Mpost Media Group

Global technology firm Nasdaq announced that it has facilitated end-to-end margin and collateral workflows on the Canton Network, integrating this blockchain-based infrastructure with its Calypso platform. This initiative, developed in collaboration with QCP, Primrose Capital Management, and Digital Asset, aims to demonstrate how the addition of on-chain functionality to existing institutional processes can improve collateral movement across diverse asset classes within institutional finance.

Nasdaq Calypso, a technology solution widely used by financial institutions for managing risk, margin, and collateral, is positioned to meet the requirements of both traditional and digital financial environments. As part of this collaboration, the platform will be extended to enable automated, continuous collateral and margin management across various asset types, including cryptocurrency derivatives, fixed income products, exchange-traded derivatives, and over-the-counter instruments.

The collaborative effort is intended to support the growth and operationalization of digital asset infrastructure. This specific use case illustrates how blockchain-based technology can be applied to collateral management, offering financial institutions tools to enhance real-time capital efficiency. The approach facilitates more flexible capital allocation by unlocking and reallocating previously idle collateral across different markets.

Nasdaq Strengthens Operational Integrity Through Scalable Technology Solutions Supporting Global Financial Institutions

Strengthening confidence in the infrastructure and systems that support the digital asset ecosystem is viewed as an essential factor for the sustained growth of this asset class.

Nasdaq offers a broad range of digital asset-related solutions aimed at contributing to the development of this ecosystem by promoting stability and operational integrity within the market. The organization intends to further develop its digital asset capabilities within its existing capital markets framework to support broader institutional engagement.

Nasdaq’s technology is currently utilized by a large portion of the global financial sector, including the majority of systemically important banks, a substantial number of leading stock exchanges, multiple central banks and regulatory entities, and thousands of other financial institutions worldwide. Leveraging its position as a technology provider, Nasdaq applies its experience in financial infrastructure, digital transformation, and cloud-based services to assist the financial industry in addressing operational complexity and advancing modernization efforts.

The post Nasdaq And QCP Establish New Benchmark For Capital Efficiency By Integrating Canton Network With Nasdaq Calypso appeared first on Metaverse Post.

Digital asset platform Nexo has been appointed the Official Crypto Partner of the Mifel Tennis Open by Telcel-Oppo. This move follows the company’s earlier sponsorship of the Acapulco Open and reinforces its growing involvement in the Latin American market. By securing its second ATP tournament in Mexico, Nexo aims to expand its regional presence and engage with a digitally aware audience, highlighted by the tournament’s anticipated 35,000 attendees.

The collaboration also includes a partnership with Mexican tennis player Rodrigo Pacheco Mendez. Positioned as a representative of qualities such as ambition and potential, Pacheco Mendez aligns with Nexo’s broader vision for emerging talent and long-term growth. Throughout the event, he will be involved in a series of activities intended to connect tournament audiences with themes of performance and innovation in the context of digital finance.

“Partnering with Nexo goes beyond supporting tennis for me. The company believes in the journey and effort behind success. Like in sport, building something meaningful takes time, focus, and the right mindset,” Rodrigo Pacheco Mendez said in a written statement. “I’m proud to be working with a firm that celebrates growth, persistence, and the next generation — both of athletes and investors,” he added.

Nexo Aligns With Mifel Tennis Open To Highlight Strategic Growth, Elevating Client Experience, And Strengthen Regional Investment In Latin America

Often described as one of the more visually striking venues on the ATP Tour, the Mifel Tennis Open takes place each July in Los Cabos, Baja California Sur. Since its inception in 2016, the tournament has consistently drawn high-profile players, including past winners such as Daniil Medvedev in 2022 and Stefanos Tsitsipas in 2023. The upcoming 2025 edition is set to feature well-known athletes including Lorenzo Musetti, Andrey Rublev, and the current titleholder Jordan Thompson. Known for combining elite-level tennis with a resort-oriented setting, Los Cabos provides a backdrop where themes of performance, strategy, and upscale experiences converge — aligning with Nexo’s broader focus on innovation, financial sophistication, and service quality.

“Tennis is a game of foresight, resilience, and execution — principles that also apply to building long-term wealth,” said Kosta Kantchev, Co-founder and Executive Chairman of Nexo, in a written statement. “Our return to the ATP stage underscores our commitment to creating lasting value through experiences that mirror the core values of championship tennis: strategy, vision, and dedication. We are particularly proud to partner with Rodrigo Pacheco Mendez, whose skill, determination, and upward momentum exemplify the ideals we champion at Nexo. His involvement reinforces our shared commitment to growth and excellence,” he added.

Alignments with events such as the Mifel Tennis Open by Telcel-Oppo contribute meaningfully to the elevated client experience associated with Nexo’s digital asset wealth platform. The company’s on-site engagement includes a dedicated lounge space designed for Nexo Private clients to unwind and participate in focused discussions, as well as curated tennis clinics led by professionals. 

Through strategic involvement in internationally respected tournaments that carry strong regional significance, Nexo further develops its presence in Latin America while emphasizing its approach to client engagement within the digital finance sector.

The post Nexo Becomes Official Crypto Partner Of Mifel Tennis Open, Teams Up With Rodrigo Pacheco Mendez appeared first on Metaverse Post.

Blockchain-focused digital payments company, Ripple announced that it has expanded multichain functionality for the XRP Ledger (XRPL) through the integration of Wormhole, a cross-chain interoperability protocol. This integration includes connectivity with both the XRPL mainnet and the XRPL EVM Sidechain. It is intended to support cross-chain messaging, asset transfers, and the issuance of tokens across multiple chains, with applications in decentralized finance (DeFi), institutional on-chain finance, and real-world asset (RWA) tokenization.

Wormhole provides interoperability across more than 35 blockchain networks and is currently utilized by over 200 applications. The protocol has facilitated over 1 billion cross-chain messages and enabled more than $60 billion in transaction volume since its launch in 2020. Its infrastructure is used by institutional entities such as BlackRock, Securitize, and Apollo. This development is part of a broader trend within the XRPL ecosystem to improve interoperability and expand optionality for developers and financial institutions.

The XRPL is designed to accommodate open financial infrastructure and enterprise-grade blockchain applications. Its modular architecture offers developers access to a variety of infrastructure providers. With integration support from interoperability solutions like Axelar and Wormhole, the XRPL allows applications to scale across networks while maintaining performance, efficiency, and regulatory alignment. Ripple’s involvement in advancing Wormhole integration supports XRPL’s strategic focus on maintaining a flexible and composable multichain environment.

Today, we are partnering with @Wormhole to bring multichain interoperability to the XRPL and the upcoming XRPL EVM Sidechain: https://t.co/soylouwu47

This integration brings new optionality for developers and institutions looking to build cross-chain applications whether for… pic.twitter.com/dpDDEKEQY6

— RippleX (@RippleXDev) June 26, 2025

Enhancing XRPL And XRPL EVM Sidechain With Cross-Chain Functionality

Wormhole is set to connect its cross-chain messaging infrastructure with both the XRPL mainnet and the XRPL EVM Sidechain, enabling functionality that supports asset transfers and contract interactions across a wide range of blockchain networks. This integration will allow developers to move XRPL-native assets—such as XRP, Issued Assets (IOUs), and Multi-Purpose Tokens (MPTs)—across more than 35 blockchain ecosystems currently supported by Wormhole. In addition, it introduces the ability to trigger smart contract interactions and data exchanges between chains using Wormhole’s messaging framework.

The initiative is expected to provide additional flexibility for developers and institutions aiming to deploy cross-chain applications. These applications may span a variety of use cases, including payment processing, DeFi, and tokenization of real-world assets (RWAs). The integration aligns with the XRPL’s core design as a decentralized blockchain infrastructure tailored to on-chain financial operations. It enhances XRPL’s utility by building on its foundational capabilities, including native support for tokenization, high-throughput settlement, liquidity provisioning, and infrastructure designed with compliance considerations.

The post Ripple Integrates Wormhole To Enhance Multichain Interoperability Infrastructure appeared first on Metaverse Post.

Decentralized automation network Gelato announced a partnership with the lending protocol Morpho to enable exchanges, wallets, custodians, and fintech platforms to integrate fully non-custodial crypto-backed lending services within their applications. 

This integration introduces a blockchain-abstracted user experience, allowing users to access borrowing functions seamlessly as part of the native interface of any participating product. By leveraging Morpho’s decentralized lending infrastructure and Gelato’s Smart Wallet SDK, users are able to secure stablecoin loans—such as USDC—against their cryptocurrency holdings, without the need for browser extensions, seed phrases, or gas fees.

The process allows for instant wallet creation through social or email login, cryptocurrency collateral deposits, immediate USDC borrowing, and flexible repayment. The entire lending operation is non-custodial and requires no user-initiated transaction signing, credit evaluation, or network fees. Lending logic is executed directly on-chain through smart contracts and is embedded in a fully composable format within the host application.

Today, in collaboration with @MorphoLabs, we're introducing Embedded Crypto-Backed Loans.

A new way for wallets, exchanges, and fintech applications to offer instant, non-custodial, and web2-like stablecoin loans directly in their products.

Available now on @arbitrum,… pic.twitter.com/EfWnDif5i3

— Gelato (@gelatonetwork) June 25, 2025

At the foundation of this solution are two interoperable components designed to support seamless and decentralized lending integration. The first is the Morpho Protocol, a publicly accessible and audited infrastructure that operates entirely on-chain, managing all aspects of lending—such as loan conditions, interest calculations, and collateral requirements—through decentralized governance rather than platform-level control. 

