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Spectral Capital Announces Strategic Acquisition of Quantomo, Pioneering the Future of Quantum Search Technology

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SEATTLE, Sept. 10, 2024 /PRNewswire/ — Spectral Capital Corporation (OTCQB: FCCN), a pioneering innovator in Quantum as a Service (QaaS) computing and decentralized cloud services, is excited to announce the acquisition of Quantomo, a groundbreaking company at the forefront of quantum tomography technology. Quantomo, short for “Quantum Tomography,” has developed revolutionary quantum algorithms designed to redefine the future of search engines, surpassing traditional search methods employed by industry giants such as Google.

Quantomo’s unique combination of classical and quantum computing, working with Spectral’s newly acquired company Vogon Cloud’s use of a distributed quantum ledger database (DQLDB), is set to usher in a new era of information retrieval. The core technology leverages quantum parallelism, enabling exponentially faster searches across multiple dimensions of data simultaneously. Unlike classical search engines, which rely on deterministic processes and centralized indexing, Quantomo’s quantum algorithms perform searches across distributed networks using quantum superposition and cooperative distributed inferencing.

“Quantomo’s acquisition represents a quantum leap in our strategic roadmap to transform how industries handle data and search optimization. In a world where there is so much data, that we don’t know what we don’t know and what questions to ask, this acquisition helps us solve that problem,” said Sean Michael Brehm, Chairman of Spectral Capital. “The integration of their advanced quantum tomography technology with our Quantum Bridge framework enables us to create the world’s most efficient, scalable, and contextually aware search engine—one that will revolutionize how data is is processed, retrieved, and leveraged in the form of information that is relevant to you.”

How Quantomo Redefines Search Technology

Quantomo’s proprietary technology offers several groundbreaking features:

Quantum Parallelism for Accelerated Search: Quantum algorithms, such as Grover’s Algorithm, perform searches exponentially faster than classical methods, using qubits to process vast datasets in parallel. Unlike Google’s PageRank, which operates in a stepwise manner, Quantomo explores all possible solutions simultaneously across a distributed ledger.

Quantomo’s innovative framework allows agents across a decentralized network to collaborate on search queries. Instead of relying on a centralized index, search agents infer results collectively, optimizing queries in real-time and adapting to evolving data patterns.

Epoch-Based Data Organization: Quantomo’s use of Vogon’s epochs allows users to track the evolution of data over time, enabling context-rich, time-based searches that go beyond static page rankings.

Semantic and Ontological Structures: Quantomo employs an ontological framework for a richer semantic understanding of data. Unlike traditional keyword-based search engines, this technology explores multidimensional relationships within the data, offering more contextually relevant results.

Polyglot Compatibility via QuantumVM: The integration with Vogon’s QuantumVM ensures seamless compatibility between quantum and classical algorithms, enabling flexible, scalable search solutions that can adapt to various data formats and languages.

“With Quantomo, we are not only advancing the field of quantum computing but also setting a new standard for collective intelligence-powered search engines,” said Jenifer Osterwalder, CEO of Spectral Capital. “This acquisition strengthens our commitment to building a decentralized, quantum-driven future that promotes scalability, security, and sustainable innovation across industries.”

A Vision for the Quantum Era

The acquisition of Quantomo aligns perfectly with Spectral’s ambitious Q4 strategy to lead the global transition into the quantum era. As part of Spectral’s broader Quantum Bridge initiative, Quantomo’s quantum-enhanced search technology will be deployed across a network of decentralized, energy-efficient micro data centers, established through Spectral’s recent expansion efforts.

This acquisition underscores Spectral’s commitment to securing its position as a global leader in quantum computing, decentralized cloud services, and next-generation data management. Quantomo’s cutting-edge technology will accelerate the development of collective intelligence solutions and drive efficiencies across sectors such as energy, healthcare, finance, and more.

Forward-Looking Statements

This press release contains forward-looking statements (as defined in Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended) concerning future events and FCCN’s growth and business strategy. Words such as “expects,” “will,” “intends,” “plans,” “believes,” “anticipates,” “hopes,” “estimates,” and variations on such words and similar expressions are intended to identify forward-looking statements. Although FCCN believes that the expectations reflected in such forward-looking statements are reasonable, no assurance can be given that such expectations will prove to have been correct. These statements involve known and unknown risks and are based upon a number of assumptions and estimates that are inherently subject to significant uncertainties and contingencies, many of which are beyond the control of FCCN. Actual results may differ materially from those expressed or implied by such forward-looking statements. Factors that could cause actual results to differ materially include, but are not limited to, changes in FCCN’s business; competitive factors in the market(s) in which FCCN operates; risks associated with operations outside the United States; and other factors listed from time to time in FCCN’s filings with the Securities and Exchange Commission. FCCN expressly disclaims any obligations or undertaking to release publicly any updates or revisions to any forward-looking statements contained herein to reflect any change in FCCN’s expectations with respect thereto or any change in events, conditions or circumstances on which any statement is based.

