complete and fair. Doing so prevents the perception that they are using these tools to dodge challenging topics. This tactic also guarantees more purposeful discussions as AI provides a balanced representation of the issues, resulting in more trust and open communication. Balancing Efficiency and Transparency Balancing Efficiency and Transparency As more businesses use AI tools to manage internal communications, it is vital to ensure these technologies do not come at the cost of transparency. AI may be able to streamline meetings, but companies must be careful not to let these systems dilute employees’ concerns. Instead, they should strike a balance to preserve workers’ trust and ensure the technology builds connection rather than division.

Why Google's 'Polite' AI Tool Is Raising Employee Eyebrows

ለምን የጎግል 'ጨዋ' AI መሳሪያ የሰራተኛ ቅንድብን እያሳደገ ነው።

Proč „zdvořilý“ nástroj umělé inteligence od Googlu zvedá obočí zaměstnanců

Hvorfor Googles 'høflige' AI-værktøj hæver medarbejdernes øjenbryn

Γιατί το «Ευγενικό» εργαλείο AI της Google αυξάνει τα φρύδια των εργαζομένων

Por qué la herramienta de inteligencia artificial 'Polite' de Google está sorprendiendo a los empleados

Poukisa zouti AI 'Polite' Google la ap ogmante sousi anplwaye yo

Ինչու է Google-ի «քաղաքավարի» AI գործիքը բարձրացնում աշխատակիցների հոնքերը

מדוע כלי ה-AI 'מנומס' של גוגל מרים גבות לעובדים

Google の「礼儀正しい」 AI ツールが従業員の眉をひそめさせる理由

किन गुगलको 'विनम्र' एआई उपकरणले कर्मचारीको भौं उठाइरहेको छ

Waarom Google's 'Polite' AI-tool de wenkbrauwen van werknemers doet fronsen

Чому «ввічливий» інструмент штучного інтелекту від Google піднімає брови у співробітників

