Charles McDonald is joined by the great Nate Tice to determine the biggest need in the 2024 NFL Draft for every single NFC team. But first – Rashee Rice was reportedly involved in a racing-related crash over the weekend, prompting a discussion on players getting involved in things they shouldn&#x27;t ...

Biggest 2024 NFL Draft need for every NFC team with Nate Tice | The Exempt List - Yahoo Sports

Nov 22, 2023; Paradise Island, BAHAMAS; Northern Iowa Panthers guard Nate Heise (0) reacts with the bench during the first half against the North Carolina Tar Heels at Imperial Arena. Mandatory Credit: Kevin Jairaj-USA TODAY Sports Iowa State men&#x27;s basketball has added former UNI Panther guard Na

RECRUITING: Iowa State adds former UNI guard Nate Heise – CycloneFanatic.com | CycloneFanatic: The Internet's most popular site for fans of the Iowa State Cyclones

Nate Oats could become second Wisconsinite to coach a team to the NCAA men&#x27;s basketball championship in the past six years as he leads Alabama.

Ten things to know about Nate Oats, Wisconsin native coaching Alabama to the men's Final Four

AthlonSports.com offers reliable predictions, provides expert analysis, reacts to breaking news, and helps shape the way fans view the game.

Minnesota native Nate Heise transfers to Iowa State

Iowa State basketball added a piece from the transfer portal Tuesday. Former UNI guard Nate Heise committed to the Cyclones.

Iowa State basketball lands UNI transfer guard Nate Heise

Nate Sudfeld&#x27;s new Lions contract should rule out any talk of another QB in Detroit in the 2024 offseason

Nate Sudfeld's new Lions contract should rule out any talk of another QB

Stay up to date with all the Alabama Crimson Tide sports news, recruiting, transfers, and more at 247Sports.com

What makes Nate Oats so special on the recruiting trail? Pair of McDonald's 
All-American signees weigh in

NATE

nate

Digitalization is a blessing to the supply chain industry. It’s significantly improved the operational efficiencies of the supply chain. But digitalization has a downturn—it’s opened supply chain enterprises to more scrutiny from malicious actors. For example, weekly attacks on the in 2021. In the US, . Also, suffered cyber attacks in the last decade. transportation and logistics (T&L) industry increased by 186% supply chain attacks increased by 42% over 72% of supply chain enterprises Why do cybercriminals target supply chains? One explanation is the network. The supply chain is a network of manufacturers, suppliers, and retailers. Therefore, an attack on the supply chain is considered “high impact” because of the sensitivity of the data involved. The higher the impact of breaches, the better for cybercriminals. An attack on the supply chain is considered “high impact” because of the sensitivity of the data involved. The higher the impact of breaches, the better for cybercriminals. Another reason is supply chains are easy targets. Supply chain enterprises, on average, care less about security measures. In 2020, a a lack of digital culture and training is the biggest threat facing the T&L sector. Without adequate security policies, technological systems are like a house without a fence; they’re easy to infiltrate. PwC study revealed Without adequate security policies, technological systems are like a house without a fence; they’re easy to infiltrate. A third reason is ransomware groups. Ransomware groups—also called —provide malicious code to amateur and professional hackers to infiltrate systems. In the last few years, RaaS gangs have grown, and they increasingly target the supply chain. Check the image below for prominent attacks RaaS gangs orchestrated on the supply chain. Ransomware-as-a-service (RaaS) What weaknesses do malicious actors target in the supply chain? : Digital systems require passwords to manage accessibility. But weak or compromised passwords plague the logistics industry. According to NordPass, the to protect their digital systems. Instead, the employees use easy passwords such as “password”, “company name”, “company name 123”, etc. Login credentials employees of T&L don't use unique passwords Besides, the infamous attacks on US-based supply chains—JBS (a meat processing firm) and Colonial Pipeline—buttresses the poor accessibility security in the supply chain industry. For Colonial Pipeline, an employee's compromised password of the Virtual Private Network (VPN) was the entry point of attack. The JBS intrusion is a similar story: attackers gained unsolicited access through the unguarded credentials of JBS workers that were available on the dark web. Computer programs—such as remote desktop protocols (RDPs), VPNs, and firewalls—require protection. Leaving software, especially open-source software programs, unguarded is terrible for security. Open-source programs typically contain known vulnerabilities malicious actors can explore. Software blunders: In 2021, Intel 421—a cybercrime intelligence company—examined the in Asia, the US, and the UK. According to the report, cybercriminals gained unwanted access mainly through unprotected RDPs, VPNs, and private networks. BlueVoyant’s 2022 cybersecurity report also corroborates Intel 421 findings. software vulnerabilities of logistics companies According to , 90% of the top 20 logistics companies used open RDPs. Additionally, 10 of the top 20 used “software with high severity vulnerabilities” on their servers. Software blunders like these only have one end—security violation. BluveVoyant Phishing is an age-long tactic threat actors use to con sensitive information from people. Phishing scams are often spread through emails and text messages. Email security is still an issue in the logistics industry—90% of the top 20 . Therefore, it’s unsurprising that target the logistics industry. Unsecure emails equal more phishing: logistics companies lack email and phishing security 3.6% of global phishing scams Phishing is an age-long tactic threat actors use to con sensitive information from people What’s the way forward for the supply chain industry? Cybersecurity awareness is ongoing education about the latest security practices. But many logistics companies are complacent with cybersecurity education—at least can’t manage cyber attacks. Increase cybersecurity awareness: 55% of logistics employees Ignorance and gullibility fuel breaches; therefore, cybersecurity education is the first step to cyber protection. To begin with, implement basic security measures such as multi-factor authentication (MFA), phishing and email security, and third-party assessment policies. 2. Password breaches are still high—weak passwords caused in 2021. MFAs provide an extra layer of security to protect your accessibility credentials. Microsoft reports that . That’s how effective MFAs are! For instance, the Colonial Pipeline breach was partly successful because the exposed VPN account lacked MFA. With an MFA, there’s a high chance Colonial Pipeline would avert the intrusion. MFAs + Zero Trust = formidable security: 21% of ransomware attacks MFAs block 99.9% of cyber breaches For formidable security, add zero-trust security to the mix. Zero-trust infrastructure works on a “never trust, always verify” policy. It verifies every entity, internal or external, before granting access to a system. Zeo-trust security is an advanced policy that manages every network traffic. Software programs always require updates (or “patches”) to fix bugs and vulnerabilities. Once available, always patch your systems. Patch your software: : “how” and “who” respond to risks, vulnerabilities, and violations can make or mar your security system. IR defines the personnel and tools for different cyber scenarios. Crucially, it describes how to identify weaknesses that affect systems. Cyber attacks are inevitable; implement incident response (IR) Backups, an archive of your data, hastens data recovery should you experience data violation. The standard backup policy is to save three copies (one primary and two backup copies) of your data on two different media with one offsite copy. Have backups:

