CertiK and Lambda256 Partner to Strengthen Blockchain Security in APAC

TheNewsCryptoPublicado em 2026-05-29Última atualização em 2026-05-29

Resumo

CertiK, a leading Web3 security services provider, and Lambda256, Dunamu's blockchain technology subsidiary, have signed a strategic Memorandum of Understanding (MOU) to strengthen blockchain security and compliance infrastructure for enterprises and financial institutions in Korea and the APAC region. As a distributor for CertiK's products, including the real-time AML and risk intelligence platform SkyInsights and the AI-powered smart contract pre-audit tool AI Auditor, Lambda256 will integrate its blockchain infrastructure and regional expertise with CertiK's security capabilities. The partnership will initially focus on enterprise deployments in Korea, including consortium blockchains and regulated digital asset use cases, with plans for joint go-to-market activities, events, and thought leadership across APAC. The collaboration aims to provide institutionally-tailored solutions for integrated risk assessment and compliance, helping organizations adopt digital assets with greater security and operational confidence.

Enterprises, financial institutions, and virtual asset service providers (VASPs) across Korea and the broader APAC region will benefit from the expanded digital asset security and compliance infrastructure thanks to a strategic Memorandum of Understanding (MOU) between CertiK, the leading Web3 security services provider, and Lambda256, a blockchain technology subsidiary of Dunamu.

A distributor and channel partner for CertiK’s compliance and security products, including CertiK SkyInsights and CertiK AI Auditor, will be Lambda256 under the terms of the relationship. This partnership brings together the blockchain infrastructure and compliance ecosystem of Lambda256 with the on-chain security intelligence and smart contract security capabilities of CertiK to provide institutionally-tailored solutions for integrated risk assessment and compliance.

Enterprise deployments in Korea will be the primary emphasis of the relationship at first. This will include consortium blockchain efforts, on-premises installations, and infrastructure assistance for use cases including public-sector and regulated digital assets. Joint go-to-market efforts throughout APAC are also in the works, as are industry seminars, events, and research-driven thought leadership campaigns centered on blockchain compliance and security.

Exchanges, Web3 initiatives, and security teams may benefit from CertiK SkyInsights, a platform that provides real-time anti-money laundering and risk information. In addition to real-time monitoring, multi-chain information, risk assessments at the wallet and transaction levels, and over 2,990 documented security events, the platform has 400 million address labels.

Before doing manual audits, smart contract teams may use CertiK AI Auditor, an AI-powered pre-audit security solution, to find vulnerabilities, decrease false positives, and accelerate remedial procedures. With its multi-scanner detection and organized triage operations, the platform is compatible with Solidity, Move, and Rust environments.

Enterprises and financial institutions across Korea and APAC can benefit from CertiK’s globally recognized security intelligence and compliance capabilities, along with Lambda256’s infrastructure and regional market expertise. This partnership will enable them to adopt digital assets with greater operational confidence and strengthened security.

“By combining CertiK’s globally recognized security intelligence and compliance capabilities with Lambda256’s infrastructure and regional market expertise, we aim to help enterprises and financial institutions across Korea and APAC navigate digital asset adoption with stronger security and operational confidence.” said Jason Jiang, CBO at CertiK.

Among the many blockchain-related services offered by the New York-based CertiK are audits of smart contracts, penetration tests, formal verification, evaluations of infrastructure, and assistance with compliance. More than five thousand business customers across the world have used the company’s digital asset ecosystem services since its inception in 2017.

While digital asset usage expands throughout Asia’s regulated financial markets, the firms anticipate the alliance to boost institutional access to enterprise-grade blockchain security infrastructure.

A frontrunner in Web3 technology, Dunamu’s blockchain subsidiary Lambda256 is revolutionizing digital finance, infrastructure, and data. Nodit for infrastructure, Clair for data intelligence, and Scope for stablecoin issuance are just a few of the platforms that Lambda256 has established since its spin-off in 2019. The future of digital transformation is being shaped by Lambda256’s enterprise-grade blockchain solutions. The company has a proven track record of serving financial institutions, multinational partners, and large-scale Web3 applications.

Perguntas relacionadas

QWhat is the main purpose of the partnership between CertiK and Lambda256?

AThe main purpose is to strengthen digital asset security and compliance infrastructure for enterprises, financial institutions, and VASPs across Korea and the broader APAC region. The partnership combines CertiK's security intelligence with Lambda256's infrastructure and regional expertise to provide institutionally-tailored solutions for integrated risk assessment and compliance.

QWhich CertiK products will Lambda256 distribute as part of the partnership?

ALambda256 will distribute CertiK's compliance and security products, specifically CertiK SkyInsights and CertiK AI Auditor, as a channel partner.

QWhat are the key features of CertiK SkyInsights?

ACertiK SkyInsights provides real-time anti-money laundering and risk information. Its key features include real-time monitoring, multi-chain information, wallet and transaction-level risk assessments, over 2,990 documented security events, and 400 million address labels.

QWhat does CertiK AI Auditor help smart contract teams do?

ACertiK AI Auditor is an AI-powered pre-audit security solution that helps smart contract teams find vulnerabilities, decrease false positives, and accelerate remedial procedures before conducting manual audits. It is compatible with Solidity, Move, and Rust environments.

QWhat are the main platforms offered by Lambda256?

ALambda256 offers several platforms, including Luniverse (for blockchain infrastructure), Nodit (for infrastructure), Clair (for data intelligence), and Scope (for stablecoin issuance).

Leituras Relacionadas

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbitHá 4h

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbitHá 4h

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手Há 6h

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手Há 6h

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbitHá 7h

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbitHá 7h

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbitHá 7h

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbitHá 7h

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbitHá 9h