**SILVER SPRING, United States, September 12th, 2024/CyberNewsWire/--**Aembit, the leading non-human identity and access management (IAM) company, has secured $25 million in Series A funding, bringing its total capital raised to nearly $45 million. Acrew Capital led the round, with participation from existing investors Ballistic Ventures, Ten Eleven Ventures, Okta Ventures, and CrowdStrike Falcon Fund. Aembit’s funding comes in the wake of continued high-profile non-human identity attacks on organizations such as Cloudflare, The New York Times, and Microsoft. Non-human identity (NHI) refers to the applications, scripts, and bots that businesses use to automate their operations, as well as the credentials used by NHIs to communicate to sensitive databases, applications, and infrastructure. These incidents exposed secrets such as API keys, access tokens, and other non-human access credentials, which were used to penetrate enterprise environments. In a newly published survey of security professionals, Aembit found that most organizations still struggle with managing NHI credentials securely: Over 30% still storing credentials in code, and 23% using email and chat to share credentials. Over 60% of respondents are looking for a comprehensive solution across their entire organization. Security professionals are recognizing the need for an access-focused approach that automates identity-driven, secretless, centrally enforced, and auditable access between distributed applications and SaaS services to sensitive resources in the cloud and on-premises. Aembit has led the market in solving this emerging challenge by pioneering non-human IAM. It enables policy-based access management between workloads and the sensitive resources they access, moving beyond reactive visibility and governance to proactively shrink the attack surface of rapidly growing and highly distributed non-human identities. Aembit was recently lauded as a Top 2 finalist in the prestigious 2024 RSA Innovation Sandbox competition and is a finalist for Best Identity Management Solution at the 2024 SC Awards. Aembit continues to advance access management with capabilities such as MFA-strength conditional access, policy automation via infrastructure-as-code, and robust auditing for NHI access. “Aembit is tackling one of the most pressing challenges in modern enterprise security,” said Mark Kraynak, founding partner at Acrew Capital. “The shift to cloud and SaaS, and AI has driven an order-of-magnitude expansion in non-human identities. With the proliferation of microservices and APIs across diverse environments, IAM has become the critical first line of defense for protecting sensitive data. Legacy access management approaches weren’t designed with this level of scale and automation in mind. We are thrilled to be partnering with Aembit to bring a new approach to the market.” Co-Founders David Goldschlag and Kevin Sapp have spent their careers innovating across the identity landscape, most recently creating New Edge Labs (acquired by Netskope), one of the first user zero trust products on the market. “Kevin and I founded Aembit with a vision to help enterprises secure access between non-human workloads, applications, and software resources with the same principles used today to secure human access,” said David Goldschlag, co-founder and CEO of Aembit. “Talking to hundreds of enterprises, and working closely with design partners, our approach centers on proactively securing access between non-human identities, while eliminating friction for developers and security teams.” “By solving non-human IAM, Aembit is tackling an essential security challenge,” said Brad Jones, CISO at Snowflake and an Aembit customer. “Not only is their approach to non-human access innovative, but Aembit is a provider we can rely on.” The Aembit Workload IAM Platform enforces secure access between non-human workloads and the services that authorize access to sensitive data and infrastructure. Aembit’s policy engine grants secretless access, just-in-time, based on the workload’s identity and posture. Leveraging native identities and sophisticated automation, organizations use Aembit to eliminate storage of sensitive secrets within applications or vaults by moving to short-lived access tokens with a no-code auth approach. With Aembit, businesses proactively secure non-human access while eliminating the manual and fragmented work required today by security, engineering, and DevSecOps teams. About Aembit Aembit is the non-human identity and access management platform that secures access between workloads across clouds, SaaS, and data centers. With Aembit’s identity control plane, DevSecOps can fully automate secretless, policy-based, and Zero Trust workload access with MFA-strength capabilities. For more information, users can visit https://aembit.io/ and follow us on LinkedIn. Contact CMO Apurva Davé Aembit info@aembit.io This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **SILVER SPRING, United States, September 12th, 2024/CyberNewsWire/--**Aembit, the leading non-human identity and access management (IAM) company, has secured $25 million in Series A funding, bringing its total capital raised to nearly $45 million. Acrew Capital led the round, with participation from existing investors Ballistic Ventures, Ten Eleven Ventures, Okta Ventures, and CrowdStrike Falcon Fund. Aembit’s funding comes in the wake of continued high-profile non-human identity attacks on organizations such as Cloudflare, The New York Times, and Microsoft. Non-human identity (NHI) refers to the applications, scripts, and bots that businesses use to automate their operations, as well as the credentials used by NHIs to communicate to sensitive databases, applications, and infrastructure. Acrew Capital Acrew Capital These incidents exposed secrets such as API keys, access tokens, and other non-human access credentials, which were used to penetrate enterprise environments. In a newly published survey of security professionals, Aembit found that most organizations still struggle with managing NHI credentials securely: Over 30% still storing credentials in code, and 23% using email and chat to share credentials. Over 60% of respondents are looking for a comprehensive solution across their entire organization. Security professionals are recognizing the need for an access-focused approach that automates identity-driven, secretless, centrally enforced, and auditable access between distributed applications and SaaS services to sensitive resources in the cloud and on-premises. Aembit has led the market in solving this emerging challenge by pioneering non-human IAM. It enables policy-based access management between workloads and the sensitive resources they access, moving beyond reactive visibility and governance to proactively shrink the attack surface of rapidly growing and highly distributed non-human identities. Aembit was recently lauded as a Top 2 finalist in the prestigious 2024 RSA Innovation Sandbox competition and is a finalist for Best Identity Management Solution at the 2024 SC Awards. Aembit continues to advance access management with capabilities such as MFA-strength conditional access, policy automation via infrastructure-as-code, and robust auditing for NHI access. Top 2 finalist Top 2 finalist “Aembit is tackling one of the most pressing challenges in modern enterprise security,” said Mark Kraynak, founding partner at Acrew Capital. “The shift to cloud and SaaS, and AI has driven an order-of-magnitude expansion in non-human identities. With the proliferation of microservices and APIs across diverse environments, IAM has become the critical first line of defense for protecting sensitive data. Legacy access management approaches weren’t designed with this level of scale and automation in mind. We are thrilled to be partnering with Aembit to bring a new approach to the market.” “Aembit is tackling one of the most pressing challenges in modern enterprise security,” said Mark Kraynak, founding partner at Acrew Capital. “The shift to cloud and SaaS, and AI has driven an order-of-magnitude expansion in non-human identities. With the proliferation of microservices and APIs across diverse environments, IAM has become the critical first line of defense for protecting sensitive data. Legacy access management approaches weren’t designed with this level of scale and automation in mind. We are thrilled to be partnering with Aembit to bring a new approach to the market.” Co-Founders David Goldschlag and Kevin Sapp have spent their careers innovating across the identity landscape, most recently creating New Edge Labs (acquired by Netskope), one of the first user zero trust products on the market. “Kevin and I founded Aembit with a vision to help enterprises secure access between non-human workloads, applications, and software resources with the same principles used today to secure human access,” said David Goldschlag, co-founder and CEO of Aembit. “Talking to hundreds of enterprises, and working closely with design partners, our approach centers on proactively securing access between non-human identities, while eliminating friction for developers and security teams.” “Kevin and I founded Aembit with a vision to help enterprises secure access between non-human workloads, applications, and software resources with the same principles used today to secure human access,” said David Goldschlag, co-founder and CEO of Aembit. “Talking to hundreds of enterprises, and working closely with design partners, our approach centers on proactively securing access between non-human identities, while eliminating friction for developers and security teams.” “By solving non-human IAM, Aembit is tackling an essential security challenge,” said Brad Jones, CISO at Snowflake and an Aembit customer. “Not only is their approach to non-human access innovative, but Aembit is a provider we can rely on.” “By solving non-human IAM, Aembit is tackling an essential security challenge,” said Brad Jones, CISO at Snowflake and an Aembit customer. “Not only is their approach to non-human access innovative, but Aembit is a provider we can rely on.” The Aembit Workload IAM Platform enforces secure access between non-human workloads and the services that authorize access to sensitive data and infrastructure. Aembit’s policy engine grants secretless access, just-in-time, based on the workload’s identity and posture. Leveraging native identities and sophisticated automation, organizations use Aembit to eliminate storage of sensitive secrets within applications or vaults by moving to short-lived access tokens with a no-code auth approach. With Aembit, businesses proactively secure non-human access while eliminating the manual and fragmented work required today by security, engineering, and DevSecOps teams. About Aembit Aembit is the non-human identity and access management platform that secures access between workloads across clouds, SaaS, and data centers. With Aembit’s identity control plane, DevSecOps can fully automate secretless, policy-based, and Zero Trust workload access with MFA-strength capabilities. For more information, users can visit https://aembit.io/ and follow us on LinkedIn . Aembit Aembit https://aembit.io/ https://aembit.io/ follow us on LinkedIn follow us on LinkedIn Contact CMO Apurva Davé Aembit info@aembit.io This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Aembit Raises $25 Million In Series A Funding For Non-Human Identity And Access Management

