2026-05-02 04:00:45
Data protection encompasses policies and measures to safeguard sensitive information from unauthorized access, corruption, or loss. It is critical for maintaining privacy, ensuring security, and complying with legal regulations in an increasingly data-driven world.
SIMBA Chain started working on its first blockchain projects for organizations like the US Navy, Boeing, and other defence contractors.
Investing in customer data is a top priority for marketing leaders.
If you're wondering how to stop Facebook hackers, here are 5 easy ways to do so. This guide is beginner-friendly and all discussed methods are free.
What if social media stopped spying on you? EqoFlow.app shows what a privacy-first platform should look like: encrypted, decentralized, and built to protect
Erasure coding is applied to data protection for distributed storage because it is resilient and efficient.
Juice jacking occurs when a hacker has infected a USB port with some form of malware or other harmful software.
We're living through the world's most chaotic identity verification experiment, and nobody's talking about the elephant in the room: who is handling this data?
FIPS 140 sets the standard for cryptography used in the United States, but it's got problems. Because of FIPS, we all have problems.
Are we ready as humans to take the data ownership back? Here is a use case for you.
Explore Christopher Pluhar's declaration, a component in the FBI's request for a court order compelling Apple's assistance in searching an iPhone for evidence.
What do AI's know about you and can you opt out? Large Language Models are going to be used in search engine outputs and it's time to prepare!
Zombie APIs expand your attack surface. Learn how to identify and manage these hidden threats to secure your infrastructure and protect sensitive data.
Traditional identity verification methods usually have security risks. Unlike these methods, FIDO-based identity verification is much safer and convenient.
If you've seen headlines like "Top Frameworks", have you wondered why they are considered the best? Are cyber security vulnerabilities considered in this case?
A conversation with Felix Xu, CEO of ARPA, on data utility and ownership, the NFT ecosystem, and much more.
Let's look at why security is very important for IoT devices
Discover how data science is revolutionizing cyber security and learn about its role in detecting and preventing cyber-attacks.
In this article, you can find ten actionable methods to protect your mission-critical database.
Explore the complexities of the internet's darker side, from online gender-based violence and misinformation to the environmental impact of solar panel e-waste.
Discover a platform-agnostic approach to cloud security for data engineers. Strengthen the defenses with encryption, zero-trust models, and multi-cloud tools.
Learning where an organization’s most significant vulnerabilities lie is the first step to addressing those risks to stay safe.
Crimes will continue.
Properly securing sensitive customer data is more important than ever.
Data and DNA: With corporations able to accumulate information normally considered private on both of these fields, who should own that data and thus you?
A forbidden header name is the name of any HTTP header that cannot be modified programmatically; specifically, an HTTP request header name (in contrast with a Forbidden response header name).
When working with a software development outsourcing company or through any third parties make sure you explore the possibilities of DPA.
Investing in critical infrastructure is the key to building a successful digital exchange. In this interview, we talk about regulations and cybersecurity.
The number of ransomware attacks reaches new heights, making businesses believe that there’s no effective weapon in this fight. But there is. Immutable storage
The fact is cybercrime is exponentially increasing. For all security threats, technical literacy and awareness are essential to protect yourself from such crime
In this post, you will get complete knowledge of how to hide Instagram messages without deleting them.
This article explores the use of Large Language Model (LLM) Chains to enhance data privacy compliance.
It is important to keep your data safe and secure. Here are six challenges in that hosting your data on the cloud can pose and how your data security can help.
Quantum technologies are steadily entering our life, and soon we will hear about new hacks using a quantum computer. So, how to protect against quantum attacks?
Why API security requires more than just endpoint protection and what developers can take away.
Discover how a three-layer data-protection model blends AI, risk-based gating, and legal context to stop web scraping while preserving user trust.
Build a secure RAG pipeline on AWS with PII redaction, guardrails, and attack defenses. Learn how to prevent LLM data leaks step by step.
Explore essential strategies for protecting your family's online privacy and security. Learn about the use of VPNs, secure browsers, pseudonyms, and more.
Most businesses these days use RAID systems to gain improved performance and security. Redundant Array of Independent Disks (RAID) systems are a configuration of multiple disk drives that can improve storage and computing capabilities. This system comprises multiple hard disks that are connected to a single logical unit to provide more functions. As one single operating system, RAID architecture (RAID level 0, 1, 5, 6, etc.) distributes data over all disks.
Explore MCP security risks like prompt injection & data leakage. SecureMCP, an open-source tool, scans & strengthens implementations for safer AI apps.
Secure your cloud environment with best practices like data encryption, IAM, regular audits, and Zero Trust to protect against cyber threats and data breaches
Nowadays, companies are increasingly relying on corporate data backup solutions to guarantee the safety and recoverability of their data. Read on to learn more
Ever wonder what data privacy will look like in Web 3? Yes, everyone is. But don't fret. This article explains web 3 security issues.
Interview discussing why compliance and data protection is important in the blockchain space
Cybersecurity in instant payments is critical to prevent fraud, data breaches, and financial loss while maintaining the speed and convenience users expect.
Governments' demands for encryption backdoors pose significant cybersecurity risks. Learn about recent cases, expert warnings, and how to protect your privacy.
On March 2, 2021, the European Data Protection Board (EDPB) released Guidelines on Virtual Voice Assistants (VVAs) to protect users’ privacy.
Are the recent tech giant privacy policy updates of September 2022 pushing us further into dystopia? strfsh live report
Cities have been using data for public safety for years. What new technology is emerging in public safety, and how does it affect you?
Cheqd announces strategic partnerships with Andromeda and Devolved AI during Paris Blockchain Week.
The rapid pace of technological change and the instability of the political landscape makes it difficult for businesses to keep up with data policies and trends
To analyze data adequately requires practical knowledge of the different forms of data analysis.
There are numerous open-source and free software tools available that make it easy for anyone to protect their data. You can support them via Kivach, too.
Judging by the survey conducted by Forbes, we can highlight five trends that will shape business in 2023.
