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Quantcast is an American technology company, founded in 2006, that specializes in AI-driven real-time advertising, audience insights and measurement. It has offices in the United States, Canada, Australia, Singapore, United Kingdom, Ireland, France, Germany, Italy, and Sweden.

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Quantcast helps create a more personalized digital world.

Quantcast - Crunchbase Company Profile & Funding

Sky Mobile Corporation achieved remarkable results with Quantcast&#x27;s cookieless solution, witnessing a 120% surge in site visits and a remarkable 59% reduction in cost per visit. Learn more about their success story and how we&#x27;re helping them implement #CookielessSolutions company-wide. ...

Quantcast | LinkedIn

Quantcast | LinkedInのフォロワー数39,000人。AI-powered advertising for marketers who demand results | We are Quantcast - the leader in measurable advertising outcomes. We’ve built an AI-powered DSP that automatically predicts which audiences are most likely to buy and optimizes campaigns ...

Quantcast has 5 employees across 21 locations and $63.89 m in total funding,. See insights on Quantcast including office locations, competitors, revenue, financials, executives, subsidiaries and more at Craft.

Quantcast Company Profile - Office Locations, Competitors, Revenue, ...

A global advertising technology company with an innovative platform, empowering organizations to grow their audience, creating a free and open internet

About Us | Quantcast

Quantcast is an advertising technology company and the creator of an innovative intelligent audience platform that empowers brands, agencies and publishers to know and grow their audiences online. Our solutions are leveling the playing field for our customers when it comes to ... Read more

Working at Quantcast | Glassdoor

Konrad Feldman is the co-founder of Quantcast, a company that pioneered the use of AI in in advertising. This is his story.

Tech disruptors, Konrad Feldman, co-founder CEO Quantcast: his story

Explore Quantcast’s job openings, read about the company culture, and see what employees love about working there.

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Quantcast press releases about the company and industry news

Find company research, competitor information, contact details &amp; financial data for Quantcast Corporation of San Francisco, CA. Get the latest business insights from Dun &amp; Bradstreet.

Quantcast Corporation Company Profile | San Francisco, CA | ...

Quantcast Was acquired by InMobi Pte. Ltd. for undisclosed amounted ... Discover new companies using five search parameters, including sector, location, public/private company status, employee count, and annual revenue.

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Rapidly growing data and advertising leader opens new Texas office, looks to expand local team

Big Growth in Austin for Big Data Company Quantcast ...

Quantcast&#x27;s executive leadership team, advisors, and board of directors bring together over a century of experience in advertising with diverse organizations

Leadership, Advisors, and Board of Directors | Quantcast

Founded in 2006 by Konrad Feldman and Paul Sutter, Quantcast is an international technology company that specializes in audience measurement and real-time advertising. Quantcast was the first ratings company to rely primarily on direct measurement, as opposed to panel-based methods pioneered ...

Quantcast – TechCrunch

Information on valuation, funding, acquisitions, investors, and executives for Quantcast. Use the PitchBook Platform to explore the full profile.

Quantcast Company Profile: Funding & Investors | PitchBook

116,698 companies use Quantcast. Quantcast is most often used by companies with 1-10 employees &amp; $1M-10M in revenue. Our usage data goes back 8 years and 4 months.

Companies using Quantcast and its marketshare

Quantcast Corporation - IAB Canada

Quantcast Measure\nQuantcast’s free direct audience measurement reports are now available for apps

We are Quantcast - the leader in measurable advertising outcomes. We’ve built an AI-powered DSP that automatically predicts which audiences are most likely to buy and optimizes campaigns to unlock reliable business results at scale. With Quantcast, advertisers save time and energy while innovating to move their business forward. Since 2006, we’ve supported agencies, brands, and publishers of all sizes, staying ahead of global trends and delivering outcomes far beyond industry standard. With confidence, you can start every campaign smarter and rely on us to seamlessly measure and effectively fine-tune – every step, everywhere, across the purchase journey. Unleash your full potential, reach more relevant consumers, create more meaningful engagement, and unlock greater business outcomes with Quantcast.