Η Aembit συγκεντρώνει 25 εκατομμύρια δολάρια σε σειρά χρηματοδότησης για διαχείριση μη ανθρώπινης ταυτότητας και πρόσβασης

Aembit recauda 25 millones de dólares en financiación de serie A para la gestión de identidad y acceso no humano

Aembit מגייסת 25 מיליון דולר בסדרה A לניהול זהות וגישה לא אנושית

Aembit prikuplja 25 milijuna dolara u seriji A financiranja za neljudski identitet i upravljanje pristupom

Aembit-ը հավաքում է 25 միլիոն դոլար մի շարք ֆինանսավորում ոչ մարդկային ինքնության և հասանելիության կառավարման համար

Aembit Mengumpulkan $25 Juta dalam Pendanaan Seri A untuk Manajemen Identitas dan Akses Non-Manusia

Aembit、非人間IDおよびアクセス管理向けシリーズA資金調達で2,500万ドルを調達

Aembit აგროვებს 25 მილიონი აშშ დოლარის სერიებში დაფინანსებას არაადამიანური იდენტობისა და წვდომის მენეჯმენტისთვის

Aembit Mengumpul $25 Juta Dalam Pembiayaan Siri A Untuk Pengurusan Identiti Dan Akses Bukan Manusia

Aembit waxay u ururisay $25 Milyan oo Taxane ah Maalgelinta Aqoonsiga Aan Aadanaha ahayn iyo Maaraynta Helitaanka