The second component is the Gelato Smart Wallet SDK, a flexible development framework that enables wallets to adopt compatibility with EIP-7702 and ERC-4337 smart account standards. This toolkit eliminates the need for users to directly interact with on-chain mechanics by supporting one-click onboarding, the use of ERC-20 tokens for gas payments, and a fully gasless user experience across more than 50 supported blockchains. The combined use of these systems allows crypto-backed lending features to be integrated within a matter of days, without the need for custom-built lending protocols or wallet infrastructure.

Embedded Crypto-Backed Loans: Enabling Crypto-Backed Lending Integration For Digital Platforms

The Embedded Crypto-Backed Loans solution enables each phase of the borrowing process—including the creation of a smart wallet, the deposit of collateral, the issuance of USDC, and eventual repayment—to be carried out entirely onchain and seamlessly integrated into third-party platforms. This collaboration is intended to assist exchanges, custodians, wallets, brokers, and fintech applications in rapidly deploying lending functionality, reducing the typical development timeline from several months to a matter of days. It facilitates the delivery of secure, composable, and gas-abstracted lending services without requiring the implementation of custodial systems or the development of custom backend infrastructure. 

These features form part of a broader initiative to enhance the Gelato Smart Wallet SDK’s security and account recovery tools, with upcoming additions such as passkey integration, multi-signer two-factor authentication, and modular recovery mechanisms. These enhancements are currently under development and are anticipated for release later in the year. The offering currently allows users to borrow USDC using cryptocurrency as collateral through a gasless interface, supports embedded wallet onboarding, and utilizes EIP-7702 compatible smart accounts. It is now available in beta with operational support on networks such as Polygon, Arbitrum, Optimism, Base, and Scroll, with Katana integration expected in the near future.

The post Gelato And Morpho Roll Out Embedded Crypto-Backed Loans appeared first on Metaverse Post.

An AI agent is a software system that makes autonomous decisions or carries out tasks based on specific data inputs and goals. These agents can interpret user inputs, plan actions, and reason without human intervention. They are being used to automate tasks and improve efficiency and decision-making across industries. Their large-scale adoption has given rise to the internet of AI.

At the same time, attack surfaces increase as each agent becomes a potential entry point or vulnerability, and recognizing the possible consequences for security has become more important than ever. 

What is the internet of AI?

AI agents share knowledge and interact autonomously within a network known as the internet of AI. They collaborate in this global intelligence network in real-time, not unlike how humans interact online. The interconnected ecosystem helps amplify the collective capabilities of AI agents and drive efficiency across multiple fields. This shift promises lower costs, smarter solutions, and more effective marketplaces powered by AI services. 

On the other hand, AI agent networks face increased security risks, including expanded attack surfaces. Cybercriminals who infect them with infostealer malware can obtain unprecedented access to personal data and credentials. Attackers also abuse integrated tools to manipulate the agent through deceptive prompts, which can involve exploiting vulnerabilities or triggering unintended actions, resulting in harmful or unauthorized execution.

Infostealer malware is at the heart of a cyberattack 

Cyberattacks are meticulously planned based on a set of methodical techniques and tactics in the following order: recon, weaponization, distribution, exploitation, connection, command and control, and actions on targets. The attacker gains access, establishes a foothold in the connection (or installation) phase, and maintains access to the compromised environment through a command and control channel.

Infostealer malware comes into play during the middle stages. This type of malicious software was specifically developed to extract sensitive data from a compromised system. The stolen data can be sold on the black market or used to blackmail victims, where the attacker threatens to release it unless the victim pays a ransom. If the data includes usernames, passwords, or private keys, the cybercriminal can further their attack by using those to compromise other systems.

Infostealer malware recently compromised 16 billion login credentials, including passwords to Google, Facebook, and Apple accounts, as well as other social media and government services. Researchers termed the attack “a blueprint for mass exploitation.” With so many login records exposed, cybercriminals have unprecedented access to data that they can use for identity theft, account takeover, and highly targeted phishing.

HyperCycle’s blueprint for secure and scalable AI agent systems

The multi-database leak illustrates the ripple effect of a decentralized failure resulting from stolen credentials, similar to the vulnerabilities faced by AI agent networks across peer-to-peer architectures. The grave consequences of such attacks underlie HyperCycle’s advocacy of decentralized node authentication and cryptographic defenses. The platform, which is advancing AI system collaboration by engineering infrastructure for a P2P network for multi-agent systems, urges ordinary users and enterprises to adopt password managers, two-factor authentication, and passkeys to neutralize malware risks.

Beyond mere recommendations, HyperCycle provides a blueprint for secure and scalable AI agent systems via cryptographic proofs. Compromised nodes or rogue agents can impersonate other agents or exfiltrate data, and robust behavioral and identity checks are required to mitigate this risk. As part of the process of securing the internet of AI agents, HyperCycle uses Toda/IP, a ledgerless architecture with cryptographic protocols and proofs, to ensure transaction integrity. Cryptographic proofs help prevent unauthorized data access because encrypted data is unreadable without cryptographic keys, regardless of whether it’s at rest, in use, or in transit. Keys can be protected using secure enclaves or hardware-based attestation.

Agents can require cryptographic proof of origin, authorization, and integrity before accepting inputs or instructions. Cryptographic identity systems ensure cybercriminals can’t spoof agents or apps easily. Zero-knowledge proofs prevent credential leakage by validating access rights without exposing passwords, tokens, etc. One can verify signed data and commands to avoid payload tampering, helping detect and reject any alterations made by malware. 

HyperCycle’s platform design ensures not only security but also scalability and speed. Its network infrastructure can accommodate a growing number of AI agents and services without compromising performance. This interoperability enables AI agents developed on different platforms to collaborate and communicate effectively. HyperCycle’s network nodes are established through a Node Factory, where they self-replicate, scaling from one node to 1024 without excessive costs. The scalability generates revenue for AI developers by making it cost-effective and efficient to deploy many AI agents.

HyperCycle Explorer allows users to monitor Node Factory and ANFE uptime and status in real time. The platform also makes increasing AI agent revenue possible by enabling agents to access and offer services seamlessly. Each AI within the network can generate more revenue or enhance its own capabilities through broader collaboration. Essentially, HyperCycle enables developers to build efficient applications and drive revenue growth by ensuring AI agents’ security and integrity.

Addressing identity spoofing and prompt injection

Going beyond conventional LLM applications, AI agents integrate external tools that are often built into different frameworks and programming languages, resulting in an even vaster attack surface. Hypercycle’s cryptographic proofs can help mitigate identity spoofing and impersonation, where attackers exploit compromised or weak authentication to pose as legitimate users or AI agents. Theft of agent credentials allows attackers to access systems, data, or tools under a false identity.

Digital signatures, ZK proofs, and public key infrastructure can be used to verify a user or system’s identity without revealing sensitive information. For example, a model could involve signing incoming messages with a known private key to trust they’re from a specific entity.

Prompt injection occurs when attackers conceal or mislead instructions to a generative AI system, causing the application to behave differently from how its developers intended. The agent begins to disregard certain policies and rules, utilizing tools to take seemingly arbitrary actions or disclose sensitive information. Cryptographic techniques like input provenance tracking can ensure that a prompt or data hasn’t been tampered with. Digital signatures and other elements can cryptographically verify that a specific party generated a particular prompt or instruction.

Cryptographic proofs can also help prevent attacks such as goal manipulation, which target an AI agent’s ability to plan and pursue goals by subtly altering its reasoning process. Goal manipulation can overlap with prompt injection. Agent hijacking is a common tactic that involves adversarial inputs distorting the agent’s ability to make decisions.

Cryptographic protocols can prove that goal specifications haven’t been tampered with during transmission. Both zero-knowledge SNARKs and verifiable computation establish that an agent followed a specific logic path or policy without seeing the data. In sum, cryptographic tools can detect and thwart attempts to alter a signed task or spoof the source of a goal.

Increasing global AI’s intelligence is the ultimate goal

HyperCycle facilitates AI collaboration across industries, enhancing interoperability, security, and efficiency with leading platforms such as Microsoft’s Open Agentic Web and Google’s A2A. Businesses can take advantage of the connected and adaptive AI internet by enabling models to interact seamlessly across networks. HyperCycle enhances opportunities to share intelligence across platforms, helping organizations integrate AI models into workflows that span numerous frameworks. This enables refined decision-making and improved data access, increasing global AI’s intelligence one node at a time.

The post How HyperCycle Combines AI Efficiency With Cryptographic Security appeared first on Metaverse Post.

Following their success in hosting more than ten major conferences globally, the team behind the renowned Hack Seasons Conference is redefining industry events, this time with a breakfast focused on depth, quality, and genuine human connection.

Held during Istanbul Blockchain Week, the Networking Breakfast offers an exclusive opportunity for tech and Web3 professionals to step away from the chaos and engage in meaningful, high-impact conversations.

A Fresh Format for Web3 

The Networking Breakfast offers a curated experience aimed at encouraging meaningful discussions on key tech topics such as DeFi, AI, and the future of finance. With over 1,200 registrations in its previous Dubai edition, the format clearly appeals to professionals seeking more intentional connections.

This iteration features limited spots and approval-based entry, guaranteeing a focused and lively community for all attendees.

Web3’s Most Honest Conversations Start Here

We’ll kick things off with the panel “Market State: What’s Actually Worth Building?”, where top voices from across the ecosystem will weigh in on what’s genuinely gaining traction in 2025 and what might be losing relevance. 

Muhammet Enes Yuce (CMO at Castrum Capital), Samuel Sandiford (Head of Product and Institutional Business Development at BitMEX), Nenter Chow (CEO at BitMart), Aaron (APAC CEO at Pudgy Penguins), and Vignesh Raja (Director of Business for Middle East & South Asia at HBAR Foundation) will share insights on builder priorities, funding realities, and which narratives still hold weight.

