Standing Tall Through Storms, Gathering in Hong Kong to Ride the Tide | Registration Opens for Conflux Tree-Graph Digital Finance & Ecosystem Development Conference

marsbitОпубліковано о 2026-05-09Востаннє оновлено о 2026-05-09

Анотація

Every technological wave quietly reshapes the world. From the steam engine to the internet and the digital economy, each has been a growth engine. Today, the convergence of blockchain, AI, and digital finance is accelerating a new transformation. Digital assets are reaching new peaks, global regulatory attitudes are clarifying, and concepts like RWA and stablecoins are scaling into real-world applications. AI Agents are beginning to participate in human production. We stand at a historic juncture where traditional and digital finance are deeply integrating, presenting clear opportunities and tangible challenges. Having operated stably for five years and connected multiple regions globally, Conflux Network continues to serve digital finance and on-chain applications. Hong Kong, as an international financial hub bridging China and the world, is seeing progressive exploration and improved frameworks for Web3 and digital finance. Leveraging Conflux's technical foundation, the Conflux Digital Finance and Ecosystem Development Summit will be held in Hong Kong from May 13 to 15, 2026. This event will foster in-depth dialogue on the future: discussing the evolution of digital financial infrastructure, the compliant implementation of RWA and stablecoins, ecosystem reshaping and on-chain governance in the age of AI Agents, and Web3 security frameworks. It will gather global scholars, entrepreneurs, investors, financial institutions, and industry representatives for cross-disciplina...

Every wave of technological advancement quietly reshapes the world's operating paradigms. The steam engine propelled the rise of industry, information technology and the internet turned the "global village" into a reality, and the digital economy has gradually become a key engine of global growth. Today, the convergence of blockchain, AI, and digital finance is accelerating the arrival of a new transformative era.

The scale of digital assets continues to hit historical highs, global regulatory attitudes toward digital financial innovation are becoming increasingly clear, RWA and stablecoins are evolving from concepts to large-scale implementation, and AI Agents are beginning to actively participate in human production and creation. We stand at a historical juncture where traditional finance and digital finance are deeply integrating. Opportunities have never been so clear, and challenges have never been so tangible.

The Conflux Network tree-graph public blockchain has been operating stably for five years, connecting multiple countries and regions globally, and continuously serving the development of digital finance and on-chain applications. Hong Kong, as an international financial center, has long played a pivotal role as a hub connecting Mainland China with the world, where capital, information, and industries converge and flow. In recent years, explorations around digital finance and Web3 have been steadily advancing here, with institutional and market environments gradually improving, providing clearer space for the experimentation and implementation of new technologies.

Leveraging Conflux's technological foundation, the Conflux Tree-Graph Digital Finance & Ecosystem Development Conference will be held in Hong Kong from May 13th to 15th, 2026.

This will be an in-depth dialogue about the present and the future: centering on core topics such as the evolution of digital financial infrastructure, the compliant implementation of RWA and stablecoins, ecosystem restructuring and on-chain governance in the era of AI Agents, and the construction of Web3 security systems, the event will gather academics, entrepreneurs, investors, financial institutions, and industry representatives from around the world for profound cross-disciplinary exchanges. On the final day of the conference, Conflux will co-host a themed salon with The University of Hong Kong, planting seeds of future-oriented thought in the minds of the younger generation.

Scan the QR code above to register

As technology transitions from "usable" to "scalably applicable," and as finance evolves from "digitalization" to "on-chain and intelligent" operation, the restructuring of industrial architecture has already begun. Whether you are a developer, researcher, entrepreneur, or an explorer keeping a watchful eye on digital finance and Web3, we look forward to meeting you in Hong Kong.

Пов'язані питання

QWhat is the main topic of the "Conflux Tree-Graph Digital Finance and Ecosystem Development Conference"?

AThe main topic is the intersection of blockchain, AI, and digital finance, focusing on infrastructure evolution, RWA and stablecoin compliance, ecosystem reshaping in the AI Agent era, on-chain governance, and Web3 security.

QWhen and where will the "Conflux Tree-Graph Digital Finance and Ecosystem Development Conference" take place?

AThe conference will take place in Hong Kong from May 13th to 15th, 2026.

QAccording to the article, why is Hong Kong considered a suitable location for this conference?

AHong Kong is a global financial hub connecting Mainland China and the world. It has increasingly clear regulations and a supportive market environment for exploring digital finance and Web3, providing space for the practical implementation of new technologies.

QWhat specific event is mentioned for the final day of the conference?

AOn the final day, Conflux will co-host a thematic salon with the University of Hong Kong, aiming to share insights about the future with the younger generation.

QWho is the target audience for this conference according to the article?

AThe target audience includes developers, researchers, entrepreneurs, investors, financial institutions, industry representatives, and anyone exploring or interested in digital finance and Web3.

Пов'язані матеріали

Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

A US researcher's visit to China's top AI labs reveals distinct cultural and organizational factors driving China's rapid AI development. While talent, data, and compute are similar to the West, Chinese labs excel through a pragmatic, execution-focused culture: less emphasis on individual stardom and conceptual debate, and more on teamwork, engineering optimization, and mastering the full tech stack. A key advantage is the integration of young students and researchers who approach model-building with fresh perspectives and low ego, prioritizing collective progress over personal credit. This contrasts with the US culture of self-promotion and "star scientist" narratives. Chinese labs also exhibit a strong "build, don't buy" mentality, preferring to develop core capabilities—like data pipelines and environments—in-house rather than relying on external services. The ecosystem feels more collaborative than tribal, with mutual respect among labs. While government support exists, its scale is unclear, and technical decisions appear driven by labs, not state mandates. Chinese companies across sectors, from platforms to consumer tech, are building their own foundational models to control their tech destiny, reflecting a broader cultural drive for technological sovereignty. Demand for AI is emerging, with spending patterns potentially mirroring cloud infrastructure more than traditional SaaS. Despite challenges like a less mature data industry and GPU shortages, Chinese labs are propelled by vast talent, rapid iteration, and deep integration with the open-source community. The competition is evolving beyond a pure model race into a contest of organizational execution, developer ecosystems, and industrial pragmatism.