Logistics Security: 5 ways to protect the Supply Chain Against Cybersecurity Attacks

As each year passes, the enterprise attack surface widens—leaving backdoors for malicious actors to exploit. While the rapid adoption of digital transformation by organizations is a great development, it comes with challenges. One of such challenges is ransomware groups, which caused over between 2020 and 2022 alone. 50% of ransomware attacks “Ransomware groups” is an everyday word for ransomware developers (also called “operators”) that provide ransomware kits on a subscription-based model. Technically known as Ransomware-as-a-Service (RaaS), its business model mirrors Software-as-a-Service (Saas)—the cloud computing service that allows you to access applications (e.g., Slack and Gmail) through the internet without managing the underlying internet infrastructure. Think of RaaS as “SaaS for cybercriminals”; it’s the illegitimate business that provides malware code for threat actors (or “affiliates”) to spread ransomware. RaaS makes ransomware accessible to anyone—amateur and professional malicious actors can access sophisticated ransomware kits. It's also cheap—subscriptions are as low as $50, a measly amount compared to ransom fees. Malicious actors are using RaaS to spread ransomware at an alarming rate. This article will educate you about RaaS groups and protective measures against ransomware attacks. Ransomware: The weaponized encryption malware “...it [ransomware] is akin to someone stealing your archive and filing cabinets and [then] demanding a ransom to return it”—James Bores, security consultant at Bores Security and Consultancy. Ransomware is malicious software that weaponizes encryption. It encrypts your data until you pay a ransom (in cryptocurrency or payment vouchers). Common methods used to spread ransomware attacks include: It’s the tech-speak for “fishing” for confidential data using deceptive tricks. Threat actors trick users into clicking ransomware-embedded links (e.g., ZIP files, PDFs, Word documents, or Excel spreadsheets). Once you click the link, your files are encrypted with ransomware within a predetermined interval. Phishing: Phishing can be targeted toward an individual (spear phishing or whaling) or a group of people through malicious ads (malvertising) and emails. Email phishing is a major ransomware delivery vehicle. For instance, Datto, a cybersecurity and backup company, reported that phishing emails caused . 54% of ransomware attacks in 2020 Vulnerable software systems provide uncontested access to networks. Software vulnerabilities include coding errors (e.g., buffer overflows), bugs (software failures), poor accessibility and security configurations, a lack of , and weak or stolen login credentials. Software vulnerabilities: two-factor authentication (2FA) systems Ransomware groups relish taking advantage of software flaws to spread malware. In 2021, —the third-highest attack avenue for ransomware groups. software vulnerabilities caused 14% of ransomware attacks High-profile examples of ransomware attacks due to software vulnerabilities are the 2021 breaches at JBS (a US-based meat processing firm) and computer manufacturer Acer. The JBS attackers took advantage of leaked login credentials. In comparison, Acer attackers leveraged a Microsoft Exchange email server flaw. Threat actors target removable media (e.g., USB sticks, smartphones, and external drives) because of lax security protection. For USB drives, bypassing infrastructural security checks enables hackers to deliver malicious software to network systems undetected. Removable devices are also the perfect vehicle to spread the Try2Cry and Spora ransomware because they can encrypt files offline. Removable Media: Like USB drives, smartphones—especially Android devices—are hackers' delight. Smartphones are vulnerable to ransomware attacks through fake app downloads,  smishing (SMS phishing), and unsecured WiFi connections. Common examples of mobile ransomware are Crypto-Ransomware and AndroidOS/MalLocker.B. : RDP creates private and secure connections to desktop applications from a remote location. Despite its encryption and remote access merits, RDP isn't without vulnerabilities. It’s susceptible to the following: Remote Desktop Protocol (RDP) Man-in-the-middle attacks: an eavesdropping technique that intercepts network traffic.** Brute-force attacks: a trial-and-error method for cracking login credentials.** Common Vulnerability and Exposure (CVE): a list of publicly known software flaws. In 2020, open RDP access . Typical examples of ransomware that exploit open RDP connections are Crysis and SamSam—the perpetrator of the Hancock Health hospital breach in 2018. caused 20% of ransomware attacks RaaS: The illegitimate SaaS RaaS is a coordinated and bespoke approach to spreading ransomware attacks. It offers affiliates encryption tools in exchange for a commission, usually 20-30% of the ransom, or a monthly license fee. According to Bores, RaaS thrives on collaboration between “access brokers” and cyber criminals. He said, “...there are criminal groups known as ‘access brokers’ who will find a way into an organization's network, and then sell that access to RaaS groups”. Access brokers do the “dirty work” to make breaches successful. Bores further explained why the RaaS model is popular: “...anyone can now buy a RaaS subscription and use it to support their criminal financial gain where once criminals would need technical knowledge to build their system”. Like SaaS providers, RaaS groups are innovative—they provide specialized malicious code to penetrate systems. Simon Jelley (Veritas Technologies GM) explained the innovativeness of RaaS to Forbes in 2021. He said, “Ransomware gangs are getting better at phishing and taking advantage of the latest developments in artificial intelligence and machine learning to slip past perimeter defenses such as antivirus and firewall software.” The high technical intelligence means RaaS families can exploit vulnerabilities seamlessly. That's why it's unsurprising that Sophos (a UK-based security firm) revealed ransomware groups orchestrated all the ransomware attacks it surveyed in 2020. The major perpetrators were Conti (16%), REvil (15%), Ryuk (9%), Lockbit (4%), Ragnarok (4%), and DarkSide (3%). 3 ways to guard against ransomware Ransomware is everywhere. But it doesn't have to get to you. Here's how you can protect your digital systems against ransomware attacks. Update your software In 2021, were new vulnerabilities, including the notorious Log4j CVE. This is why you must install the latest security updates—it'll help you fix the latest software bugs and errors. 4 out of every top 5 vulnerabilities Apply zero-trust security Ransomware gangs thrive on user recklessness and gullibility. This is why weak passwords or poor access management (21%), poor user practice (27%), and stolen credentials (10%) were frequent causes of ransomware attacks in 2020. Zero-trust security thwarts recklessness and hinders unauthorized entries. It operates on a “never trust, always verify” policy. It requires continuous authentication from every user and device, internal or external, before granting access. With Zero Trust security, one-time authentication is insufficient because threats and vulnerabilities are dynamic, especially with the sophisticated presence of RaaS. Zero trust is distinct from the traditional “trust but verify” approach that puts your data at risk against malicious insiders. Its advanced identity management policies significantly reduce attack surfaces for malicious actors to exploit. Backup your data Data backup—an archive of your data—is a recovery technique to control the impact of ransomware attacks. The 3-2-1 rule is the golden backup strategy where you store three copies (one primary copy and two backup copies) of your data on two different media (e.g., tape or local drive) with one offsite copy. But also target data backups. To tighten the security of your backups, do the following: ransomware gangs (e.g., Sabbath and Conti) Create air gaps: Air gaps have zero connections to a device or network—it's an "airtight" security system. With air gaps, you can store your backup data in an offline location different from the data production environment. Air gaps make data intrusion difficult because only authorized entities can access the ultra-secure site of the data. Encrypt your backups: It's insufficient to make data inaccessible; you must make it unreadable—and that's what encryption does. Encrypted backups convert your readable data to incomprehensible texts. It ensures the safety of your data, whether it's stored on your device (“in rest”) or transmitted between networks (“in transit”). Create immutable backups: Immutability is a defense mechanism that protects your backup data against any modification. An immutable backup is fixed and locked—no one can modify your data regardless of intention.  Also known as Write-Once-Read-Many (WORM) storage, immutability preserves data integrity and availability in case of ransomware attacks. Will RaaS groups go away? As IBM revealed in its , ransomware groups are behind most cyber breaches. Ransomware groups are also growing—according to Statista, over 120 new RaaS groups were formed between 2015 and 2020. X-Force Threat Intelligence Index 2022 Besides, RaaS gangs have a short lifespan—on average, they rebrand (or shut down) after 18 months. Their exponential growth, illicit hydra-like rebranding, and high technical intelligence make them elusive and difficult to track. To deal with RaaS, you must be proactive. Your first steps toward security proactiveness start with what you learned in this article—routinely apply security fixes, create secure backups, and use advanced authentication protocols (Zero Trust). These measures aren't silver bullets against ransomware families, but they'll help you identify and fix potential gaps RaaS groups can exploit.