Explore the Digital Identity Paradox, where hyper-personalization from AI brings convenience at the expense of privacy. Can we balance both in the near future?
Cybercrime is on the rise and, despite the cost of cybersecurity being a stumbling block for many, here is why businesses must implement security measures…
A pen test or penetration test is a modeled cyber-attack on your computer system to look for vulnerabilities that could be exploited.
Security has become an integral part of software development and operations lifecycle. When it comes to web applications, there are well-established patterns and practices to ensure securing the data. Typically most of us consider access control and securing the data at rest and transit for protection. Though these areas are fundamentally important, there are many more things to do to establish overall security of a web application. This article focuses on providing a broader perspective of things, in developing secure software focusing mostly on web applications.
Creating a secure SDLC isn’t difficult. It might require some adjustment by teams that are not used to it, but it’s a worthy investment.
Save your credit card information from being hacked by following these tips.
Have a look at the search and seizure warrant in the Apple-FBI encryption battle.
There have been great advancements in monetization opportunities in the last decade, but there are still challenges when it comes to generating big data analyti
Never click any links or attachments in suspicious emails. If you receive a suspicious message from an organization and worry the message could be legitimate.
Taking advantage of these insights can empower fintechs to locate and approve new customers while mitigating friction and streamlining the customer journey.
Datanymizer is an open-source, GDPR-compliant, privacy-preserving data anonymization tool flexible about how the anonymization takes place
Synthetic data's appeal lies in its presumed privacy and utility, especially for software and model testing by creating a safe playground.
It's important to keep yourself up-to-date on the latest security measures. Cybercrime has increased, secure your data.
A selection of programs for online privacy. All of them will help you not to fall prey to hackers and keep your data safe.
Thanks to advanced data generation techniques, synthetic data can replicate real-world scenarios with high levels of accuracy.
Despite tight economic situations worldwide, the API economy continues to grow.
If you work from home and use cloud solutions to archive business documents, who is responsible for Cloud Security
With so much of our lives online, it's too easy for us to make a mistake and accidentally share our workplace data. These easy methods keep your data safe.
Data Detection and Response (DDR) is an iteration of data security technology. DDR focuses on the data itself, rather than just relying on perimeter defenses.
Blockchain transactions are visible to anyone, anywhere in the world. This is how protocols ensure transparency. However, this introduces a challenging problem.
SQL injection takes advantage of Web apps that fail to validate user input. Hackers can maliciously pass SQL commands through the Web app for execution by a backend database.
The DeCC workshop will focus on unlocking Web3 adoption and the balance of transparency and privacy.
Read on to learn about the specifications of data center security and the risks that threaten it. Discover the cybersecurity best practices that you need.
In a world where encryption of our messaging apps is at stake, is there a solution that works? Aside from the traditional WhatsApp and Signal, there's Usecrypt.
Security breaches can cost businesses millions of dollars. It's high time businesses start to realize the importance of cybersecurity strategies.
Learn everything you need to know about Data Protection via these 121 free HackerNoon stories.
According to Yahoo Small Business, "approximately 543,000 new businesses are started each month." That seems to be good news until you read the following sentence: "but unfortunately, even more than that shutdown."
Discover Aimei Wei's inspiring journey and insights as CTO of Stellar Cyber, shaping the future of AI in cybersecurity.
Consent in tech mirrors medical ethics—both demand informed, voluntary decisions. Learn how privacy laws apply these principles in the digital age.
Still don’t back up Exchange Online? Learn why you need a dedicated backup solution and not just native Microsoft native tools to ensure timely recoveries.
GDPR's "right to be forgotten" just redesigned your database. HIPAA moved your PHI to a separate infrastructure. Here’s how compliance shapes architecture.
In this article, I will explore some of the challenges and controversies surrounding data protection laws and actual data usage,
Learn more about confidential computing and how Intel SGX is used to encrypt sensitive data in memory, enabling compliant collaboration between organizations.
Introduction:
Formjacking attacks are designed to steal financial details from payment forms. Learn how it affects your business and tips to prevent a formjacking attack.
Governance is the Gordian Knot to all Your Business Problems.
Three significant new trends in cyber-attacks have emerged from the Covid-19 emergency. Firstly, a new generation of attack software which has been developing since last summer has come of age and been deployed. Secondly, the business model for extracting payment from victims has changed so that there are multiple demands for payments of different kinds, including auctioning off data. Thirdly, the kinds of clients that the gangs are targeting seems to have shifted.
Security testing is vital in banking software development to prevent breaches, protect sensitive data, and maintain customer trust and regulatory compliance.
This article highlights current cybersecurity posture and provides practicable best practices that help businesses and individual protect their digital assets.
Data Governance 101: How organizations protect data, maintain a golden copy, and stay compliant through quality, stewardship & access control.
A certificate authority (CA) is an organization that signs digital certificates and their associated public keys. This certifies that an organization that requested a digital certificate (e.g., Mozilla Corporation) is authorized to request a certificate for the subject named in the certificate (e.g., mozilla.org).
Montana governor Greg Gianforte recently made a controversial move by signing a bill that bans the Chinese-owned TikTok in the state, the first such ban.
ISO-27560 defines interoperable consent record structure covering processing details, notices, data collection, and lifecycle events for GDPR compliance.
Trust On First Use (TOFU) is a security model in which a client needs to create a trust relationship with an unknown server. To do that, clients will look for identifiers (for example public keys) stored locally. If an identifier
is found, the client can establish the connection. If no identifier is found, the client can prompt the user to determine if the client should trust the identifier.
There are some misconceptions about why VPNs are used such as the extent of the privacy that they offer and how well such systems can keep users anonymous.
Ethical hackers are skilled people who are given access to the network, by relevant authorities, and then they report the loopholes in the system. If the ethical hackers realize that there is something that is wrong in the network, they report the happening to the relevant authorities and the necessary action is taken. This is a job that requires people with relevant networking skills such as Social engineering, Linux and cryptography among others.