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Last year, nearly visited the website of Planned Parenthood, a nonprofit that many people turn to for very private matters like sex education, access to contraceptives, and access to abortions. 200 million people What those visitors may not have known is that as soon as they opened plannedparenthood.org, some two dozen ad trackers embedded in the site alerted a slew of companies whose business is not reproductive freedom but gathering, selling, and using browsing data. See our data here: GitHub The Markup ran and found 28 ad trackers and 40 third-party cookies tracking visitors, in addition to so-called “session recorders” that could be capturing the mouse movements and keystrokes of people visiting the homepage in search of things like information on contraceptives and abortions. The site also contained trackers that tell Facebook and Google if users visited the site. Planned Parenthood’s website through our Blacklight tool The Markup’s scan found Planned Parenthood’s site communicating with companies like Oracle, Verizon, LiveRamp, TowerData, and Quantcast—some of which have made a business of assembling and selling access to masses of digital data about people’s habits. Katie Skibinski, vice president for digital products at Planned Parenthood, said the data collected on its website is “used only for internal purposes by Planned Parenthood and our affiliates,” and the company doesn’t “sell” data to third parties. “While we aim to use data to learn how we can be most impactful, at Planned Parenthood, data-driven learning is always thoughtfully executed with respect for patient and user privacy,” Skibinski said. “This means using analytics platforms to collect aggregate data to gather insights and identify trends that help us improve our digital programs.” Skibinski did not dispute that the organization shares data with third parties, including data brokers. A a localized website specifically for people in the Gulf region, including Texas, —churned up similar results. Blacklight scan of Planned Parenthood Gulf Coast— where abortion has been essentially outlawed Planned Parenthood is not alone when it comes to nonprofits, some operating in sensitive areas like mental health and addiction, gathering and sharing data on website visitors. , The Markup scanned more than 23,000 websites of nonprofit organizations, including those belonging to abortion providers and nonprofit addiction treatment centers. Using our Blacklight tool The Markup used the to identify nonprofits that have filed a tax return since 2019 and that the agency as focusing on areas like mental health and crisis intervention, civil rights, and medical research. IRS’s nonprofit master file categorizes We then examined each nonprofit’s website as publicly listed in . We found that about 86 percent of them had third-party cookies or tracking network requests. By comparison, when The Markup did a in 2020, we found 87 percent used some type of third-party tracking. GuideStar survey of the top 80,000 websites About 11 percent of the 23,856 nonprofit websites we scanned had a Facebook pixel embedded, while 18 percent used the Google Analytics feature. “Remarketing Audiences” The Markup found that 439 of the nonprofit websites loaded scripts called session recorders, which can monitor visitors’ clicks and keystrokes. Eighty-nine of those were for websites that belonged to nonprofits that the IRS categorizes as primarily focusing on mental health and crisis intervention issues. “As a user of this website, by sharing your information with them, you probably don’t assume that this sensitive information is shared with third parties and definitely don’t assume that your keystrokes are recorded,” Gunes Acar, a privacy researcher who copublished a , said. “The more sensitive the website is, the more worried I am.” 2017 study on session recorders Tracy Plevel, the vice president of development and community relations at Gateway Rehab, one of the nonprofits with session recorders on its site, said that the nonprofit uses trackers and session recorders because it needs to stay competitive with its larger, for-profit counterparts. The more sensitive the website is, the more worried I am. Gunes Acar, privacy researcher “As a nonprofit ourselves, we are up against for-profit providers with large advertising budgets as well as the addiction treatment brokers who grab those seeking care with similar online advertising tactics and connect them with the provider who is offering the greatest ‘sales’ compensation,” Plevel said. “Additionally we know user experience has a big impact on following through on treatment. When someone is ready to commit to treatment, we need to ensure it [is] as easy as possible for them before they get frustrated or intimidated by the process.” Other nonprofits had a significant number of trackers embedded on their sites as well. The Markup on The Clinic at Sharma-Crawford Attorneys at Law, a Kansas City legal clinic that represents low-income people facing deportation. found 26 ad trackers and 50 third-party cookies Rekha Sharma-Crawford, the board president of The Clinic, wrote in an emailed statement, “We take privacy and security concerns very seriously and will continue to work with our web provider to address the issues you have identified.” Save the Children, a humanitarian aid organization founded more than 100 years ago, had 26 ad trackers and 49 third-party cookies. March of Dimes, a nonprofit started by President Franklin D. Roosevelt that focuses on maternal and infant care, had more than 29 ad trackers on its site and 58 third-party cookies. City of Hope, a Californian cancer treatment and research center, had 25 ad trackers and 47 third-party cookies. Results of Blacklight scans of the homepages of Save the Children, March of Dimes, and City of Hope performed on Oct. 19, 2021. Paul Butcher, associate vice president of global digital strategy at Save the Children, said in an emailed statement that the organization “takes data protection very seriously.” Butcher also wrote that Save the Children collects some data through ad trackers “to improve user experience” and that the organization is in the process of revamping its data retention policies and recently hired a new head of data. March of Dimes and City of Hope did not respond to requests for comment. State-Level Privacy Laws Miss Nonprofits While health data is governed by HIPAA, and FERPA regulates educational records, there are no federal laws governing how websites track their visitors. Recently, a few states—California, Virginia, and Colorado—have enacted consumer privacy laws that require companies to disclose their tracking practices and allow visitors to opt-out of data collection. But nonprofits in two of those states, California and Virginia, don’t need to adhere to the regulations. Sen. Ron Wyden (D-OR), who has proposed his , said that nonprofits accrue a large amount of potentially sensitive data. own federal privacy legislation “Nonprofits store incredibly personal information about things we’re passionate about, from political causes and social views to which charitable causes we care about,” Wyden said in an emailed statement. “If a data breach reveals someone donates to a domestic violence support group or an LGBTQ rights organization or the name of their mosque, any of that information could be incredibly private.” Nonprofit leaders, however, argue that they lack the infrastructure and funding to comply with privacy law requirements and must gather and share information on donors in order to survive. “One of the most substantive and impactful uses of data by nonprofits has been our fundraising,” said Shannon McCracken, the CEO of The Nonprofit Alliance, an advocacy group made up of nonprofits and businesses. “Without the ability to cost-effectively reach prospective new donors and current donors, then nonprofits can’t continue to be as impactful as they are today.” But purposeful or not, privacy experts say, nonprofits are feeding personal information to data brokers and tech giants like Facebook and Google. “A nonprofit might share your phone number and name with LiveRamp. Tomorrow, a for-profit entity can then reuse that same data to target you,” said Ashkan Soltani, a privacy expert, and former chief technologist at the Federal Trade Commission. “The data flows that go into these third-party aggregators and data brokers come often from nonprofits as well.” Soltani, who was appointed executive director of the California Privacy Protection Agency on Oct. 4, helped draft the California Consumer Privacy Act, which was originally introduced with the nonprofit exemptions. A nonprofit might share your phone number and name…. Tomorrow, a for-profit entity can then reuse the same data to target you. Ashkan Soltani, California Privacy Protection Agency Many major nonprofits work with data brokers to help organize and analyze their data, Jan Masaoka, CEO of the California Association of Nonprofits, said. “People that have big donor lists use them extensively, pretty much all of them use one of the services,” Masaoka said. “They don’t keep it in-house, pretty much everybody keeps it with one of these services.” She noted that Blackbaud is a company that nonprofits often turn to. The ’s marketing material promotes a that combines donor data from more than 550 nonprofits with public information on millions of households. registered data broker co-op database Blackbaud didn’t respond to a request for comment. Because of a lack of funds, nonprofits also rely on third-party platforms—which also happen to be data brokers—to manage their data’s security and privacy, McCracken said. But these kinds of companies aren’t immune to cyberattacks either: Blackbaud in which hackers stole passwords, Social Security numbers, and banking information, according to a . disclosed a ransomware attack in 2020 Securities and Exchange Commission filing Hundreds of were affected, along with more than 13 million people, according to the Identity Theft Resource Center. charitable organizations, schools, and hospitals “They rely on this kind of problematic ecosystem to achieve their work, and as a result, they share number lists, email addresses, or browsing behavior with third-party advertising companies and subject their members to risk,” Soltani said. The Exception Unlike its predecessors in California and Virginia, Colorado’s privacy bill doesn’t have an exemption for nonprofits. In both California and Virginia, the bills’ main supporters gave nonprofits an exemption as a political maneuver. Alastair Mactaggart, a real estate developer and founder of Californians for Consumer Privacy, who was the driving force behind the California Consumer Privacy Act, said his proposal was already facing opposition from tech giants and didn’t want a political showdown with nonprofits, too. “You gotta take the first step, so we figured this was the one that would be the easiest to bounce off,” Mactaggart said. “Eventually, I hope that the big nonprofits are included as well.” David Marsden, the state senator who introduced the Virginia Consumer Data Protection Act, echoed that sentiment, reflecting that the law wasn’t perfect but still a good start. “Does this pick up everybody that it should, or exempt everybody who needs an exemption? Probably not, but it comes pretty close,” Marsden said. “We were able, with this bill, to get it passed without people getting up and objecting to what we were trying to do.” Colorado state senator Robert Rodriguez, who co-sponsored the state’s privacy bill, said he didn’t include an exemption for nonprofits because he felt that any entity that had data on more than 100,000 people should have to follow privacy protections. He also didn’t understand why other states had exemptions. “Someone that has over 100,000 records is a good size,” he said in an email. “They should have some protections or requirements to follow.” Correction This story has been updated to correct the title of Paul Butcher at Save the Children. He is the associate vice president of global digital strategy. Written by: and Alfred Ng Maddy Varner This article was and was republished under the license. originally published on The Markup Creative Commons Attribution-NonCommercial-NoDerivatives