For more information, please visit www.spectralcapital.com.

 

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BinBase Expands 2026 BIN Dataset with Instant Payout Intelligence for iGaming, Gambling, and Cross-Border Transfers

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BinBase updates its 2026 dataset with specialized Fast Funds, Visa Direct, and Mastercard MoneySend indicators to help iGaming operators and payout platforms execute seamless, instant card disbursements.

MIAMI, July 23, 2026 /PRNewswire-PRWeb/ — BinBase, a global provider of payment routing intelligence and card issuing data, has introduced specialized instant payout indicators as part of its upgraded 2026 BIN Database. Tailored for iGaming operators, online gambling platforms, crypto-to-fiat ramps, and payout aggregators, the updated dataset helps platform engineers streamline real-time card disbursements and Push-to-Card (P2C) transactions.

In high-velocity sectors such as online betting and gaming, instantaneous player payouts are a primary driver of customer retention. However, executing Push-to-Card transactions through protocols like Visa Direct and Mastercard MoneySend requires knowing whether the receiving card issuer supports Fast Funds for specific merchant category codes (MCCs). Attempting instant payouts on non-eligible cards leads to declined transactions, elevated processing fees, and poor user experiences.

The 2026 BinBase release solves this operational bottleneck by delivering dedicated attributes for real-time fund disbursements:

Fast Funds Eligibility: Granular indicators identifying domestic and cross-border Fast Funds support across global Visa and Mastercard ranges.Online Gambling Fast Funds (OG FF): Dedicated flags specifically identifying card ranges authorized to receive real-time gambling and betting payouts.Mastercard MoneySend & Visa Direct Indicators: Precise protocol compatibility markers (MS Ind & MT Ind) ensuring push transactions are routed only to eligible recipient cards.Direct Debit & Pull-Funds Support: Indicators for recurring collections and account-funding transactions.

“Player payouts in iGaming cannot wait for standard 2-to-3-day ACH settlements,” said a spokesperson for Damiko Inc. “By embedding our Fast Funds and Gambling FF flags into their payment engines, operators can instantly validate recipient cards before initiating a transfer, guaranteeing high success rates and instant liquidity for their users.”

Fintech engineers and payout architects can examine the full 29-field database schema and access a free 2026 sample dataset on GitHub.

To explore commercial licensing, bulk database downloads, or custom data feeds, visit BinBase at https://binbase.com.

About Damiko Inc

Damiko Inc is a US-based fintech data provider specializing in card issuer analytics, payment routing data, and global BIN database solutions. Operating through its flagship product, BinBase.com, the company supplies high-precision transaction intelligence to help merchants and payment facilitators worldwide optimize approval rates and mitigate processing fees.

Media Contact
Fedor Lavrikoff, BinBase, 1 7866133334, sales@binbase.com, www.binbase.com 

View original content:https://www.prweb.com/releases/binbase-expands-2026-bin-dataset-with-instant-payout-intelligence-for-igaming-gambling-and-cross-border-transfers-302829344.html

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Walnut Coding’s Young Coders Serve as ‘Instructors’ at Huawei Cloud Developer Training Camp

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Ages 8 and 15, students showcase AI-era project-building skills from concept to working application

BEIJING, July 23, 2026 /PRNewswire/ — Walnut Coding (the “Company”), a leading online platform for youth coding education, said two of its students – ages 8 and 15 – have joined the “instructor” lineup at Huawei Cloud Developer Training Camp, making them among the youngest “instructors” in the program’s history. The Company cites the pair as a prime example of how young learners can combine coding fundamentals with AI tools to turn ideas into working applications.

The two students, Bolin Du, 8, and Peiqi Gao, 15, built working applications using Huawei Cloud CodeArts, an AI coding assistant, then presented the projects to the training camp themselves – walking the audience through their design choices, technical builds, and debugging process.

The move lands at a moment when AI coding tools are forcing a rethink across the education sector. Tools that can generate functioning code from a plain-language prompt have undercut the traditional argument for teaching children to program – that they need the skill to build things themselves. Walnut Coding’s answer is that the more valuable skill now is judgment – knowing what problem to solve, breaking it into parts, and determining whether an AI’s output actually works.