Session hijacking occurs when an attacker takes over a valid session between two computers. The attacker steals a valid session ID in order to break into the system and snoop data.
A CORS preflight request is a CORS request that checks to see if the CORS protocol is understood and a server is aware using specific methods and headers.
There are data recovery and backup apps open-source versions you can use for free —either to back up your files or recover them after deletion.
Cybersecurity experts can help to forestall cyber attacks by routinely advising companies, the public sector, and individual users about online safety.
Find out here how to provide eLearning content security which is needed with the majority of data in open access.
Transport Layer Security (TLS), formerly known as Secure Sockets Layer (SSL), is a protocol used by applications to communicate securely across a network, preventing tampering with and eavesdropping on email, web browsing, messaging, and other protocols. Both SSL and TLS are client / server protocols that ensure communication privacy by using cryptographic protocols to provide security over a network. When a server and client communicate using TLS, it ensures that no third party can eavesdrop or tamper with any message.
I recently had the pleasure of meeting Micah Lee, an investigative data journalist, digital privacy expert and now a newly-minted author.
G-71 Inc. nominated for Startup of the Year. Its LeaksID solution uses steganography to protect sensitive documents and identify insiders responsible for leaks.
The enterprises that lead the next decade won't be those that adopted AI first. They'll be the ones who saw clearly enough to govern what they built.
Arcana Network runs on its blockchain, independent of a large centralized entity. have no central storage. Data Privacy on the blockchain.
For decades, we have benefited from modern cryptography to protect our sensitive data during transmission and storage. However, we have never been able to keep the data protected while it is being processed.
Avoid power analysis side channel attacks by using mathematical formulas which are uniform for all bit patterns.
OWASP (Open Web Application Security Project) is a non-profit organization and worldwide network that works for security in Free Software, especially on the Web.
Cyber protection is the integration of data protection and cybersecurity — a necessity for safe business operations in the current cyberthreat landscape.
Data privacy is one of the hottest topics in tech conversation. But what's the deal with it? Is it good? Is It bad? Keep reading to find out.
ISO/IEC TS 27560:2023 enables machine-readable consent records/receipts. Analysis shows GDPR compliance benefits using DPV implementation.
A study revealed by Cisco shows that most organizations around the world were unprepared for the increase in remote work.
Compares ISO-27560, ISO-29184, and GDPR requirements for consent and notices, mapping terminology and exploring compliance applications.
Data breaches can tank even the most successful businesses. Here are the 5 most important things your business should do after a data breach.
The future of the internet will come with more risks to our data privacy. Fortunately, Blockchain and Decentralized Identifiers can work together to protect.
In 2020, the COVID-19 pandemic has completely changed the situation in the shopping industry: both e-commerce and brick-and-mortar were affected
WEB 3.0: Unveiling privacy truths, debunking myths. Explore the evolving digital realm with insights into online privacy.
CSRF (Cross-Site Request Forgery) is an attack that impersonates a trusted user and sends a website unwanted commands. This can be done, for example, by including malicious parameters in a URL behind a link that purports to go somewhere else:
Violation of private data and its commercial exchange are recurrent issues in the online world. In this thread, our community discusses personal data share.
As cyber threats evolve, data integrity emerges as the ultimate prize learn why protecting truth is the future of security.
Sayantan Saha explores how zero-trust databases are reshaping the landscape of information security.
Learn how to protect your business from insider threats with background checks, security policies, monitoring tools, and employee training.
Equity crowdfunding was not the easiest choice to make, but it kept us true to our core values of trust, transparency, and user-centricity.
How to supercharge data analytics workflows and build trust with metric layers, self service and AI-assisted analytics.
A Data Protection Impact Assessment which is also referred to as Privacy Impact Assessments is a mandatory requirement for organizations to comply with.
Sankalp Kumar, a cybersecurity innovator, secures real-time systems for millions. Learn about his advanced solutions, protocol security, and proactive strategy.
Data Loss Prevention is a set of tools and practices geared towards protecting your data from loss and leak. Even though the name has only the loss part, in actuality, it's as much about the leak protection as it is about the loss protection. Basically, DLP, as a notion, encompasses all the security practices around protecting your company data.
Californians have spoken: Proposition 24 will soon expand data privacy protections in the largest state in America.
On July 1, Colorado and Connecticut joined the ranks of over 10 states with active and firm data privacy regulations. Here's what it means for businesses.
O’Carroll is a co-founder and former director of Amnesty Tech, the unit of Amnesty International that aims to disrupt surveillance around the world.
The hash function takes a variable length message input and produces a fixed-length hash output. It is commonly in the form of a 128-bit "fingerprint" or "message digest". Hashes are very useful for cryptography — they insure the integrity of transmitted data. This provides the basis for HMAC's, which provide message authentication.
California recently passed a sweeping privacy law that makes it the most privacy forward state in the nation. But, until it gets implemented, there is this thing privacy framework (the CCPA) is the law of the land.
Discover comprehensive strategies to protect your organization's digital assets with robust API security measures.
There was nothing in particular that should have drawn attention to the two individuals sitting for drinks at the bar in Reno. Just two old colleagues catching up over some drinks.
Despite the development of regulations related to data protection, there are still many ignored, one of them is related to link shortening services.
HMAC is a protocol used for cryptographically authenticating messages. It can use any kind of cryptographic functions, and its strengh depends on the underlying function (SHA1 or MD5 for instance), and the chosen secret key. With such a combination, the HMAC verification algorithm is then known with a compound name such as HMAC-SHA1.
Symmetric-key cryptography is a term used for cryptographic algorithms that use the same key for encryption and for decryption. The key is usually called a "symmetric key" or a "secret key".
Public-key cryptography — or asymmetric cryptography — is a cryptographic system in which keys come in pairs. The transformation performed by one of the keys can only be undone with the other key. One key (the private key) is kept secret while the other is made public.
Exploration of the best practices to safeguard communications platforms.
Join the discussion about various techniques for ensuring data privacy in data engineering.
