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 

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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.

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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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