Aembit 25 миллион долларро дар як силсила маблағгузорӣ барои идоракунии шахсияти ғайриинсонӣ ва дастрасӣ ба даст меорад

Aembit 在 A 轮融资中筹集 2500 万美元，用于非人类身份和访问管理

In this exclusive interview, Dmitry Zhelezov, co-founder of Subsquid (SQD), discusses the platform's recent $17 million funding round, its groundbreaking "modular indexing" technology, and the future of decentralized data access. Ishan Pandey: Hi Dmitry, welcome to our interview series. SQD recently launched its SQD token and has raised over $17 million in funding. Can you share the vision behind Subsquid and how the SQD token fits into your ecosystem? Dmitry Zhelezov: A lot of effort and billions of VC funding have been put into scaling the throughput of blockchains, namely how fast you put in new transactions. But at the same time, at the internet scale, the data is read on average 10-100 times more frequently than it’s written – you can think of how many twitter posts you read and compare to how many you write. Yet, the read access to real-time and historical blockchain data is more like an afterthought. Our mission at SQD is to enable permissionless, trustless access to the entirety of Web3 data at the Internet scale. Ishan Pandey: Indexing platforms are crucial for Web3 scalability. How does SQDd's approach to indexing differentiate itself from other solutions in the market, and what unique benefits does it offer to developers and users? Dmitry Zhelezov: Data Indexing is a complex multi-step process and is not dissimilar to how crude oil is refined into high-octane petrol. And likewise specialization is key to scale data indexing to the industrial scale. We at SQD pioneered what we call modular indexing design, where the data indexing pipeline is split into multiple completely independent steps. The key bottleneck we addressed is the step to extract the raw data. We are replacing the highly inefficient RPC nodes with read-optimized SQD Network for data access. The SQD Network is not only decentralized but is at least 100x more performant than the traditional way of indexing data. Ishan Pandey: SQD has recently partnered with Deutsche Telekom to run dedicated worker nodes. How do you see this collaboration enhancing the security and efficiency of blockchain data retrieval, and what does it mean for the future of decentralized infrastructure? Dmitry Zhelezov: Deutsche Telekom is one of the most notable players in the Web3 Infra space. For us, it’s not merely a stamp of quality approval but a strong boost for network liveness and decentralization. By design, the data in the SQD is spread across all nodes in the network with the maximal redundancy, so with the top-tier DT Infra team joining the network, we feel confidence in the quality of service it provides. Ishan Pandey: Your platform started in the Polkadot ecosystem but has since expanded to Ethereum and is now in beta for Solana. How do you manage the challenges of cross-chain indexing, and what opportunities does this multi-chain approach present? Dmitry Zhelezov: The SQD Network is designed to address the data access problem across the whole Web3 space, and can accommodate virtually any sort of on-chain data. This is the power of the modular indexing design, where we managed to make the SQD Network (the base data access layer part) truly chain-agnostic, putting the work of data decoding and transformation on the client side, close to the application business logic. Ishan Pandey: The concept of a decentralized "data lake" is intriguing. Can you explain how this works in practice and how it contributes to the overall efficiency and resilience of Web3 applications? Dmitry Zhelezov: Well, in fact SQD Network is more like a “Data Ocean” rather than a “Data Lake”. You can think of it as a planetary-scale database distributed across more than a thousand nodes each holding a chunk of data. The protocol ensures that there’s enough redundancy that no data is lost, and the nodes themselves act as mini-databases to query the chunk data they have. The access to the data as well as the rewards for the nodes is powered by the $SQD token, ensuring censorship-resistant, non-KYC access to the entirety of the data from anywhere in the world. Finally, the advanced cryptography and economic security guarantess that the data served by the nodes is not corrupted and sound. Ishan Pandey: As blockchain networks continue to grow and generate more data, what do you see as the biggest challenges for indexing platforms in the coming years, and how is SQD preparing to address these challenges? Dmitry Zhelezov: The exponential pace at which the amount of historical data is growing is probably the biggest challenge by far. Unfortunately, many ecosystems, like Solana Foundation or like Lens protocol, go for stop-gap centralized solutions like putting their data into Google Big Query. While it may solve some immediate developer needs, having a permissionless, extremely low-cost way to query and access this data is a must. Ishan Pandey: Looking ahead, how do you envision the role of indexing platforms evolving in the Web3 ecosystem, and what potential breakthroughs or innovations in this space excite you the most? Dmitry Zhelezov: The most exciting innovations are definitely coming from the advancements in modern cryptography. The two things I am particularly looking forward to is the further reduction of ZK proof generation by another two orders of magnitude (going from from affordable to extremely cheap), and then the mainstream adoption of the Fully Homorphic Encryption. The progress on ZK would allow it to fully leverage the compute power of very low-cost, untrusted nodes combined with the mathematical level of confidence in the validity of the data queries. Right now we are almost there, yet one has to resort to some trade-offs in terms to mitigate to yet still relatively high cost of generating a ZK proof. The FHE is even more exciting as it will allow it to incorporate private data into the SQD Network, utilize the full power of its query engine to filter and aggregate the data, without compromising the privacy. Only the clients then will be able to decode the query results. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR In this exclusive interview, Dmitry Zhelezov, co-founder of Subsquid (SQD), discusses the platform's recent $17 million funding round, its groundbreaking "modular indexing" technology, and the future of decentralized data access. Ishan Pandey: Hi Dmitry, welcome to our interview series. SQD recently launched its SQD token and has raised over $17 million in funding. Can you share the vision behind Subsquid and how the SQD token fits into your ecosystem? Ishan Pandey: Hi Dmitry, welcome to our interview series. SQD recently launched its SQD token and has raised over $17 million in funding. Can you share the vision behind Subsquid and how the SQD token fits into your ecosystem? Ishan Pandey: Hi Dmitry, welcome to our interview series. SQD recently launched its SQD token and has raised over $17 million in funding. Can you share the vision behind Subsquid and how the SQD token fits into your ecosystem? Ishan Pandey: Hi Dmitry, welcome to our interview series. SQD recently launched its SQD token and has raised over $17 million in funding. Can you share the vision behind Subsquid and how the SQD token fits into your ecosystem? Ishan Pandey: Hi Dmitry, welcome to our interview series. SQD recently launched its SQD token and has raised over $17 million in funding. Can you share the vision behind Subsquid and how the SQD token fits into your ecosystem? Dmitry Zhelezov: A lot of effort and billions of VC funding have been put into scaling the throughput of blockchains, namely how fast you put in new transactions. But at the same time, at the internet scale, the data is read on average 10-100 times more frequently than it’s written – you can think of how many twitter posts you read and compare to how many you write. Yet, the read access to real-time and historical blockchain data is more like an afterthought. Our mission at SQD is to enable permissionless, trustless access to the entirety of Web3 data at the Internet scale. Dmitry Zhelezov: Ishan Pandey: Indexing platforms are crucial for Web3 scalability. How does SQDd's approach to indexing differentiate itself from other solutions in the market, and what unique benefits does it offer to developers and users? Ishan Pandey: Indexing platforms are crucial for Web3 scalability. How does SQDd's approach to indexing differentiate itself from other solutions in the market, and what unique benefits does it offer to developers and users? Ishan Pandey: Indexing platforms are crucial for Web3 scalability. How does SQDd's approach to indexing differentiate itself from other solutions in the market, and what unique benefits does it offer to developers and users? Ishan Pandey: Indexing platforms are crucial for Web3 scalability. How does SQDd's approach to indexing differentiate itself from other solutions in the market, and what unique benefits does it offer to developers and users? Ishan Pandey: Indexing platforms are crucial for Web3 scalability. How does SQDd's approach to indexing differentiate itself from other solutions in the market, and what unique benefits does it offer to developers and users? Dmitry Zhelezov : Data Indexing is a complex multi-step process and is not dissimilar to how crude oil is refined into high-octane petrol. And likewise specialization is key to scale data indexing to the industrial scale. We at SQD pioneered what we call modular indexing design, where the data indexing pipeline is split into multiple completely independent steps. The key bottleneck we addressed is the step to extract the raw data. We are replacing the highly inefficient RPC nodes with read-optimized SQD Network for data access. The SQD Network is not only decentralized but is at least 100x more performant than the traditional way of indexing data. Dmitry Zhelezov Ishan Pandey: SQD has recently partnered with Deutsche Telekom to run dedicated worker nodes. How do you see this collaboration enhancing the security and efficiency of blockchain data retrieval, and what does it mean for the future of decentralized infrastructure? Ishan Pandey: SQD has recently partnered with Deutsche Telekom to run dedicated worker nodes. How do you see this collaboration enhancing the security and efficiency of blockchain data retrieval, and what does it mean for the future of decentralized infrastructure? Ishan Pandey: SQD has recently partnered with Deutsche Telekom to run dedicated worker nodes. How do you see this collaboration enhancing the security and efficiency of blockchain data retrieval, and what does it mean for the future of decentralized infrastructure? Ishan Pandey: SQD has recently partnered with Deutsche Telekom to run dedicated worker nodes. How do you see this collaboration enhancing the security and efficiency of blockchain data retrieval, and what does it mean for the future of decentralized infrastructure? Dmitry Zhelezov : Deutsche Telekom is one of the most notable players in the Web3 Infra space. For us, it’s not merely a stamp of quality approval but a strong boost for network liveness and decentralization. By design, the data in the SQD is spread across all nodes in the network with the maximal redundancy, so with the top-tier DT Infra team joining the network, we feel confidence in the quality of service it provides. Dmitry Zhelezov Ishan Pandey: Your platform started in the Polkadot ecosystem but has since expanded to Ethereum and is now in beta for Solana. How do you manage the challenges of cross-chain indexing, and what opportunities does this multi-chain approach present? Ishan Pandey: Your platform started in the Polkadot ecosystem but has since expanded to Ethereum and is now in beta for Solana. How do you manage the challenges of cross-chain indexing, and what opportunities does this multi-chain approach present? Ishan Pandey: Your platform started in the Polkadot ecosystem but has since expanded to Ethereum and is now in beta for Solana. How do you manage the challenges of cross-chain indexing, and what opportunities does this multi-chain approach present? Ishan Pandey: Your platform started in the Polkadot ecosystem but has since expanded to Ethereum and is now in beta for