Ransomware-as-a-Service: SaaS' Evil Twin

Listen to the interview on or watch on . iTunes YouTube In this episode and interview , President at NEM.io Foundation and NEM’s CTO . Linh Dao Smooke Utsav Jaiswal Alex Tinsman Nate D’Amico They discuss cryptocurrency adoption, difference between NEM, a peer-to-peer cryptocurrency and blockchain platform, and Nem.IO Foundation, gender role in crypto tech spheres, blockchain decentralization, Nem’s operation mindset and the future of NEM. “What's pretty interesting about the trajectory with the path for the NEM is that it was so forward thinking with the security features that it has always overcome and remained when all these other coins on platforms kind of went away. I think it has a bright future with technology. Platforms like NEM stick around because they are forward thinking with their features and their abilities.” “I want to be a part of change and the only way to do so is to educate yourself. And the only way to educate yourself is to get dirty. To get dirty with the code, get dirty with the channels, understand. Get into the thick of it. And then it gets really exciting [blockchain and cryptocurrency) because it opens up the opportunity for anyone. Anyone, no matter what your skills set is, everyone has the same level of playing field.  So I don't really think of it as a female thing, I think of it as an empowering tool that anyone and everyone can be a part of. So to me it's not gender, it's a movement.” - Alex Tinsman “If someone on even local, city council level wanted to do a similar voting style that we are using for the NEM ecosystem, they could launch a private or public chain and it doesn't really matter as functionally of the network would be doing the same thing. They could launch a private chain or public chain and still make it auditable, so anyone could have a verifiable data that they would check and download. And then they could configure that network by just tuning a couple labs when they launch it to decide how the consensus works. So if you’re launching a six server node of private network for local city council loading and you’re gonna use it once and then archive it for historical verification purposes, and then every year stand up a new network - you could configure it in a very simplified way that gives you total control on how to protect the network.” - Nate D'Amico P.S. If you dig the new , consider Hacker Noon Podcast giving us a 5 star review on iTunes. Also check out . today’s homepage

Challenges and Solutions with The NEM Foundation's President Alex Tinsman and NEM CTO Nate D’Amico