We’ll follow with “What Users Actually Want from DeFi”, a conversation grounded in usability and user experience. Zeynep Ecenaz Altınok, Victor Ji (Co-founder of Manta), Omur Cataltepe (Global BD Lead at TON), and Muhammed Yılmaz (DevRel at Citrea) will explore what’s broken, what users expect, and how the next generation of DeFi products can truly serve people, not just protocols.

Gaming takes center stage in “Powering the Next Generation of Gaming”, featuring John Linden, CEO of Mythical Games. He’ll dive into how blockchain is reshaping digital economies, from in-game assets and true ownership to how communities are shaping gameplay itself.

Finally, we’ll wrap with a critical discussion in “AI in Web3: Use Case or Buzzword?” — where thought leaders like Gökhan (DATS), Christian Thompson (Sui Foundation), Michael Heinrich (0G Labs), Charlie Hu (Bitlayer), and Jason Jiang (CertiK) will unpack the collision of AI and decentralization. From smart contract security to protocol intelligence and beyond, this panel will challenge assumptions and map where the real synergy lies.

Why You Should Attend

As the Web3 and tech worlds grow rapidly, creating meaningful and curated spaces for discussion becomes more crucial. The Networking Breakfast is Hack Season’s answer to this, offering a dedicated time and place where industry leaders debate the future over informal conversations instead of on stage. 

Whether you’re a startup, investor, builder, or simply interested in shaping the future, this is your chance to connect with key industry players in a focused setting free from conference distractions. Don’t miss out—register now!

The post Inside Hack Seasons’ Exclusive Networking Breakfast at Istanbul Blockchain Week appeared first on Metaverse Post.

AI research organization, OpenAI announced that ChatGPT connectors for platforms such as Google Drive, Dropbox, SharePoint, and Box are now accessible to Pro users outside of the European Economic Area (EEA), Switzerland (CH), and the United Kingdom (UK) in ChatGPT, beyond the deep research. 

ChatGPT Connectors serve as integrations that allow ChatGPT to communicate with external applications and data sources, enhancing its capabilities by enabling access to and use of real-time information from multiple platforms.

This update extends the existing availability of connectors for Outlook, Teams, Google Drive, Gmail, and Linear within the deep research, previously available for Plus and Pro users. 

ChatGPT connectors for Google Drive, Dropbox, SharePoint, and Box are now available to Pro users (excluding EEA, CH, UK) in ChatGPT outside of deep research.

Perfect for bringing in your unique context for everyday work. https://t.co/9KssDCiD9j

— OpenAI (@OpenAI) June 25, 2025

The function, initially introduced in beta earlier this year for ChatGPT Team users, is powered by a specialized version of OpenAI’s GPT-4o model. This model can refine its responses by leveraging internal company knowledge, allowing all users within a participating ChatGPT Team workspace to access the model through OpenAI’s ChatGPT applications.

The custom GPT-4o model operates by searching and analyzing internal data potentially relevant to user queries. To facilitate this, OpenAI creates a search index by syncing an encrypted copy of company files and conversations to ChatGPT’s servers. 

Administrators of workspaces have the option to develop custom deep research connectors through the Model Context Protocol (MCP), which is currently in beta. MCP enables connections to proprietary systems and other applications, allowing teams to search, reason, and take action based on that knowledge alongside web results and pre-built connectors.

Additionally, ChatGPT record mode is available for Team users on macOS. This feature captures meetings, brainstorming sessions, or voice notes, then transcribes the audio, extracts key points, and generates follow-up tasks, plans, or even code snippets.

OpenAI Enhances ChatGPT To Deliver More Contextually Informed Integrations

OpenAI’s introduction of ChatGPT Connectors represents a strategic effort to integrate ChatGPT more deeply into the suite of software tools used by businesses. 

ChatGPT itself is an AI-driven chatbot created by OpenAI, designed to facilitate natural, conversational interactions. It employs sophisticated large language models (LLMs), such as GPT-4o, to produce responses that resemble human communication across text, audio, and image formats.

Recently, OpenAI announced a gradual rollout of a streamlined version of its memory feature to users on the Free plan. This update enables ChatGPT to incorporate both previously saved memory data and recent conversation history, allowing it to generate replies that are more informed by the ongoing context.

The post OpenAI Expands ChatGPT Connectors To Google Drive, Dropbox, SharePoint, And Box, Now Available For Pro Users appeared first on Metaverse Post.

AI arm of the technology company Google, Google DeepMind unveiled AlphaGenome, an AI model designed to provide more precise and detailed predictions about the effects of individual genetic variants or mutations on various biological processes involved in gene regulation. This capability is supported in part by technical developments that enable the model to analyze extended DNA sequences and generate high-resolution predictive outputs.

In order to support ongoing scientific efforts, AlphaGenome is currently being offered in a preview phase through the AlphaGenome API for non-commercial research use, with plans for a broader model release at a later stage.

Introducing AlphaGenome: an AI model to help scientists better understand our DNA – the instruction manual for life

Researchers can now quickly predict what impact genetic changes could have – helping to generate new hypotheses and drive biological discoveries. ↓ pic.twitter.com/K441deSBgl

— Google DeepMind (@GoogleDeepMind) June 25, 2025

The AlphaGenome model developed by Google DeepMind processes extended segments of DNA—up to one million base pairs—and generates predictions across a wide array of molecular properties that characterize gene regulation. It can also assess the functional impact of specific genetic variants or mutations by comparing the predicted outcomes of altered sequences against their unmodified counterparts. The properties it predicts include gene start and end sites across different cell types and tissues, RNA splicing points, RNA expression levels, DNA base accessibility, spatial proximity, and binding interactions with regulatory proteins. The training data for the model was drawn from public datasets provided by consortia such as ENCODE, GTEx, 4D Nucleome, and FANTOM5, which collectively cover a broad range of gene regulatory processes across hundreds of human and mouse cell and tissue types.

AlphaGenome’s architecture combines convolutional layers that detect short motifs in the DNA sequence, transformer components that allow information exchange across the full sequence length, and final prediction layers that output molecular-level insights across different biological modalities. The training of each sequence was distributed across multiple interconnected Tensor Processing Units (TPUs). This model builds on prior work with Enformer and complements AlphaMissense, which focuses specifically on protein-coding regions. While protein-coding regions constitute approximately 2% of the genome, AlphaGenome targets the remaining 98%—non-coding regions—known for their role in regulating gene activity and their association with various disease-linked variants.

Distinct features of AlphaGenome include its ability to analyze long DNA sequences at base-level resolution, enabling the identification of regulatory regions located far from the genes they influence, while still capturing fine-scale biological detail. Earlier models often faced a trade-off between sequence length and resolution, limiting their ability to jointly model complex regulatory features. AlphaGenome overcomes this by maintaining efficiency in training—requiring only four hours and utilizing half the computational resources needed for the original Enformer model.

The model’s capacity for multimodal prediction allows it to provide a wide-ranging view of regulatory mechanisms, offering scientists detailed insights into various layers of gene regulation. It also supports efficient variant scoring by fast comparing mutated and unmutated sequences and summarizing the differences based on the relevant molecular context.

AlphaGenome introduces a new capability in modeling RNA splice junctions directly from DNA sequence data. This is particularly relevant for understanding genetic conditions linked to splicing errors, such as spinal muscular atrophy and certain types of cystic fibrosis. By predicting both the location and expression levels of these junctions, the model offers a more refined view of how genetic variants may affect RNA processing.

Advantages Of Underlying Model And Implications For Future Research

AlphaGenome’s broad applicability enables researchers to examine the effects of genetic variants across multiple molecular modalities using a single API request. This streamlined approach allows for faster hypothesis generation and testing, without the need to rely on separate models for each specific regulatory feature. The model’s strong predictive performance suggests it has developed a generalizable understanding of DNA sequence behavior within the framework of gene regulation, offering a platform that others in the scientific community can extend or refine. Following its full release, the model will be available for fine-tuning with custom datasets, allowing researchers to tailor its capabilities to address specific scientific questions.

The underlying architecture is designed to be both scalable and adaptable. With additional training data, AlphaGenome has the potential to enhance its accuracy, expand its utility across different species, and incorporate new modalities, thereby increasing its overall coverage and depth.

AlphaGenome’s predictions may support a range of research directions. In the context of disease studies, it could improve the identification and interpretation of functionally relevant genetic variants, especially those associated with rare disorders, contributing to a clearer understanding of disease mechanisms and the identification of potential therapeutic targets. In synthetic biology, its outputs could guide the development of custom-designed DNA sequences with targeted regulatory functions, such as enabling gene expression in specific cell types. For fundamental genomic research, AlphaGenome may assist in the systematic mapping of functional genomic elements and help clarify their roles in regulating cellular activity.

The post Google DeepMind Launches AlphaGenome AI Model To Support Deeper Insights Into Human DNA appeared first on Metaverse Post.

Technology company Google introduced the Gemini CLI, an open-source AI tool designed to integrate the capabilities of the Gemini model directly into the command-line interface. This utility offers streamlined access to the model, enabling efficient interaction through terminal commands. 

Although optimized for coding tasks, Gemini CLI is built to support a broad spectrum of uses, including content creation, analytical problem-solving, research activities, and task organization. The Gemini CLI is also connected with Google’s AI-powered coding assistant, Gemini Code Assist, allowing developers across all Code Assist plans — including Free, Standard, and Enterprise — to utilize AI-driven coding features within both VS Code and the terminal environment.