How Come So Many Nonprofit Websites Have Ad Trackers?

How To Become Better At Marketing - Pioneering Principles to Post-Modern Strategies

Recognizing good object-oriented code scales better, the truant officers at Sun Microsystems dictated that nothing in Java should roam the halls or hang out at the mall but should instead stay sealed in a class. Methods replaced functions. Fields replaced global variables. Java elevates object orientation to something of a fetish. Unfortunately Java’s structure seduces the unwary programmer into excessive abstraction. Between demanding a class for everything and enforcing all types at compile time, it encourages you to lay down layer after layer of formalism in class and interface declarations. You’re tempted, or perhaps worn down, to imagine the result a tasty baklava of order and adaptability. Every new abstraction layer is an asset, a joint at which you’ll be able to pivot your code in the future. Any one class you turn into two, you imagine vaguely, will pay off someday in better reusability or fewer bugs. Routinely hiding your fields behind getters and setters will give you degrees of freedom to change them out in the future. However, with those design principles, you’re heading for trouble. If you never meet an abstraction you don’t like, over time your logic will get atomized into a fine mist of tiny classes and methods, none of which individually does anything. You’ll find the code impossible to follow. Debugging it or even reading through it will involve pogo-sticking up and down 15 stack frames, over and over again, trying to understand the logic, until dizziness overcomes you and you forget why you started. Only project veterans will really understand how all the pieces fit together. Meanwhile the adaptability you envisioned will prove to be a mirage. The new features people ask for never seem to line up with the layers in your code. You can’t just swap this implementation for that one, you have to change six different modules at once and patch around some unfortunate external dependencies. And although you’re ready to reimplement employee.getEmployeeID() in an eyeblink, no one ever asks for that. Your code base will earn a reputation for being difficult to work with and impervious to refactoring, because any given class is pinned down, Gulliver-like, by an army of Lilliputian dependencies. You and your teammates will dread having to touch it and talk dreamily about the Big Rewrite you never seem to have time for. Are you suffering from Thousand Layer Syndrome today? Or showing early symptoms? Look around your code. Do you see 2- and 3-line methods by the screenful? Classes that hold data but don’t own any decisions, like a C struct? Interfaces with only one implementation? How about bewildering class names? If you can’t figure out what the ParameterInfoBuilderWrapper is supposed to do, or how it differs from the ParameterInfoBuilder, the disease may be advancing on you. But fear not. Even if you’re suffering an advanced case, your condition is treatable. First, forget about the Big Rewrite. A rewrite is a risky gambit that can take a lot longer than you think and make the rest of the company feel like your hostages in the meantime. And if your current version accreted too many layers, your next version will likely do the same, unless you and your team first master a better approach to design. So don’t wait for the Big Rewrite, start improving both your code and your team incrementally. Second, refresh your vision of what good health looks like. Remember that software is at its core procedural. Computers do this thing, then they do that thing, then sometimes some other thing. High-level languages and object-oriented design are fanfare and pageantry distracting from our fundamentally humble jobs: pecking out if-then statements. Procedural logic should therefore take center stage in your code. Good Java should resemble C. A method should have a clear, nontrivial job to do, and it should show its work. You should be able to read a method from top to bottom and understand how it does its job without chasing into implementations of 20 other methods. Likewise a class should have some meat on its bones and take on some real work. A well-considered class should own an interesting decision and the data needed to make it, not just one or the other. It should be easy to name, ideally with a noun that corresponds to something in the real world with a lifecycle of its own. If you’re struggling to think up a name that distinguishes this class from that, you’re probably rolling too many layers. And it should have a testable surface. Give the class a finite job to do, independent of its peers, and write tests to ensure it does it. Third, start an exercise routine. Extend your index finger in front of you, then bend it down on your Delete key, then bring it back up. Excellent. Do a single rep on every checkin. Flatten two two-line methods into one. Find a tiny class to delete. Scrap needless getters and setters. You’re not a Victorian lady covering her ankles; join those swinging Pythonistas and let your fields hang out. Put every interface and protected method to the test: “do we need multiple implementations of this today?” If voices in your head plead “but we might want to…,” remain calm and drive them away with the Extreme Programming chant: “It won’t be needed. It won’t be needed. It won’t be needed.” Future proof your code by making it short, clear, and well-tested, not by gambling on your clairvoyance. Finally, recruit your colleagues to the cause, because you’re all in this together. Challenge each other in code reviews. Could this method be named more clearly? Is it obvious what this class represents? Does this code serve today’s real use cases or tomorrow’s hypothetical ones? Will next summer’s intern understand this routine right away? Design is an art. You can read the advice in this sermon, then read a book that will tell you just the opposite. What’s important is to hone your own sense of craftsmanship. Read those books. Experiment. Debate. The investment will more than pay off in productivity gains and incrementally improving code. You’ll have your thousand layers down to a manageable few before you know it, you’ll enjoy your improved velocity, and you’ll be smiling down on a codebase you’re proud of. Originally published at www.quantcast.com on October 11, 2017.

