Binance LCX Launches Fiat-to-Crypto Exchange 8398

Binance LCX, a joint venture between Binance and Liechtenstein Cryptoassets Exchange (LCX), has announced the launch of a fiat-to-crypto exchange, according to a press release published August 16. The new trading platform will be located in Liechtenstein and offer trading between Swiss Francs (CHF) and euros (EUR) against major digital currencies pairs, subsequently adding more trading pairs following regulatory approvals.

Binance will provide and support the platform, while Binance LCX will lead customer support, regulatory compliance, and government communication. Adrian Hasler, Prime Minister of Liechtenstein, commented the launch:

“We are confident that Liechtenstein’s existing and future legal framework and practice provide a robust foundation for the Binance LCX and other blockchain companies to provide exceptional services here in Liechtenstein.”

Liechtenstein has taken a friendly and open stance towards cryptocurrencies and blockchain technology, echoing developments in neighbouring Switzerland known for its “Crypto Valley” in the canton of Zug.

Recently, Liechtenstein introduced a new blockchain law which provides legal and regulatory certainty for businesses and customers. In an interview with Cointelegraph, Hasler said that the country sees great potential in blockchain technologies, adding:

“Blockchain can serve as an important base for a variety of economic applications, covering not only payment transactions but broader financial solutions, industry use cases and general applications.”

In March, Liechtenstein lending institution Bank Frick began offering “direct investment” and cold storage for five cryptocurrencies; Bitcoin, Bitcoin Cash, Litecoin, Ripple and Ethereum. The service is aimed primarily at “professional market participants and financial intermediaries.” The bank’s Chief client officer Hubert Büchel claimed that their crypto-related services “are in demand from companies across the whole of Europe.“

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Bedroom Trader OLI: Trade the World’s Crypto Markets Without Leaving Your Pillow 674

Why Establish an Office or Construct a Lavish Trading Desk?

Bedroom Trader OLI (OLI) demonstrates that participation in global markets requires nothing more than a smartphone, a Wi‑Fi connection, and a comfortable setting.

What Is OLI?

Bedroom Trader OLI represents a meme-driven cryptocurrency collective tailored for casual traders who prioritise convenience over traditional workspaces. The initiative is grounded in three foundational principles: unrestricted accessibility, decentralised community governance, and inclusive meme culture. Trading activities may commence at any time and location, free from conventional entry barriers. Token holders are empowered to direct major organisational decisions through decentralised autonomous organisation (DAO) voting mechanisms. A culture of humorous exchange and viral creativity encourages ease of entry and fosters a welcoming environment. OLI redefines participation in cryptocurrency by transforming it into a familiar daily activity that accommodates both newcomers and experienced market participants.

Tokenomics in Plain English

The OLI token supply dynamically adjusts in response to community activity. At the close of each month, a tally of active wallets is conducted, followed by the minting of 50 to 150 billion OLI tokens based on that figure. Fifty percent is allocated as a universal reward for all holders. Every wallet in possession of OLI at the time of the monthly snapshot receives an equal share, ensuring equity and preventing concentration of tokens among large holders. The remaining fifty percent is dedicated to ecosystem development. This allocation supports airdrops, marketing initiatives, strategic partnerships, liquidity provisioning, and the operational development fund. By linking token issuance to verified engagement, OLI cultivates a self-regulating economy that prioritises collective growth.

Roadmap Highlights

Q4 2024 – Launch of the Bedroom Trading Challenge, featuring the initial airdrop, social media competitions, and a beta leaderboard.
Q1 2025 – Implementation of DAO Voting Module, enabling live on-chain proposal submissions and governance procedures.
Q2 2025 – Release of OLI Swap, offering one-tap token swaps and liquidity farming directly via mobile platforms.
Q3 2025 – Initiation of Meme Collaboration Season, including partnerships with leading meme tokens to enhance reach and utility.

Join the Pillow‑Powered Revolution

The need for conventional office settings is obsolete. With the use of a mobile device and a comfortable environment, market participation becomes straightforward, interactive, and enjoyable.

Website – https://tradeoli.io

Trustee Plus Revolution: Hundreds of Visitors Instantly Received a Fraction of Bitcoin at Money20/20. How Did It Happen? 602

This year at Money20/20 in Amsterdam, the financial community witnessed a real sensation at the booth of the crypto wallet Trustee Plus. For the first time in the event’s history, anyone could receive a fraction of Bitcoin using only a mobile phone number — even if they had never used cryptocurrency before.