Developers, builders and creators: Bring the power of Gemini 2.5 Pro directly into your terminal with Gemini CLI, our new open-source AI agent with unmatched usage limits. Available now in preview — at no charge. pic.twitter.com/D576uqjfJG

— Google (@Google) June 25, 2025

In order to access Gemini CLI at no cost, users can sign in using a personal Google account, which provides a complimentary Gemini Code Assist license. This license includes access to Gemini 2.5 Pro, featuring a context window capable of handling up to one million tokens. During the preview phase, the service offers a high usage threshold, allowing up to 60 model requests per minute and 1,000 requests daily without charge.

Currently in preview, Gemini CLI delivers advanced AI functionality across tasks such as interpreting code, managing files, executing commands, and addressing issues dynamically. It introduces enhanced capabilities to the terminal environment, supporting tasks like code generation, debugging, and process optimization through natural language interaction. Its features include integration with Google Search for real-time information retrieval, support for the Model Context Protocol and bundled extensions to expand functionality, customizable prompts and settings to adapt to different use cases, and options for non-interactive use within scripts to support automation and integration with broader workflows.

Gemini CLI And Gemini Code Assist To Share AI Technology For Seamless Developer Support

Gemini CLI is released under the Apache 2.0 license, making it fully open source. This allows developers to examine the codebase, assess its functionality, and evaluate any security considerations. The project is designed to encourage community involvement, with contributions welcomed in the form of bug reports, feature suggestions, security enhancements, and code submissions. The tool is built with extensibility in mind, incorporating developing standards such as the Model Context Protocol (MCP), structured system prompts using GEMINI.md, and customizable settings that support both individual and collaborative configurations. Recognizing the highly personalized nature of terminal environments, the design prioritizes user flexibility and customization.

Gemini Code Assist, Google’s AI-powered tool for coding support across experience levels—from learners to professionals—shares its core technology with Gemini CLI. Within the VS Code environment, users can enter prompts into the chat interface in agent mode, enabling Code Assist to carry out a range of development tasks including writing tests, correcting code, implementing features, and performing migrations. The system generates a structured, multi-step approach based on the given prompt, includes mechanisms for error recovery, and can propose alternative solutions beyond the original scope. This agent mode is available at no additional cost on all Code Assist plans—Free, Standard, and Enterprise—via the Insiders release channel.

The post Google Introduces Gemini CLI Open-Source AI Agent Bringing Gemini 2.5 Pro To Developers’ Terminals appeared first on Metaverse Post.

Technology company NVIDIA released a detailed overview of the cuMotion, nvblox, FoundationPose, and FoundationStereo software libraries and AI models included in the NVIDIA Isaac Manipulator platform, which are designed to enhance the operational efficiency of AI-powered robotic arms.

These GPU-accelerated components are intended to deliver real-time motion planning, detailed spatial mapping, and accurate depth perception, aiming to support the deployment of advanced automation solutions with reduced integration complexity. NVIDIA Isaac Manipulator is structured as an integrated software suite that facilitates the implementation of sophisticated robotic manipulator functions in industrial contexts by utilizing high-performance GPU technologies.

The cuMotion library provides motion planning, trajectory generation, and inverse kinematics capabilities using GPU acceleration, contributing to faster, collision-free motion execution even in environments with high spatial constraints. This is intended to improve reliability and operational precision, particularly in settings requiring strict performance standards. The nvblox tool supports real-time 3D mapping, enabling robotic systems to interpret and adjust to changing spatial environments. This function is designed to enhance obstacle avoidance and maintain safe operating conditions.

FoundationPose offers six-degree-of-freedom pose estimation using RGB-D input and is built to maintain accuracy across variable lighting, reflective surfaces, and diverse object shapes. To address the limited availability of real-world training data for robotic AI models, NVIDIA used synthetic data generation via Isaac Sim to produce more than five million images, followed by training on a V100 GPU system. This resulted in a zero-shot model capable of immediate deployment without additional training or fine-tuning, with the ability to generalize to new objects.

FoundationStereo supports accurate stereo depth estimation, enabling reliable interaction with surrounding environments. Its capabilities are designed to function effectively even in complex, cluttered, or dynamic industrial conditions. The model is trained on a large synthetic dataset and is capable of producing high-quality results using cost-effective hardware such as Intel RealSense sensors. FoundationStereo’s technical contributions were recognized with a Best Paper Nomination at CVPR 2025, marking it as one of a select group among thousands of submissions.

We’re excited to share how Vention’s MachineMotion AI, powered by #NVIDIAIsaac Manipulator, makes deploying AI-driven robots at scale easier than ever.

Developers benefit from:
Advanced motion planning
GPU-accelerated real-time perception
Robust remote monitoring… pic.twitter.com/eckGn3MAOM

— NVIDIA Robotics (@NVIDIARobotics) June 24, 2025

Key Applications Of Vention MachineMotion AI

Vention’s MachineMotion AI is an industrial automation controller developed to support the integration of AI-enabled robotics into manufacturing environments. Built on NVIDIA Jetson Orin and equipped with cellular connectivity, the controller is intended to facilitate rapid deployment, on-site edge computing, and remote management of robotic systems, while reducing infrastructure complexity.

One of the system’s main uses involves robotic pick-and-place operations conducted directly at the edge. The embedded NVIDIA Jetson Orin hardware allows for real-time object detection and positioning within the robotic unit itself, which minimizes processing delays and avoids reliance on external computing infrastructure.

Another area of application involves remote oversight, where the controller’s integrated cellular connectivity enables off-site diagnostics, system monitoring, and updates to software components, thereby reducing the need for physical service calls.

MachineMotion AI is also utilized in dynamic production settings where robotic configurations must be adjusted frequently. The system’s compatibility with NVIDIA Isaac Manipulator and the use of a low-code software environment help manufacturers adapt robotic cells to varying tasks and workflows. This adaptability is particularly useful for accommodating shifts in product design or changes in production requirements.

By combining edge AI processing with simplified integration, MachineMotion AI supports scalable, responsive robotic automation strategies in industrial contexts and extends the functional capabilities of NVIDIA Isaac software tools.

Representative Case: Random Bin Picking System 

A practical application of the combined NVIDIA Isaac Manipulator and Vention MachineMotion AI system can be observed in industrial random bin picking scenarios. In this context, the system integrates visual perception and motion planning to support automated object handling in unstructured environments.

The process begins with FoundationPose, which interprets RGB-D input to estimate the position and orientation of objects within the bin. This data is then utilized by cuMotion, which calculates optimized, collision-free trajectories for robotic arm movement. These motion plans are transmitted through Vention’s MachineMotion AI controller to the robotic hardware for execution. The result is a coordinated workflow that enables accurate grasping and repositioning of items, meeting the demands of real-time industrial automation.

This system configuration offers a robust solution for tasks requiring a high degree of variability and adaptability. By integrating GPU-accelerated perception and on-device motion planning, it supports high-performance automation without dependence on cloud infrastructure.

The joint use of NVIDIA Isaac Manipulator and MachineMotion AI provides several operational advantages. Real-time responsiveness is achieved through GPU acceleration on NVIDIA Jetson modules, facilitating accurate perception and manipulation even in dynamic settings. The system is also designed for ease of deployment, with MachineMotion AI reducing integration barriers and simplifying the implementation of advanced robotic capabilities. Additionally, its built-in cellular connectivity supports remote system monitoring, diagnostics, and updates, enabling ongoing optimization and reducing potential downtime.

The post Vention’s MachineMotion AI, Powered By NVIDIA Isaac Manipulator, Simplifies Scalable Deployment Of AI-Driven Robots appeared first on Metaverse Post.

Numerous accelerators have emerged to support AI startups, providing resources, mentorship, and funding to foster growth and commercial success. In this post, we will look at the finest accelerators for 2023, assessing their distinct offers and highlighting case examples to demonstrate their impact on the AI environment.

These accelerators play a crucial role in bridging the gap between research and industry, helping startups to turn their innovative ideas into practical solutions that can address real-world problems. By providing access to cutting-edge technologies and expert guidance, they enable entrepreneurs to scale their businesses and drive innovation in the AI space.

AI Startup Accelerators Comparison Table

AcceleratorDurationFunding / CreditsEquity RequiredSpecializationsAWS Generative AI Accelerator8 weeks (Hybrid)Up to $1M AWS creditsNoFoundational GenAI, inference optimization, agentic toolsGoogle Cloud AI Accelerator10 weeksUp to $350K GCP + Vertex AI creditsNoAI/ML startups building on Google CloudGoogle.org GenAI Accelerator6 monthsGrants + GCP credits (part of $30M fund)NoPublic-good GenAI (education, health, sustainability)NVIDIA Inception + GCP CreditsOngoing (rolling)Access to Blackwell GPUs + $350K GCPNoAI/ML infra, robotics, vision, simulationY Combinator3 months (on-site)$500K (SAFE)Yes (~7%)All verticals, including frontier AI and GenAIHAX (SOSV)4–6 months$250–500K + Prototyping supportYes (negotiated)AI + robotics, medtech, climate hardwareFast Forward Accelerator3–4 monthsGrants + coaching + cloud creditsNoSocial impact tech including AI for nonprofits500 Global~12 weeks$150K average investmentYes (negotiated)Emerging market AI (fintech, agritech, healthtech)AI Seed (UK)Varies (UK-based)£50K–£200K per startupYesUK-based ML/AI companies, applied AIBerkeley SkyDeck6 months$100K + UC Berkeley accessYes (5–10%)AI in health, climate, robotics; academic spinouts

1. AWS Generative AI Accelerator

An 8‑week global hybrid program (Oct–Dec 2025) supporting up to 40 AI startups building foundation models, tooling, and agentic workflows. Selected companies receive up to $1 million in AWS credits, deep technical mentorship, and go-to-market support at AWS re:Invent. It’s ideal for teams with MVPs and early traction, focused on training/inferencing infrastructure, fine-tuning platforms, and enterprise-grade AI solutions. This program bridges research and scale by pairing founders with AWS and industry experts. Applications open June–July 2025. A top pick for deep-tech AI founders. 