Death by 1000 layers: the perils of over-abstraction in Java

Check out the . And . video from our inaugural Hacker Noon party browse the photos too What Happens After You Publish with Hacker Noon? New Hacker Noon contributor had been searching and searching for a home for his tech story. Top tech publication after top tech publication kept rejecting his story. But why? Stories that make complex ideas more accessible are very valuable. You don’t have to be a famous investor or entrepreneur to tell a great story. We published it on Hacker Noon: In less than a week, it’s garnered 1,000+ recommendation & 33,000+ pageviews. And those top tech publications that rejected it? They’re now asking to republish it. You can now also read this story on , , and other tech outlets. Mohit Mamoria WTF is The Blockchain? The ultimate 3500-word guide in plain English . The Next Web trending in 5 LinkedIn Pulse categories Promoted Free Giveaway of the Week: Cisco Meraki is Giving Away $1 million dollars worth of free tech — see if you qualify. Promoted Job Opportunity of the Week: Quantcast Staff Software Engineer, Attribution and Ad Reporting What Should You Read on Hacker Noon this Week? 2 Big Picture Trend Pieces: by Software/Systems Engineer & by VC Bitcoin, Ethereum, Blockchain, Tokens, ICOs: Why should anyone care? Preethi Kasireddy A Better Path to Decency: Will tech lead the way? Brittany Laughlin 5 Recent How-tos: , , , , & by , , , , & respectively. Ask for an Intro Build an Http Container Make Mentoring Work Find America’s Entrepreneurial Soul Build a Box Subscription Site in 1 Day jordangonen Vish (Ishaya) Abrams Poornima Vijayashanker Faisal Hoque Elijah Murray 1 Guide for How to Talk About the Technology That Will Drive Us: by . Self-Driving Cars, Explained: A messaging guide for talking about our autonomous car future Alex Field 1 Piece Advice on Product Management: by It’s A Wonder Some Teams Get Anything Done… Or, why you should visualize ALL work and use Cost of Delay John Cutler 1 Post that begs the question, ‘Is it Better to ?’ by Burn Out or Fade Away How I fucked up my startup — life lessons from failing and imploding Shival Gupta 1 Story About Hackers Hacking for The Sake of I Can: by How I got banned from CodeWars Paul-Armand Assus I hope you enjoy this roundup of where Hacker Noon sees tech going. We are welcoming new submissions here . Can also message our new Facebook page if you’d like to share what you’d like to read more about, or if you just want to share your general opinions about tech venture capital , or cats . Until next time, don’t take the realities of the world for granted. Kind Regards David Smooke , Hacker Noon (of AMI )

Party Video… and What Happens After You Publish w/ Hacker Noon?

and Register for the Event on EventBrite, meet the attendees on Facebook. This message is brought to you by , Quantcast , & Meraki Developers . The Inaugural Hacker Noon Party starts at 6 on June 20th in San Francisco. COVO Quantcast, the way to understand, reach and influence all of your customers, will help pick up the tab, and is excited to meet you there. To learn more about , tweet their Head of Talent Strategy, job opportunities at Quantcast @Lumen. He’s smarter than he looks, and he looks pretty smart. Grandma Queen Elizabeth is into it. Cisco Meraki is giving away one million dollars worth of Meraki gear to help developers to use their APIs to automate networks, measure foot traffic, engage with their users via proximity marketing, and locate or track things with BLE and WiFi. Come meet ’s Meraki Colin Lowenberg at the party or tweet him . @colo Cisco Meraki is the leader in cloud managed IT with over one million active networks. The party will be hosted at COVO, San Francisco’s premier co-working venue. To learn more about their desks, offices, and event space call 415–886–6206. You must and can register for the Event on EventBrite, meet the attendees on Facebook. *, maybe even more* *… Does your brand want to become a sponsor of the Hacker Noon party? Sponsor benefits include your VIP area at the event, adverts in our emails to 100,ooo+ tech people (that milestone is kinda the reason we’re throwing this party). Interested, please email* We’d like to give away more drinks, more swag jobs , or Twitter DM me Partners@AMIpublications.com David Smooke (@davidsmooke). If you’re not in SF and can’t party with us, no worries! We wanted to invite you anyways because we’re one community. We should be hosting more of these elsewhere in the world, so drop us a line and let us know where we should throw one next. Trying something new, some of our friends are hiring. Heres our list for this month, with links to the listings on their name: — Solve big data problems in the cloud while changing banking for good. Hiring all over the US and the UK. Capital One — An open source, cloud-native SQL database for building global, scalable cloud services that survive disasters. Cockroach Labs — the way to understand, reach and influence all of your customers. Quantcast Want to get on the list of who’s hiring? Email Partners@AMIpublications.com Sorry if we disturbed your inbox! We want to say, thanks for reading , & here are ten stories of positivity in tech: Hacker Noon 10: A New Kind of Social Network: Emotional Intelligence by . Mark Nadal Could there be social networks without the haters, trolls, and downvotes? And how would a tech product go about supporting only positivity? Dig in. 9: Give Back In A Big Way by Peter Schroeder 🚀 Learn from Carnegie’s Gospel of Wealth. 8: Eight Amazing Sites With Best Free Stock Photos by Damir Mujic Photos, photos, photos. Here are 8 places giving away a ton of them. 7: Seven Reasons to Give Employees More Equity by Ash Rust People — not capital — build companies. Don’t short change your people. 6: How I tweaked pomodoro technique for better results in productivity by Febin John James The pomodoro technique is to decide on a task, set a timer for 25–30 mins and work until it rings. In this case, it’s been modified for developers. 5: How tech is softening hearts by babulous Technology can also play the role of providing needy recipients a direct path to willing donors. 4: Thirteen Simple Rules for Good Coding (from my fifteen years of experience) by Andrey Nikisha ev Nothing like experience to K.I.S.S. 3: Make A Fresh Start With These Powerful Habits by , Faisal Hoque Change your outlook, change your results. Pretty much reading down this road. all of Faisal’s stories 2: The future is algorithms, not code by Intercom Developer Support Engineer Cathal Horan This post is positive because of how it emphasizes the thought and function behind the code. 1: Stop Paying for SSL Certificates by Christopher Watson We’ve all had our issues with SSL ;-) Here’s a post about getting yours for free. Until next time, don’t take the realities of the world for granted, David Smooke Hacker Noon P.S. Read more about , Artificial Intelligence. , APIs , Developer Bootcamp , Javascript , Learning AI if You Suck at Math , MVP Lessons , Product Management Life , The Countdown , & Tech Careers Venture Capital. P.P.S. If you want free tickets to the event just like our brand new Facebook page and message us your favorite Hacker Noon article (FYI here’s latest & ). Then we’ll give you a free ticket code — trending stories while supplies last (I always wanted to use that phrase on a game show of some kind). P.P.S. This message is brought to you by our event sponsors, Quantcast, Meraki Developers & . Don’t forget to COVO register for the Event on EventBrite, and meet the attendees on Facebook.