This innovative technology allows users to initiate a crypto transfer to a mobile number, even if the recipient is not yet a Trustee Plus user. Once the recipient downloads the app, the funds automatically appear in their balance. The received Bitcoin can then be held, exchanged, or spent.

This solution sparked significant interest among Money20/20 attendees. According to company representatives, nearly 500 visitors enriched their crypto portfolios thanks to the Trustee Plus booth.

“One of Trustee Plus’s core missions is to unlock the potential of future finance for everyday people. We believe cryptocurrency should work much more simply and intuitively than traditional banking services. And when we hear from new users, ‘I’ll remember this gifted piece of Bitcoin for the rest of my life’, it reminds us that everything we do truly matters,” said Vadym Hrusha, Founder of Trustee Plus.

Effortless Bitcoin Spending with Trustee

Trustee Plus also enables seamless conversion of Bitcoin to euros directly within the app. The converted funds can be used in several ways: via SEPA transfers to any European IBAN, or through the Quicko Digital virtual card, which is currently available for free issuance. All card operations are commission-free.

By combining an intuitive interface, instant transfers, and the ability to use crypto in everyday transactions, Trustee Plus takes another step toward the mass adoption of digital assets in Europe’s financial ecosystem.

Earlier, the largest crypto media outlet in Eastern Europe, Incrypted, included Trustee Plus in its list of the “Top 12 Best Cryptocurrency Projects for Paying with Bitcoin or Ethereum in 2025.”

Skywork-Reward-V2: Leading the New Milestone for Open-Source Reward Models 561

In September 2024, Skywork first open-sourced the Skywork-Reward series models and related datasets. Over the past nine months, these models and data have been widely adopted by the open-source community for research and practice, with over 750,000 cumulative downloads on the HuggingFace platform, helping multiple frontier models achieve excellent results in authoritative evaluations such as RewardBench.

On July 4, 2025, Skywork continues to open-source the second-generation reward models – the Skywork-Reward-V2 series, comprising 8 reward models based on different base models of varying sizes, with parameters ranging from 600 million to 8 billion. These models have achieved top rankings across seven major mainstream reward model evaluation benchmarks.

Skywork-Reward-V2 Download Links
HuggingFace: https://huggingface.co/collections/Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84
GitHub: https://github.com/SkyworkAI/Skywork-Reward-V2
Technical Report: https://arxiv.org/abs/2507.01352

Reward models play a crucial role in the Reinforcement Learning from Human Feedback (RLHF) process. In developing this new generation of reward models, we constructed a hybrid dataset called Skywork-SynPref-40M, containing a total of 40 million preference pairs.

To achieve large-scale, efficient data screening and filtering, Skywork specially designed a two-stage human-machine collaborative process that combines high-quality human annotation with the scalable processing capabilities of models. In this process, humans provide rigorously verified high-quality annotations, while Large Language Models (LLMs) automatically organize and expand based on human guidance.

Based on the above high-quality hybrid preference data, we developed the Skywork-Reward-V2 series, which demonstrates broad applicability and excellent performance across multiple capability dimensions, including general alignment with human preferences, objective correctness, safety, resistance to style bias, and best-of-N scaling capability. Experimental validation shows that this series of models achieved the best performance on seven mainstream reward model evaluation benchmarks.

01 Skywork-SynPref-40M: Human-Machine Collaboration for Million-Scale Human Preference Data Screening

Even the most advanced current open-source reward models still perform inadequately on most mainstream evaluation benchmarks. They fail to effectively capture the subtle and complex characteristics of human preferences, particularly when facing multi-dimensional, multi-level feedback.

Additionally, many reward models tend to excel on specific benchmark tasks but struggle to transfer to new tasks or scenarios, exhibiting obvious “overfitting” phenomena. Although existing research has attempted to improve performance through optimizing objective functions, improving model architectures, and recently emerging Generative Reward Models, the overall effectiveness remains quite limited.

We believe that the current fragility of reward models mainly stems from the limitations of existing preference datasets, which often have limited coverage, mechanical label generation methods, or lack rigorous quality control.

Therefore, in developing the new generation of reward models, we not only continued the first generation’s experience in data optimization but also introduced more diverse and larger-scale real human preference data, striving to improve data scale while maintaining data quality.