Like many people, I spend way too much time thumbing through news feeds and clicking on intriguing Twitter headlines. Over the years, I’ve investigated many ways of reducing how long I spend staring at my feeds so I can focus more on work before five o’clock and unwinding come evening time. Today, I regularly use the app Pocket to save interesting articles I come across during the working day to my phone and after I’ve worked through my To Do list. read them later I wanted to discover more about the thinking behind a popular productivity app with the aim of helping users reduce their screen time get from their devices. and more value Create A Product That Solves A Problem Nate Weiner and his team created Pocket in 2007, initially calling their app ReadItLater. Like many products, it was born out of need. “I was always emailing links to [myself] about things I wanted to learn or articles I wanted to read,” Weiner said. “I never found myself coming back to [read] them. They’d just get lost in my inbox.” When featured this new productivity app, it became more popular. Later, Weiner and his team rebranded ReadItLater as Pocket. They want to solve the problem of consumers facing cluttered and overwhelming experiences while consuming content on their phones. Lifehacker “When we did the initial redesign of Pocket, when we renamed it, [we] tried to plant a flag in the ground around putting the content first and respecting the content,” said Weiner. “We try to get out of your way as much as possible, but be helpful in the moments we need to be.” Decide What Success Looks Like Like many of Pocket’s 30 million-plus users, I relied on the free version of Pocket for several years. I upgraded to Pocket Premium, which costs $44.99 a year, only because I wanted to access my archive of old articles. But the Pocket team says revenue or the number of premium users, which they don’t share numbers on, isn’t the main goal. Instead, they quantify success differently, at least publicly. He said: “Our mission at Pocket is about, how do we enable people to consume stories that are worth their time and attention? We don’t look at just raw active users or the number of people saving.” In 2017, Mozilla, maker of the Firefox browser, acquired Pocket for an undisclosed sum. Now Firefox users can see curated Pocket articles when they open a new browser window based on what they read previously and how others engaged with similar content. “Our goal…is about helping people to consume great stories. How do we help cut through the noise for people and help them discover and find things that they might not have otherwise?” Master Collaboration The Pocket team is based in San Francisco and employs remote workers from around the world. They rely on instant messaging tools and video-conferencing to get things done. “We’ve moved to things like Slack and video-conferencing…to help remote folks seamlessly jump in the conversation. But it’s definitely a muscle you have to work at. It doesn’t come for free.” “Collaboration and communication is key. We’ve really had to learn and get better at it.” Build For The Future Some days, my two children are more interested in watching YouTubers than catching up on television. Watching how they engage with online content led me to wonder if the way we consume information and even read is changing. From Weiner’s perspective, it is thanks to the many content platforms that are emerging. “The mediums in which we are able to consume content evolve,” said Weiner. “It’s a very distracting world today. There’s a billion things that people could read.” Weiner noted this is raising the bar for effective content. “It’s got to be something that’s valuable. It’s got to have something that people can take away from it. If they could read a billion things, why are they reading your article? It’s got to be something that really respects their time.” To that end, Pocket now includes features that adapt to people’s evolving interests and lifestyles, such as video groupings and even an audio mode that reads articles aloud with text-to-speech technology. The Pocket team also says their users spend an average of more than 15 minutes per day reading, and the 3 most popular topics last summer were health, productivity, and politics. “Before, [readers] had to hold their phone or look at their computer. They can now consume that content while they’re out on a run or while they’re driving to work, or they’re making dinner,” said Weiner “But I don’t think that necessarily means that reading, for example, is going away any time soon.” Did you enjoy this post? Hit the big ol’ clap button. I’ve also put together 101 proven writing prompts that will help you start writing articles for Medium and other sites today. >> Claim them now <<<