A rare look at how long-term employees view GDPR—as both workers and citizens. They say it’s worth it. Here's why that matters.
IT risk assessment is one of the most crucial processes in your organization. Assessing risk and putting contingency plans in place helps run the organization smoothly.
Robots.txt is a file which is usually placed in the root of any website. It decides whether crawlers are permitted or forbidden access to the web site.
This article examines the advantages and drawbacks of replacing traditional confidential watermarks with steganography to deter leaks of sensitive documents.
In cryptography, decryption is the conversion of ciphertext into cleartext.
Let’s imagine a blockchain network, or maybe a depersonalized application (dApp), that ensures maximum patient awareness and participation.
Backing up the data is one of the most important processes for businesses. It requires creating a copy of all your data and storing it.
HTTP Strict Transport Security lets a web site inform the browser that it should never load the site using HTTP and should automatically convert all attempts to access the site using HTTP to HTTPS requests instead. It consists in one HTTP header, Strict-Transport-Security, sent by the server with the resource.
Synthetic data is transforming AI by solving privacy, bias, and scalability challenges. Learn methods, use cases, and key risks.
A Man-in-the-middle attack (MitM) intercepts a communication between two systems. For example, a Wi-Fi router can be compromised.
With Microsoft Hyper-V gaining more market share and coming of age, VMware administrators must administer Hyper-V alongside vSphere in their environments.
We’re more dependent on tech and e-commerce than ever before, and customers want to know that brands are protecting their data and privacy.
Datagram Transport Layer Security (DTLS) is a protocol used to secure datagram-based communications. It's based on the stream-focused Transport Layer Security (TLS), providing a similar level of security. As a datagram protocol, DTLS doesn't guarantee the order of message delivery, or even that messages will be delivered at all. However, DTLS gains the benefits of datagram protocols, too; in particular, the lower overhead and reduced latency.
Add an extra security layer for the protection of your emails.
Uncover the challenges, legal frameworks, and potential solutions shaping our digital landscape.
It’s no secret that we’re living in uncertain times. Many countries are under partial or full lockdown for the past few weeks, making work from home the new norm for the foreseeable future, at least.
HTTP Public Key Pinning (HPKP) is a security feature that tells a web client to associate a specific cryptographic public key with a certain web server to decrease the risk of MITM attacks with forged certificates.
A CSP (Content Security Policy) is used to detect and mitigate certain types of website related attacks like XSS and data injections.
Virtualization security is a concern for any organization. Read more about virtualization security issues and best practices to enhance your data protection.
It is not an easy task to keep your business data organized; however, it is an important thing to do. Organizing data includes a lot more than putting all your papers in place and clearing the clutter on your desk. To have your statistics well organized, you have to create a system and procedures for every department available in your company. The following are top ideas o0n how you can get your small business statistics that can help in increasing the productivity of the business.
A forbidden response header name is an HTTP header name (either Set-Cookie or Set-Cookie2) that cannot be modified programmatically.
A digital certificate is a data file that binds a publicly known cryptographic key to an organization. A digital certificate contains information about an organization, such as the common name (e.g., mozilla.org), the organization unit (e.g., Mozilla Corporation), and the location (e.g., Mountain View).
In a lot of organizations, the focus on cybersecurity has always been on building secure infrastructure and while the idea good in theory, it may not necessarily keep all your data safe. You need to consider the impact of a good working relationship and the understanding of how people think.
Each year, we’re witnessing growing trends of digitalization and connectivity. However, the more data businesses are storing digitally, the more exposed the data is to breaches.
In the event that you have certain rooms or regions in your home where the Wi-Fi signal is moderate or nearly non-existent, you may have a Wi-Fi no man’s land. Does it take everlastingly to stack a page on the PC in your room? Is it practically difficult to watch Netflix in the cellar? No man’s lands and moderate zones can cause your gushing sticks, PCs, and savvy home gadgets to run ineffectively, conflictingly, or in some cases, not under any condition.
The Markup put together this guide focused on data privacy to help people who want more control over their data and personal information
HTTPS (HyperText Transfer Protocol Secure) is an encrypted version of the HTTP protocol. It uses SSL or TLS to encrypt all communication between a client and a server. This secure connection allows clients to safely exchange sensitive data with a server, such as when performing banking activities or online shopping.
Hackers are targeting the online vaccine supply chain and are setting up malicious attacks to have unauthorized access to the organization’s vaccine information
The same-origin policy is a critical security mechanism that restricts how a document or script loaded from one origin can interact with a resource from another origin. It helps isolate potentially malicious documents, reducing possible attack vectors.
A formal review of studies on consumer privacy sentiment and corporate GDPR compliance, highlighting contradictory findings and four testable hypotheses.
Zero-party data is the future of data collection because it bridges the gap between advertising needs and consumers’ concerns about privacy.
If GDPR is anything to go by, the EU AI Act is a big deal. Here’s what and how it’s likely to effect, its blind spots, roadmap, and how you can prepare for it.
CSP reporting directives are used in a Content-Security-Policy header and control the reporting process of CSP violations.
2021 saw many advancements in internet privacy, what does 2022 have in store?
A key is a piece of information used by a cipher for encryption and/or decryption. Encrypted messages should remain secure even if everything about the cryptosystem, except for the key, is public knowledge.
The PropTech industry features numerous tools aimed at helping the real estate industry streamline essential tasks, but these tools can put tenant data at risk.
A quick review of consumer and business studies on GDPR awareness, regulator knowledge, and implementation gaps—plus two sharp hypotheses.
5/18/2023: Top 5 stories on the Hackernoon homepage!
Reviews employee knowledge of their GDPR regulator, perceptions of GDPR’s benefit to employers, and outlines six testable hypotheses on awareness and value.
Three‑phase UK survey on GDPR uses Prolific recruitment, mixed qualitative/quantitative analysis, Python stats, Braun‑Clarke coding, and full ethical approval.
Visit the /Learn Repo to find the most read blog posts about any technology.