Solana. How do you manage the challenges of cross-chain indexing, and what opportunities does this multi-chain approach present? Ishan Pandey: Your platform started in the Polkadot ecosystem but has since expanded to Ethereum and is now in beta for Solana. How do you manage the challenges of cross-chain indexing, and what opportunities does this multi-chain approach present? Dmitry Zhelezov : The SQD Network is designed to address the data access problem across the whole Web3 space, and can accommodate virtually any sort of on-chain data. This is the power of the modular indexing design, where we managed to make the SQD Network (the base data access layer part) truly chain-agnostic, putting the work of data decoding and transformation on the client side, close to the application business logic. Dmitry Zhelezov Ishan Pandey: The concept of a decentralized "data lake" is intriguing. Can you explain how this works in practice and how it contributes to the overall efficiency and resilience of Web3 applications? Ishan Pandey: The concept of a decentralized "data lake" is intriguing. Can you explain how this works in practice and how it contributes to the overall efficiency and resilience of Web3 applications? Ishan Pandey: The concept of a decentralized "data lake" is intriguing. Can you explain how this works in practice and how it contributes to the overall efficiency and resilience of Web3 applications? Ishan Pandey: The concept of a decentralized "data lake" is intriguing. Can you explain how this works in practice and how it contributes to the overall efficiency and resilience of Web3 applications? Ishan Pandey: The concept of a decentralized "data lake" is intriguing. Can you explain how this works in practice and how it contributes to the overall efficiency and resilience of Web3 applications? Dmitry Zhelezov: Well, in fact SQD Network is more like a “Data Ocean” rather than a “Data Lake”. You can think of it as a planetary-scale database distributed across more than a thousand nodes each holding a chunk of data. The protocol ensures that there’s enough redundancy that no data is lost, and the nodes themselves act as mini-databases to query the chunk data they have. The access to the data as well as the rewards for the nodes is powered by the $SQD token, ensuring censorship-resistant, non-KYC access to the entirety of the data from anywhere in the world. Finally, the advanced cryptography and economic security guarantess that the data served by the nodes is not corrupted and sound. Dmitry Zhelezov: Ishan Pandey: As blockchain networks continue to grow and generate more data, what do you see as the biggest challenges for indexing platforms in the coming years, and how is SQD preparing to address these challenges? Ishan Pandey: As blockchain networks continue to grow and generate more data, what do you see as the biggest challenges for indexing platforms in the coming years, and how is SQD preparing to address these challenges? Ishan Pandey: As blockchain networks continue to grow and generate more data, what do you see as the biggest challenges for indexing platforms in the coming years, and how is SQD preparing to address these challenges? Ishan Pandey: As blockchain networks continue to grow and generate more data, what do you see as the biggest challenges for indexing platforms in the coming years, and how is SQD preparing to address these challenges? Ishan Pandey: As blockchain networks continue to grow and generate more data, what do you see as the biggest challenges for indexing platforms in the coming years, and how is SQD preparing to address these challenges? Dmitry Zhelezov: The exponential pace at which the amount of historical data is growing is probably the biggest challenge by far. Unfortunately, many ecosystems, like Solana Foundation or like Lens protocol, go for stop-gap centralized solutions like putting their data into Google Big Query. While it may solve some immediate developer needs, having a permissionless, extremely low-cost way to query and access this data is a must. Dmitry Zhelezov: Ishan Pandey: Looking ahead, how do you envision the role of indexing platforms evolving in the Web3 ecosystem, and what potential breakthroughs or innovations in this space excite you the most? Ishan Pandey: Looking ahead, how do you envision the role of indexing platforms evolving in the Web3 ecosystem, and what potential breakthroughs or innovations in this space excite you the most? Ishan Pandey: Looking ahead, how do you envision the role of indexing platforms evolving in the Web3 ecosystem, and what potential breakthroughs or innovations in this space excite you the most? Ishan Pandey: Looking ahead, how do you envision the role of indexing platforms evolving in the Web3 ecosystem, and what potential breakthroughs or innovations in this space excite you the most? Ishan Pandey: Looking ahead, how do you envision the role of indexing platforms evolving in the Web3 ecosystem, and what potential breakthroughs or innovations in this space excite you the most? Dmitry Zhelezov: The most exciting innovations are definitely coming from the advancements in modern cryptography. The two things I am particularly looking forward to is the further reduction of ZK proof generation by another two orders of magnitude (going from from affordable to extremely cheap), and then the mainstream adoption of the Fully Homorphic Encryption. Dmitry Zhelezov: The progress on ZK would allow it to fully leverage the compute power of very low-cost, untrusted nodes combined with the mathematical level of confidence in the validity of the data queries. Right now we are almost there, yet one has to resort to some trade-offs in terms to mitigate to yet still relatively high cost of generating a ZK proof. The FHE is even more exciting as it will allow it to incorporate private data into the SQD Network, utilize the full power of its query engine to filter and aggregate the data, without compromising the privacy. Only the clients then will be able to decode the query results. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Subsquid Raises $17M To Build A New Era for Blockchain Data Indexing