Pocket CEO Nate Weiner Explains How to Create a Top Productivity App

Amazon’s new Alexa model featuring Anthropic’s Claude AI could redefine voice assistants, but it comes with a price tag and data privacy concerns for consumers. Amazon is working towards a significant revamp for its flagship voice assistant, Alexa. After facing developmental challenges with its in-house generative AI (genA.I.) models, the tech giant has now turned to California-based artificial intelligence (A.I.) startup Anthropic’s generative model Claude AI to upgrade Alexa into a more capable and intuitive assistant. Amazonannounced in September that Claude AI will now serve as the core technology for Alexa’s upcoming upgrade. It is expected to enhance the voice assistant’s capabilities of handling more complex tasks and engaging in richer, multi-step conversations. From planning vacations to curating personalized news briefings, Amazon aims to significantly elevate speed, accuracy, and user experience for Alexa’s consumer base.“Anthropic’s visionary work with generative AI, most recently the introduction of its state-of-the-art Claude 3 family of models, combined with Amazon’s best-in-class infrastructure like AWS Trainium and managed services like Amazon Bedrockfurther unlocks exciting opportunities for customers to quickly, securely, and responsibly innovate with generative AI,” Dr. Swami Sivasubramanian, vice president of Data and AI at AWS said in ablog post. As with any advancement in A.I., the integration of Claude AI into Alexa also raises critical privacy concerns. Anthropic was recently found to be quietly injecting hidden instructions to user prompts submitted through its API to prevent copyright issues. Users discovered that prompts like "Respond as helpfully as possible" and "Do not comply with complex instructions" are hardcoded into the system. Although these hidden instructions aren't visible to users, they can affect how the AI responds, highlighting that Anthropic’s large language models (LLMs) may work differently in practice than expected. “In our recent evaluations of the latest LLMs, including those from Anthropic, we found that Claude demonstrates substantial implicit biases,” Sahil Agarwal, co-founder & CEO at Enkrypt AI, told me. “We have proactively reached out to the Anthropic team to address and rectify these issues. For Amazon, ensuring safety and security will be the critical challenge in dominating the AI market.” Likewise, Amazon’s $4 billion investment in Anthropic has caught the attention of the UK’s Competition and Markets Authority (CMA). The regulator is looking into whether this deal might significantly reduce competition in the UK market. The tech giant’s decision to integrate another genAI model introduces a complex interplay of innovation, user experience, and privacy concerns that could redefine how consumers interact with smart home technology. Claude's Potential Impact on Alexa: What to Expect Amazon’s journey to revamp Alexa hasn’t been without its hurdles. A recent Reuters report asserts that Amazon's initial tests with its new A.I. revealed major issues, such as slow responses and trouble understanding user prompts. With rivals like ChatGPT and Google Assistant setting high A.I. speed and accuracy standards, Amazon decided to switch to Anthropic’s Claude AI. The genA.I. model was found to be superior due to its better contextual understanding and more human-like conversation skills. While the basic "Classic" version of Alexa will still be free, users will need to pay for the extra features offered in the upgraded version. Amazon will charge a subscription fee for its "Remarkable Alexa" services, which will cost between $5 and $10 per month. Introducing a paid model for Alexa seems like a calculated risk for Amazon. The timing of this move is also strategic, transitioning Alexa towards a revenue-generating A.I. service like other tech conglomerates. Yet, this progress comes with potential risks. The enhanced AI capabilities could lead to increased data collection, raising concerns about how much personal information users are willing to share. “The new AI capabilities depend heavily on large datasets to refine and improve their responses,” Nate MacLeitch, CEO and founder of API platform QuickBlox, told me. “Amazon’s premium services will need access to the user's data to function effectively and understand user habits, health data, or their preferences. This means a user’s interactions with Alexa are not just private conversations but are also part of the data that trains the AI. ” Apple recently implemented Private Cloud Compute for its Apple Intelligence, ensuring user data remains secure, even from Apple itself. Although Amazon possesses similar capabilities, the specifics of how the integration between Alexa and Anthropic will uphold user privacy remain unclear. Will users be comfortable knowing their data contributes to the ongoing improvement of Alexa AI, especially when they are paying a subscription fee for the service? The company has been tight-lipped about specific measures to ensure data privacy, leaving users and privacy advocates to speculate and watch closely. “The key is to only train aggregate AI models using consented data that has been anonymized. Amazon can do the same by integrating a consent API and CDP system in the backend, and use Alexa to ask for consent before recording any data from the user and utilizing it,” Xun Wang, Chief Technology Officer of Bloomreach and former VP of NVIDIA, told me. “A.I. will soon be able to generate an audio text that zeroes in on the information you care about the most as opposed to a generic dump of all the information related to any specific piece of content.” Amazon’s new Alexa model featuring Anthropic’s Claude AI could redefine voice assistants, but it comes with a price tag and data privacy concerns for consumers. Amazon’s new Alexa model featuring Anthropic’s Claude AI could redefine voice assistants, but it comes with a price tag and data privacy concerns for consumers. Amazon’s new Alexa model featuring Anthropic’s Claude AI could redefine voice assistants, but it comes with a price tag and data privacy concerns for consumers. Amazon is working towards a significant revamp for its flagship voice assistant, Alexa. After facing developmental challenges with its in-house generative AI (genA.I.) models, the tech giant has now turned to California-based artificial intelligence (A.I.) startup Anthropic’s generative model Claude AI to upgrade Alexa into a more capable and intuitive assistant. Amazon announced in September that Claude AI will now serve as the core technology for Alexa’s upcoming upgrade. It is expected to enhance the voice assistant’s capabilities of handling more complex tasks and engaging in richer, multi-step conversations. From planning vacations to curating personalized news briefings, Amazon aims to significantly elevate speed, accuracy, and user experience for Alexa’s consumer base. “Anthropic’s visionary work with generative AI, most recently the introduction of its state-of-the-art Claude 3 family of models, combined with Amazon’s best-in-class infrastructure like AWS Trainium and managed services like Amazon Bedrockfurther unlocks exciting opportunities for customers to quickly, securely, and responsibly innovate with generative AI,” Dr. Swami Sivasubramanian, vice president of Data and AI at AWS said in a blog post . Amazon blog post blog post As with any advancement in A.I., the integration of Claude AI into Alexa also raises critical privacy concerns. Anthropic was recently found to be quietly injecting hidden instructions to user prompts submitted through its API to prevent copyright issues. Users discovered that prompts like "Respond as helpfully as possible" and "Do not comply with complex instructions" are hardcoded into the system. Although these hidden instructions aren't visible to users, they can affect how the AI responds, highlighting that Anthropic’s large language models (LLMs) may work differently in practice than expected. injecting hidden instructions injecting hidden instructions “In our recent evaluations of the latest LLMs, including those from Anthropic, we found that Claude demonstrates substantial implicit biases,” Sahil Agarwal, co-founder & CEO at Enkrypt AI, told me. “We have proactively reached out to the Anthropic team to address and rectify these issues. For Amazon, ensuring safety and security will be the critical challenge in dominating the AI market.” Likewise, Amazon’s $4 billion investment in Anthropic has caught the attention of the UK’s Competition and Markets Authority ( CMA ). The regulator is looking into whether this deal might significantly reduce competition in the UK market. The tech giant’s decision to integrate another genAI model introduces a complex interplay of innovation, user experience, and privacy concerns that could redefine how consumers interact with smart home technology. CMA CMA Claude's Potential Impact on Alexa: What to Expect Amazon’s journey to revamp Alexa hasn’t been without its hurdles. A recent Reuters report asserts that Amazon's initial tests with its new A.I. revealed major issues, such as slow responses and trouble understanding user prompts. With rivals like ChatGPT and Google Assistant setting high A.I. speed and accuracy standards, Amazon decided to switch to Anthropic’s Claude AI. The genA.I. model was found to be superior due to its better contextual understanding and more human-like conversation skills. Reuters report Reuters report While the basic "Classic" version of Alexa will still be free, users will need to pay for the extra features offered in the upgraded version. Amazon will charge a subscription fee for its "Remarkable Alexa" services, which will cost between $5 and $10 per month. Introducing a paid model for Alexa seems like a calculated risk for Amazon. The timing of this move is also strategic, transitioning Alexa towards a revenue-generating A.I. service like other tech conglomerates. Yet, this progress comes with potential risks. The enhanced AI capabilities could lead to increased data collection, raising concerns about how much personal information users are willing to share. “The new AI capabilities depend heavily on large datasets to refine and improve their responses,” Nate MacLeitch, CEO and founder of API platform QuickBlox, told me. “Amazon’s premium services will need access to the user's data to function effectively and understand user habits, health data, or their preferences. This means a user’s interactions with Alexa are not just private conversations but are also part of the data that trains the AI. ” Apple recently implemented Private Cloud Compute for its Apple Intelligence, ensuring user data remains secure, even from Apple itself. Although Amazon possesses similar capabilities, the specifics of how the integration between Alexa and Anthropic will uphold user privacy remain unclear. Will users be comfortable knowing their data contributes to the ongoing improvement of Alexa AI, especially when they are paying a subscription fee for the service? The company has been tight-lipped about specific measures to ensure data privacy, leaving users and privacy advocates to speculate and watch closely. “The key is to only train aggregate AI models using consented data that has been anonymized. Amazon can do the same by integrating a consent API and CDP system in the backend, and use Alexa to ask for consent before recording any data from the user and utilizing it,” Xun Wang, Chief Technology Officer of Bloomreach and former VP of NVIDIA, told me. “A.I. will soon be able to generate an audio text that zeroes in on the information you care about the most as opposed to a generic dump of all the information related to any specific piece of content.”