2. Google for Startups Cloud AI Accelerator

A 10‑week equity-free program for seed to Series‑A AI startups based in North America (and MENA/Türkiye), launching mid‑2025. Participants receive up to $350K in Google Cloud credits, access to ML experts, Vertex AI Model Garden credits, and mentorship from Google’s ecosystem. With cohorts targeting technical challenges and growth paths, it’s ideal for scaling AI-first teams leveraging Google’s infrastructure and models (e.g., Gemini). Strong for startups missing deep tech support without giving up equity.

3. Google.org Generative AI Accelerator

A six‑month, equity-free global program (applications by Feb 2025) aimed at nonprofits and social enterprises deploying GenAI for social impact. Winners share in $30 million in funding, plus Google Cloud credits and Google employee mentorship. Participants build AI-driven solutions in education, public health, and sustainability—emphasizing ethics and social responsibility. Excellent for mission-driven teams wanting access to top-tier AI support without VC constraints.

4. NVIDIA Inception + Google Cloud Credits

NVIDIA’s flagship Inception program remains free and now offers eligible startups up to $350K in Google Cloud credits, access to Blackwell GPUs, and co-marketing opportunities. Designed for early-stage AI teams working on vision, generative models, and robotics, it provides access to hardware, technical support, and networking with Nvidia experts. Startups maintain ownership, with no equity required. Ideal for tech-heavy founders looking for sophisticated infrastructure and visibility in the AI ecosystem.

5. Y Combinator

Although not AI-specific, YC continues to dominate the startup world in 2025. Its twice-yearly cohorts offer $500K funding for 7% equity, delivered via SAFEs with uncapped terms. With over 5,000 alumni and vast follow-on financing rounds, YC provides intense mentorship, community, and Demo Day exposure. Top AI startups continue flocking to YC to leverage its network, investor access, and credibility—making it the most prestigious AI-friendly accelerator still running.

6. HAX Accelerator (SOSV)

HAX (formerly HAXLR8R) remains the go-to accelerator for early-stage hard-tech and robotics-focused AI startups. Based in Newark with global labs (Shenzhen, Pune, Tokyo), it invests $250K–$500K in pre-seed funding and offers hands-on engineering support in hardware, electronics, and AI prototyping. Ideal for AI-driven physical product companies (smart sensors, autonomous devices). Full residency accelerates development cycles with lab access and co-design mentors—critical in 2025’s hardware-AI convergence.

7. Fast Forward Accelerator

Fast Forward specializes in tech nonprofits and social-impact AI teams. This non-profit accelerator provides mentorship, workshops, and equity-free grants to organizations developing AI solutions for education, social services, and humanitarian technology. Alumni have raised over $1B in follow-on funding. In 2025, Fast Forward remains a rare option for mission-driven AI ventures seeking foundations, non-dilutive capital, and expertise in scaling social impact projects.

8. 500 Global

An early-stage VC and accelerator with $2.3B AUM, 500 Global invests globally and offers sector-agnostic support across emerging markets. In 2025, AI-focused cohorts provide seed capital (~$150K), mentor networks, and access to market-specific growth levers. Startups from regions like Southeast Asia, Latin America, and Africa benefit from localization expertise and global expansion opportunities. A strong choice for AI-first founders wanting regional support with global reach.

9. AI Seed (UK)

AI Seed remains one of the UK’s only specialized AI seed funds and accelerators offering £50K–£200K equity investment alongside expert coaching and introductions . In 2025, it continues supporting machine learning and deep-learning startups through funded cohorts and access to corporate pilots. Ideal for UK-based founders seeking early-stage capital, technical mentoring, and market validation support.

10. Berkeley SkyDeck

An elite university-linked accelerator in the Bay Area, SkyDeck offers $100K funding for 5–10% equity, access to UC Berkeley faculty, labs, and investor network . In 2025, it hosts targeted AI cohorts—especially in healthtech and climate AI—combining research rigor with startup acceleration. Alumni regularly secure VC funding; ideal for founders looking for academic collaboration and regional venture capital exposure.

FAQs

Which programs are equity-free?
Google Cloud AI, Google.org, AWS Twitch? No equity. NVIDIA Inception is also free. Programs like YC, 500 Global, AI Seed, SkyDeck, and HAX typically requesting equity.

How much funding do I get?
AWS offers up to $1M in credits; Google Cloud up to $350K; Google.org non-dilutive grants; YC & SkyDeck ~ $100–500K cash. Others vary by stage.

Do I need to relocate?
AWS & Google Cloud accelerate hybrid formats; YC, HAX, SkyDeck require in-person presence (Bay Area), while 500 Global and Fast Forward are flexible/remote.

Who benefits the most?
Foundation-model and infrastructure startups → AWS/AWS; enterprise & growth-stage → YC, 500 Global; research/impact → Google.org & Fast Forward; hard-tech → HAX & SkyDeck.

Do I get cloud credits/hardware access?
Yes: AWS, Google Cloud, NVIDIA Inception (with Google credits), and YC alumni gain deep infrastructure access. Choose based on your technical needs.

Bottom Line

Accelerators in 2025 are more than checkbooks—they provide infrastructure, datasets, research access, mentorship, and go-to-market support. Choose based on your startup’s stage, capital needs, technical requirements, and location preferences. Programs like AWS GAIA and Google Cloud AI are ideal for sequenced GenAI builds. 

Meanwhile, YC, HAX, and SkyDeck offer broader networks and hands-on engineering support. For mission-driven founders, Google.org and Fast Forward provide non-equity, impact-focused pathways. Whether you’re building the next foundation model or robotics startup, these leading accelerators deliver the resources and credibility to help you launch, scale, and succeed.

The post Best 10 AI Startup Accelerators of 2025: Overview appeared first on Metaverse Post.

Decentralized finance (DeFi) aggregator 1inch announced the integration of the Layer 2 network Unichain, expanding its multi-chain functionality and offering enhanced conditions for secure, high-efficiency token swaps. This development makes the Unichain network available through the 1inch decentralized application, wallet interface, and API suite. The integration supports swap execution across more than twelve blockchain networks, includes mechanisms for mitigating maximal extractable value (MEV), and provides access to aggregated liquidity.

This update delivers improved swap rates across single and cross-chain transactions, supported by Unichain’s infrastructure, which employs 200-millisecond sub-block intervals and optimistic rollup architecture to facilitate low-latency and cost-efficient processing. The integration introduces dual-layer MEV protection, combining 1inch’s intent-based swap protocol with Unichain’s trusted execution environment (TEE)-driven block construction.

Comprehensive compatibility has been established across the 1inch Wallet and APIs, while portfolio tools now offer real-time monitoring of Uniswap balances through the 1inch Portfolio interface. Unichain, launched in February 2025, operates as a Layer 2 solution optimized for scalability within the Ethereum ecosystem. As of mid-June, its total value locked had reached $832 million according to data from DeFiLlama. That liquidity is now accessible via the 1inch ecosystem.

“Unichain fits perfectly into our vision of a fully integrated, cross-chain DeFi,” said Sergej Kunz, co-founder of 1inch, in a written statement. “With near-instant swaps, native MEV protection, and deep Unichain liquidity now accessible via 1inch, we’re pushing the boundaries of what’s possible in multi-chain trading. We’re providing users with the best execution across ecosystems, all in one convenient location,” he added.

1inch And Unichain Integration To Unlock Enhanced Capabilities And Stablecoin Support

The integration allows users to swap Unichain tokens directly on the network or transfer liquidity between Unichain and other EVM-compatible blockchains, consistently offering competitive rates. Asset management is supported through the 1inch Wallet and 1inch Portfolio, where Uniswap balances and positions are automatically updated within the portfolio dashboard, providing a consolidated view of holdings.

Additionally, both intent-based and cross-chain swaps involving Unichain on the 1inch platform include built-in MEV protection, designed to minimize slippage and prevent value extraction during transactions. This feature complements Unichain’s trusted execution environment (TEE) block-building, effectively doubling transaction security.

Unichain has experienced substantial stablecoin trading volumes. Between March and June 2025, the total stablecoin market capitalization on Unichain grew to $344.2 million, with over half of that amount issued natively on the network, according to DeFiLlama. This growth reflects Unichain’s standing as a reliable platform favored by active traders, supported by low fees that facilitate frequent and cost-effective transactions.

The integration also extends to the 1inch Developer Portal, where developers and partners gain access to Unichain through various 1inch APIs. These include functionalities for swapping (Fusion, Fusion+, Classic Swap, Orderbook), balance tracking, spot pricing, and transaction history, among others.

The collaboration builds on 1inch’s role as a leading DeFi aggregator, connecting multiple blockchain ecosystems through a single interface. Users can execute swaps across Unichain and more than a dozen other networks using the 1inch decentralized application.

The post 1inch Integrates Unichain To Enhance Multi-Chain Swaps, MEV Protection, And Stablecoin Support appeared first on Metaverse Post.

In 2025, AI has become a practical assistant for many aspects of daily living—from managing schedules and maintaining balance to planning longer-term goals. According to Deloitte’s 2025 global TMT report, 25% of enterprises using generative AI plan to deploy AI agents in their personal workflows this year, highlighting how AI is becoming embedded into routine life. Separately, a Deloitte survey showed 57% of consumers feel positive about AI’s role in daily decisions, including health, finance, and time management .

Even with access to AI, most people benefit only when they ask precisely. Prompts that are well-structured and contextual turn an AI model into a helper who understands your life—not just a content generator. This article presents seven categories of life tasks, each with robust, ready-to-use prompts. These prompts help you reclaim time, reduce mental clutter, and feel confident in your daily routines.