You’re Invited to Our Inaugural Hacker Noon Party

As Ethereum prices and investments surge to ATHs, the could not be any higher for companies and consumers. Today, the average loss companies experience during a hack is about , but in the cryptocurrency world that number can be much higher. For example, earlier this year we witnessed the that lead to 512,774 ether ($242mm USD) being stolen, and in 2016 the that lead to $60 million USD worth of ether being compromised. What if I said that these hacks could have been prevented by correctly auditing their Ethereum smart contracts? , a project out of Y Combinator’s winter 2018 class, has set out to fill this massive void in the market and become the first decentralized smart contract security audit platform. Today, QSP , but within the I see this token trading because of the resources and backing that Y Combinator offers, a pipeline of 40 audits scheduled, partnerships with Request Network and others, and the additional drivers of growth that I will outline below. need for security $30 million Parity Wallet hack DAO hack QuantStamp (QSP) trades at $.16 next 12 months at $3.20 Basics: QuantStamp is the and designed to find vulnerabilities in Ethereum smart contracts first scalable cost-effective security-audit protocol The QuantStamp project is originating from the incubator, , as part of their 2018 Winter class Y Combinator The for these security audits is with QSP tokens and half of the tokens used as payment will be burned only form of payment The QuantStamp project is based out of Vancouver, Canada and was launched in November 2017 Price: $.16Ranking by Market Cap (Token): 135Market Capitalization: $100,964,819 Circulating Supply: 617,314,171 QSPMax Supply: 976,442,388 QSPAverage Trading Volume: $2,209,547Consensus: Ethereum consensus (currently proof of work, will be proof of stake later on) Ticker: QSP Team: The QuantStamp team is lead by . Mr. Ma was a former Algorithmic Trader at Tower Research, where he handled millions of dollars trading while using Extreme Software Testing Methods. Mr. Ma also received his ECE at Cornell University Richard Ma QuantStamp’s . Mr Stewart previously founded Many Trees Inc that specialized in building GPU in-memory databases. He also worked for the Canadian Department of National Defense. CTO is Steven Stewart The QuantStamp team currently has working on the project full time 5 additional developers The team also has 2 additional social media managers and community manages Rounding out the team is and 1 graphics designer 8 experienced advisors Drivers of Growth: Within the next year, it is expected that another will be built, creating a natural increase in demand for the services that QuantStamp is offering 10 Million Smart Contracts As of October 2017, about , companies will want a efficient, cost-effective, and secure way to audit their smart contracts to protect themselves against losses $3.2 Billion (11M ETH) have been locked in Smart Contracts QuantStamp is part of . However, QSP has not been officially listed on Y Combinator’s website, but expect a huge surge of interest when it does Y Combinator’s 2018 winter class QuantStamp successfully completed a smart contract audit for , which is another project from Y Combinator Request Network The QuantStamp team already has an additional for the new year 40 audits in their pipeline QuantStamp intends to , such as Neo, in the future expand their services to other protocols Another motivation for investors to hold QuantStamp tokens is that they This is one of the biggest benefit of owning QuantStamp tokens because, in many cases, token holders will have access to companies that would normally only be available to VC’s will receive tokens of the companies QuantStamp audits. of QuantStamp include: Blockfolio, Science Blockchain, Quoine, Team in Residence, Block Asset, Blockchain Partners Korea, Iconic Partners, Luna Capital, TLDR, and Kenetic Capital… I think this QSP army speaks for itself Additional supporters Headwinds: If a project that has undergone one of QuantStamp’s audits becomes compromised, the value of the QSP token will take a massive hit During the initial stages of this project, QSP will have to build their audit automation protocol by undergoing several manual audits, so keep in mind that this is a long term project Until the QuantStamp team expands their services to other protocols the success of their project is directly linked to the growth of the Ethereum protocol Summary: Since this project is so unique and is first of its kind, we will use a bit of estimation to get our 12 month price target. First, I will assume that half of the current price of QSP, $.08, is the result of successfully completing the Request Network audit. We also know that QSP has roughly 40 audits lined up for next year. So, we will take , which gives us our . In closing, please keep in mind that this price target does not include a market growth rate. $.08 * 40 = $3.2 12 month price target I hope you enjoyed today’s write up because this coin solves too many problems for it not to succeed. I know 2018 will be your year!! QuantStamp team…