Consequently, Skywork proposes Skywork-SynPref-40M – the largest preference hybrid dataset to date, containing a total of 40 million preference sample pairs. Its core innovation lies in a “human-machine collaboration, two-stage iteration” data selection pipeline.

Stage 1: Human-Guided Small-Scale High-Quality Preference Construction

The team first constructed an unverified initial preference pool and used Large Language Models (LLMs) to generate preference-related auxiliary attributes such as task type, objectivity, and controversy. Based on this, human annotators followed a strict verification protocol and used external tools and advanced LLMs to conduct detailed reviews of partial data, ultimately constructing a small-scale but high-quality “gold standard” dataset as the basis for subsequent data generation and model evaluation.

Subsequently, we used preference labels from the gold standard data as guidance, combined with LLM large-scale generation of high-quality “silver standard” data, thus achieving data volume expansion. The team also conducted multiple rounds of iterative optimization: in each round, training reward models and identifying model weaknesses based on their performance on gold standard data; then retrieving similar samples and using multi-model consensus mechanisms for automatic annotation to further expand and enhance silver standard data. This human-machine collaborative closed-loop process continues iteratively, effectively improving the reward model’s understanding and discrimination of preferences.

Stage 2: Fully Automated Large-Scale Preference Data Expansion

After obtaining preliminary high-quality models, the second stage turns to automated large-scale data expansion. This stage no longer relies on manual review but uses trained reward models to perform consistency filtering:

  • If a sample’s label is inconsistent with the current optimal model’s prediction, or if the model’s confidence is low, LLMs are called to automatically re-annotate;
  • If the sample label is consistent with the “gold model” (i.e., a model trained only on human data) prediction and receives support from the current model or LLM, it can directly pass screening.

Through this mechanism, the team successfully screened 26 million selected data points from the original 40 million samples, achieving a good balance between preference data scale and quality while greatly reducing the human annotation burden.

02 Skywork-Reward-V2: Matching Large Model Performance with Small Model Size

Compared to the previous generation Skywork-Reward, Skywork newly released Skywork-Reward-V2 series provides 8 reward models trained based on Qwen3 and LLaMA3 series models, with parameter scales covering from 600 million to 8 billion.

On seven mainstream reward model evaluation benchmarks including Reward Bench v1/v2, PPE Preference & Correctness, RMB, RM-Bench, and JudgeBench, the Skywork-Reward-V2 series comprehensively achieved current state-of-the-art (SOTA) levels.

Compensating for Model Scale Limitations with Data Quality and Richness

Even the smallest model, Skywork-Reward-V2-Qwen3-0.6B, achieves overall performance nearly matching the previous generation’s strongest model, Skywork-Reward-Gemma-2-27B-v0.2, on average. The largest scale model, Skywork-Reward-V2-Llama-3.1-8B, achieved comprehensive superiority across all mainstream benchmark tests, becoming the currently best-performing open-source reward model overall.

Broad Coverage of Multi-Dimensional Human Preference Capabilities

Additionally, Skywork-Reward-V2 achieved leading results in multiple advanced capability evaluations, including Best-of-N (BoN) tasks, bias resistance capability testing (RM-Bench), complex instruction understanding, and truthfulness judgment (RewardBench v2), demonstrating excellent generalization ability and practicality.

Highly Scalable Data Screening Process Significantly Improves Reward Model Performance

Beyond excellent performance in evaluations, the team also found that in the “human-machine collaboration, two-stage iteration” data construction process, preference data that underwent careful screening and filtering could continuously and effectively improve reward models’ overall performance through multiple iterative training rounds, especially showing remarkable performance in the second stage’s fully automated data expansion.

In contrast, blindly expanding raw data not only fails to improve initial performance but may introduce noise and negative effects. To further validate the critical role of data quality, we conducted experiments on a subset of 16 million data points from an early version. Results showed that training an 8B-scale model using only 1.8% (about 290,000) of the high-quality data already exceeded the performance of current 70B-level SOTA reward models. This result again confirms that the Skywork-SynPref dataset not only leads in scale but also has significant advantages in data quality.