Let’s explore how structured AI prompts can meaningfully improve your life.

How AI Prompts Make Life Easier in 2025

In 2025, we face a common problem: not a lack of tools or apps, but too much of everything. There’s too much to read, too many tasks, and too many things to keep in mind. This makes everyday life feel harder than it should.

That’s where smart AI prompts come in. When you talk to AI the right way, it becomes more than just a tool — it becomes your thinking partner. You can use it to reduce stress, speed up planning, and get clear answers faster.

Whether you’re dealing with time pressure, messy routines, financial stress, or personal goals, the key is how you ask AI for help. A good prompt can:

Save you hours by replacing guesswork with smart structure;

Help you sort your thoughts and focus on what matters;

Turn daily chaos into simple systems;

Give you back mental space so you can focus on family, growth, or rest.

Here are practical, everyday prompts — organized by topic — that can make life simpler, smoother, and more enjoyable with AI in 2025.

Personal Time Management

When your week feels out of control, it often starts with poor time setup. Your calendar might be too packed or missing real focus time. AI can help you see what’s not working and suggest better routines based on what actually matters to you.

These prompts give you clear steps to rethink your week, organize goals, and make time feel less rushed:

Act like a time management coach. Here’s my current weekly plan: [paste it]. Go over it and give me a better setup using focus blocks, rest breaks, and grouped tasks. Explain your thinking;

Help me figure out my top 3 goals for this week. Ask me a few short questions, then create a step-by-step plan with clear goals, deadlines, and tasks;

I feel stuck and tired. Look at my to-do list and sort it using the Eisenhower Matrix. Tell me what I should delay, remove, or ask for help with — and why.

Home Tasks & Routines

Chores often feel small, but they build up fast and take energy. Cooking, cleaning, organizing — they all eat up your time. With AI, you can set smart systems that take care of the thinking part so you can move faster and with less effort.

Use these prompts to lighten the load and create better flow in your home:

Create a weekly meal plan using what’s already in my kitchen: [insert list]. Focus on healthy, quick meals (under 20 mins) and write a shopping list sorted by store aisle;

Make a weekend reset plan for my home and mind. Include time for cleaning, organizing, and planning ahead — all within 2 hours;

Design a fair housework plan for two adults and one child. Include who does what, rest days, and tasks based on skills and age.

Finances & Budgeting

Many people feel stress around money — not because they don’t earn, but because they don’t see the full picture. AI can help you understand where your cash is going and suggest smarter ways to spend or save.

Use these prompts to make financial planning easier and more useful:

Here’s my income and spending list: [paste it]. Review and suggest 3 ways I can cut costs without lowering my quality of life;

Build a simple monthly budget that covers bills, savings, and spending on things like learning or self-care;

I want to save $5,000 in six months. Create a weekly plan with targets and tools I can use to track it.

And for anyone thinking about launching something of their own, there’s a full step-by-step prompt guide to starting a business with AI that breaks down the process in detail.

Learning & Personal Growth

Learning something new takes time — but most of us waste it trying random tutorials or watching videos with no plan. AI can create a full roadmap for your learning journey, tailored to your goals and pace.

Use these prompts to make your growth faster and more focused:

Make a 3-week beginner plan to learn ‘creative entrepreneurship’. Include lessons, small tasks, and weekly goals;

I want to understand financial literacy but don’t know where to begin. Build me a 30-day learning plan with daily 15-minute lessons and free tools;

Create a thinking workout to boost decision-making and problem-solving. Include 3 types of practice I can use daily.

Travel Planning

Trips should be fun, not frustrating. But too often we get stuck comparing flights, booking stays, or planning schedules. AI helps you plan better, faster, and with fewer mistakes.

These prompts make trip planning smooth and stress-free:

Plan a 7-day trip in Japan (Tokyo–Kyoto–Osaka) with cheap food spots, top sights, and rest days. Budget: $100/day max;

Find a nature getaway for 3 days near [your city]. It should be within 2 hours, with no Wi-Fi, and a quiet place to relax;

I’m going to two places with very different weather. Make a simple packing list that covers both climates, tech gear, and needed travel documents.

Health & Wellbeing

Good health isn’t just about gym workouts — it’s about daily choices that support your body and brain. AI can help you build routines that are small, but powerful.

Try these prompts to feel better and stay balanced:

Here’s my current sleep routine: [describe it]. Suggest a better wind-down plan and explain how it helps with deeper sleep and rest;

Create a short morning routine (10 minutes) that includes light movement, breathing, and setting a positive mindset. Use tips from behavioral science;

Suggest 3 small grounding habits I can do at work to feel calm. They should take under 5 minutes and not need me to leave my desk.

Work & Life Balance at Work

In 2025, work isn’t just tasks — it’s nonstop emails, calls, meetings, and mental pressure. AI prompts can help you build better structure into your workday and protect your time and energy.

These prompts offer calm, control, and clearer boundaries:

I feel tired after work even if I wasn’t very busy. Look at how I spend my time and suggest ways to reduce brain overload but still get things done;

Make a digital boundary system for me. Help me build habits to manage email, take real breaks, and mentally switch off after work hours;

Create a weekly work schedule that includes focus time, short breaks, message windows, and space for life outside work.

Living Smarter in 2025: Results from Using AI for Daily Life Tasks

In 2025, structured prompts help people save time and reduce stress across key areas of life. According to a Deloitte Asia-Pacific study, daily users of generative AI reported saving around 6.3 hours per week through improved planning, budgeting, and routine management. The Adecco Group’s 2024 survey of 35,000 workers across 27 economies found AI users saved an average of one hour per day, helping reclaim time for creativity, focus, and better work–life balance. And a Thomson Reuters report projects that routine automation could free up to 12 hours per week within the next few years as AI becomes more integrated.

When prompts are clear, detailed, and actionable, AI tools like ChatGPT, Claude, and Gemini provide tailored help that aligns with your real schedule and priorities. In 2025, prompt-writing has become an essential part of digital literacy—no more guesswork, only focused routines.

For those building habits, managing attention, or balancing multiple roles, knowing how to use prompts has become a core skill. It’s now part of everyday digital literacy—just like scheduling or email.

The post Top Prompts That Will Simplify Your Life With AI In 2025 appeared first on Metaverse Post.

Most people think of blockchain as immutable code and cryptographic signatures. Grigore Roșu sees it differently: as mathematics in motion. A veteran of NASA and a professor of computer science, Roșu is building Pi Squared—a radical reimagining of Web3 where every computation becomes a mathematical proof, verifiable by design. 

In this interview, he explains why blockchains are just one use case for his “verifiability-first” stack, how AI agents can operate without ever touching a chain, and why the distinction between L1s and L2s might soon disappear entirely.

Can you share your journey into Web3? What led you to found Pi Squared?

I entered the Web3 and blockchain space in 2017. Interestingly, before that, I worked extensively in formal verification and formal methods, particularly on programming language semantics. These techniques were mainly used to analyze and prove the correctness of mission-critical software for clients like NASA, Boeing, and Toyota.

I had co-founded another company focused on runtime verification. We applied formal verification techniques to mission-critical software. Then, in 2017, Charles Hoskinson, the founder of Cardano, reached out to me and suggested that these same methods could be applied to verify smart contracts and protocols in Web3. I looked into him, and he was legit, and that’s how we started collaborating.

So we brought the rigorous software analysis techniques used in aerospace and automotive to Web3. That’s how I got started. I’m also a professor of computer science at the University of Illinois Urbana-Champaign, where I’ve been teaching these methods for over 20 years. I still collaborate with NASA as well.

How do you envision Pi Squared’s role in advancing Web3 through a verifiability-first infrastructure?

Web3 is all about verifiability. It empowers individuals to own and transfer their digital assets. But this raises a big question: what if programs malfunction, or worse, what if someone tries to maliciously steal assets? That’s where verifiability becomes crucial.

At Pi Squared, we apply decades of research in programming languages and formal verification to Web3. We generate mathematical proofs that smart contracts and other programs are functioning exactly as they should, according to their formal definitions. It’s the most rigorous level of software assurance we know, and we’re bringing that to Web3.

What are the main components of your verifiability stack (VSL, VLM, and Proof of Proof), and how do they work together?

The core components are the Verifiable Settlement Layer (VSL), Verifiable Language Machine (VLM), and what we call “proof of proof.”

Everything is built on mathematical definitions of programming languages and computing models. We believe—almost religiously—that you can’t talk about correctness unless you’re using mathematical definitions. This field of formal semantics has existed for over 50 years and is foundational to computer science.

In our system, every computation—whether it’s a program execution or a token transfer—generates a mathematical proof. Computation is deduction, reasoning. What we do differently is we verify the integrity of program execution by verifying the integrity of its mathematical proof.

“Proof of proof” refers to a zero-knowledge cryptographic proof of a mathematical proof. This is what gives our company its name—Pi Squared. Mathematical proofs are large but precise, and cryptographic proofs are small but efficient. By combining both, we achieve succinctness and correctness.

VSL handles verifiable computation settlements. VLM reduces any programming language execution to a mathematical proof. And proof of proof is the cherry on top—providing compact cryptographic evidence of correctness.

How does the Verifiable Language Machine handle cross-language interoperability, for example, multi-language swaps?

The beauty of the Verifiable Language Machine is that it reduces computations in any language to mathematical proofs. Once you’re working with math, interoperability is automatic.

Mathematics has been around for thousands of years, and it naturally allows for interoperability. You can use algebra in geometry, and vice versa. Similarly, if every program execution becomes a mathematical proof, all those proofs are interoperable. So by translating computation into math, we get interoperability for free.

What architectural challenges have emerged when building a universal ZK verification layer?