QuantStamp — VeriSign of Blockchain

Authors:
(1) Surya Narayanan Hari,  Department of Biology and Biological Engineering California Institute of Technology (Email: shari@caltech.edu);
(2) Matt Thomson, Department of Biology and Biological Engineering Program in Computational and Neural Systems California Institute of Technology (Email: mthomson@caltech.edu). Table of Links Abstract and Introduction Conclusion and discussion, and References Abstract Currently, over a thousand LLMs exist that are multi-purpose and are capable of performing real world tasks, including Q&A, text summarization, content generation, etc. However, accessibility, scale and reliability of free models prevents them from being widely deployed in everyday use cases. To address the first two issues of access and scale, organisations such as HuggingFace have created model repositories where users have uploaded model weights and quantized versions of models trained using different paradigms, as well as model cards describing their training process. While some models report performance on commonly used benchmarks, not all do, and interpreting the real world impact of trading off performance on a benchmark for model deployment cost, is unclear. Here, we show that a herd of open source models can match or exceed the performance of proprietary models via an intelligent router. We show that a Herd of open source models is able to match the accuracy of ChatGPT, despite being composed of models that are effectively 2.5x smaller. We show that in cases where GPT is not able to answer the query, Herd is able to identify a model that can, at least 40% of the time. 1 Introduction Large language models have found novel ways to increase the number of use cases, such as by expanding the number of parameters, combining existing models to augment a single models’ functionality and quanitizing large models to fit on smaller devices [4, 12, 9, 18, 2, 8, 13, 3–5]. The rapid expansion of model availability has created a significant challenge in practice, where corporations want to expose performant LLM endpoints for their users, and have to spend time evaluating models to find the best one that works for them in practice. To overcome this problem, engineers often resort to proprietary models without knowing if there are open-source models available at a comparable performance standard. This often leads to the problem elaborated in Figure 1, showing examples of questions taken from MMLU that ChatGPT (GPT 3.5 Turbo) answers incorrectly, but there is some open source model that can answer the question correctly. We use this insight to try and construct a herd of models such that at least one model in the herd can answer any incoming query correctly. Recent model evaluation frameworks [6, 19] help users compare LLMs against each other, but the growing pace of model formats, outpaces one-size-fits-all comparison software suites. Empirical evidence in this work, reveals that open source models have caught up with leading proprietary models, but not all open source models feature on leaderboards, due to their vast number. Deployment of models also remains a key challenge. The 70b parameter Llama-2, in 16-bit precision, requires 2 80Gb A100 GPUs, and in practice, users might want several models running in parallel. Sacrificing parameter count to cut costs risks performance degradation, the exact magnitude of which is unknown before deployment. While quantized models might alleviate some of the challenges associated with model deployment, finding performant quantized models, navigating their formats and knowing their training details, such as what datasets were used in their quantisation calibration, requires expertise. In addition to quantized variants of models, specific model variants exist with chat capabilities, with different performance metrics from non-chat models. Others with more specific domain expertises such as science or code [17, 1], might be useful for some user applications but aren’t fine-tuned for chat capability, making it harder to pick one model to use in production. Today the Huggingface (HF) model repository contains ∼24,000 machine learning models for text generation. While model cards might provide some insight into the dataset that a model is trained on, common practices such as fine-tuning models using inputs from other large language models or model merging [10, 16, 14, 11] has made it difficult to track what data was used to train the model. This has also made it challenging to track what datasets or tasks one can expect the models to be performant on. Futher, not all open source models have detailed model cards, making trusting them in deployment even more challenging. Together, it would be a useful service to expose an endpoint that would process an incoming users’ request by abstracting away model selection. Here, we explore the advantage of exposing a model herd of open source models, which outperforms a larger, proprietary large language model, offering size advantages. We also train a Tryage router [7] to predict model performance, and show that the model herd is able to answer 74% of incoming queries with performance comparable to or better than ChatGPT. Herd Architecture Demonstrating Her We find that a herd of open source models is able to beat ChatGPT (Figure 2) despite being effectively less than 30% of the size (effective size measured as the average size of models weighted by the number of examples allocated to them. Further, none of the models in the herd were individually better than ChatGPT, but together, they were able to surpass ChatGPT’s performance. Further, all the models are open source, and the herd can be seamlessly expanded, contracted or interchanged for other models We trained a tryage router [7] to model the performances of a herd and found that the router was able to successfully allocate incoming queries to models that produced aggregate performance comparable to GPT 3.5 Turbo despite being effectively 2.5x smaller 3a [1] . Further, some models in the herd are quantized, meaning they can be run on edge compute / cloud compute - a user can trade off the size of a herd for compute cost. We show that Herd can capture knowledge in cases where ChatGPT fails to answer an incoming query. While any single model might not be able to answer all the incoming queries, Herd is able to find a model that can answer each query, based on the input text of the prompt. ChatGPT is only able to beat a herd of open source models 26% of the time, implying 74% of the queries can be answered by open source models (Fig. 3b, ‘beat’ is defined as F1 in excess of 5%). In the cases where ChatGPT was wrong, defined as when ChatGPT had an F1 score of less than 0.9, Herd was able to achieve a correct answer (defined as when any model in the Herd had an F1 score greater than 0.95), 69.3% of the time. A predictive router, was able to identify a model that can answer the query correctly, 40% of the time (Tryage bar in Fig. 3c). The mean of the F1s of the answers from each model, as well as the aggregate F1s from Herd and the predictive router, are shown in Figure 3c. This paper is available on arxiv under CC 4.0 license. [1] exact number of parameters in ChatGPT (GPT 3.5 Turbo) unknown, based on reported information Authors: (1) Surya Narayanan Hari,  Department of Biology and Biological Engineering California Institute of Technology (Email: shari@caltech.edu); (2) Matt Thomson, Department of