03 Welcoming a New Milestone for Open-Source Reward Models: Helping Build Future AI Infrastructure

In this research work on the second-generation reward model Skywork-Reward-V2, the team proposed Skywork-SynPref-40M, a hybrid dataset containing 40 million preference pairs (with 26 million carefully screened pairs), and Skywork-Reward-V2, a series of eight reward models with state-of-the-art performance designed for broad task applicability.

We believe this research work and the continued iteration of reward models will help advance the development of open-source reward models and more broadly promote progress in Reinforcement Learning from Human Feedback (RLHF) research. This represents an important step forward for the field and can further accelerate the prosperity of the open-source community.

The Skywork-Reward-V2 series models focus on research into scaling preference data. In the future, the team’s research scope will gradually expand to other areas that have not been fully explored, such as alternative training techniques and modeling objectives.

Meanwhile, considering recent development trends in the field – reward models and reward shaping mechanisms have become core components in today’s large-scale language model training pipelines, applicable not only to RLHF based on human preference learning and behavior guidance, but also to RLVR including mathematics, programming, or general reasoning tasks, as well as agent-based learning scenarios.

Therefore, we envision that reward models, or more broadly, unified reward systems, are poised to form the core of AI infrastructure in the future. They will no longer merely serve as evaluators of behavior or correctness, but will become the “compass” for intelligent systems navigating complex environments, helping them align with human values and continuously evolve toward more meaningful goals.

Additionally, Skywork released the world’s first deep research AI workspace agents in May, which you can experience by visiting: skywork.ai

STONEFORM Launches a Tokenized Real Estate Platform to Open Up Investment Opportunities 525

Tokenizing real estate to Unveil global opportunities & fractional ownership for all investors.

STONEFORM is reshaping the real estate investment landscape by leveraging blockchain technology to create a decentralized platform for fractional property ownership, expanding global access and liquidity for investors. Through the power of tokenization, STONEFORM is set to make property ownership more accessible, efficient, and transparent by allowing fractional ownership of real estate assets.

STONEFORM’s Vision: A New Digital Paradigm for Real Estate Investment

STONEFORM’s goal is to integrate blockchain technology and decentralized finance (DeFi) to unveil the power of real estate investment. STONEFORM enables global participation, providing diverse investment options for individuals and institutions. Token holders can engage in real estate investments without the burdens typically associated with traditional property ownership.

“At STONEFORM,we are building more than just a platform; we are building a milestone in real estate, We believe blockchain is the key to facilitating widespread access to high-quality real estate assets, enabling anyone, regardless of their financial background, to invest in and benefit from the growth of this sector.” Ukrit Thaweerat, Founder.

Main Functionalities of STONEFORM

  • Fractional Ownership: Purchase fractional shares of premium real estate, lowering entry barriers for small investors globally.
  • Blockchain-Powered Liquidity: Tokenized assets trade on decentralized markets,ensuring faster and more cost-effective transactions.
  • Smart Contracts for Automated Management: Automates property management tasks like rent distribution, reducing costs and administrative efforts.
  • Decentralized Governance: Token holders vote on decisions,giving the community control over the platform’s governance and direction.
  • Global Access: Blockchain enables worldwide participation in real estate investment.
  • Security and Compliance: Robust security features and automated compliance checks ensure safe and regulated transactions.

A New Era for Real Estate Investment

The global real estate market is valued at trillions, but traditional investments often require large capital and have limited liquidity. STONEFORM solves these issues with blockchain-powered fractional ownership.

Conclusion

STONEFORM is redefining the way people invest in real estate by integrating blockchain and decentralized finance. With fractional ownership, smart contracts, and decentralized governance, the platform is set to make real estate investment more accessible,liquid,and transparent than ever before. The project will continue to expand its offerings, driving the future of real estate investment on a global scale.

Bitcino Casino Joins Forces with AI Powerhouse ‘Mr.House’ — The Future of Crypto Gambling Is HERE! 1091

30 6 2025 1

In a bold move that redefines the future of online gambling, Bitcino Casino has integrated Mr.House a groundbreaking AI-driven management platform into its crypto casino infrastructure. Built as a fully autonomous operational layer for iGaming platforms, Mr.House is an independent, intelligent entity now being deployed by Bitcino to automate, optimize, and scale every aspect of casino operations.

As the first casino to adopt Mr.House’s full suite of tools, Bitcino is leading the charge in bringing AI-powered automation to the crypto gambling industry blending performance, innovation, and user-centric design in a way no traditional platform can match.

What is Mr.House?