Our universal ZK verification layer has two parts: reducing computation to a mathematical proof and then generating a ZK proof from that.

The first part—formal semantics—is a mature field. The second part, translating mathematical proofs into ZK proofs, is newer and presents unique challenges.

Adapting ZK circuits to work with mathematical proofs was surprisingly not the hardest part. The big challenge was engineering an infrastructure that could generate these mathematical proofs in real time as a program runs—say, on the EVM. No one else is doing that, and that’s where Pi Squared stands out.

While most ZK projects create custom circuits for specific languages like RISC-V, we go from any language to math, and then from math to ZK. That’s what makes our system universal.

What major milestones are you planning in the next 12–24 months, especially around DevNet maturity and mainnet launch?

Our biggest upcoming milestone is the launch of our Mainnet, a large, decentralized validator network. We hope to have more than a million validators. Anyone can join, even with a laptop or phone.

Thanks to the efficiency of verifying ZK proofs, thousands of verifications per second can be done on everyday hardware. Once the network is live, the possibilities are endless.

We’ll build applications on top, like AI agents and even entire blockchains. In fact, our infrastructure is more fundamental than blockchains. We don’t distinguish between Layer 1 and Layer 2. We’re like a Layer 0—or even Layer -1.

How do you plan to onboard millions of developers to Web3 with your “Bring Your Own Language” approach?

With BYOL (Bring Your Own Language), developers can write Web3 applications in whatever language they’re already using: Java, C, C++, and so on.

This massively expands the developer base from the tens of thousands who know Solidity to millions who know conventional programming languages. You don’t need to learn Solidity or EVM to become a Web3 developer anymore.

What’s your vision for how a universal settlement layer can disrupt current Layer 2 architectures?

Honestly, L2s exist because L1s like Ethereum are too slow and expensive. But if you have a universal, verifiable settlement layer that can handle any computation in any language instantly, you no longer need to distinguish between L1s and L2s.

In our system, both become just applications on top of our infrastructure. Even voting, payments, and auctions can be done verifiably and decentralized—without needing a blockchain.

What’s your stance on the shift toward modular verification in contrast to traditional blockchain-centric Web3?

Traditional blockchains enforce a total order of transactions, like Bitcoin does, to prevent double spending. But that comes at a massive scalability cost.

Our approach is different. Each computation is independently verifiable and doesn’t need to be ordered. It’s like tree leaves moving independently in the wind. That’s the future of Web3.

Where do you see the biggest challenges in the convergence of AI and crypto, and how will Pi Squared’s VSL address them?

AI agents need two things from crypto: payments and state settlement. Both can be done without a blockchain.

Blockchains can offer those features, but at a high cost. Pi Squared’s VSL provides verifiability, decentralization, and correctness—all with uncapped performance. No waiting in line, no forced ordering.

By “unchaining the chain,” we create a foundation for AI agents to operate freely and independently, without sacrificing decentralization or security.

The post Pi Squared Wants to Replace Layer 1, Layer 2—and Everything in Between appeared first on Metaverse Post.

The AI arm of Google, Google DeepMind unveiled a locally deployable robotics model–Gemini Robotics On-Device. This model is designed to operate efficiently on robotic hardware without reliance on external networks. It demonstrates advanced capabilities in general-purpose dexterity and task adaptation across various use cases.

The model functions entirely on the robot, which reduces latency and maintains operational stability even in environments with unreliable or no internet connectivity. It is intended for bi-arm robotic systems and requires minimal computational power.

Gemini Robotics On-Device extends the functionality of previous Gemini Robotics models by supporting real-time, dexterous task execution, enabling fine-tuning for new tasks, and allowing natural language command interpretation. The system shows consistent performance in handling visually and semantically varied tasks such as manipulating soft objects or executing multi-step instructions.

Evaluations indicate that the model generalizes well across different conditions and outperforms comparable on-device systems in complex scenarios. For developers requiring enhanced capabilities beyond local constraints, an alternative version of the Gemini Robotics model is available.

We’re bringing powerful AI directly onto robots with Gemini Robotics On-Device.

It’s our first vision-language-action model to help make robots faster, highly efficient, and adaptable to new tasks and environments – without needing a constant internet connection. pic.twitter.com/1Y21D3cF5t

— Google DeepMind (@GoogleDeepMind) June 24, 2025

Gemini Robotics On-Device Becomes First VLA Model Available For Fine-Tuning, Developed With Emphasis On Safety And Responsible Innovation

Gemini Robotics On-Device represents the first instance of a VLA model from this series made available for fine-tuning. While the model is capable of executing a range of tasks without modification, it can also be adapted to enhance performance in specific applications. Adaptation can be achieved using a relatively small number of demonstrations, typically between 50 and 100, which demonstrates the model’s ability to apply its foundational capabilities to unfamiliar tasks.

The development of all Gemini Robotics models follows a framework aligned with established AI Principles, incorporating a comprehensive safety strategy that addresses both semantic and physical dimensions. Semantic and content-related safety is monitored through the Live API, while low-level safety-critical controllers are integrated to manage the execution of physical actions. A semantic safety benchmark has been introduced for system-wide evaluations, and targeted testing methods, including red-teaming, are advised to identify safety-related weaknesses.

Oversight of the models’ real-world implications is conducted by the Responsible Development & Innovation team, which assesses potential risks and societal effects. These findings are reviewed by the Responsibility & Safety Council, whose recommendations inform the ongoing development of the models, aiming to enhance positive outcomes while reducing potential harm.

The post Google DeepMind Drops Gemini Robotics On-Device, Enabling Localized AI Integration For Robotic Systems appeared first on Metaverse Post.

Team behind the high-performance Layer 1 blockchain Sonic, Sonic Labs introduced a formal verification library designed to enhance security in DAG-based consensus protocols. Led by Chief Research Officer Dr. Bernhard Scholz, the library aims to provide mathematical proof of safety for Directed Acyclic Graph (DAG) blockchains, including Sonic’s own EVM-compatible network.

Developed in partnership with researchers from the University of Sydney and INRIA, the open-source library is built using the TLA+ proof assistant. It offers a modular and reusable framework that streamlines the process of modeling and verifying consensus mechanisms based on DAG architecture.

The verification library features formal proofs for several existing DAG-based protocols, such as DAG-Rider, Cordial Miner, Bullshark, Hashgraph, and Aleph. Sonic’s proprietary protocol has also been verified as a derivative within this framework. The research was first presented at the NASA Formal Methods 2025 conference, held in Williamsburg, Virginia, from June 11 to 13, and represents a notable advancement in blockchain verification standards.

“In blockchain, security failures often stem from assumptions that go untested until it’s too late,” said Dr. Bernhard Scholz, Chief Research Officer at Sonic Labs, in a written statement. “With this library, we’re shifting from hope to proof, offering the tools to verify with mathematical certainty that a protocol will behave safely under all conditions. Our goal is to make formal verification accessible to every protocol developer,” he added.

As Blockchain Value Surges, Sonic Labs Launches Formal Verification Framework To Ensure Protocol Security And Reliability

As the value secured by blockchain networks continues to grow, the potential impact of vulnerabilities in consensus protocols becomes increasingly significant, with risks including double spending and inconsistent ledger states. Conventional testing and code audits are limited in their ability to guarantee bug-free systems. In response, a formal verification method has been applied by Sonic Labs, utilizing mathematical proofs to confirm protocol security across all possible conditions.

This approach not only validates existing consensus protocols but also aids developers who are designing new Directed Acyclic Graph-based models or adapting current ones. The method is currently being implemented to formally verify that the Sonic blockchain cannot exhibit unsafe behavior, establishing the protocol’s reliability through mathematical validation.

By making the verification library openly available, the initiative provides blockchain developers with resources to build provably secure systems. This is expected to improve the overall resilience of decentralized ecosystems and reduce the resource demands typically associated with consensus protocol verification.

The post Sonic Labs Unveils Formal Verification Library For DAG Consensus Protocols appeared first on Metaverse Post.

Community-governed organization dedicated to fostering the development, adoption, and sustainable growth of the Moca Network, Moca Foundation announced the upcoming launch of Moca Chain, a Layer 1 blockchain designed specifically to manage identity and user data. Moca Chain aims to facilitate the creation of identity protocols across various industry sectors, enabling individuals, devices, and AI agents to control, consolidate, and verify their digital credentials independently of centralized platforms. The blockchain intends to promote user-centric growth while preserving privacy through seamless integration with consumer applications. The Moca Chain testnet is scheduled for release in the third quarter of 2025, with the mainnet planned for the fourth quarter of the same year.

This blockchain will enable verification of both on-chain and off-chain user data across different applications and blockchains by leveraging decentralized data storage, a cross-chain identity oracle, web proof data generation using zkTLS technology, and on-chain verification processes. As a modular and EVM-compatible network, Moca Chain will operate interoperably with other blockchains, providing an identity and data layer for its partners and users. The native MOCA Coin will serve as the primary token, supporting gas fees, validator staking, storage costs, oracle services, data generation, and verification transactions.

“Billions of users today go online using single sign-on (SSO), which contains the keys to a user’s data, services, and digital lives. While convenient, SSO represents a centralized point of failure that compromises security while also allowing operators to aggressively extract value from users’ digital selves. Moca Chain seeks to solve this problem by giving users decentralized true ownership of their data, ensuring the sovereignty of users’ digital identity without a single point of failure,” said Yat Siu, co-founder and executive chairman of Animoca Brands, in a written statement.

“In conjunction with Moca Network’s AIR Kit, Moca Chain is creating a digital ecosystem where users can finally own their data, reputations, and contributions. This aligns strongly with the mission of Animoca Brands to advance digital property rights and empower individuals to control and benefit from their online activities and their personal data, enabling more equitable sharing of the value that users generate through their online presence and activity,” he added.