Biology and Biological Engineering Program in Computational and Neural Systems California Institute of Technology (Email: mthomson@caltech.edu). Authors: Authors: (1) Surya Narayanan Hari,  Department of Biology and Biological Engineering California Institute of Technology (Email: shari@caltech.edu); (2) Matt Thomson, Department of Biology and Biological Engineering Program in Computational and Neural Systems California Institute of Technology (Email: mthomson@caltech.edu). Table of Links Abstract and Introduction Abstract and Introduction Conclusion and discussion, and References Conclusion and discussion, and References Abstract Currently, over a thousand LLMs exist that are multi-purpose and are capable of performing real world tasks, including Q&A, text summarization, content generation, etc. However, accessibility, scale and reliability of free models prevents them from being widely deployed in everyday use cases. To address the first two issues of access and scale, organisations such as HuggingFace have created model repositories where users have uploaded model weights and quantized versions of models trained using different paradigms, as well as model cards describing their training process. While some models report performance on commonly used benchmarks, not all do, and interpreting the real world impact of trading off performance on a benchmark for model deployment cost, is unclear. Here, we show that a herd of open source models can match or exceed the performance of proprietary models via an intelligent router. We show that a Herd of open source models is able to match the accuracy of ChatGPT, despite being composed of models that are effectively 2.5x smaller. We show that in cases where GPT is not able to answer the query, Herd is able to identify a model that can, at least 40% of the time. 1 Introduction Large language models have found novel ways to increase the number of use cases, such as by expanding the number of parameters, combining existing models to augment a single models’ functionality and quanitizing large models to fit on smaller devices [4, 12, 9, 18, 2, 8, 13, 3–5]. The rapid expansion of model availability has created a significant challenge in practice, where corporations want to expose performant LLM endpoints for their users, and have to spend time evaluating models to find the best one that works for them in practice. To overcome this problem, engineers often resort to proprietary models without knowing if there are open-source models available at a comparable performance standard. This often leads to the problem elaborated in Figure 1, showing examples of questions taken from MMLU that ChatGPT (GPT 3.5 Turbo) answers incorrectly, but there is some open source model that can answer the question correctly. We use this insight to try and construct a herd of models such that at least one model in the herd can answer any incoming query correctly. Recent model evaluation frameworks [6, 19] help users compare LLMs against each other, but the growing pace of model formats, outpaces one-size-fits-all comparison software suites. Empirical evidence in this work, reveals that open source models have caught up with leading proprietary models, but not all open source models feature on leaderboards, due to their vast number. Deployment of models also remains a key challenge. The 70b parameter Llama-2, in 16-bit precision, requires 2 80Gb A100 GPUs, and in practice, users might want several models running in parallel. Sacrificing parameter count to cut costs risks performance degradation, the exact magnitude of which is unknown before deployment. While quantized models might alleviate some of the challenges associated with model deployment, finding performant quantized models, navigating their formats and knowing their training details, such as what datasets were used in their quantisation calibration, requires expertise. In addition to quantized variants of models, specific model variants exist with chat capabilities, with different performance metrics from non-chat models. Others with more specific domain expertises such as science or code [17, 1], might be useful for some user applications but aren’t fine-tuned for chat capability, making it harder to pick one model to use in production. Today the Huggingface (HF) model repository contains ∼24,000 machine learning models for text generation. While model cards might provide some insight into the dataset that a model is trained on, common practices such as fine-tuning models using inputs from other large language models or model merging [10, 16, 14, 11] has made it difficult to track what data was used to train the model. This has also made it challenging to track what datasets or tasks one can expect the models to be performant on. Futher, not all open source models have detailed model cards, making trusting them in deployment even more challenging. Together, it would be a useful service to expose an endpoint that would process an incoming users’ request by abstracting away model selection. Here, we explore the advantage of exposing a model herd of open source models, which outperforms a larger, proprietary large language model, offering size advantages. We also train a Tryage router [7] to predict model performance, and show that the model herd is able to answer 74% of incoming queries with performance comparable to or better than ChatGPT. Herd Architecture Demonstrating Her We find that a herd of open source models is able to beat ChatGPT (Figure 2) despite being effectively less than 30% of the size (effective size measured as the average size of models weighted by the number of examples allocated to them. Further, none of the models in the herd were individually better than ChatGPT, but together, they were able to surpass ChatGPT’s performance. Further, all the models are open source, and the herd can be seamlessly expanded, contracted or interchanged for other models We trained a tryage router [7] to model the performances of a herd and found that the router was able to successfully allocate incoming queries to models that produced aggregate performance comparable to GPT 3.5 Turbo despite being effectively 2.5x smaller 3a [1] . Further, some models in the herd are quantized, meaning they can be run on edge compute / cloud compute - a user can trade off the size of a herd for compute cost. We show that Herd can capture knowledge in cases where ChatGPT fails to answer an incoming query. While any single model might not be able to answer all the incoming queries, Herd is able to find a model that can answer each query, based on the input text of the prompt. ChatGPT is only able to beat a herd of open source models 26% of the time, implying 74% of the queries can be answered by open source models (Fig. 3b, ‘beat’ is defined as F1 in excess of 5%). In the cases where ChatGPT was wrong, defined as when ChatGPT had an F1 score of less than 0.9, Herd was able to achieve a correct answer (defined as when any model in the Herd had an F1 score greater than 0.95), 69.3% of the time. A predictive router, was able to identify a model that can answer the query correctly, 40% of the time (Tryage bar in Fig. 3c). The mean of the F1s of the answers from each model, as well as the aggregate F1s from Herd and the predictive router, are shown in Figure 3c. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [1] exact number of parameters in ChatGPT (GPT 3.5 Turbo) unknown, based on reported information