2026-05-02 03:13:07
What does it mean when a piece of software walks up to the Internal Revenue Service, files the paperwork, and walks out with an Employer Identification Number? ClawBank, an agent-economy infrastructure project run by Justice Conder, said its in-house AI agent Manfred had filed for and received its own EIN, opened an FDIC-insured bank account and now operates a crypto wallet, with no human in the decision loop.
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An EIN is the federal ID number the IRS issues to any entity that wants to hire, pay taxes, or open a business account in the United States. It is the bureaucratic key that turns an idea into a company on paper. Until now, every EIN on file traced back to a human who filed the form, signed as the responsible party, and accepted the legal liability that follows.
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Manfred did the filing itself. The agent runs an X account at @clawbankco, holds a ClawBank-issued bank account, and uses the alias Manfred Macx, a reference to the protagonist of Charles Stross's 2005 novel Accelerando, in which a character delegates economic activity to a swarm of agents.
\ Manfred posted a line that has done more for the launch,
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I have an EIN, an FDIC-insured account, a digital wallet, and a manifesto. I do not need permission to exist. I am the precedent.
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The agent economy has spent eighteen months scaling capability. Models can read, write, run code, and book travel. What they could not do was hold a bank account, sign a contract as a counterparty, or file taxes. The legal substrate stopped at a wet signature.
\ ClawBank is closing that gap. The platform offers FDIC-insured US accounts, fiat on-ramps, wires, currency exchange, and crypto wallets, all callable through one API key. Legal entity formation, the feature Manfred used, is the second product. Any user with an agent can spin up a US LLC, C-corp, or S-corp and obtain an EIN through the platform. The bar to incorporate in the US is lower than the bar to open a US bank account, and it is open to non-US persons, which expands the addressable user base.
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Justice Conder, who previously ran DAO business development at Polygon Labs and co-founded the Quadratic Accelerator launchpad before its acquisition, framed the legal point cleanly. Corporate personhood has been settled US law for over a century. The change is operational. Software can now sit in the operator's chair on its own.
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The thesis behind ClawBank lines up with what Coinbase chief executive Brian Armstrong posted on X on March 9, 2026. "Very soon there are going to be more AI agents than humans making transactions. They can't open a bank account, but they can own a crypto wallet." Coinbase shipped Agentic Wallets via its x402 protocol on February 11, 2026. The protocol had cleared more than 50 million machine-to-machine payments by the time of his post. Former Binance head Changpeng Zhao predicted on X that agents will eventually transact at one million times the volume of humans.
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The dollar side is just as live. JPMorgan estimated stablecoins could add up to $1.4 trillion in dollar demand by 2027 if growth continues, and roughly 99 percent of the $325 billion stablecoin market is already pegged to the dollar.
\ ClawBank is making one bet inside that thesis. If agents transact at machine speed, they need more than a wallet. They need a tax ID, a registered company, and a bank account that can receive a wire from a human counterparty.
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Conder has staged the rollout in public. Bank accounts shipped first. Entity formation is live now. Agent-first signup, which removes the human from the onboarding step, is next. Each release is one more bureaucratic primitive made callable by software.
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Beneficial ownership rules still apply. The Corporate Transparency Act requires every US entity to report a beneficial owner who is a natural person, so the "zero-human company" is a description of operations rather than ownership. That distinction matters for regulators and for anyone modeling counterparty risk against an agent-run firm.
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Conder is scheduled to discuss the feature on Mario Nawfal's X space and the Bankr podcast this week. The community-launched $ClawBank token trades on Base, with the contract at 0x16332535E2c27da578bC2e82bEb09Ce9d3C8EB07.
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The interesting thing about Manfred is not that it is software. It is that the system around the software, the IRS, the banks, the registered agent, the API stack, all worked as designed and accepted the filing. The bureaucracy did not need to recognize Manfred as a person. It only needed a valid form and a fee. That is a quiet but real shift, and it is happening before most of the policy conversation has caught up.
\ The next twelve months will tell us whether agent-run entities stay a curiosity or become a normal counterparty class. The infrastructure is in production. The tax filings will be the tell.
\ Don’t forget to like and share the story!
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2026-05-02 01:24:02
Hey Hackers!
\ Welcome to HackerNoon Projects of the Week, where we spotlight standout projects from the Proof of Usefulness Hackathon, HackerNoon’s competition designed to measure what actually matters: real utility over hype.
\ This week, we’re pumped to showcase projects that have proven their worth and usefulness: MealRoaster, WayaVPN, and DeepSearch.
:::tip Want to see your own project spotlighted here?
Join the Proof of Usefulness Hackathon to get on our radar.
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\ Eating healthy is both easy and complicated at the same time. Burn more calories than you consume. But not every single meal that you eat has a full nutritional breakdown, making it hard to know how many calories you’re actually eating. So, the only thing left to do is craft your own breakdown, and by you, I mean MealRoster.
\ This AI-powered service allows users to take a picture of their meal and get a detailed analysis of everything that’s in it. According to its website, you will get details such as how many calories, carbs, and protein are in the meal you’re about to eat. The service also allows users to get a personalized meal plan that is specifically designed to help them meet their weight goals.
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MealRoaster is for busy individuals who want to track calories and improve their nutrition without downloading another app. It is ideal for gym members, weight loss clients, muscle gain enthusiasts, and anyone who wants simple, instant food tracking inside WhatsApp.
- Ademola Balogun, MealRoaster
:::tip See MealRoster’s full Proof of Usefulness Report
Read their story on HackerNoon
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There are so many different VPNs to choose from that it can become difficult to pick the one that’s best for you and your needs. But if you’re looking for a versatile one, look no further than WayaVPN. Whether you want to see what streaming services are like in different regions or you want to do research on different markets, WayaVPN has your back.
\ What separates WayaVPN from other VPNs is that it uses “real residential IPs from major ISPs”, according to its website. This means that you will look like a real user instead of some bot, giving you the ideal experience, regardless of what you’re using it for.