Gao built a travel-planning application that generates routes, itineraries, and recommendations based on user input, handling the project end to end, from requirements and design through coding and debugging. Du, the younger of the two, built an interactive calendar application, using HTML for page structure, CSS for visual design, and JavaScript for interactive features. Both students then took on an instructor’s role at the camp, presenting their project goals and technical implementation to the audience – a step Walnut Coding says separated the work from a typical classroom assignment.

These were not classroom exercises but working projects, built and presented inside a professional developer-training environment. The experience demanded more from both students than simply producing something functional – they needed to articulate their reasoning, defend technical choices, and refine the final result under scrutiny. Their participation signals a broader shift underway in what youth coding education can deliver.

AI is making code generation easier, but it is also redrawing which skills actually matter. A student who relies only on one-click generation may get a rough prototype quickly, but still struggle to spot logical flaws, judge whether the output is reliable, or turn an abstract idea into a product that actually works. Students with programming foundations, by contrast, are better positioned to define requirements, evaluate what the AI produces, correct its errors, and treat the technology as a tool rather than a shortcut to lean on.

“AI can help children generate code faster, but it cannot decide for them what problem they should solve, nor can it make the final judgment about whether the result is truly effective,” said Pengxuan Zeng, founder and CEO of Walnut Coding. “What these two students demonstrated is not just coding technique, but the ability to define needs, break down tasks, verify outcomes, and turn an idea into a working product. That is why we believe young people still need to learn programming in the AI era.”

Walnut Coding structures its courses around that thesis, pairing student-led project work with teaching-assistant guidance and AI-assisted support. According to the Company, this data is continuously fed back into its systems to refine the personalization of AI-assisted feedback — a closed-loop process linking teaching, practice, feedback, and curriculum development.

The Company frames the payoffs less around producing professional software engineers than around a broader form of literacy. As AI continues to reshape how tasks get done, the ability to understand the technology, structure problems clearly and collaborate effectively with intelligent tools may prove one of the most durable skills a young learner can develop.

Walnut Coding says it plans to keep expanding opportunities for students to build practical projects, partner with industry technology platforms such as Huawei Cloud, and develop the core capabilities needed to build with technology in the AI era.

View original content:https://www.prnewswire.com/news-releases/walnut-codings-young-coders-serve-as-instructors-at-huawei-cloud-developer-training-camp-302833884.html

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MOREH Showcases High-Performance LLM Inference on AMD GPUs at AMD Advancing AI 2026

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SAN FRANCISCO, July 23, 2026 /PRNewswire/ — Moreh, an AI infrastructure software company, led by CEO Gangwon Jo, participated in AMD Advancing AI 2026, AMD’s flagship annual AI event held in San Francisco on July 22–23 (local time), where it demonstrated its distributed inference solution, the MoAI Inference Framework, running on AMD GPUs.

At the event, Moreh presented a live demonstration of the GLM-5.1 large language model (LLM) powered by the MoAI Inference Framework on a system equipped with 32 AMD Instinct™ MI300X GPUs across four nodes. Visitors experienced the chatbot firsthand, evaluating its response speed and service quality while observing performance across a range of real-world use cases.

Unlike conventional demonstrations that simply run an AI model, Moreh’s showcase displayed key inference service metrics in real time, including GPU utilization, Tokens Per Second (TPS), Time To First Token (TTFT), and Time Per Output Token (TPOT). This enabled attendees to directly verify both inference performance and GPU resource efficiency in a production-like service environment.

Global AI industry leaders and enterprise customers attending the event expressed strong interest in the system’s fast response times and stable performance. In particular, the live deployment of the computationally demanding GLM-5.1 model on AMD GPUs at production-grade service levels received positive feedback from visitors.

Moreh’s MoAI Inference Framework is widely recognized as the world’s first commercially deployed distributed inference solution built for the AMD ecosystem. Its distributed inference and heterogeneous computing technologies are designed to dramatically reduce AI service costs, enabling broader adoption of AI worldwide. The technology addresses one of the industry’s biggest challenges-the rapidly rising infrastructure and service costs caused by increasingly larger AI models-by delivering a more efficient inference infrastructure.

Moreh CEO Gangwon Jo stated, “This event provided an opportunity for global customers to verify firsthand that top-tier inference performance can be achieved on AMD GPU environments,” and added “We will continue advancing our AI infrastructure software so enterprises can operate AI services as efficiently as possible, regardless of the underlying GPU platform.”

Moreh develops its own AI infrastructure engine and has expanded its end-to-end AI capabilities through its foundation LLM subsidiary, Motif Technologies, covering both AI infrastructure and foundation models. The company is also strengthening its presence in the global AI market through strategic partnerships with leading technology companies, including AMD and Tenstorrent.

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