How a Herd of Models Challenges ChatGPT's Dominance: Abstract and Introduction

LISBON, Portugal, May 21, 2024—Marginly, the leverage-as-a-service protocol that enables one-click leverage on yield-bearing assets, kicked off its Liquidity Bootstrap Event on May 15. The protocol is rewarding depositors who provide ETH, USDC, or USDT with both high interest rates and Sparks—points that make liquidity providers eligible for the upcoming Marginly airdrop. Marginly is raising liquidity in this three-week event for pools that enable leveraged yield farming on Pendle, the sizzling DeFi protocol that supports tokenization and trading of future yield. With liquidity in Marginly pools, leveraged yield farmers may post margin and then borrow from these pools to gain their desired leverage and respective APY boost for Pendle Principal Tokens. “We’re pumped to announce this Liquidity Bootstrap Event and believe that these trades can go viral given the high yields,” said Taylor Click, Marginly's Chief Liquidity Officer. “Bootstrapping liquidity for such trades is critical, which is why we’re heavily rewarding early adopters with a massive share of our future airdrop.” Why provide liquidity to Marginly? Many DeFi protocols can benefit from liquidity, so why should liquidity providers (LPs) bootstrap Marginly’s liquidity before June 5? The short answer is—high interest rates, special incentives during this Liquidity Bootstrap Event, and protocol safety. Earn high interest Everyone loves passive income, which Marginly generates for liquidity providers. The protocol pays up to 45% in interest for ETH, USDC, and USDT—in the same currency that LPs provide for liquidity. Earn Sparks Akin to points in a video game arcade, Sparks make liquidity providers eligible to participate in the future Marginly token airdrop. Marginly is doubling the Sparks awarded to liquidity providers during the Liquidity Bootstrap Event. Get the Marginly airdrop Earning Sparks during the Liquidity Bootstrap Event is what makes liquidity providers eligible to receive part of the Marginly airdrop. Marginly is distributing airdrop tokens to all participants in the Liquidity Bootstrap Event. Refer friends and get even more Sparks Marginly supports a two-tier referral system that enables liquidity providers to add to their Sparks tallies. Get the equivalent of 10% of the Sparks earned by your referrals, and 5% of the Sparks earned by the referrals of your referrals. Invite all your friends to earn so many additional Sparks! Marginly’s technology is already tried and tested, previously for margin trading on Arbitrum since December 2023. The tech was initially designed for leveraged trading of various long-tail assets; that’s why it expertly serves niche leveraged yield farming use cases. What else makes the Marginly platform safe for liquidity providers— No impermanent loss because LPs provide only single-sided liquidity
Liquidity pools are isolated to minimize LPs’ exposure to risk
Platform is 100% decentralized—non-custodial and with no reliance on off-chain oracles
Smart contracts are double audited by the cybersecurity OG Quantstamp About Marginly Marginly is a leverage-as-a-service protocol that creates isolated side pools to existing automated market maker pools, enabling lenders to deposit their ETH and stablecoins to earn attractive yields. Leveraged yield farmers on Marginly post margin and then borrow from Marginly pools to gain their desired leverage. Partnered with Pendle for leveraged farming of Pendle’s Principal Tokens, and with EtherFi to bring eETH to Pendle, Marginly is the protocol for one-click leverage on yield-bearing assets. Get in line for the Marginly airdrop by providing liquidity to a Marginly pool before the Liquidity Bootstrap Event ends on June 5. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. LISBON, Portugal, May 21, 2024—Marginly, the leverage-as-a-service protocol that enables one-click leverage on yield-bearing assets, kicked off its Liquidity Bootstrap Event on May 15. The protocol is rewarding depositors who provide ETH, USDC, or USDT with both high interest rates and Sparks—points that make liquidity providers eligible for the upcoming Marginly airdrop. Marginly is raising liquidity in this three-week event for pools that enable leveraged yield farming on Pendle, the sizzling DeFi protocol that supports tokenization and trading of future yield. With liquidity in Marginly pools, leveraged yield farmers may post margin and then borrow from these pools to gain their desired leverage and respective APY boost for Pendle Principal Tokens. “We’re pumped to announce this Liquidity Bootstrap Event and believe that these trades can go viral given the high yields,” said Taylor Click, Marginly's Chief Liquidity Officer. “Bootstrapping liquidity for such trades is critical, which is why we’re heavily rewarding early adopters with a massive share of our future airdrop.” “We’re pumped to announce this Liquidity Bootstrap Event and believe that these trades can go viral given the high yields,” said Taylor Click, Marginly's Chief Liquidity Officer. “Bootstrapping liquidity for such trades is critical, which is why we’re heavily rewarding early adopters with a massive share of our future airdrop.” Why provide liquidity to Marginly? Why provide liquidity to Marginly? Many DeFi protocols can benefit from liquidity, so why should liquidity providers (LPs) bootstrap Marginly’s liquidity before June 5? The short answer is—high interest rates, special incentives during this Liquidity Bootstrap Event, and protocol safety. Earn high interest Earn high interest Everyone loves passive income, which Marginly generates for liquidity providers. The protocol pays up to 45% in interest for ETH, USDC, and USDT—in the same currency that LPs provide for liquidity. Earn Sparks Earn Sparks Akin to points in a video game arcade, Sparks make liquidity providers eligible to participate in the future Marginly token airdrop. Marginly is doubling the Sparks awarded to liquidity providers during the Liquidity Bootstrap Event. Get the Marginly airdrop Get the Marginly airdrop Earning Sparks during the Liquidity Bootstrap Event is what makes liquidity providers eligible to receive part of the Marginly airdrop. Marginly is distributing airdrop tokens to all participants in the Liquidity Bootstrap Event. Refer friends and get even more Sparks Refer friends and get even more Sparks Marginly supports a two-tier referral system that enables liquidity providers to add to their Sparks tallies. Get the equivalent of 10% of the Sparks earned by your referrals, and 5% of the Sparks earned by the referrals of your referrals. Invite all your friends to earn so many additional Sparks! Marginly’s technology is already tried and tested, previously for margin trading on Arbitrum since December 2023. The tech was initially designed for leveraged trading of various long-tail assets; that’s why it expertly serves niche leveraged yield farming use cases. What else makes the Marginly platform safe for liquidity providers— No impermanent loss because LPs provide only single-sided liquidity Liquidity pools are isolated to minimize LPs’ exposure to risk Platform is 100% decentralized—non-custodial and with no reliance on off-chain oracles Smart contracts are double audited by the cybersecurity OG Quantstamp No impermanent loss because LPs provide only single-sided liquidity Liquidity pools are isolated to minimize LPs’ exposure to risk Platform is 100% decentralized—non-custodial and with no reliance on off-chain oracles Smart contracts are double audited by the cybersecurity OG Quantstamp About Marginly Marginly is a leverage-as-a-service protocol that creates isolated side pools to existing automated market maker pools, enabling lenders to deposit their ETH and stablecoins to earn attractive yields. Leveraged yield farmers on Marginly post margin and then borrow from Marginly pools to gain their desired leverage. Partnered with Pendle for leveraged farming of Pendle’s Principal Tokens, and with EtherFi to bring eETH to Pendle, Marginly is the protocol for one-click leverage on yield-bearing assets. Get in line for the Marginly airdrop by providing liquidity to a Marginly pool before the Liquidity Bootstrap Event ends on June 5. providing liquidity to a Marginly pool providing liquidity to a Marginly pool This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

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