Subsquid prikuplja 17 miliona dolara za izgradnju nove ere za indeksiranje podataka na bloku

Subsquid recauda 17 millones de dólares para crear una nueva era en la indexación de datos de blockchain

Subsquid מגייס 17 מיליון דולר כדי לבנות עידן חדש לאינדקס נתונים בלוקצ'יין

Subsquid が 1,700 万ドルを調達し、ブロックチェーン データ インデックスの新時代を築く

Subsquid Blockchain маалыматтарын индекстөө үчүн жаңы доорду куруу үчүн 17 миллион доллар чогултат

Subsquid pozyskuje 17 mln USD na budowę nowej ery indeksowania danych blockchain

Subsquid Inokwidza $ 17M Kuvaka A New Era yeBlockchain Data Indexing

Subsquid барои бунёди як давраи нав барои индексатсияи маълумотҳои Blockchain $17 миллион ҷамъ овард

Subsquid, Blockchain maglumatlary indeksirlemek üçin täze döwür gurmak üçin 17 million dollar ýygnaýar

Subsquid Yi Hlengeleta $17M Ku Aka Nguva Leyintshwa Ya Blockchain Data Indexing

I-Subsquid Iphakamisa i-17M yeedola Ukwakha iXesha elitsha leBlockchain Data Indexing

Subsquid 融资 1700 万美元，打造区块链数据索引新时代

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