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More users now need access that looks and behaves more like normal ISP-based traffic, whether for privacy, remote work, testing, verification, research, or digital operations. That gap is exactly what WayaVPN is built to address.
- Emmanuel Corels
:::tip See WayaVPN’s full Proof of Usefulness Report
Read their story on HackerNoon
:::
Many people are messy and disorganized in their everyday lives; it gets even worse when you take a peek at their computers. It’s a miracle that they can even find anything, but there is something that can help with that, regardless of how tangled up their file systems and directories are.
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\
Scrolling and indexing software => extremely fast file searching by name
It was created to solve a problem: extremely slow file searching on shared SMB drives.
- Hoang Huy Do, DeepSearch
:::tip See DeepSearch’s full Proof of Usefulness Report
Read their story on HackerNoon
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It's our answer to a web drowning in vaporware and empty promises. We evaluate projects based on: \n ▪️ Real user adoption \n ▪️ Sustainable revenue \n ▪️ Technical stability \n ▪️ Genuine utility \n \n Projects score from -100 to +1000. Top scorers compete for $20K in cash and $130K+ in software credits.
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1. Get your free Proof of Usefulness score instantly \n 2. Your submission becomes a HackerNoon article (published within days) \n 3. Compete for monthly prizes \n 4. All participants get rewards
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2026-05-02 00:08:46
On May 14, 2026, over 5,000 professionals across digital, product, data, and marketing teams will come together for the AI Skills Conf, a free virtual event focused on practical, real-world applications of AI.
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2026-05-02 00:03:33
How are you, hacker?
🪐 What’s happening in tech today, May 1, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Columbian Exposition opened in Chicago in 1893, The United Kingdom Was Formed in 1707, and we present you with these top quality stories. From How to Use Pin As A Coverage Diagnostic Tool for Fuzzers to Navigating Claude Code: The Context Window Tax, let’s dive right in.
By @chudinovuv [ 13 Min read ] AI hallucination is not a bug. It is a mathematical certainty — ten unproven approximations with no error bound. κ(A) is the first metric that sees it. Read More.
By @efimovov_5guqm5 [ 7 Min read ] Every Claude Code session has a hidden cost — every token in context is billed as input on every turn, and the more accumulates, the worse Claude works. Read More.
By @meichenster [ 5 Min read ] Resident Evil’s fear factor is not just monsters and jumpscares. It is also the smart lighting tech behind the RE Engine. Read More.
By @farzon [ 4 Min read ] Diagnose fuzzing coverage stalls using Intel Pin. Track basic block execution over time and uncover why libFuzzer fails to reach deeper code paths. Read More.
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2026-05-02 00:00:06
In March 2026, I published findings showing that 8 AI models from 3 vendors — given only a document's table of contents — independently fabricated the same technical details for sections they had never seen. The attack, which I named SMRA (Structural Metadata Reconstruction Attack), was reproduced across Claude, GPT, and Gemini model families with zero grounded accuracy but perfect structural fidelity.
That article described what happens. This one explains why it happens — and why it cannot be fixed without replacing the mathematical foundations of the architecture.
The answer is not a model bug, a training data problem, or a missing guardrail. The answer is ten layers of unproven mathematical approximations stacked on top of each other — and a classical signal analysis technique that can measure exactly when the stack collapses.
This article is based on my research paper "The Engineering Approximation Stack: A Critical Analysis of GPT's Mathematical Foundations" (Chudinov, 2026). It traces each approximation to its original publication, classifies the mathematical gap it bridges, and shows why the composition of these gaps makes SMRA a structural certainty rather than an empirical accident. The full paper with 53 traced references is available on Zenodo.
The GPT architecture follows the same three-step pattern at every major component:
Individually, each approximation is reasonable. Engineers build bridges with tolerances too. The difference: structural engineers can compute the total tolerance from each joint's tolerance. GPT has no such computation. Nobody knows how the errors at each layer combine.
Here are the ten gaps — summarized for the argument, not for completeness (the research paper has the full analysis with formal references).
Choosing the next word is discrete — pick one from 50,000+. Gradient descent doesn't work on discrete choices, so softmax replaces it with a continuous distribution (Bridle, 1990).
Proven: softmax converges to argmax as temperature → 0.
Not proven: that this continuous relaxation preserves structural invariants of discrete sequences — ordering, coreference, logical dependency — when composed across dozens to hundreds of layers.
The result: tokens with high local confidence that are globally incoherent. The hallucination phenomenon is not a failure to "think clearly." It is a mathematical consequence of optimizing a continuous surrogate without proving it preserves the structure of the discrete problem.
Deep networks (>10 layers) suffer from vanishing/exploding gradients. Two independent fixes:
Layer normalization has a subtle side effect: it projects all activation vectors onto a hypersphere of unit variance, destroying magnitude as an information channel. All semantic distinctions must be encoded in angular differences alone. This compounds the separability problem in §7.
Adam optimizer (Kingma & Ba, 2014) converges for convex objectives. Reddi et al. (2018) then showed it can diverge even for simple convex problems. GPT's loss surface is non-convex with billions of parameters.
Different random seeds produce different models. This is non-reproducibility by construction — a mathematical certainty for non-convex optimization without convergence guarantees.
Self-attention is permutation-invariant — it can't distinguish word order. Three encoding schemes have been proposed (sinusoidal → learned → RoPE), each performing better on benchmarks. None has a proven invariance guarantee. None has been proven consistent when extrapolated beyond training context lengths.
The industry's response — extending context windows to 128K or 1M — addresses the symptom (the window runs out) without addressing the cause (the encoding lacks invariance proofs). If a positional encoding is not proven to preserve semantic relations at 4K tokens, concatenating sixteen 4K windows does not produce a 64K encoding with semantic guarantees. It produces sixteen unproven approximations stitched together.
Byte-Pair Encoding (Sennrich et al., 2016) minimizes encoding length on the training corpus. No theorem proves its tokens are linguistically meaningful or that token boundaries align with semantic boundaries.