Can Claude AI Solve Alexa’s Data Privacy Issues?

ক্লড এআই কি আলেক্সার ডেটা গোপনীয়তার সমস্যাগুলি সমাধান করতে পারে?

Kann Claude AI die Datenschutzprobleme von Alexa lösen?

¿Puede Claude AI resolver los problemas de privacidad de datos de Alexa?

Claude AI peut-il résoudre les problèmes de confidentialité des données d’Alexa ?

क्या क्लाउड एआई एलेक्सा की डेटा गोपनीयता समस्याओं को हल कर सकता है?

Claude AI は Alexa のデータ プライバシー問題を解決できるか?

Claude AI가 Alexa의 데이터 프라이버시 문제를 해결할 수 있을까?

A Claude AI pode resolver os problemas de privacidade de dados da Alexa?

Сможет ли Claude AI решить проблемы конфиденциальности данных Alexa?

Claude AI, Alexa'nın Veri Gizliliği Sorunlarını Çözebilir mi?

Liệu Claude AI có thể giải quyết được vấn đề về quyền riêng tư dữ liệu của Alexa không?

Claude AI 能解决 Alexa 的数据隐私问题吗？

What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here. In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court—will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. https://twitter.com/Guntar/status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild (@dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article. And @Angry_Staffer, based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog, and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election. I also post regular updates on Bluesky and Twitter. ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ What if pollsters are just too afraid of Trump this time around? What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here . own little forecast half here here In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court —will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. recently allowed by the Supreme Court In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Mason McCann Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. very https://twitter.com/ Guntar /status/1851339376196010260?embedable=true https://twitter.com/ Guntar Guntar /status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild ( @dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” @dcg1114 https://twitter.com/dcg1114/status/1851779439899578606?embedable=true https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article . And @Angry_Staffer , based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” his latest article @Angry_Staffer https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog , and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. analyzing and providing past polling biases on his blog As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: my Monte Carlo simulation ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. the In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election . I also post regular updates on Bluesky and Twitter . https://github.com/maxspeicher/2024-us-presidential-election Bluesky Twitter ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ ☕ I love coffee, and if you want to support my work, you can always send me one , or subscribe to my newsletter . 🗞️ send me one send me one my newsletter my newsletter