Mr.House is an advanced network of AI agents purpose-built for online casinos and sportsbooks. Designed to run 24/7 with zero downtime, Mr.House handles everything from dynamic marketing and SEO to risk management, player engagement, and affiliate automation all without human intervention.

Bitcino is the first casino to fully integrate Mr.House’s capabilities, instantly setting a new standard in the world of decentralized online gambling.

Core functions include:

  • Targeted marketing across Telegram, Discord, and Twitter/X 
  • Personalized live chat support and player interaction 
  • Real-time sportsbook odds and automated betting suggestions 
  • Custom bonus delivery based on player behavior and game history 
  • Automated affiliate and referral program tracking 
  • Intelligent content generation for SEO growth 
  • Ongoing fraud detection and risk-managed bankroll allocation

Bitcino: The First Mr.House-Powered Crypto Casino

Bitcino is a fully anonymous, no-KYC, crypto-based casino and sportsbook offering high-speed gameplay, fast crypto transactions, and global access. With Mr.House embedded into its core systems, Bitcino now delivers a completely reimagined online gambling experience smarter, faster, and more responsive than ever before.

Bitcino’s standout features include:

  • A rich library of provably fair casino games, including slots, blackjack, roulette, live dealer tables, and crypto-native games like Crash and Plinko 
  • Support for major cryptocurrencies with instant deposits and withdrawals 
  • Fully AI-managed referral and affiliate programs, allowing users to earn from every dollar wagered by players they invite 
  • A unique revenue-sharing token model, distributing 50% of platform profits to token holders 
  • Real-time platform optimization, bonus tuning, and content updates — all powered by Mr.House

Referral Rewards Reimagined

With Mr.House overseeing Bitcino’s affiliate and referral infrastructure, both users and partners can benefit from a seamless, performance-driven system. Affiliates earn up to 30% of profit generated by their referred players, while regular users earn a share of total wagering volume creating a sustainable, long-term incentive for community growth.

This smart referral structure offers a competitive edge over traditional casino affiliate programs, thanks to its real-time tracking and transparent payouts.

Mr.House and Bitcino: A New Era for Crypto Casinos

As Mr.House prepares for broader deployment across the iGaming space, Bitcino stands as the flagship example of what’s possible when AI is given full operational control of a casino platform. The partnership doesn’t just improve efficiency it redefines the player experience, removes human bottlenecks, and enables Bitcino to scale globally with minimal friction.

By integrating bleeding-edge technology and focusing on key concepts such as:

  • Crypto casino 
  • Bitcoin gambling 
  • No KYC casino 
  • Fast payout casino 
  • Online casino referral program 
  • Decentralized sportsbook 
  • AI-powered online casino

.. Bitcino is rapidly establishing itself as a dominant force in the online gambling space, building a strong and unmistakable identity that resonates with players worldwide.

Try Bitcino Today Powered by Mr.House, Built for the Future

Whether you’re a player looking for fast, private, and fair crypto gambling, or a partner ready to monetize your traffic with real earning potential, Bitcino Casino offers an experience like no other fully optimized by AI, fully built for Web3.

Check out Bitcino and stay connected:

Visit the official site: bitcino.com
Explore the latest sports insights on the blog: betting.bitcino.com/
Follow us on Twitter/X for real-time updates: x.com/bitcinocasino

Choosing Your Right Trading Partner with SignalPlus 1230

SignalPlus, founded in 2021 and headquartered in Hong Kong, is an industry-leading provider of trading software and infrastructure. Trusted by institutional partners across the ecosystem, the company delivers advanced analytical tools for options, perpetuals, and spot markets to traders worldwide.

Key features designed for high-pressure decision-making include:

  • Smart Dealing – live P&L visualisations with one-click order management
  • Risk Scenario – instant stress tests that model extreme market moves
  • Trading Compass – AI-curated news flow and alt-coin signals highlight emerging trends
  • RFQ for Block Trades – transparent and competitive pricing for large trades executed on Deribit

Backed by the recent closing of its $11 million Series B round led by AppWorks and OKX Ventures, SignalPlus reaffirms its commitment to building the best-in-class infrastructure that brings institutional-grade tools to the public domain. Looking ahead, SignalPlus will continue to scale R&D and compliance operations from our Hong Kong base, delivering durable, globally relevant infrastructure for the next chapter of digital-asset markets.