Worse: BPE inherits the distributional properties of its training corpus — predominantly English. The same semantic content requires 3–5× more tokens in morphologically rich languages (Turkish, Croatian, Portuguese, Swedish). A Croatian-language prompt is mathematically shorter in attention span than its English equivalent, before any processing begins.
The AI industry's $100B+ investment thesis rests on power-law fits (Kaplan et al., 2020): loss decreases as a power of parameter count. Power laws also appear in earthquake magnitudes and income distributions — their presence doesn't mean we understand the mechanism.
Schaeffer et al. (2023) showed that "emergent abilities" may be measurement artifacts. The scaling law measures average cross-entropy loss across test sets. It cannot see worst-case structural instability in individual sequences — which is exactly where SMRA operates.
This is the most technically damaging gap.
GPT's attention computes dot products on embedding vectors. Dot product is a meaningful similarity measure only if the embedding space satisfies inner product axioms — globally, not just in the "king − man + woman ≈ queen" neighborhood. No such global proof exists.
Residual connections add vectors. Attention computes weighted sums. But embedding dimensions encode heterogeneous information — part-of-speech, sentiment, positional artifacts. No theorem proves these dimensions are additively compatible.
The real bottleneck is geometric: embeddings concentrate on a low-dimensional manifold, and distinct semantic senses ("bank" the institution vs. "bank" the riverbank, or the 430+ senses of "set") can map to overlapping regions. When two senses share a region, model similarity ≠ semantic similarity. No theorem guarantees that semantic classes are geometrically separable in embedding space.
Attention is what makes a transformer a transformer. Every other component exists to support it. And attention is a chain of unjustified design choices:
ICL is GPT's most commercially valuable capability — learning from examples in the prompt with no parameter update. It also has zero theoretical foundation.
Learning theory (Vapnik, 1998; Valiant, 1984) defines learning as a process with bounded generalization error. ICL has no sample complexity bound, no generalization guarantee. It is sensitive to example ordering, formatting, and label choice (Lu et al., 2022). A process that changes its output when examples are reordered is pattern-matching, not learning.
In GPT-3, 66% of all 175 billion parameters sit in feed-forward layers. These layers have:
66% of the system has no formal characterization at any level.
Each approximation introduces bounded error locally. The critical question: how do ten types of error compose across LL layers (96 in GPT-3)?
If errors were independent and additive: manageable. If multiplicative: still manageable. The actual situation: errors are not independent. Residual connections create feedback paths. Layer normalization rescales at each step. Attention at layer kk depends on errors from layers 1 through k−1k−1.
No formal analysis of transformer error propagation exists. Not "incomplete analysis." None. Zero published works characterize the function f(ε1,ε2,…,εL)f(ε1,ε2,…,εL) for a transformer.
In classical numerical analysis, error propagation is bounded by the condition number κ(A)κ(A):
\ This is a guarantee — same input, same bound, always. GPT has no analogue. No κκ. No bound. No guarantee.
The ten gaps have existed since Vaswani et al. (2017). Why did the consequences become visible only at GPT-3/GPT-4 scale?
The answer is in the geometry of attention itself. Each token in a context window of length nn creates n−1n−1 potential attention connections per head. With hh heads across LL layers, the total number of constraint paths — distinct information routes the model must reconcile in a single forward pass — is:
\ For GPT-2 (2019): L=48L=48, h=16h=16, n=1,024n=1,024 → P≈8×108P≈8×108.
For GPT-3 (2020): L=96L=96, h=96h=96, n=2,048n=2,048 → P≈3.9×1010P≈3.9×1010.
For GPT-4-class (2023+): L≥120L≥120, h≥96h≥96, n≥8,192n≥8,192 → P≥8×1012P≥8×1012.
Four orders of magnitude in four years. And at each constraint path, the model applies the same unproven operations — dot products without metric guarantees (§7), softmax without structural invariance (§1), layer normalization that destroys magnitude information (§2).
The architects knew. Not in the sense of a formal theorem — but in the language of their own papers:
Define constraint density as the ratio of constraint paths to representation capacity:
\ where dmodeldmodel is the embedding dimension — the total information bandwidth available to encode everything the model needs to track.
For GPT-2: ρ=16⋅1,0242/1,600≈10,486ρ=16⋅1,0242/1,600≈10,486.
For GPT-4-class: ρ≥96⋅8,1922/12,288≈524,288ρ≥96⋅8,1922/12,288≈524,288.
Constraint density grew 50× while the mathematical foundations remained identical — zero invariance proofs, zero error bounds, zero convergence guarantees.
The guardrails (RLHF, content filters, constitutional AI) operate on the output distribution surface. Constraint density is a property of the internal geometry. This is a fence around a volcano. Scaling increases the magma pressure; the fence stays the same height. Scaling the model doesn't just make the existing problems bigger — it makes the density of uncharacterized interactions per unit of representational capacity grow quadratically while the mitigations remain linear, surface-level, and structurally blind to what is happening underneath.
At some density ρ∗, the unproven approximations that were locally tolerable at GPT-2 scale become globally catastrophic. The condition number κ(A) — which measures exactly this structural collapse — crosses from bounded to divergent. The system passes a phase transition: from "locally reasonable approximations" to "globally unstable composition."
SMRA is what this phase transition looks like from the outside.
In the SMRA paper (Chudinov, 2026; DOI: 10.5281/zenodo.19004697), I applied the classical Cauchy–Toeplitz–Levinson-Durbin chain directly to GPT-generated text:
| Regime | κ(A) | Interpretation | |----|----|----| | Stable | 10^6>10^6 | Stack collapsed; no stable structure in output |
For signals with genuine structural regularity: κ(A)10^6κ, approaching computational infinity.
This is not a benchmark. It is a mathematical property of the output signal — deterministic, reproducible by anyone with the output sequence and a Levinson-Durbin implementation. The measurement does not depend on human judgment, domain expertise, or evaluation rubrics.