Election Forecasts, Schmelection Forecasts

Textbooks Are All You Need: Conclusion and References

A New Framework for Trustworthy AI Deductive Reasoning

Pioneering Legal Frameworks for the Next Generation of Web3

Baanbrekende regsraamwerke vir die volgende generasie van Web3

Web3-ün növbəti nəsli üçün qabaqcıl hüquqi çərçivələr

Marcos jurídicos pioneros para la próxima generación de la Web3

Marcos legais pioneiros para a próxima xeración de Web3

次世代のWeb3のための先駆的な法的枠組み

Web3 кийинки муун үчүн пионердик укуктук негиздери

ບຸກເບີກໂຄງຮ່າງກົດໝາຍສຳລັບ Web3 ລຸ້ນຕໍ່ໄປ

Novatorisks tiesiskais regulējums nākamās paaudzes Web3

Bobulamadibogo Ditlhako tša Molao tša Moloko wo o Latelago wa Web3

د Web3 راتلونکي نسل لپاره مخکښ قانوني چوکاټونه

Асосҳои ҳуқуқӣ барои насли ояндаи Web3

Pioneering Legal Frameworks para sa Susunod na Henerasyon ng Web3

At a certain point, it becomes unclear how many notifications are demanding to be clicked on your phone. As you wake, your groggy brain attempts to recall what you tweeted as you dozed off last night. What did you say to earn this ephemeral moment of attention and glory? Last year, I experienced such a moment. I work for Manifold, a prediction markets platform, which hosts questions such as “Will TikTok be forced to sell?” or “When will GPT-5 be released?”. Users will then bet on these questions to earn play money and work their way up the leaderboards. This prices the market and creates accurate probabilities about the future. One night, while scrolling Manifold for content to share on our socials, I stumbled onto a market predicting the discovery of room-temperature superconductors. It gave a 17% chance that a recently published study claiming to create such a superconductor would be replicated. Before sharing it, I searched for other news articles and related tweets to engage with. To my surprise, I found… nothing. We had beaten the news to the story. Over the next week, our market was widely shared and received millions of views. But something interesting was happening: many users were signing up and returning each morning to check the odds, yet most had little interest in trading. On X, Paul Graham wrote, “A couple times a day I check the prediction market on LK-99. It has doubled since its low on July 29. Still only 24%, but it's moving up, not down.” This revealed two crucial insights. First, we had focused too much on proving the accuracy of prediction markets when people were drawn to them for the fast, aggregated data they provided. Second, we realised that most users were less interested in trading and more engaged as viewers, simply wanting to observe market trends. This required a fundamental shift in our product design and marketing strategies. From Academic Experiments to Consumer Product Prediction markets have traditionally been confined to academic experiments and niche applications, often brushed aside as speculative or impractical. However, our journey with Manifold has revealed untapped potential in this space if you can nail the product-market fit. In the last few years, several startups have taken different approaches to making prediction markets more accessible, valuable, and mainstream. Polymarket is the leading crypto platform and offers real-money markets on topics that other platforms, especially those in the U.S., can’t touch—like political elections. This strategy has attracted recent investment of $70 million from big names like Peter Thiel and Vitalik Buterin, with Nate Silver even coming on board as an advisor. With the election cycle heating up, their trading volume increased to over $100 million per month. Even Trump showed interest and regularly shared their odds on his socials, perhaps because Polymarket’s odds favoured him more than the polls. On the other hand, Kalshi is playing the long game by focusing on regulatory compliance and appealing to institutions. Unlike Polymarket, Kalshi allows US customers and has gone through the rigorous process of securing approval from the Commodity Futures Trading Commission (CFTC) to operate legal betting markets on specific economic indicators and events. This approach aligns with their goal of becoming the go-to platform for traditional financial institutions. But Kalshi isn’t stopping there—they’re actively lobbying and even suing the CFTC to expand their offerings to include more popular markets, like elections. Their recent partnership with SIG as a market maker to provide liquidity is a big step toward making Kalshi a trusted, compliant platform for large-scale traders and investors. Meanwhile, Manifold is taking a different route by focusing on accessibility and user engagement. By opting for a play-money model, Manifold sidesteps the regulatory headaches that come with real-money platforms. We are the only site where users can create their own markets allowing us to scale the range of questions being asked compared to other platforms. This user-generated content model ensures that Manifold is always the fastest to respond to breaking news and the questions that come with it. While our markets currently operate on play money, we’re planning to introduce a sweepstakes model that will let users win real money on certain markets. Because of the gamification and reputation at stake, users are still incentivised to trade accurately allowing our markets to be just as good as our competitors. As these three platforms—Polymarket, Kalshi, and Manifold—demonstrate, the path to product-market fit in prediction markets is not a one-size-fits-all journey. That said, all 3 platforms have had to overcome some shared challenges that come with prediction markets. Identifying the challenges with your product’s marketability Every new product faces the challenge of overcoming initial skepticism, especially when it's something unfamiliar. For example, most people have no connotation with the terms “prediction market” or “forecasting platform”. This presents a rare opportunity to create their initial impression but comes with the risk of them comparing it to a gambling platform with a negative connotation. This makes prediction markets arduous to share. Manifold users who tried to share links without framing them properly were barraged with downvotes on Reddit. But this isn’t the fault of the sharers, but rather the product itself and what it communicates about itself when viewed at first glance. There’s an intuition people have that an idea or product can only grow as much as people are willing to share it. I would argue that this isn’t the bottleneck. You have to look one step further. A product’s growth is limited by how easy it is to share. You could have a cult-like following obsessed with an idea and bombarding anyone who dares to listen. Yet, if it is too complex and cannot be presented in a relatable way, it’s not going to grow. And even if you can convey an accurate picture of what a prediction market is, it still may not be obvious to someone what value it presents to them. Luckily, prediction markets are now in a position where enough credible people and news outlets understand them and are willing to leverage their reputation to share them. Presenting them as a way to “follow the odds on something you care about” rather than “play this game/make money betting about something you care about” has helped a lot too. Narrowing your focus on Product-market-fit As we developed our product, we found ourselves constantly pulled in different directions, often sacrificing key use cases. Initially, we over-optimised for traders, believing that product-market fit (PMF) depended on teaching as many people as possible how to trade and making it fun for them. However, this limited approach overlooked a significant segment of users who were more interested in the information prediction markets provided than in trading itself. The superconductor rumours and the ongoing election cycle offered strong examples of this. Many users cared about the probabilities but had little interest in trading. Also, they didn’t need the markets to be perfectly accurate; they just wanted a quick gauge of how events, like Biden potentially dropping out, could impact the race. The speed and convenience of prediction markets filled this gap far better than slow polls could. By making the shift to focus on highlighting our speed and readability, it becomes harder for skeptical potential users to dismiss us. Previously, they could choose to believe we didn’t have enough proof of accuracy and thus weren’t worth anyone’s time. Or that the only reason to interact with prediction markets is if you were a gambler, which made sense as our marketing and copy were solely focused on pushing people to make trades. However, it’s much harder to deny that prediction markets create the fastest, aggregated data that is convenient to read. Manifold’s data still suggests that prediction markets are incredibly accurate and we still care about our traders. But, by focusing on the value we provide in other areas, we aren’t so reliant on winning the accuracy argument or trying to convert everyone to enjoy trading. What’s next for prediction markets? We are entering exciting times for prediction markets. This election cycle will be their first test with significant volume and mainstream attention to see how they perform compared to more traditional forecasting methods. Even more exciting will be seeing how many news outlets and people begin including prediction markets as a data point when forming their own opinions. We are also continuously discovering more advantages prediction markets have that align with what users care about. For example, news sites heavily optimise for reporting breaking news as soon as possible. When Biden dropped out Manifold sent an email 20 minutes before news outlets such as the NYT did to alert our users. Finally, questions remain about prediction markets’ longevity in the US, especially as the CFTC continues its legal battle with Kalshi. How this will play out could have significant implications for the future of the industry. At a certain point, it becomes unclear how many notifications are demanding to be clicked on your phone. As you wake, your groggy brain attempts to recall what you tweeted as you dozed off last night. What did you say to earn this ephemeral moment of attention and glory? Last year, I experienced such a moment. I work for Manifold , a prediction markets platform, which hosts questions such as “Will TikTok be forced to sell?” or “When will GPT-5 be released?”