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3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

Corning, a 175-year-old glass company, is experiencing a dramatic revival as a key player in AI infrastructure, driven by surging demand for high-performance optical fiber in data centers. AI data centers require vastly more fiber than traditional ones—5 to 10 times as much per rack—to handle high-speed data transmission between GPUs. This structural demand shift, coupled with supply constraints from the lengthy expansion cycle for fiber preforms, has created a significant supply-demand gap. Nvidia has invested in Corning, along with Lumentum and Coherent, in a $4.5 billion total commitment to secure the optical supply chain for AI. Corning's competitive edge lies in its expertise in producing ultra-low-loss, high-density, and bend-resistant specialty fiber, which is critical for 800G+ and future 1.6T data rates. Its deep involvement in co-packaged optics (CPO) with partners like Nvidia further solidifies its position. While not the largest fiber manufacturer globally, Corning's revenue from enterprise/data center clients now exceeds 40% of its optical communications sales, and it has secured multi-year supply agreements with major hyperscalers including Meta and Nvidia. Financially, Corning's optical communications revenue has surged, doubling from $1.3 billion in 2023 to over $3 billion in 2025. Its stock price has risen nearly 6-fold since late 2023. Key future catalysts include the rollout of Nvidia's CPO products and the scale of undisclosed customer agreements. However, risks include high current valuations and potential disruption from next-generation technologies like hollow-core fiber. The company's long-term bet on light over electricity, maintained even through the telecom bubble crash, is now being validated by the AI boom.

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In the Age of AI, the Organization Itself Is the Moat

In the AI era, where products, interfaces, and narratives are easily replicated, a company's true moat is its organizational structure. The article argues that exceptional companies like OpenAI, Anthropic, and Palantir differentiate themselves not merely through technology but by inventing new organizational forms that allow a specific type of talent to thrive and become a version of themselves they couldn't elsewhere. These companies compete on identity, offering ambitious individuals a sense of being special, chosen, close to power, and part of a historic mission. However, this emotional commitment must be matched by structural commitment—real power, ownership, status, and economic participation. For founders, the key question is not how to tell a better story, but what kind of person can only truly realize their potential within their specific company structure. For individuals evaluating opportunities, the distinction between "being chosen" (an emotional feeling) and "being seen" (a structural reality of tangible power and rewards) is crucial. The most dangerous promises are those priced in future time. While AI makes copying visible elements easy, it does not make building a great, novel organization any easier. The next frontier of competition is creating organizational vessels that attract, structure, and compound the judgment of the right people—those whom traditional boxes cannot contain. The company itself becomes the moat.

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I've Been a Divorce Lawyer for 26 Years: How Has Cryptocurrency Become a New Tool for the Wealthy to Hide Assets?

Natalie Brunell reports on insights from divorce lawyer James Sexton, who has 26 years of experience. He argues that money itself is not the root of marital breakdown; rather, emotional disconnection is the core issue. While financial hardship increases divorce risk, excessive wealth can also make divorce easier by reducing the incentive to work on the relationship. Sexton discusses financial management in marriages, advocating for transparency and a "yours, mine, and ours" system that balances shared finances with individual autonomy and privacy. He notes the growing normalization of prenuptial agreements, especially among younger generations. A significant portion focuses on cryptocurrency's role in divorce. Sexton explains that crypto became a new tool for hiding assets due to its early anonymity and complexity. He highlights that many lawyers and spouses lack understanding, allowing knowledgeable parties to gain advantages. He cites a New York legal form that only added a specific crypto disclosure field in 2026. On saving relationships, Sexton emphasizes small, consistent acts of reconnection, affirmation, and expressing appreciation, which he finds more effective than criticism. He concludes that fostering warmth and kindness is a simple yet powerful way to strengthen bonds and, in his words, "put divorce lawyers out of business."

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Turing Award Laureate Sutton's New Work: Using a Formula from 1967 to Solve a Major Flaw in Streaming Reinforcement Learning

New research titled "Intentional Updates for Streaming Reinforcement Learning" (arXiv:2604.19033v1), involving Turing Award laureate Richard Sutton, addresses a core challenge in deep reinforcement learning (RL): the "stream barrier." Current deep RL methods typically rely on replay buffers and batch training for stability, failing catastrophically when learning online from single data points (streaming). The authors propose a fundamental shift: instead of prescribing how far to move parameters (a fixed step size), their "Intentional Updates" method specifies the desired change in the function's output (e.g., a 5% reduction in value prediction error). It then calculates the step size needed to achieve that intent. This idea is inspired by the Normalized Least Mean Squares (NLMS) algorithm from 1967. Applied to value and policy learning, this yields algorithms like Intentional TD(λ) and Intentional AC. The method inherently stabilizes learning by adapting the step size based on the local gradient landscape, preventing overshooting/undershooting. In experiments on MuJoCo continuous control and Atari discrete tasks, Intentional AC achieved performance rivaling batch-based algorithms like SAC in a streaming setting (batch size=1, no replay buffer), while being ~140x more computationally efficient per update. The work demonstrates significant robustness, reducing reliance on numerous stabilization tricks. A remaining challenge is bias in policy updates due to action-dependent step sizes. Overall, this approach advances efficient, online, "learn-as-you-go" RL, enabling adaptive systems without massive data buffers or compute clusters.

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