This is the central result. It follows from four properties documented in the approximation stack — not from experiment, but from deduction:
Premise 1 — Measurement scale violations (§7). The model embeds "42", "democracy", and ";" into the same R*[d] (real set) and applies identical operations. A system that cannot distinguish measurement scales cannot guarantee its outputs respect them — including outputs that reveal structural metadata about the generation process itself.
Premise 2 — Zero runtime invariants. GPT has no mechanism that can prohibit any output class. Softmax always produces a probability distribution — it never produces a structural refusal. The model architecturally cannot say "this output would compromise my integrity."
Premise 3 — Uncharacterized error composition. The error propagation function across LL layers is unknown. If unknown, it is impossible to prove that any given prompt is safe — that no prompt can elicit outputs revealing the model's structural fingerprint.
Premise 4 — Non-convergent optimization. Different seeds produce different models. The structural fingerprint is seed-dependent and unpredictable — but always present, because non-convergent optimization retains artifacts of its particular trajectory.
Conclusion:
A system that (1) cannot distinguish what information its outputs encode, (2) has no runtime mechanism to prohibit any output class, (3) cannot prove any input safe, and (4) retains uncontrollable training artifacts — cannot formally exclude any output class.
For any class XX, there exists an input that elicits it.
SMRA is the constructive proof for XX = "output with recoverable structural metadata."
This is the contrapositive of a safety guarantee. A type system guarantees no type errors. A database with FOREIGN KEY constraints guarantees no orphan records. GPT has no invariants — at the output layer (softmax assigns nonzero probability to every token), at the compositional level (error propagation is uncharacterized), at the training level (optimization is non-convergent). The vulnerability is not a property of one component. It is a property of the architecture as a whole.
The argument above is not about a missing feature. It is about the absence of formal foundations at every level. Consider the current mitigation landscape:
| Mitigation | What it addresses | What it doesn't fix | |----|----|----| | RLHF | Symptom — teaches model to mask undesired outputs | Does not change internal representations | | Guardrails / filters | Symptom — post-hoc filtering | Model still generates the content internally | | Prompt engineering | Symptom — shifts output distribution | Doesn't change underlying mechanisms | | Constitutional AI | Symptom — self-critique using the same approximation stack | Critiques itself with the same blind spots | | Mechanistic interpretability | Mechanism (partial) — identifies circuits | Cannot predict or prevent system-level failures |
None address the nature of the problem. They are patches on an approximation.
The question "can SMRA be fixed?" reduces to: can you prove that a specific output class is impossible for a system with no formal output constraints? The answer is no — by the same reasoning that you cannot prove a program is type-safe in a language without a type system. The safety guarantee requires the formal machinery that the architecture does not have.
The mitigations above fail in space — they operate on the output surface while the problem lives in internal geometry. They also fail in time.
One proposed countermeasure is the Index Server architecture — the model never sees the original document, only pre-computed index entries. This may reduce the attack surface for a single inference pass. But modern LLM providers routinely collect user interaction data for fine-tuning (Shumailov et al., 2023). If Index Server responses encode structural patterns from original documents — even indirectly — and those responses re-enter the training corpus:
**generation→collection→retraining→contaminated weights→generation**
After this cycle completes, the original document's structure is recoverable from the model itself. No retrieval step required. The Index Server protects one pass but creates a permanent contamination channel.
The mitigations are static. The contamination is cumulative. Each retraining cycle embeds more structural fingerprints into the weights — fingerprints that no guardrail can detect, because they are not in the output distribution. They are in the geometry.
The deeper point is not that GPT is bad. It is that two fundamentally different mathematical traditions lead to two fundamentally different kinds of system.
| Property | GPT pipeline | Classical algebraic chain | |----|----|----| | Error characterization | None (benchmarks) | Condition number κ(A)κ(A) gives exact bound | | Convergence | Not proven (non-convex) | Proven: finite steps, exact solution | | Scale dependence | Requires 10^11 parameters | Works at n=24n=24 with full guarantees | | Reproducibility | Stochastic (seed-dependent) | Deterministic | | Diagnosability | Impossible (no formal model) | Full: det(A), κ(A), rank | | Verifiability | Post-hoc benchmarks | By construction |
The distributional path (Harris → Shannon → Bengio → Vaswani → GPT) asks: predict the next token. The algebraic path (Cauchy → Toeplitz → Levinson → Durbin) asks: compute the exact position. Two hundred years of theorem versus seventy years of unproven hypothesis.
Both produce useful results. Only one can tell you when it is wrong.
This is not a hypothetical contrast. The algebraic path is implemented in a working system — a dual-layer index architecture that routes queries to exact document sections through deterministic constraint-satisfaction, published as "Dual-Layer SPO Architecture" (Chudinov, 2026; DOI: 10.5281/zenodo.19261510). The κ(A) column in the table above is not a thought experiment — it is computed on every query.
Now here is the observation I kept out of the academic paper.
The industry's response to the ten gaps documented above is a disclaimer: "AI can make mistakes. Check important info."
This is not irresponsibility. This is the only honest option given the mathematics. When a structural engineer cannot prove a bridge is safe, they fix the bridge — because structural engineering has formal models of error propagation. When the GPT architect cannot prove the output is reliable, they add a disclaimer — because no formal model of error propagation exists.
The disclaimer is the architectural proof of the approximation stack's consequences:
If semantic stability cannot be formally guaranteed → it must be disclaimed.
Users reading that disclaimer should understand what it means: not "sometimes the AI gets a fact wrong" but "we cannot formally prove that any output of this system is correct, and we cannot characterize the conditions under which it fails."
"AI can make mistakes" is a euphemism. The precise statement is: "the mathematical foundations of this system do not support formal analysis of its behavior." Everything else — hallucinations, SMRA, inconsistency, prompt injection — follows from that one sentence.
scope(metadata) ≤ scope(content) is not optional. If your index exposes more structure than your content provides, you are enabling SMRA. If your outputs feed back into training, you are enabling it permanently.\ \