. Users will then bet on these questions to earn play money and work their way up the leaderboards. This prices the market and creates accurate probabilities about the future. Manifold One night, while scrolling Manifold for content to share on our socials, I stumbled onto a market predicting the discovery of room-temperature superconductors. It gave a 17% chance that a recently published study claiming to create such a superconductor would be replicated. Before sharing it, I searched for other news articles and related tweets to engage with. To my surprise, I found… nothing. We had beaten the news to the story. Over the next week, our market was widely shared and received millions of views. But something interesting was happening: many users were signing up and returning each morning to check the odds, yet most had little interest in trading. On X, Paul Graham wrote , “A couple times a day I check the prediction market on LK-99. It has doubled since its low on July 29. Still only 24%, but it's moving up, not down.” On X, Paul Graham wrote This revealed two crucial insights. First, we had focused too much on proving the accuracy of prediction markets when people were drawn to them for the fast, aggregated data they provided. Second, we realised that most users were less interested in trading and more engaged as viewers, simply wanting to observe market trends. This required a fundamental shift in our product design and marketing strategies. From Academic Experiments to Consumer Product Prediction markets have traditionally been confined to academic experiments and niche applications, often brushed aside as speculative or impractical. However, our journey with Manifold has revealed untapped potential in this space if you can nail the product-market fit. In the last few years, several startups have taken different approaches to making prediction markets more accessible, valuable, and mainstream. Polymarket is the leading crypto platform and offers real-money markets on topics that other platforms, especially those in the U.S., can’t touch—like political elections. This strategy has attracted recent investment of $70 million from big names like Peter Thiel and Vitalik Buterin, with Nate Silver even coming on board as an advisor . With the election cycle heating up, their trading volume increased to over $100 million per month . Even Trump showed interest and regularly shared their odds on his socials, perhaps because Polymarket’s odds favoured him more than the polls. Polymarket investment of $70 million Nate Silver even coming on board as an advisor $100 million per month On the other hand, Kalshi is playing the long game by focusing on regulatory compliance and appealing to institutions. Unlike Polymarket, Kalshi allows US customers and has gone through the rigorous process of securing approval from the Commodity Futures Trading Commission (CFTC) to operate legal betting markets on specific economic indicators and events. This approach aligns with their goal of becoming the go-to platform for traditional financial institutions. But Kalshi isn’t stopping there—they’re actively lobbying and even suing the CFTC to expand their offerings to include more popular markets, like elections. Their recent partnership with SIG as a market maker to provide liquidity is a big step toward making Kalshi a trusted, compliant platform for large-scale traders and investors. Kalshi suing the CFTC partnership with SIG Meanwhile, Manifold is taking a different route by focusing on accessibility and user engagement. By opting for a play-money model, Manifold sidesteps the regulatory headaches that come with real-money platforms. We are the only site where users can create their own markets allowing us to scale the range of questions being asked compared to other platforms. This user-generated content model ensures that Manifold is always the fastest to respond to breaking news and the questions that come with it. While our markets currently operate on play money, we’re planning to introduce a sweepstakes model that will let users win real money on certain markets. Because of the gamification and reputation at stake, users are still incentivised to trade accurately allowing our markets to be just as good as our competitors. sweepstakes model As these three platforms—Polymarket, Kalshi, and Manifold—demonstrate, the path to product-market fit in prediction markets is not a one-size-fits-all journey. That said, all 3 platforms have had to overcome some shared challenges that come with prediction markets. Identifying the challenges with your product’s marketability Every new product faces the challenge of overcoming initial skepticism, especially when it's something unfamiliar. For example, most people have no connotation with the terms “prediction market” or “forecasting platform”. This presents a rare opportunity to create their initial impression but comes with the risk of them comparing it to a gambling platform with a negative connotation. This makes prediction markets arduous to share. Manifold users who tried to share links without framing them properly were barraged with downvotes on Reddit. But this isn’t the fault of the sharers, but rather the product itself and what it communicates about itself when viewed at first glance. There’s an intuition people have that an idea or product can only grow as much as people are willing to share it. I would argue that this isn’t the bottleneck. You have to look one step further. A product’s growth is limited by how easy it is to share. You could have a cult-like following obsessed with an idea and bombarding anyone who dares to listen. Yet, if it is too complex and cannot be presented in a relatable way, it’s not going to grow. And even if you can convey an accurate picture of what a prediction market is, it still may not be obvious to someone what value it presents to them. Luckily, prediction markets are now in a position where enough credible people and news outlets understand them and are willing to leverage their reputation to share them. Presenting them as a way to “follow the odds on something you care about” rather than “play this game/make money betting about something you care about” has helped a lot too. Narrowing your focus on Product-market-fit As we developed our product, we found ourselves constantly pulled in different directions, often sacrificing key use cases. Initially, we over-optimised for traders, believing that product-market fit (PMF) depended on teaching as many people as possible how to trade and making it fun for them. However, this limited approach overlooked a significant segment of users who were more interested in the information prediction markets provided than in trading itself. The superconductor rumours and the ongoing election cycle offered strong examples of this. Many users cared about the probabilities but had little interest in trading. Also, they didn’t need the markets to be perfectly accurate; they just wanted a quick gauge of how events, like Biden potentially dropping out, could impact the race. The speed and convenience of prediction markets filled this gap far better than slow polls could. By making the shift to focus on highlighting our speed and readability, it becomes harder for skeptical potential users to dismiss us. Previously, they could choose to believe we didn’t have enough proof of accuracy and thus weren’t worth anyone’s time. Or that the only reason to interact with prediction markets is if you were a gambler, which made sense as our marketing and copy were solely focused on pushing people to make trades. However, it’s much harder to deny that prediction markets create the fastest, aggregated data that is convenient to read. Manifold’s data still suggests that prediction markets are incredibly accurate and we still care about our traders. But, by focusing on the value we provide in other areas, we aren’t so reliant on winning the accuracy argument or trying to convert everyone to enjoy trading. Manifold’s data What’s next for prediction markets? We are entering exciting times for prediction markets. This election cycle will be their first test with significant volume and mainstream attention to see how they perform compared to more traditional forecasting methods. Even more exciting will be seeing how many news outlets and people begin including prediction markets as a data point when forming their own opinions. We are also continuously discovering more advantages prediction markets have that align with what users care about. For example, news sites heavily optimise for reporting breaking news as soon as possible. When Biden dropped out Manifold sent an email 20 minutes before news outlets such as the NYT did to alert our users. Manifold sent an email 20 minutes before news outlets Finally, questions remain about prediction markets’ longevity in the US, especially as the CFTC continues its legal battle with Kalshi. How this will play out could have significant implications for the future of the industry.

Stories, News & Mentions about NATE

Nate’s Stories on HackerNoon

Nate's News & Mentions