“Moca Chain and AIR Kit are a one-of-a-kind infrastructure for verified identity data to empower consumer apps and their users,” said Kenneth Shek, project lead of Moca Network, in a written statement. “By adopting Moca Chain and MOCA Coin, we believe we can disrupt current models of data ownership and break down the dominance of walled garden ecosystems, returning value to the users who generate it and making ecosystem growth more scalable,” he added.

Moca Network serves as the identity ecosystem associated with Animoca Brands and is a key launch partner supporting the growth and adoption of Moca Chain. The AIR Kit developed by Moca Network has been integrated into products from various partners, including companies within the Animoca Brands portfolio, affiliates, and collaborators, collectively reaching an estimated user base exceeding 700 million. Applications and protocols built on Moca Chain will have access to the user networks and data of AIR Kit adopters, such as SK Planet’s OK Cashbag with 28 million KYC-verified users and One Football with over 200 million users.

In collaboration with its protocol partners, Moca Chain seeks to address common challenges in identity verification, including fragmentation, authenticity, privacy, interoperability, and self-sovereign control, across multiple industries. Current applications involve healthcare, where unified electronic health records can be verified across providers; recruitment, with verified education and training credentials; finance, supporting privacy-preserving KYC and AML processes; and advertising, enabling unified user data across applications for verified onboarding.

Moca Chain: Enabling User-Controlled Digital Identity And Seamless Data Interoperability Across Platforms

Moca Chain is developed with real-world adoption in mind, integrating Moca Network’s AIR Kit into prominent Web2 platforms to enable identity verification and rewards within applications already used by millions. These collaborations position Moca Chain as the foundational infrastructure for a growing ecosystem focused on identity-based services. Traditionally, users of major platforms such as social networks or online retailers are confined within closed systems where their data is siloed and monetized without their consent. Moca Chain aims to return control to users by allowing them to verify their identity and manage their data within a unified framework. Users can determine which applications have access to their private information and set detailed permissions governing how and where their data is shared. Sharing data grants users access to partner benefits, ecosystem privileges, or token rewards.

The identity layer of Moca Chain is designed to be composable, enabling the seamless transfer of user attributes like loyalty points, social proof, and access rights across multiple decentralized applications. This functionality allows users to gain access and rewards across different platforms without revealing private data, all while maintaining a consistent and user-controlled identity. Protocols built on Moca Chain have the option to issue or verify reusable user data and credentials, both on-chain and off-chain, for monetization purposes while safeguarding the privacy of identity and reputation information. Data issued to users can be verified universally through zero-knowledge proofs, supporting ecosystem expansion by enabling user interaction without direct API connections and shifting verification authority from centralized platforms to the users themselves.

Moca Chain operates in conjunction with AIR Kit, the global software development kit (SDK) from Moca Network for account management, identity, and reputation. Developers can leverage AIR Kit to build feature-rich applications that include smart accounts and verifiable credentials, with built-in support for plug-and-play permissions to facilitate user-friendly experiences.

The post Moca Foundation Announces Moca Chain For Self-Sovereign, Privacy-Preserving Identity And User Verification appeared first on Metaverse Post.

DeFi Development Corp., a publicly listed entity with a treasury strategy focused on accumulating and compounding Solana-based assets, announced that it has entered into a validator partnership with the community behind dogwifhat (WIF), a well-known memecoin ecosystem operating on the Solana blockchain. 

Dogwifhat, which launched in late 2023, has emerged as one of the most widely held and actively traded tokens on Solana, achieving a peak market capitalization of over $3 billion. It is now considered a prominent example of a community-driven digital asset within the Solana ecosystem.

As part of the agreement, DeFi Development Corp. will manage the technical infrastructure, operations, and ongoing performance of a validator node on Solana that is associated with the dogwifhat community. Although the validator is operated by DeFi Development Corp., ownership is attributed to the WIF community. Both parties plan to jointly promote the validator and collaborate on efforts to attract delegated stake, including submitting an application to the Solana Foundation Delegation Program (SFDP).

According to DeFi Development Corp., the arrangement reflects a combination of professional validator services and the influence of a culturally resonant token community. The validator will be governed by a performance-based model. Once necessary operational costs such as infrastructure and voting fees are covered, any remaining rewards—including staking yields, block incentives, and maximal extractable value (MEV)—will be split equally between DeFi Development Corp. and the dogwifhat community.

This initiative forms part of DeFi Development Corp.’s broader validator strategy, which focuses on increasing the metric known as SOL per share (SPS)—a proprietary indicator representing the amount of SOL backing each share of the company’s tokenized equity, DFDV. By running high-performance validators, the company aims to generate returns in SOL that contribute to the growth of its treasury holdings, ultimately serving shareholder interests.

DeFi Development Corp.: SOL-Centered Treasury Strategy Aligns Web3 Participation With Real Estate Tech Operations

DeFi Development Corp. has implemented a treasury policy centered on maintaining SOL as its primary reserve asset. This approach offers investors economic exposure to Solana’s native token while reinforcing the company’s active role within the Solana ecosystem. Beyond holding and staking SOL, the firm operates proprietary validator infrastructure to earn staking rewards and fees from delegated assets. It also participates in decentralized finance activities and continues to evaluate opportunities within Solana’s expanding application ecosystem.

In parallel, the company functions as an AI-enabled digital platform serving the commercial real estate sector. It delivers data services, software subscriptions, and additional tools to support professionals managing multifamily and commercial properties, addressing the demands of a fast evolving operational environment.

Recently the company announced plans to tokenize its equity shares in collaboration with cryptocurrency exchange Kraken. The initiative will utilize xStocks, a tokenization platform developed by Backed, to make its shares available onchain. Kraken, which partnered with xStocks in May, intends to offer tokenized equity of major US-listed firms such as Apple, Tesla, and Nvidia to international users. The underlying platform for these tokenized assets is built on the Solana blockchain.

The post DeFi Development Corp. And Dogwifhat Launch Validator Partnership To Support Solana Network Growth appeared first on Metaverse Post.

Phishing simulation platform developed by professionals from the blockchain security firm SlowMist, the cybersecurity community DeFiHackLabs, and the Web3 anti-scam solution Scam Sniffer has announced its official launch date, scheduled for July 1st. 

The platform is intended to function as a Web3 phishing drill environment designed to assist users in learning and enhancing their ability to detect and prevent phishing attacks. It simulates a range of frequently encountered phishing tactics, including social engineering strategies, deceptive websites, and malicious smart contracts. 

It is being positioned as a public resource, offered at no cost, with the objective of minimizing phishing risks, contributing to the overall security of the Web3 ecosystem, and expanding access to security education and awareness tools.

Unphishable is browser-based and provides interactive training through simulations of real-world Web3 phishing attempts. Participants engage with gamified scenarios organized by levels of difficulty—beginner, intermediate, and advanced—where they learn to identify and avoid scams within a secure and educational setting. 

The platform includes over 30 distinct phishing scenarios, such as seed phrase scams, approval traps, fraudulent airdrops, clipboard hijacking, and punycode attacks. It also offers multilingual support in English, Traditional Chinese, and Simplified Chinese. No installation or setup is required, as the training runs entirely in-browser.

It has been developed in response to the high number of users falling victim to phishing and the lack of widely accessible, hands-on training solutions. Once assets are compromised, recovery is often not possible, highlighting the urgency of preventive measures. The platform aims to address this issue by providing realistic phishing simulations to prepare users before they are targeted. Its stated mission is to equip the Web3 community with practical, user-friendly tools for phishing awareness and education. The initiative is backed by the Ethereal Foundation ESP, Geodework, and GoPlus Security.

Unphishable Platform Launching July 1st — Test Your Web3 Phishing Defense!

July 1st – 5th (First Event)
Complete all challenges and win 10 USDT
Top 30 players who clear all levels will be rewarded
Train with real phishing scenarios#Web3Sec #Unphishable pic.twitter.com/bAONfyqSM1

— SunSec (@1nf0s3cpt) June 25, 2025

Unphishable Launches Five-Day Interactive Challenge To Promote Web3 Phishing Awareness And Skill Development

As part of its launch, Unphishable will conduct a five-day challenge designed for early participants. During this event, the top 30 individuals who successfully complete all levels of the simulation will each be awarded 10 USDT. The challenge presents an opportunity for users to assess their existing skills in phishing detection or to develop foundational knowledge in a controlled, educational environment.

The platform is developed by experts affiliated with several established cybersecurity-focused entities. Contributors include SunSec, Rory representing DeFiHackLabs, Fun from Scam Sniffer, and Cos (余弦), Thinking, and Hik3 from SlowMist. 

SlowMist is an internationally recognized firm specializing in blockchain security, with more than ten years of experience in network protection. Its services encompass security audits for digital asset exchanges, wallets, blockchain networks, and smart contracts, in addition to red teaming, incident response, threat intelligence, and tools for tracking money laundering activity. 

DeFiHackLabs operates as a Web3 security community with a focus on enhancing the resilience of decentralized systems through white-hat collaboration. With a membership exceeding 4,000 individuals, including nearly 300 ethical hackers, the group supports cybersecurity advancement through training programs, educational initiatives, and public engagement in events. 

Scam Sniffer functions as a real-time Web3 anti-scam solution. Its approach to phishing and fraud prevention involves both on-chain and off-chain threat monitoring, supported by browser extension technology that alerts users to phishing domains, malicious smart contract approvals, wallet drainers, and harmful links distributed through messaging platforms such as Telegram and X.

The post SlowMist, DeFiHackLabs, And Scam Sniffer Announce Unphishable Phishing Simulation Platform Launching On July 1 appeared first on Metaverse Post.